Protein folding analysis using features obtained by persistent homology

Abstract

Understanding the protein folding process is an outstanding issue in biophysics; recent developments in molecular dynamics simulation have provided insights into this phenomenon. However, the large freedom of atomic motion hinders the understanding of this process. In this study, we applied persistent homology, an emerging methods to analyze topological features in a dataset, to reveal protein folding dynamics. We developed a new method to characterize protein structure based on persistent homology and applied this method to molecular dynamics simulations of chignolin. Using principle component analysis or non-negative matrix factorization, our analysis method revealed two stable states and one saddle state, corresponding to the native, misfolded, and transition states, respectively. We also identified an unfolded state with slow dynamics in the reduced space. Our method serves as a promising tool to understand the protein folding process.

To be published in Biophysical Journal

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• Scikit-learn: Machine Learning in Python
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• Persistent homology analysis of protein structure, flexibility and folding
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• Persistence weighted Gaussian kernel for topological data analysis
• Categorification of persistent homology
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Cited by in corpus (4)

• Persistent Homology Analysis for Materials Research and Persistent Homology Software: HomCloud
• Microstructure Evolution of Solid Oxide Fuel Cell Anodes Characterized by Persistent Homology
• Euler Characteristic Tools For Topological Data Analysis
• Time series analysis using persistent homology of distance matrix