KScons: a Bayesian approach for protein residue contact prediction using the knob-socket model of protein tertiary structure
Author(s) -
Qiwei Li,
David B. Dahl,
Marina Vannucci,
Hyun Joo,
Jerry Tsai
Publication year - 2016
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btw553
Subject(s) - pairwise comparison , computer science , false positive paradox , casp , protein tertiary structure , construct (python library) , protein structure prediction , artificial intelligence , pattern recognition (psychology) , data mining , algorithm , protein structure , chemistry , biochemistry , programming language
By simplifying the many-bodied complexity of residue packing into patterns of simple pairwise secondary structure interactions between a single knob residue with a three-residue socket, the knob-socket construct allows a more direct incorporation of structural information into the prediction of residue contacts. By modeling the preferences between the amino acid composition of a socket and knob, we undertake an investigation of the knob-socket construct's ability to improve the prediction of residue contacts. The statistical model considers three priors and two posterior estimations to better understand how the input data affects predictions. This produces six implementations of KScons that are tested on three sets: PSICOV, CASP10 and CASP11. We compare against the current leading contact prediction methods.
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