Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2013

Abstract

Specific binding between proteins plays a crucial role in molecular functions and biological processes. Protein binding interfaces and their atomic contacts are typically defined by simple criteria, such as distance-based definitions that only use some threshold of spatial distance in previous studies. These definitions neglect the nearby atomic organization of contact atoms, and thus detect predominant contacts which are interrupted by other atoms. It is questionable whether such kinds of interrupted contacts are as important as other contacts in protein binding. To tackle this challenge, we propose a new definition called beta (β) atomic contacts. Our definition, founded on the β-skeletons in computational geometry, requires that there is no other atom in the contact spheres defined by two contact atoms; this sphere is similar to the van der Waals spheres of atoms. The statistical analysis on a large dataset shows that β contacts are only a small fraction of conventional distance-based contacts. To empirically quantify the importance of β contacts, we design βACV, an SVM classifier with β contacts as input, to classify homodimers from crystal packing. We found that our βACV is able to achieve the state-of-the-art classification performance superior to SVM classifiers with distance-based contacts as input. Our βACV also outperforms several existing methods when being evaluated on several datasets in previous works. The promising empirical performance suggests that β contacts can truly identify critical specific contacts in protein binding interfaces. β contacts thus provide a new model for more precise description of atomic organization in protein quaternary structures than distance-based contacts.

Keywords

Crystallography, X-Ray, Decision Trees, Hydrogen Bonding, Hydrophobic and Hydrophilic Interactions, Chemical Models, Molecular Models, Protein Interaction Domains and Motifs, Quaternary Protein Structure, Proteins, Support Vector Machines

Discipline

Bioinformatics | Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

PLoS ONE

Volume

8

Issue

4

First Page

e59737-1

Last Page

10

ISSN

1932-6203

Identifier

10.1371/journal.pone.0059737

Publisher

Public Library of Science

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1371/journal.pone.0059737

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