Persistent homology analysis of protein structure, flexibility and folding
By Kelin Xia, G. Wei. International Journal for Numerical Methods in Biomedical Engineering, 2014
296 citations recorded by the source corpus.
Proteins are the most important biomolecules for living organisms. The understanding of protein structure, function, dynamics, and transport is one of the most challenging tasks in biological science. In the present work, persistent homology is, for the first time, introduced for extracting molecular topological fingerprints (MTFs) based on the persistence of molecular topological invariants. MTFs are utilized for protein characterization, identification, and classification. The method of slicing is proposed to track the geometric origin of protein topological invariants. Both all‐atom and coarse‐grained representations of MTFs are constructed. A new cutoff‐like filtration is proposed to shed light on the optimal cutoff distance in elastic network models. On the basis of the correlation between protein compactness, rigidity, and connectivity, we propose an accumulated bar length generated from persistent topological invariants for the quantitative modeling of protein flexibility. To this end, a correlation matrix‐based filtration is developed. This approach gives rise to an accurate prediction of the optimal characteristic distance used in protein B‐factor analysis. Finally, MTFs are employed to characterize protein topological evolution during protein folding and quantitatively predict the protein folding stability. An excellent consistence between our persistent homology prediction and molecular dynamics simulation is found. This work reveals the topology–function relationship of proteins. Copyright © 2014 John Wiley & Sons, Ltd.
Subfields: Biophysics, Computational Theory and Mathematics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/2a8523c1482085874df9560b9c25ca7e2f1e82e1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
In Computational Theory and Mathematics
In Molecular Biology
Citations in the corpus
- TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions (2017, background): “78,79 Fig.”