A solvent often manifests itself as the key determinant of the kinetic aspect of the molecular recognition process. While the solvent is often depicted as a source of barrier in the ligand recognition process by the polar cavity, the nature of solvent’s role in the recognition process involving hydrophobic cavity and hydrophobic ligand remains to be addressed. In this work, we quantitatively assess the role of solvent in dictating the kinetic process of recognition in a popular system involving the hydrophobic cavity and ligand. In this prototypical system, the hydrophobic cavity undergoes dewetting transition as the ligand approaches the cavity, which influences the cavity–ligand recognition kinetics. Here, we build a Markov state model (MSM) using adaptively sampled unrestrained molecular dynamics simulation trajectories to map the kinetic recognition process. The MSM-reconstructed free energy surface recovers a broad water distribution at an intermediate cavity–ligand separation, consistent with a previous report of dewetting transition in this system. Time-structured independent component analysis of the simulated trajectories quantitatively shows that cavity–solvent density contributes considerably in an optimized reaction coordinate involving cavity–ligand separation and water occupancy. Our approach quantifies two solvent-mediated macrostates at an intermediate separation of the cavity–ligand recognition pathways, apart from the fully ligand-bound and fully ligand-unbound macrostates. Interestingly, we find that these water-mediated intermediates, while transient in populations, can undergo slow mutual interconversion and create possibilities of multiple pathways of cavity recognition by the ligand. Overall, the work provides a quantitative assessment of the role that the solvent plays in facilitating the recognition process involving the hydrophobic cavity.
Skip Nav Destination
Article navigation
21 February 2020
Research Article|
February 19 2020
On the role of solvent in hydrophobic cavity–ligand recognition kinetics
Navjeet Ahalawat
;
Navjeet Ahalawat
1
Tata Institute of Fundamental Research, Center for Interdisciplinary Sciences
, Hyderabad 500107, India
2
Department of Molecular Biology, Biotechnology and Bioinformatics, Chaudhary Charan Singh Haryana Agricultural University
, Hisar 125004, India
Search for other works by this author on:
Satyabrata Bandyopadhyay;
Satyabrata Bandyopadhyay
1
Tata Institute of Fundamental Research, Center for Interdisciplinary Sciences
, Hyderabad 500107, India
Search for other works by this author on:
Jagannath Mondal
Jagannath Mondal
a)
1
Tata Institute of Fundamental Research, Center for Interdisciplinary Sciences
, Hyderabad 500107, India
a)Author to whom correspondence should be addressed: jmondal@tifrh.res.in. Tel.: +914020203091.
Search for other works by this author on:
a)Author to whom correspondence should be addressed: jmondal@tifrh.res.in. Tel.: +914020203091.
J. Chem. Phys. 152, 074104 (2020)
Article history
Received:
November 21 2019
Accepted:
January 27 2020
Citation
Navjeet Ahalawat, Satyabrata Bandyopadhyay, Jagannath Mondal; On the role of solvent in hydrophobic cavity–ligand recognition kinetics. J. Chem. Phys. 21 February 2020; 152 (7): 074104. https://doi.org/10.1063/1.5139584
Download citation file:
Sign in
Don't already have an account? Register
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Pay-Per-View Access
$40.00
Citing articles via
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.
Related Content
An efficient Bayesian kinetic lumping algorithm to identify metastable conformational states via Gibbs sampling
J. Chem. Phys. (August 2018)
Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm
J. Chem. Phys. (August 2017)
Hierarchical Nyström methods for constructing Markov state models for conformational dynamics
J. Chem. Phys. (May 2013)
Mental states as macrostates emerging from brain electrical dynamics
Chaos (March 2009)
A deep encoder–decoder framework for identifying distinct ligand binding pathways
J. Chem. Phys. (May 2023)