Structural bioinformatics NeBcon: protein contact map prediction using neural network training coupled with naı̈ve Bayes classifiers
نویسندگان
چکیده
Motivation: Recent CASP experiments have witnessed exciting progress on folding large-size nonhumongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact prediction methods for the high volume of sequence homologs that are not available to most of the non-humongous protein targets. Development of efficient methods that can generate balanced and reliable contact maps for different type of protein targets is essential to enhance the success rate of the ab initio protein structure prediction. Results: We developed a new pipeline, NeBcon, which uses the naı̈ve Bayes classifier (NBC) theorem to combine eight state of the art contact methods that are built from co-evolution and machine learning approaches. The posterior probabilities of the NBC model are then trained with intrinsic structural features through neural network learning for the final contact map prediction. NeBcon was tested on 98 non-redundant proteins, which improves the accuracy of the best coevolution based meta-server predictor by 22%; the magnitude of the improvement increases to 45% for the hard targets that lack sequence and structural homologs in the databases. Detailed data analysis showed that the major contribution to the improvement is due to the optimized NBC combination of the complementary information from both co-evolution and machine learning predictions. The neural network training also helps to improve the coupling of the NBC posterior probability and the intrinsic structural features, which were found particularly important for the proteins that do not have sufficient number of homologous sequences to derive reliable co-evolution profiles. Availiablity and Implementation: On-line server and standalone package of the program are available at http://zhanglab.ccmb.med.umich.edu/NeBcon/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online. VC The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected] 1 Bioinformatics, 2017, 1–11 doi: 10.1093/bioinformatics/btx164 Advance Access Publication Date: 28 March 2017
منابع مشابه
NeBcon: protein contact map prediction using neural network training coupled with naı̈ve Bayes classifiers
Motivation: Recent CASP experiments have witnessed exciting progress on folding large-size nonhumongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact prediction methods for the high volume of sequence homologs that are not available to most of the non-humongous protein targets. Development of effici...
متن کاملNeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers
Motivation Recent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact prediction methods for the high volume of sequence homologs that are not available to most of the non-humongous protein targets. Development of effici...
متن کاملDeep architectures for protein contact map prediction
MOTIVATION Residue-residue contact prediction is important for protein structure prediction and other applications. However, the accuracy of current contact predictors often barely exceeds 20% on long-range contacts, falling short of the level required for ab initio structure prediction. RESULTS Here, we develop a novel machine learning approach for contact map prediction using three steps of...
متن کاملPrediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners
MOTIVATION Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction. RESULTS We develop a new set of flexible machine learning architectures for the predict...
متن کاملPrediction of Protein Coarse Contact Maps
Prediction of topological representations of proteins that are geometrically invariants can contribute towards the solution of fundamental open problems in structural genomics like folding. In this paper we focus on coarse grained protein contact maps, a representation that describes the spatial neighborhood relation between secondary structure elements such as helices, beta sheets, and random ...
متن کامل