Topological Data Analysis for Predicting Protein Folding Pathways

Abstract

Protein folding, the process by which a polypeptide chain attains its functional three-dimensional structure, is a fundamental problem in biochemistry and bioinformatics. Traditional approaches to predicting protein folding pathways have faced significant challenges due to the complex, high-dimensional nature of the underlying data. This work introduces a novel methodology leveraging Topological Data Analysis (TDA) to address this challenge. We hypothesize that underlying topological features within protein folding data – specifically, the presence and connectivity of loops, cavities, and other topological structures – can be used to predict and understand the pathways proteins take to fold. This paper outlines the theoretical framework, describes the application of TDA techniques (persistent homology, Mapper, etc.) to protein folding data, and presents preliminary results demonstrating the potential of this approach. We demonstrate how TDA can extract meaningful insights from complex protein folding landscapes that are often missed by conventional methods. The core claim is that understanding protein folding pathways is a major challenge in bioinformatics, and our approach provides a new lens through which to examine this problem. This work contributes to the development of more accurate and efficient protein folding prediction tools.