Methods for Detecting Critical Residues in Proteins
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 wordsDocking Evolutionary conservation Machine learning Protein binding interfaces Protein–protein interaction
Protein Data Bank
van der Waals
Least root mean square deviation
Support vector machine
The work described here was partially funded by NSF grant CCF-1116060. The authors thank Dr. Bahar Akbal-Delibas for her collaboration.
- 17.Akbal-Delibas B, Pomplun M, Haspel N (2014) Accurmsd: a machine learning approach to predicting structure similarity of docked protein complexes. In: Proc. of ACM-BCB (5th ACM International conference on Bioinformatics and Computational Biology). pp 289–296Google Scholar
- 19.Jagodzinski F, Akbal-Delibas B, Haspel N (2013) An evolutionary conservation & rigidity analysis machine learning approach for detecting critical protein residues. In: CSBW (Computational Structural Bioinformatics Workshop), in proc. of ACM-BCB (ACM International conference on Bioinformatics and Computational Biology), pp 780–786Google Scholar
- 20.Lichtarge O-Evolutionary trace server. http://mammoth.bcm.tmc.edu/ETserver.html
- 23.Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intel Syst Technol 2(3)Google Scholar
- 30.Akbal-Delibas B, Pomplun M, Haspel N (2015) AccuRefiner: a machine learning guided refinement method for protein-protein docking. In: proceedings of BICoB (7th international conference on Bioinformatics and Computational Biology)Google Scholar