Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.
REFERENCES
1.
V.
Bianco
,
G.
Franzese
, and
I.
Coluzza
, “
In silico evidence that protein unfolding is a precursor of protein aggregation
,” ChemPhysChem
21
, 377
–384
(2020
).2.
V. N.
Uversky
, “
Introduction to intrinsically disordered proteins (IDPs)
,” Chem. Rev.
114
, 6557
–6560
(2014
).3.
J.
Frydman
, “
Folding of newly translated proteins in vivo: The role of molecular chaperones
,” Annu. Rev. Biochem.
70
, 603
–647
(2001
).4.
P. L.
Clark
, “
Protein folding in the cell: Reshaping the folding funnel
,” Trends Biochem. Sci.
29
, 527
–534
(2004
).5.
M.
Liutkute
,
E.
Samatova
, and
M. V.
Rodnina
, “
Cotranslational folding of proteins on the ribosome
,” Biomolecules
10
, 97
(2020
).6.
H. H.
Kampinga
,
M. P.
Mayer
, and
A.
Mogk
, “
Protein quality control: From mechanism to disease: EMBO Workshop Costa de la Calma (Mallorca), Spain, April 28–May 03, 2019
,” Cell Stress Chaperones
24
, 1013
–1026
(2019
).7.
A. U.
Müller
and
E.
Weber-Ban
, “
The bacterial proteasome at the core of diverse degradation pathways
,” Front. Mol. Biosci.
6
, 23
(2019
).8.
J. L.
Silva
,
E. A.
Cino
,
I. N.
Soares
,
V. F.
Ferreira
, and
G. A. P.
de Oliveira
, “
Targeting the prion-like aggregation of mutant p53 to combat cancer
,” Acc. Chem. Res.
51
, 181
–190
(2018
).9.
A. M.
Morris
,
M. A.
Watzky
, and
R. G.
Finke
, “
Protein aggregation kinetics, mechanism, and curve-fitting: A review of the literature
,” Biochim. Biophys. Acta, Proteins Proteomics
1794
, 375
–397
(2009
).10.
L.-N.
Schaffert
and
W. G.
Carter
, “
Do post-translational modifications influence protein aggregation in neurodegenerative diseases: A systematic review
,” Brain Sci.
10
, 232
(2020
).11.
P. J.
Barrett
and
J. T.
Greenamyre
, “
Post-translational modification of [INEQ-START] [55-EQN-28] [INEQ-END]-synuclein in Parkinson s disease
,” Brain Res.
1628
, 247
–253
(2015
).12.
P.
Alam
,
K.
Siddiqi
,
S. K.
Chturvedi
, and
R. H.
Khan
, “
Protein aggregation: From background to inhibition strategies
,” Int. J. Biol. Macromol.
103
, 208
–219
(2017
).13.
A.
Kumar
,
N. K.
Singh
,
D.
Ghosh
, and
M.
Radhakrishna
, “
Understanding the role of hydrophobic patches in protein disaggregation
,” Phys. Chem. Chem. Phys.
23
, 12620
–12629
(2021
).14.
A.
Kumar
,
D.
Ghosh
, and
M.
Radhakrishna
, “
Surface patterning for enhanced protein stability: Insights from molecular simulations
,” J. Phys. Chem. B
123
, 8363
–8369
(2019
).15.
P.
Ciryam
,
R.
Kundra
,
R. I.
Morimoto
,
C. M.
Dobson
, and
M.
Vendruscolo
, “
Supersaturation is a major driving force for protein aggregation in neurodegenerative diseases
,” Trends Pharmacol. Sci.
36
, 72
–77
(2015
).16.
K. L.
Moreau
and
J. A.
King
, “
Protein misfolding and aggregation in cataract disease and prospects for prevention
,” Trends Mol. Med.
18
, 273
–282
(2012
).17.
C. M.
Dobson
, “
Principles of protein folding, misfolding and aggregation
,” Semin. Cell Dev. Biol.
15
, 3
–16
(2004
).18.
W.
Wang
,
S.
Nema
, and
D.
Teagarden
, “
Protein aggregation—pathways and influencing factors
,” Int. J. Pharm.
390
, 89
–99
(2010
).19.
Y.
Yoshimura
,
Y.
Lin
,
H.
Yagi
,
Y.-H.
Lee
,
H.
Kitayama
,
K.
Sakurai
,
M.
So
,
H.
Ogi
,
H.
Naiki
, and
Y.
Goto
, “
Distinguishing crystal-like amyloid fibrils and glass-like amorphous aggregates from their kinetics of formation
,” Proc. Natl. Acad. Sci. U. S. A.
109
, 14446
–14451
(2012
).20.
A.
Pastore
and
P.
Temussi
, “
Protein aggregation and misfolding: Good or evil?
,” J. Phys.: Condens. Matter
24
, 244101
(2012
).21.
M.
Stefani
and
C. M.
Dobson
, “
Protein aggregation and aggregate toxicity: New insights into protein folding, misfolding diseases and biological evolution
,” J. Mol. Med.
81
, 678
–699
(2003
).22.
F.
Chiti
and
C. M.
Dobson
, “
Protein misfolding, functional amyloid, and human disease
,” Annu. Rev. Biochem.
75
, 333
–366
(2006
).23.
K. J.
Bari
and
S.
Sharma
, “
A perspective on biophysical studies of crystallin aggregation and implications for cataract formation
,” J. Phys. Chem. B
124
, 11041
–11054
(2020
).24.
A.
Andersson
,
S.
Bohman
,
L.
Borg
,
J. F.
Paulsson
,
S. W.
Schultz
,
G. T.
Westermark
, and
P.
Westermark
et al., “
Amyloid deposition in transplanted human pancreatic islets: A conceivable cause of their long-term failure
,” J. Diabetes Res.
2008
, 562985
.25.
M.
Sugiyama
,
N.
Fujii
,
Y.
Morimoto
,
K.
Itoh
,
K.
Mori
,
T.
Fukunaga
, and
N.
Fujii
, “
SAXS and SANS observations of abnormal aggregation of human α-crystallin
,” Chem. Biodiversity
7
, 1380
–1388
(2010
).26.
R. J.
Truscott
, “
Age-related nuclear cataract-oxidation is the key
,” Exp. Eye Res.
80
, 709
–725
(2005
).27.
F.
Ding
,
J. J.
LaRocque
, and
N. V.
Dokholyan
, “
Direct observation of protein folding, aggregation, and a prion-like conformational conversion
,” J. Biol. Chem.
280
, 40235
–40240
(2005
).28.
J.
Lei
,
M.
Cai
,
Y.
Shen
,
D.
Lin
, and
X.
Deng
, “
Molecular dynamics study on the inhibition mechanisms of ReACp53 peptide for p53–R175H mutant aggregation
,” Phys. Chem. Chem. Phys.
23
, 23032
–23041
(2021
).29.
A.
Morriss-Andrews
and
J.-E.
Shea
, “
Computational studies of protein aggregation: Methods and applications
,” Annu. Rev. Phys. Chem.
66
, 643
–666
(2015
).30.
S.
Navarro
and
S.
Ventura
, “
Computational methods to predict protein aggregation
,” Curr. Opin. Struct. Biol.
73
, 102343
(2022
).31.
E. M.
Moussa
,
J. P.
Panchal
,
B. S.
Moorthy
,
J. S.
Blum
,
M. K.
Joubert
,
L. O.
Narhi
, and
E. M.
Topp
, “
Immunogenicity of therapeutic protein aggregates
,” J. Pharm. Sci.
105
, 417
–430
(2016
).32.
C. J.
Roberts
, “
Protein aggregation and its impact on product quality
,” Curr. Opin. Biotechnol.
30
, 211
–217
(2014
).33.
G.
Baird
,
C.
Farrell
,
J.
Cheung
,
A.
Semple
,
J.
Blue
, and
P. L.
Ahl
, “
FTIR spectroscopy detects intermolecular β-sheet formation above the high temperature T m for two monoclonal antibodies
,” Protein J.
39
, 318
–327
(2020
).34.
D.
Gao
,
L.-L.
Wang
,
D.-Q.
Lin
, and
S.-J.
Yao
, “
Evaluating antibody monomer separation from associated aggregates using mixed-mode chromatography
,” J. Chromatogr. A
1294
, 70
–75
(2013
).35.
Z.
Hamrang
,
N. J.
Rattray
, and
A.
Pluen
, “
Proteins behaving badly: Emerging technologies in profiling biopharmaceutical aggregation
,” Trends Biotechnol.
31
, 448
–458
(2013
).36.
O.
Obrezanova
,
A.
Arnell
,
R. G.
De La Cuesta
,
M. E.
Berthelot
,
T. R.
Gallagher
,
J.
Zurdo
, and
Y.
Stallwood
, “
Aggregation risk prediction for antibodies and its application to biotherapeutic development
,” MAbs
7
, 352
–363
(2015
).37.
J. A.
Housmans
,
G.
Wu
,
J.
Schymkowitz
, and
F.
Rousseau
, “
A guide to studying protein aggregation
,” FEBS J.
290
, 554
–583
(2023
).38.
J. C.
Boatz
,
M. J.
Whitley
,
M.
Li
,
A. M.
Gronenborn
, and
P. C.
van der Wel
, “
Cataract-associated P23T γD-crystallin retains a native-like fold in amorphous-looking aggregates formed at physiological pH
,” Nat. Commun.
8
, 15137
(2017
).39.
J. S.
Ebo
,
N.
Guthertz
,
S. E.
Radford
, and
D. J.
Brockwell
, “
Using protein engineering to understand and modulate aggregation
,” Curr. Opin. Struct. Biol.
60
, 157
–166
(2020
).40.
J. M.
Andrews
and
C. J.
Roberts
, “
A Lumry–Eyring nucleated polymerization model of protein aggregation kinetics: 1. Aggregation with pre-equilibrated unfolding
,” J. Phys. Chem. B
111
, 7897
–7913
(2007
).41.
C. J.
Roberts
, “
Kinetics of irreversible protein aggregation: Analysis of extended Lumry–Eyring models and implications for predicting protein shelf life
,” J. Phys. Chem. B
107
, 1194
–1207
(2003
).42.
C. J.
Roberts
, “
Non-native protein aggregation kinetics
,” Biotechnol. Bioeng.
98
, 927
–938
(2007
).43.
R.
Wälchli
,
P.-J.
Vermeire
,
J.
Massant
, and
P.
Arosio
, “
Accelerated aggregation studies of monoclonal antibodies: Considerations for storage stability
,” J. Pharm. Sci.
109
, 595
–602
(2020
).44.
W.
Wang
and
C. J.
Roberts
, “
Non-Arrhenius protein aggregation
,” AAPS J.
15
, 840
–851
(2013
).45.
W. F.
Weiss
IV
,
T. M.
Young
, and
C. J.
Roberts
, “
Principles, approaches, and challenges for predicting protein aggregation rates and shelf life
,” J. Pharm. Sci.
98
, 1246
–1277
(2009
).46.
H.
Mori
,
K.
Takio
,
M.
Ogawara
, and
D. J.
Selkoe
, “
Mass spectrometry of purified amyloid beta protein in Alzheimer's disease
,” J. Biol. Chem.
267
, 17082
–17086
(1992
).47.
M. G.
Spillantini
and
M.
Goedert
, “
Tau protein pathology in neurodegenerative diseases
,” Trends Neurosci.
21
, 428
–433
(1998
).48.
M.
Goedert
, “
Alpha-synuclein and neurodegenerative diseases
,” Nat. Rev. Neurosci.
2
, 492
–501
(2001
).49.
B.
Lorber
,
F.
Fischer
,
M.
Bailly
,
H.
Roy
, and
D.
Kern
, “
Protein analysis by dynamic light scattering: Methods and techniques for students
,” Biochem. Mol. Biol. Educ.
40
, 372
–382
(2012
).50.
A. M.
Tsai
,
J. H.
van Zanten
, and
M. J. I.
Betenbaugh
, “
Study of protein aggregation due to heat denaturation: A structural approach using circular dichroism spectroscopy, nuclear magnetic resonance, and static light scattering
,” Biotechnol. Bioeng.
59
, 273
–280
(1998
).51.
F.
Hillger
,
D.
Nettels
,
S.
Dorsch
, and
B.
Schuler
, “
Detection and analysis of protein aggregation with confocal single molecule fluorescence spectroscopy
,” J. Fluorescence
17
, 759
–765
(2007
).52.
R. R.
Ansari
,
K. I.
Suh
,
A.
Arabshahi
,
W. W.
Wilson
,
T. L.
Bray
, and
L. J.
DeLucas
, “
A fiber optic probe for monitoring protein aggregation, nucleation and crystallization
,” J. Cryst. Growth
168
, 216
–226
(1996
).53.
J. D.
Harper
and
P. T.
Lansbury
, Jr.
, “
Models of amyloid seeding in Alzheimer's disease and scrapie: Mechanistic truths and physiological consequences of the time-dependent solubility of amyloid proteins
,” Annu. Rev. Biochem.
66
, 385
–407
(1997
).54.
S. E.
Bondos
, “
Methods for measuring protein aggregation
,” Curr. Anal. Chem.
2
, 157
–170
(2006
).55.
L. A.
Munishkina
and
A. L.
Fink
, “
Fluorescence as a method to reveal structures and membrane-interactions of amyloidogenic proteins
,” Biochim. Biophys. Acta, Biomembr.
1768
, 1862
–1885
(2007
).56.
R.
Prabakaran
,
P.
Rawat
,
A. M.
Thangakani
,
S.
Kumar
, and
M. M.
Gromiha
, “
Protein aggregation: In silico algorithms and applications
,” Biophys. Rev.
13
, 71
–89
(2021
).57.
A.
Mishra
,
P.
Dikshit
,
S.
Purkayastha
,
J.
Sharma
,
N.
Nukina
, and
N. R.
Jana
, “
E6-AP promotes misfolded polyglutamine proteins for proteasomal degradation and suppresses polyglutamine protein aggregation and toxicity
,” J. Biol. Chem.
283
, 7648
–7656
(2008
).58.
J. L.
Klepeis
,
K.
Lindorff-Larsen
,
R. O.
Dror
, and
D. E.
Shaw
, “
Long-timescale molecular dynamics simulations of protein structure and function
,” Curr. Opin. Struct. Biol.
19
, 120
–127
(2009
).59.
Q.
Liao
, “
Enhanced sampling and free energy calculations for protein simulations
,” Prog. Mol. Biol. Transl. Sci.
170
, 177
–213
(2020
).60.
Molecular Modeling of Proteins
, edited by
A.
Kukol
(
Springer
, 2008
), Vol.
443
.61.
P.
Dey
and
P.
Biswas
, “
Exploring the aggregation of amyloid-β 42 through Monte Carlo simulations
,” Biophys. Chem.
297
, 107011
(2023
).62.
P.
Tian
,
K.
Lindorff-Larsen
,
W.
