title: KScons: a Bayesian approach for protein residue contact prediction using the knob-socket model of protein tertiary structure.
abstract: 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.
  
  
  
  The results demonstrate the usefulness as well as the limits of knob-socket based structural modeling of protein contacts. The construct is able to extract good predictions from known structural homologs, while its performance degrades when no homologs exist. Among our six implementations, KScons MST-MP (which uses the multiple structure alignment prior and marginal posterior incorporating structural homolog information) performs the best in all three prediction sets. An analysis of recall and precision finds that KScons MST-MP improves accuracy not only by improving identification of true positives, but also by decreasing the number of false positives. Over the CASP10 and CASP11 sets, KScons MST-MP performs better than the leading methods using only evolutionary coupling data, but not quite as well as the supervised learning methods of MetaPSICOV and CoinDCA-NN that incorporate a large set of structural features.
  
  
  
  qiwei.li@rice.eduSupplementary information: Supplementary data are available at Bioinformatics online.
authors: Department of Statistics, Rice University, Houston, TX, USA.
authors: Qiwei
authors: Q
authors: Li
authors: Department of Statistics, Brigham Young University, Provo, UT, USA.
authors: David B
authors: DB
authors: Dahl
authors: Department of Statistics, Rice University, Houston, TX, USA.
authors: Marina
authors: M
authors: Vannucci
authors: Department of Chemistry, University of the Pacific, Stockton, CA, USA.
authors: Hyun
authors: H
authors: Joo
authors: Department of Chemistry, University of the Pacific, Stockton, CA, USA.
authors: Jerry W
authors: JW
authors: Tsai
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D015233:Models, Statistical; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
chemical_list: D000596:Amino Acids; D011506:Proteins