NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers

He, Baoji; Mortuza, S. M.; Wang, Yanting; Shen, Hong; Zhang, Yang

doi:10.1093/bioinformatics/btx164

Cited by 76 publications

(66 citation statements)

References 39 publications

(60 reference statements)

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“…To highlight this impact, in Figure A we summarize the folding performance for FM targets based on the best models submitted by either “Zhang‐Server” or “QUARK” since CASP8. Here, a new contact prediction module that implemented the previous version of NeBcon without using deep‐learning‐based predictors was incorporated into “Zhang‐Server” and “QUARK” during CASP12. This resulted in an average TM‐score of 0.459 for the 30 FM targets in CASP12, which was at least 22.4% better than the results of the former CASP experiments.…”

Section: Resultsmentioning

confidence: 99%

“…Table S4 shows the performance of NeBcon and its nine component contact prediction methods. The accuracies of the deep‐learning‐based methods (ResPRE, DeepPLM, Deepcontact, DNCON2, DeepCOV and MetaPSICOV2) that were newly added to NeBcon are significantly better than those of the co‐evolution‐based methods used in the former version of NeBcon . Among all of the individual deep‐learning‐based methods, ResPRE has the best performance, followed by another in‐house program, DeepPLM.…”

Section: Resultsmentioning

confidence: 99%

“…In the last decade, we have been constantly working on extending the capabilities of these pipelines by introducing new modules in each of the previous CASP experiments. In CASP12, for instance, we included contact information, derived from a meta‐method, NeBcon, into the “QUARK” pipeline, which greatly improved the accuracy of the FM target models. However, the modeling accuracy was often limited by low contact prediction precision.…”

Section: Introductionmentioning

confidence: 99%

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Deep‐learning contact‐map guided protein structure prediction in CASP13

et al. 2019

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We report the results of two fully automated structure prediction pipelines, “Zhang‐Server” and “QUARK”, in CASP13. The pipelines were built upon the C‐I‐TASSER and C‐QUARK programs, which in turn are based on I‐TASSER and QUARK but with three new modules: (a) a novel multiple sequence alignment (MSA) generation protocol to construct deep sequence‐profiles for contact prediction; (b) an improved meta‐method, NeBcon, which combines multiple contact predictors, including ResPRE that predicts contact‐maps by coupling precision‐matrices with deep residual convolutional neural‐networks; and (c) an optimized contact potential to guide structure assembly simulations. For 50 CASP13 FM domains that lacked homologous templates, average TM‐scores of the first models produced by C‐I‐TASSER and C‐QUARK were 28% and 56% higher than those constructed by I‐TASSER and QUARK, respectively. For the first time, contact‐map predictions demonstrated usefulness on TBM domains with close homologous templates, where TM‐scores of C‐I‐TASSER models were significantly higher than those of I‐TASSER models with a P‐value <.05. Detailed data analyses showed that the success of C‐I‐TASSER and C‐QUARK was mainly due to the increased accuracy of deep‐learning‐based contact‐maps, as well as the careful balance between sequence‐based contact restraints, threading templates, and generic knowledge‐based potentials. Nevertheless, challenges still remain for predicting quaternary structure of multi‐domain proteins, due to the difficulties in domain partitioning and domain reassembly. In addition, contact prediction in terminal regions was often unsatisfactory due to the sparsity of MSAs. Development of new contact‐based domain partitioning and assembly methods and training contact models on sparse MSAs may help address these issues.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep‐learning contact‐map guided protein structure prediction in CASP13

et al. 2019

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“…Therefore, most research has focused on making contact prediction reliable enough for medium- 35 sized protein families [21,24,29,38,47,57].…”

Section: Introductionmentioning

confidence: 99%

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Vorberg

Seemayer

Soeding

2018

Preprint

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Compensatory mutations between protein residues that are in physical contact with each other can manifest themselves as statistical couplings between the corresponding columns in a multiple sequence alignment (MSA) of the protein family. Conversely, high coupling coefficients predict residues contacts. Methods for de-novo protein structure prediction based on this approach are becoming increasingly reliable. Their main limitation is the strong systematic and statistical noise in the estimation of coupling coefficients, which has so far limited their application to very large protein families. While most research has focused on boosting contact prediction quality by adding external information, little progress has been made to improve the statistical procedure at the core. In that regard, our lack of understanding of the sources of noise poses a major obstacle. We have developed CCMgen, the first method for simulating protein evolution by providing full control over the generation of realistic synthetic MSAs with pairwise statistical couplings between residue positions. This procedure requires an exact statistical model that reliably reproduces observed alignment statistics. With CCMpredPy we also provide an implementation of persistent contrastive divergence (PCD), a precise inference technique that enables us to learn the required high-quality statistical models. We demonstrate how CCMgen can facilitate the development and testing of contact prediction methods by analysing the systematic noise contributions from phylogeny and entropy. For that purpose we propose a simple entropy correction (EC) strategy which disentangles the correction for both sources of noise. We find that entropy contributes typically roughly twice as much noise as phylogeny.

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“…These methods effectively reduce false positive predictions by globally considering all inter-residue correlations. More recently, methods like MetaPSICOV [19], SAE-DNN [20], DeepConPred [21], NeBcon [22] and RaptorX-Contact [23] integrated sophisticated machine-learning techniques to further enhance the prediction accuracy. In the latest CASP12 competition, RaptorX-Contact achieved the best performance in the category of template-free modeling targets.…”

Section: Author Summarymentioning

confidence: 99%

Identification of residue pairing in interacting β-strands from a predicted residue contact map

Mao

Wang

Zhang

et al. 2017

Preprint

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Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β -β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we introduce a novel ridge-detection-based β -β contact predictor, RDb 2 C, to identify residue pairing in β strands from any predicted residue contact map. The algorithm adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb 2 C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~62% and ~76%at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb 2 C achieves impressively higher performance, with F1-scores reaching ~76% and ~86% at the residue level and strand level, respectively. According to our tests on 61 mainly β proteins, improvement in the β -β contact prediction can further ameliorate the structural prediction. Availability:All source data and codes are available at http://166.111.152.91/Downloads.html or at the GitHub address of https://github.com/wzmao/RDb2C. Author summaryDue to the topological complexity, mainly β proteins are challenging targets in protein structure prediction. Knowledge of the pairing between β strands, especially the residue pairing pattern, can greatly facilitate the tertiary structure prediction of mainly β proteins. In this work, we developed a novel algorithm to identify the residue pairing in β strands from a predicted residue contact map.This method adopts the ridge detection technique to capture the characteristic pattern of β -β interactions from the map and then utilizes a multi-stage random forest framework to predict β -β contacts at the residue level. According to our tests, our method could effectively improve the prediction of β -β contacts even from a highly noisy contact map. Moreover, the refined β -β contact information could effectively improve the structural modeling of mainly β proteins.

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NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 76 publications

References 39 publications

Deep‐learning contact‐map guided protein structure prediction in CASP13

Deep‐learning contact‐map guided protein structure prediction in CASP13

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Identification of residue pairing in interacting β-strands from a predicted residue contact map

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