Boomsma
,
M. H.
Jensen
, and
D. E.
Otzen
, “
A Monte Carlo study of the early steps of functional amyloid formation
,” PLoS One
11
, e0146096
(2016
).63.
S.
Mohanty
, “
Aggregation and coacervation with Monte Carlo simulations
,” Prog. Mol. Biol. Transl. Sci.
170
, 505
–520
(2020
).64.
J. A.
McCammon
,
B. R.
Gelin
, and
M.
Karplus
, “
Dynamics of folded proteins
,” Nature
267
, 585
–590
(1977
).65.
A.
Rahman
,
B.
Saikia
,
C. R.
Gogoi
, and
A.
Baruah
, “
Advances in the understanding of protein misfolding and aggregation through molecular dynamics simulation
,” Prog. Biophys. Mol. Biol.
175
, 31
(2022
).66.
B.
Ma
and
R.
Nussinov
, “
Simulations as analytical tools to understand protein aggregation and predict amyloid conformation
,” Curr. Opin. Chem. Biol.
10
, 445
–452
(2006
).67.
T.
Hansson
,
C.
Oostenbrink
, and
W.
van Gunsteren
, “
Molecular dynamics simulations
,” Curr. Opin. Struct. Biol.
12
, 190
–196
(2002
).68.
H. A.
Scheraga
,
M.
Khalili
, and
A.
Liwo
, “
Protein-folding dynamics: Overview of molecular simulation techniques
,” Annu. Rev. Phys. Chem
58
, 57
–83
(2007
).69.
R. O.
Dror
,
R. M.
Dirks
,
J.
Grossman
,
H.
Xu
, and
D. E.
Shaw
, “
Biomolecular simulation: A computational microscope for molecular biology
,” Annu. Rev. Biophys.
41
, 429
–452
(2012
).70.
M.
Bixon
and
S.
Lifson
, “
Potential functions and conformations in cycloalkanes
,” Tetrahedron
23
, 769
–784
(1967
).71.
M. T.
Allen
and
D. J.
Tildesley
, Computer Simulation of Liquids
(
Oxford Academic
, 1987
).72.
J. A.
Coleman
,
E. M.
Green
, and
E.
Gouaux
, “
X-ray structures and mechanism of the human serotonin transporter
,” Nature
532
, 334
–339
(2016
).73.
S.
Sinha
,
B.
Tam
, and
S. M.
Wang
, “
Applications of molecular dynamics simulation in protein study
,” Membranes
12
, 844
(2022
).74.
A.
Rader
, “
Coarse-grained models: Getting more with less
,” Curr. Opin. Pharmacol.
10
, 753
–759
(2010
).75.
R. L.
Redler
,
D.
Shirvanyants
,
O.
Dagliyan
,
F.
Ding
,
D. N.
Kim
,
P.
Kota
,
E. A.
Proctor
,
S.
Ramachandran
,
A.
Tandon
, and
N. V.
Dokholyan
, “
Computational approaches to understanding protein aggregation in neurodegeneration
,” J. Mol. Cell Biol.
6
, 104
–115
(2014
).76.
C.
Wu
and
J.-E.
Shea
, “
Coarse-grained models for protein aggregation
,” Curr. Opin. Struct. Biol.
21
, 209
–220
(2011
).77.
R. I.
Dima
,
G.
Settanni
,
C.
Micheletti
,
J. R.
Banavar
, and
A.
Maritan
, “
Extraction of interaction potentials between amino acids from native protein structures
,” J. Chem. Phys.
112
, 9151
–9166
(2000
).78.
M.
Heo
,
S.
Kim
,
E.-J.
Moon
,
M.
Cheon
,
K.
Chung
, and
I.
Chang
, “
Perceptron learning of pairwise contact energies for proteins incorporating the amino acid environment
,” Phys. Rev. E
72
, 011906
(2005
).79.
R. L.
Jernigan
and
I.
Bahar
, “
Structure-derived potentials and protein simulations
,” Curr. Opin. Struct. Biol.
6
, 195
–209
(1996
).80.
J.
Meller
,
M.
Wagner
, and
R.
Elber
, “
Maximum feasibility guideline in the design and analysis of protein folding potentials
,” J. Comput. Chem.
23
, 111
–118
(2002
).81.
L. A.
Mirny
and
E. I.
Shakhnovich
, “
How to derive a protein folding potential? A new approach to an old problem
,” J. Mol. Biol.
264
, 1164
–1179
(1996
).82.
B.
Park
and
M.
Levitt
, “
Energy functions that discriminate x-ray and near-native folds from well-constructed decoys
,” J. Mol. Biol.
258
, 367
–392
(1996
).83.
M. J.
Sippl
, “
Knowledge-based potentials for proteins
,” Curr. Opin. Struct. Biol.
5
, 229
–235
(1995
).84.
P. D.
Thomas
and
K. A.
Dill
, “
An iterative method for extracting energy-like quantities from protein structures
,” Proc. Natl. Acad. Sci. U. S. A.
93
, 11628
–11633
(1996
).85.
F.
Musiani
and
A.
Giorgetti
, “
Protein aggregation and molecular crowding: Perspectives from multiscale simulations
,” Int. Rev. Cell Mol. Biol.
329
, 49
–77
(2017
).86.
S.
Auer
,
C. M.
Dobson
,
M.
Vendruscolo
, and
A.
Maritan
, “
Self-templated nucleation in peptide and protein aggregation
,” Phys. Rev. Lett.
101
, 258101
(2008
).87.
S.
Auer
,
F.
Meersman
,
C. M.
Dobson
, and
M.
Vendruscolo
, “
A generic mechanism of emergence of amyloid protofilaments from disordered oligomeric aggregates
,” PLoS Comput. Biol.
4
, e1000222
(2008
).88.
A.
Irbäck
,
S.
Jonsson
,
N.
Linnemann
,
B.
Linse
, and
S.
Wallin
, “
Aggregate geometry in amyloid fibril nucleation
,” Phys. Rev. Lett.
110
, 058101
(2013
).89.
R.
Vácha
and
D.
Frenkel
, “
Relation between molecular shape and the morphology of self-assembling aggregates: A simulation study
,” Biophys. J.
101
, 1432
–1439
(2011
).90.
J.
Zhang
and
M.
Muthukumar
, “
Simulations of nucleation and elongation of amyloid fibrils
,” J. Chem. Phys.
130
, 035102
(2009
).91.
S.
Abeln
,
M.
Vendruscolo
,
C. M.
Dobson
, and
D.
Frenkel
, “
A simple lattice model that captures protein folding, aggregation and amyloid formation
,” PLoS One
9
, e85185
(2014
).92.
G.
Bellesia
and
J.-E.
Shea
, “
Self-assembly of β-sheet forming peptides into chiral fibrillar aggregates
,” J. Chem. Phys.
126
, 245104
(2007
).93.
G.
Bellesia
and
J.-E.
Shea
, “
Diversity of kinetic pathways in amyloid fibril formation
,” J. Chem. Phys.
131
, 111102
(2009
).94.
G.
Bellesia
and
J.-E.
Shea
, “
Effect of β-sheet propensity on peptide aggregation
,” J. Chem. Phys.
130
, 145103
(2009
).95.
X.
Li
and
E. L.
Mehler
, “
Simulation of molecular crowding effects on an Alzheimer's β-amyloid peptide
,” Cell Biochem. Biophys.
46
, 123
–141
(2006
).96.
M. S.
Li
,
D.
Klimov
,
J.
Straub
, and
D.
Thirumalai
, “
Probing the mechanisms of fibril formation using lattice models
,” J. Chem. Phys.
129
, 175101
(2008
).97.
R.
Ni
,
S.
Abeln
,
M.
Schor
,
M. A. C.
Stuart
, and
P. G.
Bolhuis
, “
Interplay between folding and assembly of fibril-forming polypeptides
,” Phys. Rev. Lett.
111
, 058101
(2013
).98.
R.
Pellarin
and
A.
Caflisch
, “
Interpreting the aggregation kinetics of amyloid peptides
,” J. Mol. Biol.
360
, 882
–892
(2006
).99.
R.
Pellarin
,
E.
Guarnera
, and
A.
Caflisch
, “
Pathways and intermediates of amyloid fibril formation
,” J. Mol. Biol.
374
, 917
–924
(2007
).100.
L.
Monticelli
,
S. K.
Kandasamy
,
X.
Periole
,
R. G.
Larson
,
D. P.
Tieleman
, and
S.-J.
Marrink
, “
The MARTINI coarse-grained force field: Extension to proteins
,” J. Chem. Theory Comput.
4
, 819
–834
(2008
).101.
Y.
Chebaro
,
S.
Pasquali
, and
P.
Derreumaux
, “
The coarse-grained OPEP force field for non-amyloid and amyloid proteins
,” J. Phys. Chem. B
116
, 8741
–8752
(2012
).102.
M.
Cheon
,
I.
Chang
, and
C. K.
Hall
, “
Extending the PRIME model for protein aggregation to all 20 amino acids
,” Proteins: Struct., Funct., Bioinf.
78
, 2950
–2960
(2010
).103.
L.
Darré
,
M. R.
Machado
,
A. F.
Brandner
,
H. C.
González
,
S.
Ferreira
, and
S.
Pantano
, “
SIRAH: A structurally unbiased coarse-grained force field for proteins with aqueous solvation and long-range electrostatics
,” J. Chem. Theory Comput.
11
, 723
–739
(2015
).104.
S. J.
Marrink
,
H. J.
Risselada
,
S.
Yefimov
,
D. P.
Tieleman
, and
A. H.
De Vries
, “
The MARTINI force field: Coarse grained model for biomolecular simulations
,” J. Phys. Chem. B
111
, 7812
–7824
(2007
).105.
A.
Voegler Smith
and
C. K.
Hall
, “
α-helix formation: Discontinuous molecular dynamics on an intermediate-resolution protein model
,” Proteins: Struct., Funct., Bioinf.
44
, 344
–360
(2001
).106.
N.
Singh
and
W.
Li
, “
Recent advances in coarse-grained models for biomolecules and their applications
,” Int. J. Mol. Sci.
20
, 3774
(2019
).107.
B. M.
Bruininks
,
P. C.
Souza
, and
S. J.
Marrink
, “
A practical view of the martini force field
,” Biomol. Simul.: Methods Protoc.
2022
, 105
–127
(2019
).108.
H.
Li
and
A. A.
Gorfe
, “
Aggregation of lipid-anchored full-length H-Ras in lipid bilayers: Simulations with the MARTINI force field
,” PLoS One
8
, e71018
(2013
).109.
N.
Thota
,
Z.
Luo
,
Z.
Hu
, and
J.
Jiang
, “
Self-assembly of amphiphilic peptide (AF) 6H5K15: Coarse-grained molecular dynamics simulation
,” J. Phys. Chem. B
117
, 9690
–9698
(2013
).110.
M.
Seo
,
S.
Rauscher
,
R.
Pome's
, and
D. P.
Tieleman
, “
Improving internal peptide dynamics in the coarse-grained MARTINI model: Toward large-scale simulations of amyloid-and elastin-like peptides
,” J. Chem. Theory Comput.
8
, 1774
–1785
(2012
).111.
O.-S.
Lee
,
V.
Cho
, and
G. C.
Schatz
, “
Modeling the self-assembly of peptide amphiphiles into fibers using coarse-grained molecular dynamics
,” Nano Lett.
12
, 4907
–4913
(2012
).112.
C.
Guo
,
Y.
Luo
,
R.
Zhou
, and
G.
Wei
, “
Probing the self-assembly mechanism of diphenylalanine-based peptide nanovesicles and nanotubes
,” ACS Nano
6
, 3907
–3918
(2012
).113.
J.
Sørensen
,
X.
Periole
,
K. K.
Skeby
,
S.-J.
Marrink
, and
B.
Schiøtt
, “
Protofibrillar assembly toward the formation of amyloid fibrils
,” J. Phys. Chem. Lett.
2
, 2385
–2390
(2011
).114.
P. W.
Frederix
,
R. V.
Ulijn
,
N. T.
Hunt
, and
T.
Tuttle
, “
Virtual screening for dipeptide aggregation: Toward predictive tools for peptide self-assembly
,” J. Phys. Chem. Lett.
2
, 2380
–2384
(2011
).115.
S. J.
Marrink
and
D. P.
Tieleman
, “
Perspective on the Martini model
,” Chem. Soc. Rev.
42
, 6801
–6822
(2013
).116.
E. M.
Phelps
and
C. K.
Hall
, “
Structural transitions and oligomerization along polyalanine fibril formation pathways from computer simulations
,” Proteins: Struct., Funct., Bioinf.
80
, 1582
–1597
(2012
).117.
M.
Cheon
,
I.
Chang
, and
C. K.
Hall
, “
Influence of temperature on formation of perfect tau fragment fibrils using PRIME20/DMD simulations
,” Protein Sci.
21
, 1514
–1527
(2012
).118.
V. A.
Wagoner
,
M.
Cheon
,
I.
Chang
, and
C. K.
Hall
, “
Fibrillization propensity for short designed hexapeptides predicted by computer simulation
,” J. Mol. Biol.
416
, 598
–609
(2012
).119.
V. A.
Wagoner
,
M.
Cheon
,
I.
Chang
, and
C. K.
Hall
, “
Computer simulation study of amyloid fibril formation by palindromic sequences in prion peptides
,” Proteins: Struct., Funct., Bioinf.
79
, 2132
–2145
(2011
).120.
M.
Cheon
,
I.
Chang
, and
C. K.
Hall
, “
Spontaneous formation of twisted Aβ16–22 fibrils in large-scale molecular-dynamics simulations
,” Biophys. J.
101
, 2493
–2501
(2011
).121.
V. A.
Wagoner
,
M.
Cheon
,
I.
Chang
, and
C. K.
Hall
, “
Impact of sequence on the molecular assembly of short amyloid peptides
,” Proteins: Struct., Funct., Bioinf.
82
, 1469
–1483
(2014
).122.
S. Y.
Joshi
and
S. A.
Deshmukh
, “
A review of advancements in coarse-grained molecular dynamics simulations
,” Mol. Simul.
47
, 786
–803
(2021
).123.
N. J.
Dunn
,
T. T.
Foley
, and
W. G.
Noid
, “
Van der Waals perspective on coarse-graining: Progress toward solving representability and transferability problems
,” Acc. Chem. Res.
49
, 2832
–2840
(2016
).124.
S.
Kmiecik
,
D.
Gront
,
M.
Kolinski
,
L.
Wieteska
,
A. E.
Dawid
, and
A.
Kolinski
, “
Coarse-grained protein models and their applications
,” Chem. Rev.
116
, 7898
–7936
(2016
).125.
J.
Zhang
,
W.
Li
,
J.
Wang
,
M.
Qin
,
L.
Wu
,
Z.
Yan
,
W.
Xu
,
G.
Zuo
, and
W.
Wang
, “
Protein folding simulations: From coarse-grained model to all-atom model
,” IUBMB Life
61
, 627
–643
(2009
).126.
A. P.
Heath
,
L. E.
Kavraki
, and
C.
