title: Accurate prediction of helix interactions and residue contacts in membrane proteins.
abstract: Accurate prediction of intra-molecular interactions from amino acid sequence is an important pre-requisite for obtaining high-quality protein models. Over the recent years, remarkable progress in this area has been achieved through the application of novel co-variation algorithms, which eliminate transitive evolutionary connections between residues. In this work we present a new contact prediction method for α-helical transmembrane proteins, MemConP, in which evolutionary couplings are combined with a machine learning approach. MemConP achieves a substantially improved accuracy (precision: 56.0%, recall: 17.5%, MCC: 0.288) compared to the use of either machine learning or co-evolution methods alone. The method also achieves 91.4% precision, 42.1% recall and a MCC of 0.490 in predicting helix-helix interactions based on predicted contacts. The approach was trained and rigorously benchmarked by cross-validation and independent testing on up-to-date non-redundant datasets of 90 and 30 experimental three dimensional structures, respectively. MemConP is a standalone tool that can be downloaded together with the associated training data from http://webclu.bio.wzw.tum.de/MemConP.
authors: Department of Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, Maximus-von-Imhof Forum 3, 85354 Freising, Germany.
authors: Peter
authors: P
authors: Hönigschmid
authors: Department of Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, Maximus-von-Imhof Forum 3, 85354 Freising, Germany; Helmholtz Zentrum Munich, German Research Center for Environmental Health (GmbH), Institute of Bioinformatics and Systems Biology, 85764 Neuherberg, Germany; Laboratory of Bioinformatics, RASA Research Center, St Petersburg State Polytechnical University, St Petersburg 195251, Russia. Electronic address: d.frishman@wzw.tum.de.
authors: Dmitrij
authors: D
authors: Frishman
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry / Q000378:metabolism; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D020407:Internet; D008565:Membrane Proteins / Q000737:chemistry* / Q000378:metabolism; D008958:Models, Molecular; D000072417:Protein Domains; D017433:Protein Structure, Secondary; D015203:Reproducibility of Results
chemical_list: D000596:Amino Acids; D008565:Membrane Proteins
keywords: Machine learning; Molecular interactions; Protein structure prediction; Sequence analysis