Open AccessUsing Topological Data Analysis and RRT to Investigate Protein Conformational Spaces
Author(s) -
Ramin Dehghanpoor,
Fatemeh Afrasiabi,
Nurit Haspel
Publication year - 2022
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
ISSN - 2398-7340
DOI - 10.29007/57fw
Subject(s) - computer science , monte carlo method , task (project management) , work (physics) , space (punctuation) , topology (electrical circuits) , protein structure , algorithm , chemistry , physics , mathematics , engineering , biochemistry , combinatorics , statistics , thermodynamics , operating system , systems engineering
An essential step to understanding how different functionalities of proteins work is to explore their conformational space. However, because of the fleeting nature of conforma- tional changes in proteins, investigating protein conformational spaces is a challenging task to do experimentally. Nonetheless, computational methods have shown to be practical to explore these conformational pathways. In this work, we use Topological Data Analysis (TDA) methods to evaluate our previously introduced algorithm called RRTMC, that uses a combination of Rapidly-exploring Random Trees algorithm and Monte Carlo criteria to explore these pathways. TDA is used to identify the intermediate conformations that are generated the most by RRTMC and examine how close they are to existing known inter- mediate conformations. We concluded that the intermediate conformations generated by RRTMC are close to existing experimental data and that TDA can be a helpful tool to analyze protein conformation sampling methods.