title: Accurate contact predictions using covariation techniques and machine learning.
abstract: Here we present the results of residue-residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effective sequences, our server achieved an average top-L/5 long-range contact precision of 27%. MetaPSICOV method bases on a combination of classical contact prediction features, enhanced with three distinct covariation methods embedded in a two-stage neural network predictor. Some unique features of our approach are (1) the tuning between the classical and covariation features depending on the depth of the input alignment and (2) a hybrid approach to generate deepest possible multiple-sequence alignments by combining jackHMMer and HHblits. We discuss the CONSIP2 pipeline, our results and show that where the method underperformed, the major factor was relying on a fixed set of parameters for the initial sequence alignments and not attempting to perform domain splitting as a preprocessing step. Proteins 2016; 84(Suppl 1):145-151. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
authors.affiliation: Department of Computer Science, Bioinformatics Group, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
authors.forename: Tomasz
authors.initials: T
authors.lastname: Kosciolek
authors.affiliation: Department of Computer Science, Bioinformatics Group, University College London, Gower Street, London, WC1E 6BT, United Kingdom. d.t.jones@ucl.ac.uk.
authors.forename: David T
authors.initials: DT
authors.lastname: Jones
mesh_terms: D000595:Amino Acid Sequence; D001419:Bacteria / Q000737:chemistry; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data*; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D020407:Internet; D000069550:Machine Learning; D008958:Models, Molecular; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017510:Protein Folding; D054730:Protein Interaction Domains and Motifs; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D012984:Software; D014780:Viruses / Q000737:chemistry
chemical_list: D011506:Proteins
keywords: CASP; ab initio prediction; amino acid covariation; protein structure prediction; residue-residue contact prediction