Chapter title |
Methods for Detecting Critical Residues in Proteins.
|
---|---|
Chapter number | 15 |
Book title |
In Vitro Mutagenesis
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6472-7_15 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6470-3, 978-1-4939-6472-7
|
Authors |
Nurit Haspel, Filip Jagodzinski |
Editors |
Andrew Reeves |
Abstract |
In proteins, certain amino acids may play a critical role in determining their structure and function. Examples include flexible regions, which allow domain motions, and highly conserved residues on functional interfaces, which play a role in binding and interaction with other proteins. Detecting these regions facilitates the analysis and simulation of protein rigidity and conformational changes, and aids in characterizing protein-protein binding. We present a protocol that combines graph-theory rigidity analysis and machine-learning-based methods for predicting critical residues in proteins. Our approach combines amino-acid specific information and data obtained by two complementary methods. One method, KINARI, performs graph-based analysis to find rigid clusters of amino acids in a protein, while the other method relies on evolutionary conservation scores to find functional interfaces in proteins. Our machine learning model combines both methods, in addition to amino acid type and solvent-accessible surface area. |
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Demographic breakdown
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Unknown | 1 | 14% |
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