Clementi
, “
From coarse-grain to all-atom: Toward multiscale analysis of protein landscapes
,” Proteins: Struct., Funct., Bioinf.
68
, 646
–661
(2007
).127.
A.
Samiotakis
,
D.
Homouz
, and
M. S.
Cheung
, “
Multiscale investigation of chemical interference in proteins
,” J. Chem. Phys.
132
, 175101
(2010
).128.
B.
Urbanc
,
L.
Cruz
,
F.
Ding
,
D.
Sammond
,
S.
Khare
,
S.
Buldyrev
,
H.
Stanley
, and
N.
Dokholyan
, “
Molecular dynamics simulation of amyloid beta dimer formation
,” Biophys. J.
87
, 2310
–2321
(2004
).129.
A. V.
Onufriev
and
S.
Izadi
, “
Water models for biomolecular simulations
,” Wiley Interdiscip. Rev.: Comput. Mol. Sci.
8
, e1347
(2018
).130.
W. L.
Jorgensen
, “
Transferable intermolecular potential functions for water, alcohols, and ethers. Application to liquid water
,” J. Am. Chem. Soc.
103
, 6651764
(1981
).131.
W. L.
Jorgensen
,
J.
Chandrasekhar
,
J. D.
Madura
,
R. W.
Impey
, and
M. L.
Klein
, “
Comparison of simple potential functions for simulating liquid water
,” J. Chem. Phys.
79
, 926
–935
(1983
).132.
M. W.
Mahoney
and
W. L.
Jorgensen
, “
A five-site model for liquid water and the reproduction of the density anomaly by rigid, nonpolarizable potential functions
,” J. Chem. Phys.
112
, 8910
–8922
(2000
).133.
X.
Li
,
Z.
Yang
,
Y.
Chen
,
S.
Zhang
,
G.
Wei
, and
L.
Zhang
, “
Dissecting the molecular mechanisms of the Co-aggregation of Aβ40 and Aβ42 Peptides: A REMD simulation study
,” J. Phys. Chem. B
127
, 4050
–4060
(2023
).134.
A.
Onufriev
, “
Implicit solvent models in molecular dynamics simulations: A brief overview
,” Annu. Rep. Comput. Chem.
4
, 125
–137
(2008
).135.
D.
Bashford
and
D. A.
Case
, “
Generalized born models of macromolecular solvation effects
,” Annu. Rev. Phys. Chem.
51
, 129
–152
(2000
).136.
P.
Das
,
J. A.
King
, and
R.
Zhou
, “
Aggregation of γ-crystallins associated with human cataracts via domain swapping at the C-terminal β-strands
,” Proc. Natl. Acad. Sci. U. S. A.
108
, 10514
–10519
(2011
).137.
H.
Bloemendal
and
O.
Hockwin
, “
Lens protein
,” Crit. Rev. Biochem.
12
, 1
–38
(1982
).138.
M. A.
DiMauro
,
S. K.
Nandi
,
C. T.
Raghavan
,
R. K.
Kar
,
B.
Wang
,
A.
Bhunia
,
R. H.
Nagaraj
, and
A.
Biswas
, “
Acetylation of Gly1 and Lys2 promotes aggregation of human γD-crystallin
,” Biochemistry
53
, 7269
–7282
(2014
).139.
D. R.
Goulet
,
K. M.
Knee
, and
J. A.
King
, “
Inhibition of unfolding and aggregation of lens protein human gamma D crystallin by sodium citrate
,” Exp. Eye Res.
93
, 371
–381
(2011
).140.
A.
Basak
,
O.
Bateman
,
C.
Slingsby
,
A.
Pande
,
N.
Asherie
,
O.
Ogun
,
G. B.
Benedek
, and
J.
Pande
, “
High-resolution x-ray crystal structures of human γD crystallin (1.25 Å) and the R58H mutant (1.15 Å) associated with aculeiform cataract
,” J. Mol. Biol.
328
, 1137
–1147
(2003
).141.
C.
Slingsby
and
N. J.
Clout
, “
Structure of the crystallins
,” Eye
13
, 395
–402
(1999
).142.
S.
Brudar
and
B.
Hribar-Lee
, “
The mechanism of self-association of human γ-D crystallin from molecular dynamics simulations
,” J. Mol. Liq.
386
, 122461
(2023
).143.
S.
Chaudhury
,
A.
Dutta
,
S.
Bag
,
P.
Biswas
,
A. K.
Das
, and
S.
Dasgupta
, “
Probing the inhibitory potency of epigallocatechin gallate against human γB-crystallin aggregation: Spectroscopic, microscopic and simulation studies
,” Spectrochim. Acta, Part A
192
, 318
–327
(2018
).144.
H.
Ebersbach
,
E.
Fiedler
,
T.
Scheuermann
,
M.
Fiedler
,
M. T.
Stubbs
,
C.
Reimann
,
G.
Proetzel
,
R.
Rudolph
, and
U.
Fiedler
, “
Affilin–novel binding molecules based on human γ-B-crystallin, an all β-sheet protein
,” J. Mol. Biol.
372
, 172
–185
(2007
).145.
D.
Ghosh
,
K. A.
Sojitra
,
A.
Biswas
,
M.
Agarwal
, and
M.
Radhakrishna
, “
Effect of mutations on the folding and stability of γD-crystallin protein
,” J. Biomol. Struct. Dyn.
2023
, 1
–15
.146.
J.
Gaugler
,
T. J.
Bryan James
,
J.
Reimer
, and
J.
Weuve
, “
2021 Alzheimer's disease facts and figures
,” Alzheimer's Dementia
17
, 327
–406
(2021
).147.
A.
Burns
, “
Alzheimer's disease: On the verges of treatment and prevention
,” Lancet Neurol.
8
, 4
–5
(2009
).148.
M. C.
Childers
and
V.
Daggett
, “
Insights from molecular dynamics simulations for computational protein design
,” Mol. Syst. Des. Eng.
2
, 9
–33
(2017
).149.
Y.
Ihara
,
N.
Nukina
,
R.
Miura
, and
M.
Ogawara
, “
Phosphorylated tau protein is integrated into paired helical filaments in Alzheimer's disease
,” Alzheimer Dis. Assoc. Disord.
1
, 201
(1987
).150.
S. S.
Shafiei
,
M. J.
Guerrero-Muñoz
, and
D. L.
Castillo-Carranza
, “
Tau oligomers: Cytotoxicity, propagation, and mitochondrial damage
,” Front. Aging Neurosci.
9
, 83
(2017
).151.
X.
He
,
V. H.
Man
,
J.
Gao
, and
J.
Wang
, “
Investigation of the structure of full-length tau proteins with coarse-grained and all-atom molecular dynamics simulations
,” ACS Chem. Neurosci.
14
, 209
–217
(2022
).152.
E.
Hill
,
M. J.
Wall
,
K. G.
Moffat
, and
T. K.
Karikari
, “
Understanding the pathophysiological actions of tau oligomers: A critical review of current electrophysiological approaches
,” Front. Mol. Neurosci.
13
, 155
(2020
).153.
P.
Das
,
S-g
Kang
,
S.
Temple
, and
G.
Belfort
, “
Interaction of amyloid inhibitor proteins with amyloid beta peptides: Insight from molecular dynamics simulations
,” PLoS One
9
, e113041
(2014
).154.
S. G.
Itoh
and
H.
Okumura
, “
Promotion and inhibition of amyloid-β peptide aggregation: Molecular dynamics studies
,” Int. J. Mol. Sci.
22
, 1859
(2021
).155.
Y.
Zhang
,
Y.
Wang
,
Y.
Liu
,
G.
Wei
,
F.
Ding
, and
Y.
Sun
, “
Molecular insights into the misfolding and dimerization dynamics of the full-length α-synuclein from atomistic discrete molecular dynamics simulations
,” ACS Chem. Neurosci.
13
, 3126
–3137
(2022
).156.
M. G.
Spillantini
,
M. L.
Schmidt
,
V. M.-Y.
Lee
,
J. Q.
Trojanowski
,
R.
Jakes
, and
M.
Goedert
, “
α-synuclein in Lewy bodies
,” Nature
388
, 839
–840
(1997
).157.
M. C.
Bennett
, “
The role of α-synuclein in neurodegenerative diseases
,” Pharmacol. Ther.
105
, 311
–331
(2005
).158.
R.
Ramis
,
J.
Ortega-Castro
,
B.
Vilanova
,
M.
Adrover
, and
J.
Frau
, “
Unraveling the NaCl concentration effect on the first stages of α-synuclein aggregation
,” Biomacromolecules
21
, 5200
–5212
(2020
).159.
P. H.
Weinreb
,
W.
Zhen
,
A. W.
Poon
,
K. A.
Conway
, and
P. T.
Lansbury
, “
NACP, a protein implicated in Alzheimer's disease and learning, is natively unfolded
,” Biochemistry
35
, 13709
–13715
(1996
).160.
K.-P.
Wu
and
J.
Baum
, “
Detection of transient interchain interactions in the intrinsically disordered protein α-synuclein by NMR paramagnetic relaxation enhancement
,” J. Am. Chem. Soc.
132
, 5546
–5547
(2010
).161.
D. P.
Karpinar
,
M. B. G.
Balija
,
S.
Kügler
,
F.
Opazo
,
N.
Rezaei-Ghaleh
,
N.
Wender
,
H.-Y.
Kim
,
G.
Taschenberger
,
B. H.
Falkenburger
, and
H.
Heise
et al., “
Pre-fibrillar α-synuclein variants with impaired β-structure increase neurotoxicity in Parkinson's disease models
,” EMBO J.
28
, 3256
–3268
(2009
).162.
P. H.
Nguyen
,
A.
Ramamoorthy
,
B. R.
Sahoo
,
J.
Zheng
,
P.
Faller
,
J. E.
Straub
,
L.
Dominguez
,
J.-E.
Shea
,
N. V.
Dokholyan
,
A.
De Simone
et al, “
Amyloid oligomers: A joint experimental/computational perspective on Alzheimer's disease, Parkinson's disease, type II diabetes, and amyotrophic lateral sclerosis
,” Chem. Rev.
121
, 2545
–2647
(2021
).163.
L.
Marino
,
R.
Ramis
,
R.
Casasnovas
,
J.
Ortega-Castro
,
B.
Vilanova
,
J.
Frau
, and
M.
Adrover
, “
Unravelling the effect of N(ε)-(carboxyethyl) lysine on the conformation, dynamics and aggregation propensity of α-synuclein
,” Chem. Sci.
11
, 3332
–3344
(2020
).164.
R.
Ramis
,
J.
Ortega-Castro
,
R.
Casasnovas
,
L.
Mariño
,
B.
Vilanova
,
M.
Adrover
, and
J.
Frau
, “
A coarse-grained molecular dynamics approach to the study of the intrinsically disordered protein α-synuclein
,” J. Chem. Inf. Model.
59
, 1458
–1471
(2019
).165.
K.
Smida
,
M.
Albedah
,
R. F.
Rashid
, and
A.-R.
Al-Qawasmi
, “
Molecular dynamics method for targeting α-synuclein aggregation induced Parkinson's disease using boron nitride nanostructures
,” Eng. Anal. Boundary Elem.
146
, 89
–95
(2023
).166.
J.
Guo
,
L.
Ning
,
H.
Ren
,
H.
Liu
, and
X.
Yao
, “
Influence of the pathogenic mutations T188K/R/A on the structural stability and misfolding of human prion protein: Insight from molecular dynamics simulations
,” Biochim. Biophys. Acta, Gen. Subj.
1820
, 116
–123
(2012
).167.
J. A.
Freites
,
M. N.
Louis
, and
D. J.
Tobias
, “
Insights into the solubility of γD-crystallin from multiscale atomistic simulations
,” J. Comput. Chem.
44
, 1658
(2023
).168.
T. D.
Romo
and
A.
Grossfield
, “
Unknown unknowns: The challenge of systematic and statistical error in molecular dynamics simulations
,” Biophys. J.
106
, 1553
–1554
(2014
).169.
L.
Monticelli
and
D. P.
Tieleman
, “
Force fields for classical molecular dynamics
,” Biomol. Simul.: Methods Protoc.
2013
, 197
–213
.170.
E. A.
Cino
,
W.-Y.
Choy
, and
M.
Karttunen
, “
Comparison of secondary structure formation using 10 different force fields in microsecond molecular dynamics simulations
,” J. Chem. Theory Comput.
8
, 2725
–2740
(2012
).171.
D. J.
Price
and
C. L.
Brooks
III
, “
Modern protein force fields behave comparably in molecular dynamics simulations
,” J. Comput. Chem.
23
, 1045
–1057
(2002
).172.
K.
Lindorff-Larsen
,
P.
Maragakis
,
S.
Piana
,
M. P.
Eastwood
,
R. O.
Dror
, and
D. E.
Shaw
, “
Systematic validation of protein force fields against experimental data
,” PLoS One
7
, e32131
(2012
).173.
K. A.
Beauchamp
,
Y.-S.
Lin
,
R.
Das
, and
V. S.
Pande
, “
Are protein force fields getting better? A systematic benchmark on 524 diverse NMR measurements
,” J. Chem. Theory Comput.
8
, 1409
–1414
(2012
).174.
O. F.
Lange
,
D.
Van Der Spoel
, and
B. L.
De Groot
, “
Scrutinizing molecular mechanics force fields on the submicrosecond timescale with NMR data
,” Biophys. J.
99
, 647
–655
(2010
).175.
Y.
Gu
,
D.-W.
Li
, and
R.
Bruschweiler
, “
NMR order parameter determination from long molecular dynamics trajectories for objective comparison with experiment
,” J. Chem. Theory Comput.
10
, 2599
–2607
(2014
).176.
E. S.
O'Brien
,
A. J.
Wand
, and
K. A.
Sharp
, “
On the ability of molecular dynamics force fields to recapitulate NMR derived protein side chain order parameters
,” Protein Sci.
25
, 1156
–1160
(2016
).177.
G.
Hernández
,
J. S.
Anderson
, and
D. M.
LeMaster
, “
Experimentally assessing molecular dynamics sampling of the protein native state conformational distribution
,” Biophys. Chem.
163–164
, 21
–34
(2012
).178.
K. C.
Cunha
,
V. H.
Rusu
,
I. F.
Viana
,
E. T.
Marques
,
R.
Dhalia
, and
R. D.
Lins
, “
Assessing protein conformational sampling and structural stability via de novo design and molecular dynamics simulations
,” Biopolymers
103
, 351
–361
(2015
).179.
G.
Grasso
and
A.
Danani
, “
Molecular simulations of amyloid beta assemblies
,” Adv. Phys.: X
5
, 1770627
(2020
).180.
T.
Meyer
,
M.
D'Abramo
,
M.
Rueda
,
C.
Ferrer-Costa
,
A.
Pérez
,
O.
Carrillo
,
J.
Camps
,
C.
Fenollosa
,
D.
Repchevsky
,
J. L.
Gelpí
, and
M.
