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Methods for Detecting Critical Residues in Proteins

  • Nurit Haspel
  • Filip Jagodzinski
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1498)

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.

Key words

Docking Evolutionary conservation Machine learning Protein binding interfaces Protein–protein interaction 

Abbreviations

PDB

Protein Data Bank

VdW

van der Waals

lRMSD

Least root mean square deviation

SVM

Support vector machine

AI

Artificial intelligence

Notes

Acknowledgements

The work described here was partially funded by NSF grant CCF-1116060. The authors thank Dr. Bahar Akbal-Delibas for her collaboration.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Massachusetts BostonBostonUSA
  2. 2.Department of Computer ScienceWestern Washington UniversityBellinghamUSA

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