Simulation and data analysis have evolved into powerful methods for discovering and understanding molecular modes of action and designing new compounds to exploit these modes. The combination provides a strong impetus to create and exploit new tools and techniques at the interfaces between physics, biology, and data science as a pathway to new scientific insight and accelerated discovery. In this context, we explore the rational design of novel antimicrobial peptides (short protein sequences exhibiting broad activity against multiple species of bacteria). We show how datasets can be harvested to reveal features which inform new design concepts. We introduce new analysis and visualization tools: a graphical representation of the k-mer spectrum as a fundamental property encoded in antimicrobial peptide databases and a data-driven representation to illustrate membrane binding and permeation of helical peptides.
Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions
Flaviu Cipcigan, Anna Paola Carrieri, Edward O. Pyzer-Knapp, Ritesh Krishna, Ya-Wen Hsiao, Martyn Winn, Maxim G. Ryadnov, Colin Edge, Glenn Martyna, Jason Crain; Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions. J. Chem. Phys. 28 June 2018; 148 (24): 241744. https://doi.org/10.1063/1.5027261
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