Orozco
, “
MoDEL (Molecular Dynamics Extended Library): A database of atomistic molecular dynamics trajectories
,” Structure
18
, 1399
–1409
(2010
).181.
J. C.
Thibault
,
T. E.
Cheatham
III
, and
J. C.
Facelli
, “
iBIOMES lite: Summarizing biomolecular simulation data in limited settings
,” J. Chem. Inf. Model.
54
, 1810
–1819
(2014
).182.
P. R.
Arantes
,
M. D.
Polêto
,
C.
Pedebos
, and
R.
Ligabue-Braun
, “
Making it rain: Cloud-based molecular simulations for everyone
,” J. Chem. Inf. Model.
61
, 4852
–4856
(2021
).183.
Y.
Miao
and
J. A.
McCammon
, “
Unconstrained enhanced sampling for free energy calculations of biomolecules: A review
,” Mol. Simul.
42
, 1046
–1055
(2016
).184.
M.
Kurplus
and
J.
McCammon
, “
Dynamics of proteins: Elements and function
,” Annu. Rev. Biochem.
52
, 263
–300
(1983
).185.
Y.
Sugita
,
M.
Kamiya
,
H.
Oshima
, and
S.
Re
, “
Replica-exchange methods for biomolecular simulations
,” Biomol. Simul.: Methods Protoc.
2022
, 155
–177
(2019
).186.
P.
Tiwary
and
M.
Parrinello
, “
From metadynamics to dynamics
,” Phys. Rev. Lett.
111
, 230602
(2013
).187.
A.
Barducci
,
M.
Bonomi
, and
M.
Parrinello
, “
Metadynamics
,” Wiley Interdiscip. Rev.: Comput. Mol. Sci.
1
, 826
–843
(2011
).188.
B.
Roux
, “
The calculation of the potential of mean force using computer simulations
,” Comput. Phys. Commun.
91
, 275
–282
(1995
).189.
Y.
Sugita
and
Y.
Okamoto
, “
Replica-exchange molecular dynamics method for protein folding
,” Chem. Phys. Lett.
314
, 141
–151
(1999
).190.
M.
Chen
, “
Collective variable-based enhanced sampling and machine learning
,” Eur. Phys. J. B
94
, 1
–17
(2021
).191.
J. D.
Faraldo-Gómez
and
B.
Roux
, “
Characterization of conformational equilibria through Hamiltonian and temperature replica-exchange simulations: Assessing entropic and environmental effects
,” J. Comput. Chem.
28
, 1634
–1647
(2007
).192.
F.
Noé
and
C.
Clementi
, “
Collective variables for the study of long-time kinetics from molecular trajectories: Theory and methods
,” Curr. Opin. Struct. Boil.
43
, 141
–147
(2017
).193.
J. S.
Van Duijneveldt
and
D.
Frenkel
, “
Computer simulation study of free energy barriers in crystal nucleation
,” J. Chem. Phys.
96
, 4655
–4668
(1992
).194.
R.
Lynden-Bell
,
J. S.
Van Duijneveldt
, and
D.
Frenkel
, “
Free energy changes on freezing and melting ductile metals
,” Mol. Phys.
80
, 801
–814
(1993
).195.
C.
Desgranges
and
J.
Delhommelle
, “
Molecular simulation of the crystallization of aluminum from the supercooled liquid
,” J. Chem. Phys.
127
, 144509
(2007
).196.
D.
Quigley
and
P. M.
Rodger
, “
A metadynamics-based approach to sampling crystallisation events
,” Mol. Simul.
35
, 613
–623
(2009
).197.
F.
Trudu
,
D.
Donadio
, and
M.
Parrinello
, “
Freezing of a Lennard-Jones fluid: From nucleation to spinodal regime
,” Phys. Rev. Lett.
97
, 105701
(2006
).198.
M.
Salvalaglio
,
C.
Perego
,
F.
Giberti
,
M.
Mazzotti
, and
M.
Parrinello
, “
Molecular-dynamics simulations of urea nucleation from aqueous solution
,” Proc. Natl. Acad. Sci. U. S. A.
112
, E6
–E14
(2015
).199.
P. M.
Piaggi
,
O.
Valsson
, and
M.
Parrinello
, “
Enhancing entropy and enthalpy fluctuations to drive crystallization in atomistic simulations
,” Phys. Rev. Lett.
119
, 015701
(2017
).200.
O.
Valsson
and
M.
Parrinello
, “
Variational approach to enhanced sampling and free energy calculations
,” Phys. Rev. Lett.
113
, 090601
(2014
).201.
D.
Granata
,
F.
Baftizadeh
,
J.
Habchi
,
C.
Galvagnion
,
A.
De Simone
,
C.
Camilloni
,
A.
Laio
, and
M.
Vendruscolo
, “
The inverted free energy landscape of an intrinsically disordered peptide by simulations and experiments
,” Sci. Rep.
5
, 15449
(2015
).202.
J. C.
Gumbart
,
B.
Roux
, and
C.
Chipot
, “
Standard binding free energies from computer simulations: What is the best strategy?
,” J. Chem. Theory Comput.
9
, 794
–802
(2013
).203.
R.
Aguayo-Ortiz
,
D. C.
Guzmán-Ocampo
, and
L.
Dominguez
, “
Insights into the binding of morin to human γD-crystallin
,” Biophys. Chem.
282
, 106750
(2022
).204.
S.
Izrailev
,
S.
Stepaniants
,
B.
Isralewitz
,
D.
Kosztin
,
H.
Lu
,
F.
Molnar
,
W.
Wriggers
, and
K.
Schulten
, “
Steered molecular dynamics
,” in Computational Molecular Dynamics: Challenges, Methods, Ideas: Proceedings of the 2nd International Symposium on Algorithms for Macromolecular Modelling, Berlin, May 21–24, 1997
(
Springer
, 1999
), pp. 39
–65
.205.
S.
Park
and
K.
Schulten
, “
Calculating potentials of mean force from steered molecular dynamics simulations
,” J. Chem. Phys.
120
, 5946
–5961
(2004
).206.
C.
Jarzynski
, “
Nonequilibrium equality for free energy differences
,” Phys. Rev. Lett.
78
, 2690
(1997
).207.
D.
Suh
,
B. K.
Radak
,
C.
Chipot
, and
B.
Roux
, “
Enhanced configurational sampling with hybrid non-equilibrium molecular dynamics–Monte Carlo propagator
,” J. Chem. Phys.
148
, 014101
(2018
).208.
A.
Okur
,
D. R.
Roe
,
G.
Cui
,
V.
Hornak
, and
C.
Simmerling
, “
Improving convergence of replica-exchange simulations through coupling to a high-temperature structure reservoir
,” J. Chem. Theory Comput.
3
, 557
–568
(2007
).209.
R. C.
Bernardi
,
M. C.
Melo
, and
K.
Schulten
, “
Enhanced sampling techniques in molecular dynamics simulations of biological systems
,” Biochim. Biophys. Acta, Gen. Subj.
1850
, 872
–877
(2015
).210.
J.
Domanski
,
G.
Hedger
,
R. B.
Best
,
P. J.
Stansfeld
, and
M. S.
Sansom
, “
Convergence and sampling in determining free energy landscapes for membrane protein association
,” J. Phys. Chem. B
121
, 3364
–3375
(2017
).211.
J. K.
Marzinek
,
P. J.
Bond
,
G.
Lian
,
Y.
Zhao
,
L.
Han
,
M. G.
Noro
,
E. N.
Pistikopoulos
, and
A.
Mantalaris
, “
Free energy predictions of ligand binding to an α-helix using steered molecular dynamics and umbrella sampling simulations
,” J. Chem. Inf. Modell.
54
, 2093
–2104
(2014
).212.
G.
Ozer
,
E. F.
Valeev
,
S.
Quirk
, and
R.
Hernandez
, “
Adaptive steered molecular dynamics of the long-distance unfolding of neuropeptide Y
,” J. Chem. Theory Comput.
6
, 3026
–3038
(2010
).213.
T. T. M.
Thu
and
M. S.
Li
, “
Protein aggregation rate depends on mechanical stability of fibrillar structure
,” J. Chem. Phys.
157
, 055101
(2022
).214.
P. A.
Barredo
and
M. P.
Balanay
, “
Recent advances in molecular dynamics simulations of Tau fibrils and oligomers
,” Membranes
13
, 277
(2023
).215.
M.
Sotomayor
, “
Computational exploration of single-protein mechanics by steered molecular dynamics
,” AIP Conf. Proc.
1703
, 030001
(2015
).216.
G. M.
Torrie
and
J. P.
Valleau
, “
Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling
,” J. Comput. Phys.
23
, 187
–199
(1977
).217.
G. M.
Torrie
and
J. P.
Valleau
, “
Monte Carlo free energy estimates using non-Boltzmann sampling: Application to the sub-critical Lennard-Jones fluid
,” Chem. Phys. Lett.
28
, 578
–581
(1974
).218.
I.
McDonald
and
K.
Singer
, “
Machine calculation of thermodynamic properties of a simple fluid at supercritical temperatures
,” J. Chem. Phys.
47
, 4766
–4772
(1967
).219.
I.
McDonald
and
K.
Singer
, “
Examination of the adequacy of the 12–6 potential for liquid argon by means of Monte Carlo calculations
,” J. Chem. Phys.
50
, 2308
–2315
(1969
).220.
C.
Chen
,
Y.
Huang
, and
Y.
Xiao
, “
Enhanced sampling of molecular dynamics simulation of peptides and proteins by double coupling to thermal bath
,” J. Biomol. Struct. Dyn.
31
, 206
–214
(2013
).221.
C.
Selvaraj
,
G.
Krishnasamy
,
S. S.
Jagtap
,
S. K.
Patel
,
S. S.
Dhiman
,
T.-S.
Kim
,
S. K.
Singh
, and
J.-K.
Lee
, “
Structural insights into the binding mode of d-sorbitol with sorbitol dehydrogenase using QM-polarized ligand docking and molecular dynamics simulations
,” Biochem. Eng. J.
114
, 244
–256
(2016
).222.
T. C.
Beutler
and
W. F.
van Gunsteren
, “
The computation of a potential of mean force: Choice of the biasing potential in the umbrella sampling technique
,” J. Chem. Phys.
100
, 1492
–1497
(1994
).223.
K. C.
Mundim
and
C.
Tsallis
, “
Geometry optimization and conformational analysis through generalized simulated annealing
,” Int. J. Quantum Chem.
58
, 373
–381
(1996
).224.
S.
Kumar
,
J. M.
Rosenberg
,
D.
Bouzida
,
R. H.
Swendsen
, and
P. A.
Kollman
, “
The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method
,” J. Comput. Chem.
13
, 1011
–1021
(1992
).225.
M.
Souaille
and
B.
Roux
, “
Extension to the weighted histogram analysis method: Combining umbrella sampling with free energy calculations
,” Comput. Phys. Commun.
135
, 40
–57
(2001
).226.
J.
Kästner
and
W.
Thiel
, “
Bridging the gap between thermodynamic integration and umbrella sampling provides a novel analysis method: “Umbrella integration
,”” J. Chem. Phys.
123
, 144104
(2005
).227.
A.
Hung
,
N.
Todorova
, and
I.
Yarovsky
, “
Computer simulation studies of abnormal protein aggregation
,” in Proceedings of the 2nd WSEAS International Conference on Biomedical Electronics and Biomedical Informatics
(
WSEAS
, 2009
), pp. 41
–46
.228.
E.
Rivera
,
J.
Straub
, and
D.
Thirumalai
, “
Sequence and crowding effects in the aggregation of a 10-residue fragment derived from islet amyloid polypeptide
,” Biophys. J.
96
, 4552
–4560
(2009
).229.
C. H.
Davis
and
M. L.
Berkowitz
, “
Interaction between amyloid-β (1–42) peptide and phospholipid bilayers: A molecular dynamics study
,” Biophys. J.
96
, 785
–797
(2009
).230.
A.
Laio
and
M.
Parrinello
, “
Escaping free-energy minima
,” Proc. Natl. Acad. Sci. U. S. A.
99
, 12562
–12566
(2002
).231.
V.
Spiwok
,
P.
Lipovová
, and
B.
Králová
, “
Metadynamics in essential coordinates: Free energy simulation of conformational changes
,” J. Phys. Chem. B
111
, 3073
–3076
(2007
).232.
J.
Pfaendtner
, “
Metadynamics to enhance sampling in biomolecular simulations
,” Methods Protoc.
2022
, 179
–200
(2019
).233.
T.
Huber
,
A. E.
Torda
, and
W. F.
Van Gunsteren
, “
Local elevation: A method for improving the searching properties of molecular dynamics simulation
,” J. Comput.-Aided Mol. Des.
8
, 695
–708
(1994
).234.
G.
Bussi
and
A.
Laio
, “
Using metadynamics to explore complex free-energy landscapes
,” Nat. Rev. Phys.
2
, 200
–212
(2020
).235.
G.
Bussi
,
F. L.
Gervasio
,
A.
Laio
, and
M.
Parrinello
, “
Free-energy landscape for β hairpin folding from combined parallel tempering and metadynamics
,” J. Am. Chem. Soc.
128
, 13435
–13441
(2006
).236.
M.
Deighan
,
M.
Bonomi
, and
J.
Pfaendtner
, “
Efficient simulation of explicitly solvated proteins in the well-tempered ensemble
,” J. Chem. Theory Comput.
8
, 2189
–2192
(2012
).237.
S.
Piana
and
A.
Laio
, “
A bias-exchange approach to protein folding
,” J. Phys. Chem. B
111
, 4553
–4559
(2007
).238.
V.
Limongelli
,
M.
Bonomi
, and
M.
Parrinello
, “
Funnel metadynamics as accurate binding free-energy method
,” Proc. Natl. Acad. Sci. U. S. A.
110
, 6358
–6363
(2013
).239.
P.
Raiteri
,
A.
Laio
,
F. L.
Gervasio
,
C.
Micheletti
, and
M.
Parrinello
, “
Efficient reconstruction of complex free energy landscapes by multiple walkers metadynamics
,” J. Phys. Chem. B
110
, 3533
–3539
(2006
).240.
T.
Róg
,
M.
Girych
, and
A.
Bunker
, “
Mechanistic understanding from molecular dynamics in pharmaceutical research 2: Lipid membrane in drug design
,” Pharmaceuticals
14
, 1062
(2021
).241.
K.
Hukushima
and
K.
Nemoto
, “
Exchange Monte Carlo method and application to spin glass simulations
,” J. Phys. Soc. Jpn.
65
, 1604
–1608
(1996
).242.
D. R.
Roe
,
C.
Bergonzo
, and
T. E.
Cheatham
III
, “
Evaluation of enhanced sampling provided by accelerated molecular dynamics with Hamiltonian replica exchange methods
,” J. Phys. Chem. B
118
, 3543
–3552
(2014
).243.
K.
Ostermeir
and
M.
