Afrasiabi, F. | Search bioRxiv

bioRxiv2021

Characterizing Protein Conformational Space Using Robotics-Based Search and Topological Data Analysis

An essential step to understanding how different functionalities of proteins work is to explore their conformational space. However, because of the fleeting nature of conformational 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 intermediate 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.

bioinformatics↗

bioRxiv2021

Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways using RRT* and MC

To understand how proteins function on a cellular level, it is of paramount importance to understand their structures and dynamics, including the conformational changes they undergo to carry out their function. For the aforementioned reasons, the study of large conformational changes in proteins has been an interest to researchers for years. However, since some proteins experience rapid and transient conformational changes, it is hard to experimentally capture the intermediate structures. Additionally, computational brute force methods are computationally intractable, which makes it impossible to find these pathways which require a search in a high-dimensional, complex space. In our previous work, we implemented a hybrid algorithm that combines Monte-Carlo (MC) sampling and RRT*, a version of the Rapidly Exploring Random Trees (RRT) robotics-based method, to make the conformational exploration more accurate and efficient, and produce smooth conformational pathways. In this work, we integrated the rigidity analysis of proteins into our algorithm to guide the search to explore flexible regions. We demonstrate that rigidity analysis dramatically reduces the run time and accelerates convergence.

bioinformatics↗