Zacharias
, “
Advanced replica-exchange sampling to study the flexibility and plasticity of peptides and proteins
,” Biochim. Biophys. Acta, Proteins Proteomics
1834
, 847
–853
(2013
).244.
Y.
Sugita
,
A.
Kitao
, and
Y.
Okamoto
, “
Multidimensional replica-exchange method for free-energy calculations
,” J. Chem. Phys.
113
, 6042
–6051
(2000
).245.
J. D.
Chodera
and
M. R.
Shirts
, “
Replica exchange and expanded ensemble simulations as Gibbs sampling: Simple improvements for enhanced mixing
,” J. Chem. Phys.
135
, 194110
(2011
).246.
N.
Plattner
,
J.
Doll
,
P.
Dupuis
,
H.
Wang
,
Y.
Liu
, and
J.
Gubernatis
, “
An infinite swapping approach to the rare-event sampling problem
,” J. Chem. Phys.
135
, 134111
(2011
).247.
H.
Suwa
and
S.
Todo
, “
Markov chain Monte Carlo method without detailed balance
,” Phys. Rev. Lett.
105
, 120603
(2010
).248.
T.
Mori
,
J.
Jung
, and
Y.
Sugita
, “
Surface-tension replica-exchange molecular dynamics method for enhanced sampling of biological membrane systems
,” J. Chem. Theory Comput.
9
, 5629
–5640
(2013
).249.
H.
Fukunishi
,
O.
Watanabe
, and
S.
Takada
, “
On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: Application to protein structure prediction
,” J. Chem. Phys.
116
, 9058
–9067
(2002
).250.
M.
Moradi
and
E.
Tajkhorshid
, “
Mechanistic picture for conformational transition of a membrane transporter at atomic resolution
,” Proc. Natl. Acad. Sci. U. S. A.
110
, 18916
–18921
(2013
).251.
S.
Park
and
W.
Im
, “
Two dimensional window exchange umbrella sampling for transmembrane helix assembly
,” J. Chem. Theory Comput.
9
, 13
–17
(2013
).252.
S.
Park
,
T.
Kim
, and
W.
Im
, “
Transmembrane helix assembly by window exchange umbrella sampling
,” Phys. Rev. Lett.
108
, 108102
(2012
).253.
S.
Jamal
,
A.
Kumari
,
A.
Singh
,
S.
Goyal
, and
A.
Grover
, “
Conformational ensembles of α-synuclein derived peptide with different osmolytes from temperature replica exchange sampling
,” Front. Neurosci.
11
, 684
(2017
).254.
K.
Kappel
,
Y.
Miao
, and
J. A.
McCammon
, “
Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor
,” Q. Rev. Biophys.
48
, 479
–487
(2015
).255.
D.
Hamelberg
,
J.
Mongan
, and
J. A.
McCammon
, “
Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules
,” J. Chem. Phys.
120
, 11919
–11929
(2004
).256.
H. J.
Gim
,
J.
Park
,
M. E.
Jung
, and
K.
Houk
, “
Conformational dynamics of androgen receptors bound to agonists and antagonists
,” Sci. Rep.
11
, 15887
(2021
).257.
S.
Patel
and
R. V.
Hosur
, “
Replica exchange molecular dynamics simulations reveal self-association sites in M-crystallin caused by mutations provide insights of cataract
,” Sci. Rep.
11
, 23270
(2021
).258.
J. A.
Maier
,
C.
Martinez
,
K.
Kasavajhala
,
L.
Wickstrom
,
K. E.
Hauser
, and
C.
Simmerling
, “
ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB
,” J. Chem. Theory Comput.
11
, 3696
–3713
(2015
).259.
D.
Case
,
D.
Cerutti
,
T.
Cheatham
,
T.
Darden
,
R.
Duke
,
T.
Giese
,
H.
Gohlke
,
A.
Goetz
,
D.
Greene
,
N.
Homeyer
et al, Amber18
(
University of San Francisco
, 2017
).260.
G. A.
Tribello
,
M.
Bonomi
,
D.
Branduardi
,
C.
Camilloni
, and
G.
Bussi
, “
PLUMED 2: New feathers for an old bird
,” Comput. Phys. Commun.
185
, 604
–613
(2014
).261.
M.
Bonomi
,
G.
Bussi
,
C.
Camilloni
,
G. A.
Tribello
,
P.
Banáš
,
A.
Barducci
,
M.
Bernetti
,
P. G.
Bolhuis
,
S.
Bottaro
, and
D.
Branduardi
et al., “
Promoting transparency and reproducibility in enhanced molecular simulations
,” Nat. Methods
16
, 670
–673
(2019
).262.
Q.
Wang
,
X.
Yu
,
K.
Patal
,
R.
Hu
,
S.
Chuang
,
G.
Zhang
, and
J.
Zheng
, “
Tanshinones inhibit amyloid aggregation by amyloid-β peptide, disaggregate amyloid fibrils, and protect cultured cells
,” ACS Chem. Neurosci.
4
, 1004
–1015
(2013
).263.
T.
Takeda
,
W. E.
Chang
,
E. P.
Raman
, and
D. K.
Klimov
, “
Binding of nonsteroidal anti-inflammatory drugs to Aβ fibril
,” Proteins: Struct., Funct., Bioinf.
78
, 2849
–2860
(2010
).264.
T. T. M.
Thu
,
N. T.
Co
,
L. A.
Tu
, and
M. S.
Li
, “
Aggregation rate of amyloid beta peptide is controlled by beta-content in monomeric state
,” J. Chem. Phys.
150
, 225101
(2019
).265.
D.
Van Der Spoel
,
E.
Lindahl
,
B.
Hess
,
G.
Groenhof
,
A. E.
Mark
, and
H. J.
Berendsen
, “
GROMACS: Fast, flexible, and free
,” J. Comput. Chem.
26
, 1701
–1718
(2005
).266.
V. H.
Man
,
X.
He
,
F.
Han
,
L.
Cai
,
L.
Wang
,
T.
Niu
,
J.
Zhai
,
B.
Ji
,
J.
Gao
, and
J.
Wang
, “
Phosphorylation at Ser289 enhances the oligomerization of Tau repeat R2
,” J. Chem. Inf. Model.
63
, 1351
–1361
(2023
).267.
E.
Cline
,
M.
Bicca
,
K.
Viola
, and
W.
Klein
, “
The amyloid-beta oligomer hypothesis: Beginning of the third decade
,” J. Alzheimers Dis.
64
, S567
–S610
(2018
).268.
B.
Hyman
, “
All the Tau we cannot see
,” Annu. Rev. Med.
74
, 503
–514
(2023
).269.
A.
Surguchov
, “
Molecular and cellular biology of synucleins
,” Int. Rev. Cell Mol. Biol.
270
, 225
–317
(2008
).270.
M. G.
Spillantini
,
R. A.
Crowther
,
R.
Jakes
,
M.
Hasegawa
, and
M.
Goedert
, “
α-synuclein in filamentous inclusions of Lewy bodies from Parkinson's disease and dementia with Lewy bodies
,” Proc. Natl. Acad. Sci. U. S. A.
95
, 6469
–6473
(1998
).271.
W. S.
Davidson
,
A.
Jonas
,
D. F.
Clayton
, and
J. M.
George
, “
Stabilization of α-synuclein secondary structure upon binding to synthetic membranes
,” J. Biol. Chem.
273
, 9443
–9449
(1998
).272.
P. J.
McLean
,
H.
Kawamata
,
S.
Ribich
, and
B. T.
Hyman
, “
Membrane association and protein conformation of α-synuclein in intact neurons: Effect of Parkinson's disease-linked mutations
,” J. Biol. Chem.
275
, 8812
–8816
(2000
).273.
E.
Jo
,
J.
McLaurin
,
C. M.
Yip
,
P. S.
George-Hyslop
, and
P. E.
Fraser
, “
α-synuclein membrane interactions and lipid specificity
,” J. Biol. Chem.
275
, 34328
–34334
(2000
).274.
S.
Park
,
J.
Yoon
,
S.
Jang
,
K.
Lee
, and
S.
Shin
, “
The role of the acidic domain of α-synuclein in amyloid fibril formation: A molecular dynamics study
,” J. Biomol. Struct. Dyn.
34
, 376
–383
(2016
).275.
A.
Saurabh
and
N. P.
Prabhu
, “
Concerted enhanced-sampling simulations to elucidate the helix-fibril transition pathway of intrinsically disordered α-synuclein
,” Int. J. Biol. Macromol.
223
, 1024
–1041
(2022
).276.
P.
Bjelkmar
,
P.
Larsson
,
M. A.
Cuendet
,
B.
Hess
, and
E.
Lindahl
, “
Implementation of the CHARMM force field in GROMACS: Analysis of protein stability effects from correction maps, virtual interaction sites, and water models
,” J. Chem. Theory Comput.
6
, 459
–466
(2010
).277.
M. J.
Abraham
,
T.
Murtola
,
R.
Schulz
,
S.
Páll
,
J. C.
Smith
,
B.
Hess
, and
E.
Lindahl
, “
GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers
,” SoftwareX
1
, 19
–25
(2015
).278.
A.
Patriksson
and
D.
van der Spoel
, “
A temperature calculator for replica exchange molecular dynamics simulations
,” Phys. Chem. Chem. Phys.
10
, 2073
(2007
).279.
J. D.
Chodera
,
N.
Singhal
,
V. S.
Pande
,
K. A.
Dill
, and
W. C.
Swope
, “
Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics
,” J. Chem. Phys.
126
, 155101
(2007
).280.
F.
Noé
,
I.
Horenko
,
C.
Schütte
, and
J. C.
Smith
, “
Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of metastable states
,” J. Chem. Phys.
126
, 155102
(2007
).281.
G. R.
Bowman
,
V. S.
Pande
, and
F.
Noé
, An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation
(
Springer Science & Business Media
, 2013
). Vol.
797
.282.
W.
Wang
,
S.
Cao
,
L.
Zhu
, and
X.
Huang
, “
Constructing Markov state models to elucidate the functional conformational changes of complex biomolecules
,” Wiley Interdiscip. Rev.: Comput. Mol. Sci.
8
, e1343
(2018
).283.
B. E.
Husic
and
V. S.
Pande
, “
Markov state models: From an art to a science
,” J. Am. Chem. Soc.
140
, 2386
–2396
(2018
).284.
F.
Noé
and
E.
Rosta
, “
Markov models of molecular kinetics
,” J. Chem. Phys.
151
, 190401
(2019
).285.
M. K.
Scherer
,
B.
Trendelkamp-Schroer
,
F.
Paul
,
G.
Pérez-Hernández
,
M.
Hoffmann
,
N.
Plattner
,
C.
Wehmeyer
,
J.-H.
Prinz
, and
F.
Noé
, “
PyEMMA 2: A software package for estimation, validation, and analysis of Markov models
,” J. Chem. Theory Comput.
11
, 5525
–5542
(2015
).286.
K. A.
Beauchamp
,
G. R.
Bowman
,
T. J.
Lane
,
L.
Maibaum
,
I. S.
Haque
, and
V. S.
Pande
, “
MSMBuilder2: Modeling conformational dynamics at the picosecond to millisecond scale
,” J. Chem. Theory Comput.
7
, 3412
(2011
).287.
D.
Nagel
,
S.
Sartore
, and
G.
Stock
, “
Toward a benchmark for Markov state models: The folding of HP35
,” J. Phys. Chem. Lett.
14
, 6956
–6967
(2023
).288.
D.
Ghosh
,
M.
Agarwal
, and
M.
Radhakrishna
, “
Molecular insights into the inhibitory role of α-crystallin against γD-crystallin aggregation
,” J. Chem. Theory Comput.
20
, 1740
–1752
(2023
).289.
M. M.
Sultan
and
V. S.
Pande
, “
Automated design of collective variables using supervised machine learning
,” J. Chem. Phys.
149
, 094106
(2018
).290.
D.
Trapl
,
I.
Horvacanin
,
V.
Mareska
,
F.
Ozcelik
,
G.
Unal
, and
V.
Spiwok
, “
Anncolvar: Approximation of complex collective variables by artificial neural networks for analysis and biasing of molecular simulations
,” Front. Mol. Biosci.
6
, 25
(2019
).291.
J.
Kinoshita
and
T.
Clark
, “
Alzforum
,” Methods Mol. Biol.
401
, 365
–381
(2007
).292.
J. A.
Siepen
and
D. R.
Westhead
, “
The fibril_one on-line database: Mutations, experimental conditions, and trends associated with amyloid fibril formation
,” Protein Sci.
11
, 1862
–1866
(2002
).293.
M.
López de la Paz
and
L.
Serrano
, “
Sequence determinants of amyloid fibril formation
,” Proc. Natl. Acad. Sci. U. S. A.
101
, 87
–92
(2004
).294.
M. I.
Ivanova
,
M. J.
Thompson
, and
D.
Eisenberg
, “
A systematic screen of β2-microglobulin and insulin for amyloid-like segments
,” Proc. Natl. Acad. Sci. U. S. A.
103
, 4079
–4082
(2006
).295.
S.
Pawlicki
,
A.
Le Béchec
, and
C.
Delamarche
, “
AMYPdb: A database dedicated to amyloid precursor proteins
,” BMC Bioinf.
9
, 273
(2008
).296.
K.
Bodi
,
T.
Prokaeva
,
B.
Spencer
,
M.
Eberhard
,
L. H.
Connors
, and
D. C.
Seldin
, “
AL-Base: A visual platform analysis tool for the study of amyloidogenic immunoglobulin light chain sequences
,” Amyloid
16
, 1
–8
(2009
).297.
L.
Goldschmidt
,
P. K.
Teng
,
R.
Riek
, and
D.
Eisenberg
, “
Identifying the amylome, proteins capable of forming amyloid-like fibrils
,” Proc. Natl. Acad. Sci. U. S. A.
107
, 3487
–3492
(2010
).298.
M.
Belli
,
M.
Ramazzotti
, and
F.
Chiti
, “
Prediction of amyloid aggregation in vivo
,” EMBO Rep.
12
, 657
–663
(2011
).299.
V.
Espinosa Angarica
,
A.
Angulo
,
A.
Giner
,
G.
Losilla
,
S.
Ventura
, and
J.
Sancho
, “
PrionScan: An online database of predicted prion domains in complete proteomes
,” BMC Genomics
15
, 102
(2014
).300.
A. B.
Ahmed
,
N.
Znassi
,
M.-T.
Château
, and
A. V.
Kajava
, “
A structure-based approach to predict predisposition to amyloidosis
,” Alzheimer's Dementia
11
, 681
–690
(2015
).301.
P. P.
Wozniak
and
M.
Kotulska
, “
AmyLoad: Website dedicated to amyloidogenic protein fragments
,” Bioinformatics
31
, 3395
–3397
(2015
).302.
M.
Varadi
,
G.
De Baets
,
W. F.
Vranken
,
P.
Tompa
, and
R.
Pancsa
, “
AmyPro: A database of proteins with validated amyloidogenic regions
,” Nucl. Acids Res.
46
, D387
–D392
(2018
).303.
N.
Louros
,
K.
Konstantoulea
,
M.
De Vleeschouwer
,
M.
Ramakers
,
J.
Schymkowitz
, and
F.
Rousseau
, “
WALTZ-DB 2.0: An updated database containing structural information of experimentally determined amyloid-forming peptides
,” Nucl. Acids Res.
48
, D389
–D393
(2020
).304.
K.
Takács
,
B.
Varga
, and
V.
Grolmusz
, “
PDB_Amyloid: An extended live amyloid structure list from the PDB
,” FEBS Open Bio.
9
, 185
–190
(2019
).305.
P.
Rawat
,
R.
Prabakaran
,
R.
Sakthivel
,
A.
Mary Thangakani
,
S.
Kumar
, and
M. M.
Gromiha
, “
CPAD 2.0: A repository of curated experimental data on aggregating proteins and peptides
,” Amyloid
27
, 128
–133
(2020
).306.
C.
Pintado-Grima
,
O.
Bárcenas
,
Z.
Manglano-Artuñedo
,
R.
Vilaça
,
S.
Macedo-Ribeiro
,
I.
Pallarès
,
J.
Santos
, and
S.
Ventura
, “
CARs-DB: A database of cryptic amyloidogenic regions in intrinsically disordered proteins
,” Front. Mol. Biosci.
9
, 882160
(2022
).307.
M.
Burdukiewicz
,
D.
Rafacz
,
A.
Barbach
,
K.
Hubicka
,
L.
Bąkała
,
A.
Lassota
,
J.
Stecko
,
N.
Szymańska
,
J. W.
Wojciechowski
,
D.
Kozakiewicz
et al, “
AmyloGraph: A comprehensive database of amyloid–amyloid interactions
,” Nucl. Acids Res.
51
, D352
–D357
(2023
).308.
G.
De Baets
,
J.
Schymkowitz
, and
F.
Rousseau
, “
Predicting aggregation-prone sequences in proteins
,” Essays Biochem.
56
, 41
–52
(2014
).309.
X.
Wang
,
T. K.
Das
,
S. K.
Singh
, and
S.
Kumar
, “
Potential aggregation prone regions in biotherapeutics: A survey of commercial monoclonal antibodies
,” MAbs
1
, 254
–267
(2009
).310.
K. A.
Opoku-Nsiah
and
J. E.
Gestwicki
, “
Aim for the core: Suitability of the ubiquitin-independent 20S proteasome as a drug target in neurodegeneration
,” Transl. Res.
198
, 48
–57
(2018
).311.
P. M.
Buck
,
S.
Kumar
, and
S. K.
Singh
, “
On the role of aggregation prone regions in protein evolution, stability, and enzymatic catalysis: Insights from diverse analyses
,” PLoS Comput. Biol.
9
, e1003291
(2013
).312.
M.
Yagi-Utsumi
,
S.
Yanaka
,
C.
Song
,
T.
Satoh
,
C.
Yamazaki
,
H.
Kasahara
,
T.
Shimazu
,
K.
Murata
, and
K.
Kato
, “
Characterization of amyloid β fibril formation under microgravity conditions
,” npj Microgravity
6
, 17
(2020
).313.
M.
Kollmer
,
W.
Close
,
L.
Funk
,
J.
Rasmussen
,
A.
Bsoul
,
A.
Schierhorn
,
M.
Schmidt
,
C. J.
Sigurdson
,
M.
Jucker
, and
M.
Fändrich
, “
Cryo-EM structure and polymorphism of Aβ amyloid fibrils purified from Alzheimer's brain tissue
,” Nat. Commun.
10
, 4760
(2019
).314.
S. O.
Hoppe
,
G.
Uzunoğlu
, and
C.
Nussbaum-Krammer
, [“
α-synuclein strains: Does amyloid conformation explain the heterogeneity of Synucleinopathies?
,” Biomolecules
11
, 931
(2021
).315.
A.-M.
Fernandez-Escamilla
,
F.
Rousseau
,
J.
Schymkowitz
, and
L.
Serrano
, “
Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins
,” Nat. Biotechnol.
22
, 1302
–1306
(2004
).316.
G. G.
Tartaglia
,
A.
Cavalli
,
R.
Pellarin
, and
A.
Caflisch
, “
Prediction of aggregation rate and aggregation-prone segments in polypeptide sequences
,” Protein Sci.
14
, 2723
–2734
(2005
).317.
M. J.
Thompson
,
S. A.
Sievers
,
J.
Karanicolas
,
M. I.
Ivanova
,
D.
Baker
, and
D.
Eisenberg
, “
The 3D profile method for identifying fibril-forming segments of proteins
,” Proc. Natl. Acad. Sci. U. S. A.
103
, 4074
–4078
(2006
).318.
O.
Conchillo-Solé
,
N. S.
de Groot
,
F. X.
Avilés
,
J.
Vendrell
,
X.
Daura
, and
S.
Ventura
, “
AGGRESCAN: A server for the prediction and evaluation of” hot spots” of aggregation in polypeptides
,” BMC Bioinf.
8
, 65
(2007
).319.
S. J.
Hamodrakas
,
C.
Liappa
, and
V. A.
Iconomidou
, “
Consensus prediction of amyloidogenic determinants in amyloid fibril-forming proteins
,” Int. J. Biol. Macromol.
41
, 295
–300
(2007
).320.
S.
Zibaee
,
O. S.
Makin
,
M.
Goedert
, and
L. C.
Serpell
, “
A simple algorithm locates β-strands in the amyloid fibril core of α-synuclein, Aβ, and tau using the amino acid sequence alone
,” Protein Sci.
16
, 906
–918
(2007
).321.
A. W.
Bryan
, Jr.
,
M.
Menke
,
L. J.
Cowen
,
S. L.
Lindquist
, and
B.
Berger
, “
BETASCAN: Probable β-amyloids identified by pairwise probabilistic analysis
,” PLoS Comput. Biol.
5
, e1000333
(2009
).322.
S.
Maurer-Stroh
,
M.
Debulpaep
,
N.
Kuemmerer
,
M. L.
De La Paz
,
I. C.
Martins
,
J.
Reumers
,
K. L.
Morris
,
A.
Copland
,
L.
Serpell
,
L.
Serrano
et al, “
Exploring the sequence determinants of amyloid structure using position-specific scoring matrices
,” Nat. Methods
7
, 237
–242
(2010
).323.
C. W.
O'Donnell
,
J.
Waldispühl
,
M.
Lis
,
R.
Halfmann
,
S.
Devadas
,
S.
Lindquist
, and
B.
Berger
, “
A method for probing the mutational landscape of amyloid structure
,” Bioinformatics
27
, i34
–i42
(2011
).324.
I.
Walsh
,
F.
Seno
,
S. C.
Tosatto
, and
A.
Trovato
, “
PASTA 2.0: An improved server for protein aggregation prediction
,” Nucl. Acids Res.
42
, W301
–W307
(2014
).325.
S. A.
Bondarev
,
O. V.
Bondareva
,
G. A.
Zhouravleva
, and
A. V.
Kajava
, “
BetaSerpentine: A bioinformatics tool for reconstruction of amyloid structures
,” Bioinformatics
34
, 599
–608
(2018
).326.
G. G.
Tartaglia
and
M.
Vendruscolo
, “
The Zyggregator method for predicting protein aggregation propensities
,” Chem. Soc. Rev.
37
, 1395
–1401
(2008
).327.
S. O.
Garbuzynskiy
,
M. Y.
Lobanov
, and
O. V.
Galzitskaya
, “
FoldAmyloid: A method of prediction of amyloidogenic regions from protein sequence
,” Bioinformatics
26
, 326
–332
(2010
).328.
A. W.
Bryan
, Jr.
,
C. W.
O'Donnell
,
M.
Menke
,
L. J.
Cowen
,
S.
Lindquist
, and
B.
Berger
, “
STITCHER: Dynamic assembly of likely amyloid and prion β-structures from secondary structure predictions
,” Proteins: Struct., Funct., Bioinf.
80
, 410
–420
(2012
).329.
A. M.
Thangakani
,
S.
Kumar
,
R.
Nagarajan
,
D.
Velmurugan
, and
M. M.
Gromiha
, “
GAP: Towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies
,” Bioinformatics
30
, 1983
–1990
(2014
).330.
N.
Chennamsetty
,
V.
Voynov
,
V.
Kayser
,
B.
Helk
, and
B. L.
Trout
, “
Design of therapeutic proteins with enhanced stability
,” Proc. Natl. Acad. Sci. U. S. A.
106
, 11937
–11942
(2009
).331.
A.
Kuriata
,
V.
Iglesias
,
J.
Pujols
,
M.
Kurcinski
,
S.
Kmiecik
, and
S.
Ventura
, “
Aggrescan3D (A3D) 2.0: Prediction and engineering of protein solubility
,” Nucl. Acids Res.
47
, W300
–W307
(2019
).332.
J.
Van Durme
,
G.
De Baets
,
R.
Van Der Kant
,
M.
Ramakers
,
A.
Ganesan
,
H.
Wilkinson
,
R.
Gallardo
,
F.
Rousseau
, and
J.
Schymkowitz
, “
Solubis: A webserver to reduce protein aggregation through mutation
,” Protein Eng., Des. Sel.
29
, 285
–289
(2016
).333.
K.
Sankar
,
S. R.
Krystek
, Jr.
,
S. M.
Carl
,
T.
Day
, and
J. K.
Maier
, “
AggScore: Prediction of aggregation-prone regions in proteins based on the distribution of surface patches
,” Proteins: Struct., Funct., Bioinf.
86
, 1147
–1156
(2018
).334.
P.
Sormanni
,
F. A.
Aprile
, and
M.
Vendruscolo
, “
The CamSol method of rational design of protein mutants with enhanced solubility
,” J. Mol. Biol.
427
, 478
–490
(2015
).335.
E.
Khan
,
S.
Mishra
, and
A.
Kumar
, “
Emerging methods for structural analysis of protein aggregation
,” Protein Pept. Lett.
24
, 331
–339
(2017
).336.
F.
Oosawa
and
M.
Kasai
, “
A theory of linear and helical aggregations of macromolecules
,” J. Mol. Biol.
4
, 10
–21
(1962
).337.
E.
Reisler
,
J.
Pouyet
, and
H.
Eisenberg
, “
Molecular weights, association, and frictional resistance of bovine liver glutamate dehydrogenase at low concentrations. Equilibrium and velocity sedmintation, light-scattering studies, and settling experiments with macroscopic models of the enzyme oligomer
,” Biochemistry
9
, 3095
–3102
(1970
).338.
J.
Hofrichter
,
P. D.
Ross
, and
W. A.
Eaton
, “
Kinetics and mechanism of deoxyhemoglobin S gelation: A new approach to understanding sickle cell disease
,” Proc. Natl. Acad. Sci. U. S. A.
71
, 4864
–4868
(1974
).339.
A.
Wegner
and
J.
Engel
, “
Kinetics of the cooperative association of actin to actin filament
,” Biophys. Chem.
3
, 215
–225
(1975
).340.
D.
Thusius
, “
Mechanism of bovine liver glutamate dehydrogenase self-assembly. II. Simulation of relaxation spectra for an open linear polymerization proceeding via a sequential addition of monomer units
,” J. Mol. Biol.
94
, 367
–383
(1975
).341.
F. A.
Ferrone
,
J.
Hofrichter
,
H. R.
Sunshine
, and
W. A.
Eaton
, “
Kinetic studies on photolysis-induced gelation of sickle cell haemoglobin suggest a new mechanism
,” Biophys. J.
32
, 361
–380
(1980
).342.
M.
Firestone
,
R.
De Levie
, and
S.
Rangarajan
, “
On one-dimensional nucleation and growth of “living” polymers. I. Homogeneous nucleation
,” J. Theor. Biol.
104
, 535
–552
(1983
).343.
S.
Rangarajan
and
R.
De Levie
, “
On one-dimensional nucleation and growth of “living” polymers. II. Growth at constant monomer concentration
,” J. Theor. Biol.
104
, 553
–570
(1983
).344.
R. F.
Goldstein
and
L.
Stryer
, “
Cooperative polymerization reactions. Analytical approximations, numerical examples, and experimental strategy
,” Biophys. J.
50
, 583
–599
(1986
).345.
H.
Flyvbjerg
,
E.
Jobs
, and
S.
Leibler
, “
Kinetics of self-assembling microtubules: An “inverse problem” in biochemistry
,” Proc. Natl. Acad. Sci. U. S. A.
93
, 5975
–5979
(1996
).346.
M. A.
Watzky
and
R. G.
Finke
, “
Transition metal nanocluster formation kinetic and mechanistic studies. A new mechanism when hydrogen is the reductant: Slow, continuous nucleation and fast autocatalytic surface growth
,” J. Am. Chem. Soc.
119
, 10382
–10400
(1997
).347.
348.
M.
Kamihira
,
A.
Naito
,
S.
Tuzi
,
H.
Saitô
, and
A. Y.
Nosaka
, “
Conformational transitions and fibrillation mechanism of human calcitonin as studied by high-resolution solid-state 13C NMR
,” Protein Sci.
9
, 867
–877
(2000
).349.
F.
Chiti
,
M.
Stefani
,
N.
Taddei
,
G.
Ramponi
, and
C. M.
Dobson
, “
Rationalization of the effects of mutations on peptide and protein aggregation rates
,” Nature
424
, 805
–808
(2003
).350.
K. F.
DuBay
,
A. P.
Pawar
,
F.
Chiti
,
J.
Zurdo
,
C. M.
Dobson
, and
M.
Vendruscolo
, “
Prediction of the absolute aggregation rates of amyloidogenic polypeptide chains
,” J. Mol. Biol.
341
, 1317
–1326
(2004
).351.
T. J.
Gibson
and
R. M.
Murphy
, “
Inhibition of insulin fibrillogenesis with targeted peptides
,” Protein Sci.
15
, 1133
–1141
(2006
).352.
M. A.
Watzky
,
A. M.
Morris
,
E. D.
Ross
, and
R. G.
Finke
, “
Fitting yeast and mammalian prion aggregation kinetic data with the Finke-Watzky two-step model of nucleation and autocatalytic growth
,” Biochemistry
47
, 10790
–10800
(2008
).353.
A. M.
Morris
,
M. A.
Watzky
,
J. N.
Agar
, and
R. G.
Finke
, “
Fitting neurological protein aggregation kinetic data via a 2-step, minimal/“Ockham's razor” model: The Finke-Watzky mechanism of nucleation followed by autocatalytic surface growth
,” Biochemistry
47
, 2413
–2427
(2008
).354.
P. E.
Wright
and
H. J.
Dyson
, “
Intrinsically unstructured proteins: Re-assessing the protein structure-function paradigm
,” J. Mol. Biol.
293
, 321
–331
(1999
).355.
V. N.
Uversky
,
J. R.
Gillespie
, and
A. L.
Fink
, “
Why are “natively unfolded” proteins unstructured under physiologic conditions?
,” Proteins: Struct., Funct., Bioinf.
41
, 415
–427
(2000
).356.
V. N.
Uversky
and
A. K.
Dunker
, “
Understanding protein non-folding
,” Biochim. Biophys. Acta, Proteins Proteomics
1804
, 1231
–1264
(2010
).357.
P.
Tompa
, “
Intrinsically unstructured proteins
,” Trends Biochem. Sci.
27
, 527
–533
(2002
).358.
V. K.
Mulligan
and
A.
Chakrabartty
, “
Protein misfolding in the late-onset neurodegenerative diseases: Common themes and the unique case of amyotrophic lateral sclerosis
,” Proteins: Struct., Funct., Bioinf.
81
, 1285
–1303
(2013
).359.
J. W.
Kelly
, “
The alternative conformations of amyloidogenic proteins and their multi-step assembly pathways
,” Curr. Opin. Struct. Biol.
8
, 101
–106
(1998
).360.
V.
Bellotti
,
P.
Mangione
, and
M.
Stoppini
, “
Biological activity and pathological implications of misfolded proteins
,” Cell. Mol. Life Sci.
55
, 977
–991
(1999
).361.
J.-C.
Rochet
and
P. T.
Lansbury
, Jr.
, “
Amyloid fibrillogenesis: Themes and variations
,” Curr. Opin. Struct. Biol.
10
, 60
–68
(2000
).362.
V. N.
Uversky
and
A. L.
Fink
, “
Conformational constraints for amyloid fibrillation: The importance of being unfolded
,” Biochim. Biophys. Acta, Proteins Proteomics
1698
, 131
–153
(2004
).363.
M. S.
Hipp
,
S.-H.
Park
, and
F. U.
Hartl
, “
Proteostasis impairment in protein-misfolding and-aggregation diseases
,” Trends Cell Biol.
24
, 506
–514
(2014
).364.
L.
Breydo
and
V. N.
Uversky
, “
Role of metal ions in aggregation of intrinsically disordered proteins in neurodegenerative diseases
,” Metallomics
3
, 1163
–1180
(2011
).365.
V. N.
Uversky
, “
The triple power of D (3): Protein intrinsic disorder in degenerative diseases
,” Front. Biosci.
19
, 181
–258
(2014
).366.
D.
Piovesan
,
A. M.
Monzon
,
F.
Quaglia
, and
S. C.
Tosatto
, “
Databases for intrinsically disordered proteins
,” Acta Crystallogr., Sect. D: Struct. Biol.
78
, 144
–151
(2022
).367.
J.
Mistry
,
S.
Chuguransky
,
L.
Williams
,
M.
Qureshi
,
G. A.
Salazar
,
E. L.
Sonnhammer
,
S. C.
Tosatto
,
L.
Paladin
,
S.
Raj
,
L. J.
Richardson
et al, “
Pfam: The protein families database in 2021
,” Nucl. Acids Res.
49
, D412
–D419
(2021
).368.
M.
Necci
,
D.
Piovesan
, and
S. C.
Tosatto
, “
Critical assessment of protein intrinsic disorder prediction
,” Nat. Methods
18
, 472
–481
(2021
).369.
M.
Kumar
,
M.
Gouw
,
S.
Michael
,
H.
Sámano-Sánchez
,
R.
Pancsa
,
J.
Glavina
,
A.
Diakogianni
,
J. A.
Valverde
,
D.
Bukirova
,
J.
Čalyševa
et al, “
ELM—the eukaryotic linear motif resource in 2020
,” Nucl. Acids Res.
48
, D296
–D306
(2020
).370.
A.
Hatos
,
B.
Hajdu-Soltész
,
A. M.
Monzon
,
N.
Palopoli
,
L.
Álvarez
,
B.
Aykac-Fas
,
C.
Bassot
,
G. I.
Benítez
,
M.
Bevilacqua
,
A.
Chasapi
et al, “
DisProt: Intrinsic protein disorder annotation in 2020
,” Nucl. Acids Res.
48
, D269
–D276
(2020
).371.
S.
Fukuchi
,
T.
Amemiya
,
S.
Sakamoto
,
Y.
Nobe
,
K.
Hosoda
,
Y.
Kado
,
S. D.
Murakami
,
R.
Koike
,
H.
Hiroaki
, and
M.
Ota
, “
IDEAL in 2014 illustrates interaction networks composed of intrinsically disordered proteins and their binding partners
,” Nucl. Acids Res.
42
, D320
–D325
(2014
).372.
M.
Miskei
,
C.
Antal
, and
M.
Fuxreiter
, “
FuzDB: Database of fuzzy complexes, a tool to develop stochastic structure-function relationships for protein complexes and higher-order assemblies
,” Nucl. Acids Res.
45
, D228
–D235
(2016
).373.
E.
Fichó
,
I.
Reményi
,
I.
Simon
, and
B.
Mészáros
, “
MFIB: A repository of protein complexes with mutual folding induced by binding
,” Bioinformatics
33
, 3682
–3684
(2017
).374.
E.
Schad
,
E.
Ficho
,
R.
Pancsa
,
I.
Simon
,
Z.
Dosztányi
, and
B.
Meszaros
, “
DIBS: A repository of disordered binding sites mediating interactions with ordered proteins
,” Bioinformatics
34
, 535
–537
(2018
).375.
D.
Piovesan
and
S. C.
Tosatto
, “
Mobi 2.0: An improved method to define intrinsic disorder, mobility and linear binding regions in protein structures
,” Bioinformatics
34
, 122
–123
(2018
).376.
A. M.
Monzon
,
M.
Necci
,
F.
Quaglia
,
I.
Walsh
,
G.
Zanotti
,
D.
Piovesan
, and
S. C.
Tosatto
, “
Experimentally determined long intrinsically disordered protein regions are now abundant in the Protein Data Bank
,” Int. J. Mol. Sci.
21
, 4496
(2020
).377.
M. E.
Oates
,
P.
Romero
,
T.
Ishida
,
M.
Ghalwash
,
M. J.
Mizianty
,
B.
Xue
,
Z.
Dosztányi
,
V. N.
Uversky
,
Z.
Obradovic
,
L.
Kurgan
et al, “
D2P2: Database of disordered protein predictions
,” Nucl. Acids Res.
41
, D508
–D516
(2012
).378.
M.
Blum
,
H.-Y.
Chang
,
S.
Chuguransky
,
T.
Grego
,
S.
Kandasaamy
,
A.
Mitchell
,
G.
Nuka
,
T.
Paysan-Lafosse
,
M.
Qureshi
,
S.
Raj
et al, “
The InterPro protein families and domains database: 20 years on
,” Nucl. Acids Res.
49
, D344
–D354
(2021
).379.
M.
Ormö
,
A. B.
Cubitt
,
K.
Kallio
,
L. A.
Gross
,
R. Y.
Tsien
, and
S. J.
Remington
, “
Crystal structure of the Aequorea victoria green fluorescent protein
,” Science
273
, 1392
–1395
(1996
).380.
P. R.
Romero
,
N.
Kobayashi
,
J. R.
Wedell
,
K.
Baskaran
,
T.
Iwata
,
M.
Yokochi
,
D.
Maziuk
,
H.
Yao
,
T.
Fujiwara
,
G.
Kurusu
et al, “
BioMagResBank (BMRB) as a resource for structural biology
,” Struct. Bioinf.: Methods Protocols
2112
, 187
–218
(2020
).381.
L.
Whitmore
,
A. J.
Miles
,
L.
Mavridis
,
R. W.
Janes
, and
B. A.
Wallace
, “
PCDDB: New developments at the protein circular dichroism data bank
,” Nucl. Acids Res.
45
, D303
–D307
(2017
).382.
A. G.
Kikhney
,
C. R.
Borges
,
D. S.
Molodenskiy
,
C. M.
Jeffries
, and
D. I.
Svergun
, “
SASBDB: Towards an automatically curated and validated repository for biological scattering data
,” Protein Sci.
29
, 66
–75
(2020
).383.
T.
Lazar
,
E.
Martínez-Pérez
,
F.
Quaglia
,
A.
Hatos
,
L. B.
Chemes
,
J. A.
Iserte
,
N. A.
Méndez
,
N. A.
Garrone
,
T. E.
Saldaño
,
J.
Marchetti
et al, “
PED in 2021: A major update of the protein ensemble database for intrinsically disordered proteins
,” Nucl. Acids Res.
49
, D404
–D411
(2021
).384.
B.
Vallat
,
B.
Webb
,
J. D.
Westbrook
,
A.
Sali
, and
H. M.
Berman
, “
Development of a prototype system for archiving integrative/hybrid structure models of biological macromolecules
,” Structure
26
, 894
–904
(2018
).385.
K.
You
,
Q.
Huang
,
C.
Yu
,
B.
Shen
,
C.
Sevilla
,
M.
Shi
,
H.
Hermjakob
,
Y.
Chen
, and
T.
Li
, “
PhaSepDB: A database of liquid–liquid phase separation related proteins
,” Nucl. Acids Res.
48
, D354
–D359
(2020
).386.
B.
Mészáros
,
G.
Erdős
,
B.
Szabó
,
É.
Schád
,
Á.
Tantos
,
R.
Abukhairan
,
T.
Horváth
,
N.
Murvai
,
O. P.
Kovács
,
M.
Kovács
et al, “
PhaSePro: The database of proteins driving liquid–liquid phase separation
,” Nucl. Acids Res.
48
, D360
–D367
(2020
).387.
Q.
Li
,
X.
Peng
,
Y.
Li
,
W.
Tang
,
J.
Zhu
,
J.
Huang
,
Y.
Qi
, and
Z.
Zhang
, “
LLPSDB: A database of proteins undergoing liquid–liquid phase separation in vitro
,” Nucl. Acids Res.
48
, D320
–D327
(2020
).388.
W.
Ning
,
Y.
Guo
,
S.
Lin
,
B.
Mei
,
Y.
Wu
,
P.
Jiang
,
X.
Tan
,
W.
Zhang
,
G.
Chen
,
D.
Peng
et al, “
DrLLPS: A data resource of liquid–liquid phase separation in eukaryotes
,” Nucl. Acids Res.
48
, D288
–D295
(2020
).389.
C.
Hou
,
H.
Xie
,
Y.
Fu
,
Y.
Ma
, and
T.
Li
, “
MloDisDB: A manually curated database of the relations between membraneless organelles and diseases
,” Briefings Bioinf.
22
, bbaa271
(2021
).390.
P.
Patel
,
K.
Parmar
,
V. K.
Vyas
,
D.
Patel
, and
M.
Das
, “
Combined in silico approaches for the identification of novel inhibitors of human islet amyloid polypeptide (hIAPP) fibrillation
,” J. Mol. Graphics Modell.
77
, 295
–310
(2017
).391.
A.
Abedini
and
D. P.
Raleigh
, “
A role for helical intermediates in amyloid formation by natively unfolded polypeptides?
,” Phys. Biol.
6
, 015005
(2009
).392.
G.
De Baets
,
J.
Reumers
,
J.
Delgado Blanco
,
J.
Dopazo
,
J.
Schymkowitz
, and
F.
Rousseau
, “
An evolutionary trade-off between protein turnover rate and protein aggregation favors a higher aggregation propensity in fast degrading proteins
,” PLoS Comput. Biol.
7
, e1002090
(2011
).393.
S. V.
Kravchenko
,
P. A.
Domnin
,
S. Y.
Grishin
,
A. V.
Panfilov
,
V. N.
Azev
,
L. G.
Mustaeva
,
E. Y.
Gorbunova
,
M. I.
Kobyakova
,
A. K.
Surin
,
A. V.
Glyakina
et al “
Multiple antimicrobial effects of hybrid peptides synthesized based on the sequence of ribosomal S1 protein from Staphylococcus aureus
,” Int. J. Mol. Sci.
23
, 524
(2022
).394.
S. Y.
Grishin
,
E. I.
Deryusheva
,
A. V.
Machulin
,
O. M.
Selivanova
,
A. V.
Glyakina
,
E. Y.
Gorbunova
,
L. G.
Mustaeva
,
V. N.
Azev
,
V. V.
Rekstina
,
T. S.
Kalebina
et al, “
Amyloidogenic propensities of ribosomal S1 proteins: Bioinformatics screening and experimental checking
,” Int. J. Mol. Sci.
21
, 5199
(2020
).395.
F.
Chiti
and
C. M.
Dobson
, “
Protein misfolding, amyloid formation, and human disease: A summary of progress over the last decade
,” Annu. Rev. Biochem.
86
, 27
–68
(2017
).396.
J.
Santos
,
J.
Pujols
,
I.
Pallarès
,
V.
Iglesias
, and
S.
Ventura
, “
Computational prediction of protein aggregation: Advances in proteomics, conformation-specific algorithms and biotechnological applications
,” Comput. Struct. Biotechnol. J.
18
, 1403
–1413
(2020
).397.
M.
Baek
,
F.
DiMaio
,
I.
Anishchenko
,
J.
Dauparas
,
S.
Ovchinnikov
,
G. R.
Lee
,
J.
Wang
,
Q.
Cong
,
L. N.
Kinch
, and
D.
Schaeffer
et al. “
Accurate prediction of protein structures and interactions using a three-track neural network
,” Science
373
, 871
–876
(2021
).398.
K.
Tunyasuvunakool
,
J.
Adler
,
Z.
Wu
,
T.
Green
,
M.
Zielinski
,
A.
Žídek
,
A.
Bridgland
,
A.
Cowie
,
C.
Meyer
,
A.
Laydon
et al, “
Highly accurate protein structure prediction for the human proteome
,” Nature
596
, 590
–596
(2021
).399.
S.
Liu
,
K.
Wu
, and
C.
Chen
, “
Obtaining protein foldability information from computational models of AlphaFold2 and RoseTTAFold
,” Comput. Struct. Biotechnol. J.
20
, 4481
–4489
(2022
).400.
M.
Mohammed
,
M. B.
Khan
, and
E. B. M.
Bashier
, Machine Learning: Algorithms and Applications
(
CRC Press
, 2016
).401.
J.
Han
,
J.
Pei
, and
H.
Tong
, Data Mining: Concepts and Techniques
(
Morgan Kaufmann
, 2022
).402.
I. H.
Sarker
,
A.
Kayes
,
S.
Badsha
,
H.
Alqahtani
,
P.
Watters
, and
A.
Ng
, “
Cybersecurity data science: An overview from machine learning perspective
,” J. Big Data
7
, 1
–29
(2020
).403.
L. P.
Kaelbling
,
M. L.
Littman
, and
A. W.
Moore
, “
Reinforcement learning: A survey
,” J. Artif. Intell. Res.
4
, 237
–285
(1996
).404.
C.
Kim
,
J.
Choi
,
S. J.
Lee
,
W. J.
Welsh
, and
S.
Yoon
, “
NetCSSP: Web application for predicting chameleon sequences and amyloid fibril formation
,” Nucl. Acids Res.
37
, W469
–W473
(2009
).405.
J.
Tian
,
N.
Wu
,
J.
Guo
, and
Y.
Fan
, “
Prediction of amyloid fibril-forming segments based on a support vector machine
,” BMC Bioinf.
10
, S45
(2009
).406.
C.
Liaw
,
C.-W.
Tung
, and
S.-Y.
Ho
, “
Prediction and analysis of antibody amyloidogenesis from sequences
,” PLoS One
8
, e53235
(2013
).407.
P.
Gasior
and
M.
Kotulska
, “
FISH amyloid—a new method for finding amyloidogenic segments in proteins based on site specific co-occurence of aminoacids
,” BMC Bioinf.
15
, 54
(2014
).408.
C.
Família
,
S. R.
Dennison
,
A.
Quintas
, and
D. A.
Phoenix
, “
Prediction of peptide and protein propensity for amyloid formation
,” PLoS One
10
, e0134679
(2015
).409.
M.
Burdukiewicz
,
P.
Sobczyk
,
S.
Rödiger
,
A.
Duda-Madej
,
P.
Mackiewicz
, and
M.
Kotulska
, “
Amyloidogenic motifs revealed by n-gram analysis
,” Sci. Rep.
7
, 12961
(2017
).410.
M.
Niu
,
Y.
Li
,
C.
Wang
, and
K.
Han
, “
RFAmyloid: A web server for predicting amyloid proteins
,” Int. J. Mol. Sci.
19
, 2071
(2018
).411.
J. W.
Wojciechowski
and
M.
Kotulska
, “
Path-prediction of amyloidogenicity by threading and machine learning
,” Sci. Rep.
10
, 7721
(2020
).412.
N.
Louros
,
G.
Orlando
,
M.
De Vleeschouwer
,
F.
Rousseau
, and
J.
Schymkowitz
, “
Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities
,” Nat. Commun.
11
, 3314
(2020
).413.
Y.
Xu
,
D.
Verma
,
R. P.
Sheridan
,
A.
Liaw
,
J.
Ma
,
N. M.
Marshall
,
J.
McIntosh
,
E. C.
Sherer
,
V.
Svetnik
, and
J. M.
Johnston
, “
Deep dive into machine learning models for protein engineering
,” J. Chem. Inf. Model.
60
, 2773
–2790
(2020
).414.
D.
Raimondi
,
G.
Orlando
,
P.
Fariselli
, and
Y.
Moreau
, “
Insight into the protein solubility driving forces with neural attention
,” PLoS Comput. Biol.
16
, e1007722
(2020
).415.
W.
Yang
,
P.
Tan
,
X.
Fu
, and
L.
Hong
, “
Prediction of amyloid aggregation rates by machine learning and feature selection
,” J. Chem. Phys.
151
, 084106
(2019
).416.
G.
Orlando
,
A.
Silva
,
S.
Macedo-Ribeiro
,
D.
Raimondi
, and
W.
Vranken
, “
Accurate prediction of protein beta-aggregation with generalized statistical potentials
,” Bioinformatics
36
, 2076
–2081
(2020
).417.
Y.
Li
,
Z.
Zhang
,
Z.
Teng
, and
X.
Liu
, “
Predamyl-mlp: Prediction of amyloid proteins using multilayer perceptron
,” Comput. Math. Methods Med.
2020
, 8845133
.418.
L.
Keresztes
,
E.
Szögi
,
B.
Varga
,
V.
Farkas
,
A.
Perczel
, and
V.
Grolmusz
, “
The budapest amyloid predictor and its applications
,” Biomolecules
11
, 500
(2021
).419.
A. C.
Tsolis
,
N. C.
Papandreou
,
V. A.
Iconomidou
, and
S. J.
Hamodrakas
, “
A consensus method for the prediction of “aggregation-prone-peptides in globular proteins
,” PLoS One
8
, e54175
(2013
).420.
M.
Emily
,
A.
Talvas
, and
C.
Delamarche
, “
MetAmyl: A METa-predictor for AMYLoid proteins
,” PLoS One
8
, e79722
(2013
).421.
R.
Prabakaran
,
P.
Rawat
,
S.
Kumar
, and
M. M.
Gromiha
, “
ANuPP: A versatile tool to predict aggregation nucleating regions in peptides and proteins
,” J. Mol. Biol.
433
, 166707
(2021
).422.
P.
Rawat
,
S.
Kumar
, and
M. M.
Gromiha
, “
An in-silico method for identifying aggregation rate enhancer and mitigator mutations in proteins
,” Int. J. Biol. Macromol.
118
, 1157
–1167
(2018
).423.
P.
Rawat
,
R.
Prabakaran
,
S.
Kumar
, and
M. M.
Gromiha
, “
AggreRATE-Pred: A mathematical model for the prediction of change in aggregation rate upon point mutation
,” Bioinformatics
36
, 1439
–1444
(2020
).424.
C. J.
Oldfield
,
Y.
Cheng
,
M. S.
Cortese
,
P.
Romero
,
V. N.
Uversky
, and
A. K.
Dunker
, “
Coupled folding and binding with α-helix-forming molecular recognition elements
,” Biochemistry
44
, 12454
–12470
(2005
).425.
Y.
Cheng
,
C. J.
Oldfield
,
J.
Meng
,
P.
Romero
,
V. N.
Uversky
, and
A. K.
Dunker
, “
Mining α-helix-forming molecular recognition features with cross species sequence alignments
,” Biochemistry
46
, 13468
–13477
(2007
).426.
R. J.
Edwards
,
N. E.
Davey
, and
D. C.
Shields
, “
SLiMFinder: A probabilistic method for identifying over-represented, convergently evolved, short linear motifs in proteins
,” PLoS One
2
, e967
(2007
).427.
Z.
Dosztányi
,
B.
Mészáros
, and
I.
Simon
, “
ANCHOR: Web server for predicting protein binding regions in disordered proteins
,” Bioinformatics
25
, 2745
–2746
(2009
).428.
B.
Xue
,
A. K.
Dunker
, and
V. N.
Uversky
, “
Retro-MoRFs: Identifying protein binding sites by normal and reverse alignment and intrinsic disorder prediction
,” Int. J. Mol. Sci.
11
, 3725
–3747
(2010
).429.
N. E.
Davey
,
N. J.
Haslam
,
D. C.
Shields
, and
R. J.
Edwards
, “
SLiMSearch: A webserver for finding novel occurrences of short linear motifs in proteins, incorporating sequence context
,” in Pattern Recognition in Bioinformatics: 5th IAPR International Conference, Nijmegen, The Netherlands, September 22–24, 2010
(
Springer
, 2010
), pp. 50
–61
.430.
N. E.
Davey
,
N. J.
Haslam
,
D. C.
Shields
, and
R. J.
Edwards
, “
SLiMSearch 2.0: Biological context for short linear motifs in proteins
,” Nucl. Acids Res.
39
, W56
–W60
(2011
).431.
F. M.
Disfani
,
W.-L.
Hsu
,
M. J.
Mizianty
,
C. J.
Oldfield
,
B.
Xue
,
A. K.
Dunker
,
V. N.
Uversky
, and
L.
Kurgan
, “
MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins
,” Bioinformatics
28
, i75
–i83
(2012
).432.
C.
Mooney
,
G.
Pollastri
,
D. C.
Shields
, and
N. J.
Haslam
, “
Prediction of short linear protein binding regions
,” J. Mol. Biol.
415
, 193
–204
(2012
).433.
N. E.
Davey
,
J. L.
Cowan
,
D. C.
Shields
,
T. J.
Gibson
,
M. J.
Coldwell
, and
R. J.
Edwards
, “
SLiMPrints: Conservation-based discovery of functional motif fingerprints in intrinsically disordered protein regions
,” Nucl. Acids Res.
40
, 10628
–10641
(2012
).434.
C.
Fang
,
T.
Noguchi
,
D.
Tominaga
, and
H.
Yamana
, “
MFSPSSMpred: Identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation
,” BMC Bioinf.
14
, 300
(2013
).435.
W.
Khan
,
F.
Duffy
,
G.
Pollastri
,
D. C.
Shields
, and
C.
Mooney
, “
Predicting binding within disordered protein regions to structurally characterised peptide-binding domains
,” PLoS One
8
, e72838
(2013
).436.
D. T.
Jones
and
D.
Cozzetto
, “
DISOPRED3: Precise disordered region predictions with annotated protein-binding activity
,” Bioinformatics
31
, 857
–863
(2015
).437.
N.
Palopoli
,
K. T.
Lythgow
, and
R. J.
Edwards
, “
QSLiMFinder: Improved short linear motif prediction using specific query protein data
,” Bioinformatics
31
, 2284
–2293
(2015
).438.
Z.
Peng
and
L.
Kurgan
, “
High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder
,” Nucl. Acids Res.
43
, e121
(2015
).439.
J.
Yan
,
A. K.
Dunker
,
V. N.
Uversky
, and
L.
Kurgan
, “
Molecular recognition features (MoRFs) in three domains of life
,” Mol. BioSystems
12
, 697
–710
(2016
).440.
R.
Sharma
,
S.
Kumar
,
T.
Tsunoda
,
A.
Patil
, and
A.
Sharma
, “
Predicting MoRFs in protein sequences using HMM profiles
,” BMC Bioinf.
17
, 251
–258
(2016
).441.
I.
Krystkowiak
and
N. E.
Davey
, “
SLiMSearch: A framework for proteome-wide discovery and annotation of functional modules in intrinsically disordered regions
,” Nucl. Acids Res.
45
, W464
–W469
(2017
).442.
R.
Sharma
,
M.
Bayarjargal
,
T.
Tsunoda
,
A.
Patil
, and
A.
Sharma
, “
MoRFPred-plus: Computational identification of MoRFs in protein sequences using physicochemical properties and HMM profiles
,” J. Theor. Biol.
437
, 9
–16
(2018
).443.
R.
Sharma
,
G.
Raicar
,
T.
Tsunoda
,
A.
Patil
, and
A.
Sharma
, “
OPAL: prediction of MoRF regions in intrinsically disordered protein sequences
,” Bioinformatics
34
, 1850
–1858
(2018
).444.
B.
Mészáros
,
G.
Erdős
, and
Z.
Dosztányi
, “
IUPred2A: Context-dependent prediction of protein disorder as a function of redox state and protein binding
,” Nucl. Acids Res.
46
, W329
–W337
(2018
).445.
R.
Sharma
,
A.
Sharma
,
G.
Raicar
,
T.
Tsunoda
, and
A.
Patil
, “
OPAL+: Length-specific MoRF prediction in intrinsically disordered protein sequences
,” Proteomics
19
, 1800058
(2019
).446.
C.
Fang
,
Y.
Moriwaki
,
A.
Tian
,
C.
Li
, and
K.
Shimizu
, “
Identifying short disorder-to-order binding regions in disordered proteins with a deep convolutional neural network method
,” J. Bioinf. Comput. Biol.
17
, 1950004
(2019
).447.
E. T.
Wong
and
J.
Gsponer
, “
Predicting protein–protein interfaces that bind intrinsically disordered protein regions
,” J. Mol. Biol.
431
, 3157
–3178
(2019
).448.
J.
Hanson
,
T.
Litfin
,
K.
Paliwal
, and
Y.
Zhou
, “
Identifying molecular recognition features in intrinsically disordered regions of proteins by transfer learning
,” Bioinformatics
36
, 1107
–1113
(2020
).449.
G.
Hu
,
A.
Katuwawala
,
K.
Wang
,
Z.
Wu
,
S.
Ghadermarzi
,
J.
Gao
, and
L.
Kurgan
, “
flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions
,” Nat. Commun.
12
, 4438
(2021
).450.
A.
Katuwawala
,
B.
Zhao
, and
L.
Kurgan
, “
DisoLipPred: Accurate prediction of disordered lipid-binding residues in protein sequences with deep recurrent networks and transfer learning
,” Bioinformatics
38
, 115
–124
(2022
).451.
G. E.
Tusnády
,
L.
Dobson
, and
P.
Tompa
, “
Disordered regions in transmembrane proteins
,” Biochim. Biophys. Acta, Biomembr.
1848
, 2839
–2848
(2015
).452.
F.
Zhang
,
B.
Zhao
,
W.
Shi
,
M.
Li
, and
L.
Kurgan
, “
DeepDISOBind: Accurate prediction of RNA-, DNA-and protein-binding intrinsically disordered residues with deep multi-task learning
,” Briefings Bioinf.
23
, bbab521
(2022
).453.
J.
Pereira
,
A. J.
Simpkin
,
M. D.
Hartmann
,
D. J.
Rigden
,
R. M.
Keegan
, and
A. N.
Lupas
, “
High-accuracy protein structure prediction in CASP14
,” Proteins: Struct., Funct., Bioinf.
89
, 1687
–1699
(2021
).454.
B.
Strodel
, “
Energy landscapes of protein aggregation and conformation switching in intrinsically disordered proteins
,” J. Mol. Biol.
433
, 167182
(2021
).455.
T.
Waller
,
D.
Read
,
D.
Engelke
, and
P.
Smaldino
, “
The human tRNA-modifying protein, TRIT1, forms amyloid fibers in vitro
,” Gene
612
, 19
–24
(2017
).456.
G.
Suzuki
,
N.
Shimazu
, and
M.
Tanaka
, “
A yeast prion, Mod5, promotes acquired drug resistance and cell survival under environmental stress
,” Science
336
, 355
–359
(2012
).457.
D. A.
Nissley
,
Y.
Jiang
,
F.
Trovato
,
I.
Sitarik
,
K. B.
Narayan
,
P.
To
,
Y.
Xia
,
S. D.
Fried
, and
E. P.
O'Brien
, “
Universal protein misfolding intermediates can bypass the proteostasis network and remain soluble and less functional
,” Nat. Commun.
13
, 3081
(2022
).458.
T. N.
Lamichhane
,
S.
Mattijssen
, and
R. J.
Maraia
, “
Human cells have a limited set of tRNA anticodon loop substrates of the tRNA isopentenyltransferase TRIT1 tumor suppressor
,” Mol. Cell. Biol.
33
, 4900
–4908
(2013
).459.
J.
Garcia-Pardo
,
A. E.
Badaczewska-Dawid
,
C.
Pintado-Grima
,
V.
Iglesias
,
A.
Kuriata
,
S.
Kmiecik
, and
S.
Ventura
, “
A3DyDB: Exploring structural aggregation propensities in the yeast proteome
,” Microb. Cell Fact.
22
, 186
(2023
).460.
D.
Piovesan
,
A. M.
Monzon
, and
S. C.
Tosatto
, “
Intrinsic protein disorder and conditional folding in AlphaFoldDB
,” Protein Sci.
31
, e4466
(2022
).461.
F.
Pinheiro
,
J.
Santos
, and
S.
Ventura
, “
AlphaFold and the amyloid landscape
,” J. Mol. Biol.
433
, 167059
(2021
).© 2024 Author(s). Published under an exclusive license by AIP Publishing.
2024
Author(s)
You do not currently have access to this content.
