Forecasting residue–residue contact prediction accuracy

Protein contact prediction helps reconstruct the tertiary structure that greatly determines a protein’s function; therefore, contact prediction from the sequence is an important problem. Recently there has been exciting progress on this problem, but many of the existing methods are still low quality of prediction accuracy. In this paper, we present a new mixed integer linear programming (MILP)-based consensus method: a Consensus scheme based On a Mixed integer linear opTimization method for prOtein contact Prediction (COMTOP). The MILP-based consensus method combines the strengths of seven selected protein contact prediction methods, including CCMpred, EVfold, DeepCov, NNcon, PconsC4, plmDCA, and PSICOV, by optimizing the number of correctly predicted contacts and achieving a better prediction accuracy. The proposed hybrid protein residue–residue contact prediction scheme was tested in four independent test sets. For 239 highly non-redundant proteins, the method showed a prediction accuracy of 59.68%, 70.79%, 78.86%, 89.04%, 94.51%, and 97.35% for top-5L, top-3L, top-2L, top-L, top-L/2, and top-L/5 contacts, respectively. When tested on the CASP13 and CASP14 test sets, the proposed method obtained accuracies of 75.91% and 77.49% for top-L/5 predictions, respectively. COMTOP was further tested on 57 non-redundant ɑ-helical transmembrane proteins and achieved prediction accuracies of 64.34% and 73.91% for top-L/2 and top-L/5 predictions, respectively. For all test datasets, the improvement of COMTOP in accuracy over the seven individual methods increased with the increasing number of predicted contacts. For example, COMTOP performed much better for large number of contact predictions (such as top-5L and top-3L) than for small number of contact predictions such as top-L/2 and top-L/5. The results and analysis demonstrate that COMTOP can significantly improve the performance of the individual methods; therefore, COMTOP is more robust against different types of test sets. COMTOP also showed better/comparable predictions when compared with the state-of-the-art predictors.

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Bio-knowledge-based filters improve residue–residue contact prediction accuracy

Bioinformatics ◽

10.1093/bioinformatics/bty416 ◽

2018 ◽

Vol 34 (21) ◽

pp. 3675-3683 ◽

Cited By ~ 1

Author(s):

P P Wozniak ◽

J Pelc ◽

M Skrzypecki ◽

G Vriend ◽

M Kotulska

Keyword(s):

Prediction Accuracy ◽

Contact Prediction ◽

Residue Contact ◽

Knowledge Based

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Faculty Opinions recommendation of Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1106054.562184 ◽

2008 ◽

Author(s):

Nathan Baker

Keyword(s):

Mutual Information ◽

Contact Prediction ◽

Residue Contact

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Improved residue contact prediction using support vector machines and a large feature set

BMC Bioinformatics ◽

10.1186/1471-2105-8-113 ◽

2007 ◽

Vol 8 (1) ◽

Cited By ~ 145

Author(s):

Jianlin Cheng ◽

Pierre Baldi

Keyword(s):

Support Vector Machines ◽

Support Vector ◽

Contact Prediction ◽

Residue Contact ◽

Vector Machines

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Predicting protein inter-residue contacts using composite likelihood maximization and deep learning

BMC Bioinformatics ◽

10.1186/s12859-019-3051-7 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 2

Author(s):

Haicang Zhang ◽

Qi Zhang ◽

Fusong Ju ◽

Jianwei Zhu ◽

Yujuan Gao ◽

...

Keyword(s):

Deep Learning ◽

Conditional Probability ◽

Prediction Accuracy ◽

Tertiary Structure ◽

Likelihood Function ◽

Composite Likelihood ◽

Contact Prediction ◽

Markov Random ◽

Pseudo Likelihood ◽

Residue Contacts

Abstract Background Accurate prediction of inter-residue contacts of a protein is important to calculating its tertiary structure. Analysis of co-evolutionary events among residues has been proved effective in inferring inter-residue contacts. The Markov random field (MRF) technique, although being widely used for contact prediction, suffers from the following dilemma: the actual likelihood function of MRF is accurate but time-consuming to calculate; in contrast, approximations to the actual likelihood, say pseudo-likelihood, are efficient to calculate but inaccurate. Thus, how to achieve both accuracy and efficiency simultaneously remains a challenge. Results In this study, we present such an approach (called clmDCA) for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e., the product of conditional probability of individual residues, our approach uses composite-likelihood, i.e., the product of conditional probability of all residue pairs. Composite likelihood has been theoretically proved as a better approximation to the actual likelihood function than pseudo-likelihood. Meanwhile, composite likelihood is still efficient to maximize, thus ensuring the efficiency of clmDCA. We present comprehensive experiments on popular benchmark datasets, including PSICOV dataset and CASP-11 dataset, to show that: i) clmDCA alone outperforms the existing MRF-based approaches in prediction accuracy. ii) When equipped with deep learning technique for refinement, the prediction accuracy of clmDCA was further significantly improved, suggesting the suitability of clmDCA for subsequent refinement procedure. We further present a successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset. Conclusions Composite likelihood maximization algorithm can efficiently estimate the parameters of Markov Random Fields and can improve the prediction accuracy of protein inter-residue contacts.

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IMPContact: An Interhelical Residue Contact Prediction Method

BioMed Research International ◽

10.1155/2020/4569037 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Chao Fang ◽

Yajie Jia ◽

Lihong Hu ◽

Yinghua Lu ◽

Han Wang

Keyword(s):

Biological Activities ◽

Transmembrane Protein ◽

Prediction Method ◽

Alpha Helix ◽

Structural Features ◽

Contact Prediction ◽

Transmembrane Segments ◽

Residue Contact ◽

Residue Contacts ◽

Protein Functions

As an important category of proteins, alpha-helix transmembrane proteins (αTMPs) play an important role in various biological activities. Because the solved αTMP structures are inadequate, predicting the residue contacts among the transmembrane segments of an αTMP exhibits the basis of protein fold, which can be used to further discover more protein functions. A few efforts have been devoted to predict the interhelical residue contact using machine learning methods based on the prior knowledge of transmembrane protein structure. However, it is still a challenge to improve the prediction accuracy, while the deep learning method provides an opportunity to utilize the structural knowledge in a different insight. For this purpose, we proposed a novel αTMP residue-residue contact prediction method IMPContact, in which a convolutional neural network (CNN) was applied to recognize those interhelical contacts in a TMP using its specific structural features. There were four sequence-based TMP-specific features selected to descript a pair of residues, namely, evolutionary covariation, predicted topology structure, residue relative position, and evolutionary conservation. An up-to-date dataset was used to train and test the IMPContact; our method achieved better performance compared to peer methods. In the case studies, IHRCs in the regular transmembrane helixes were better predicted than in the irregular ones.

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A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks

BMC Bioinformatics ◽

10.1186/1471-2105-14-s14-s12 ◽

2013 ◽

Vol 14 (S14) ◽

Cited By ~ 20

Author(s):

Jesse Eickholt ◽

Jianlin Cheng

Keyword(s):

Contact Prediction ◽

Residue Contact ◽

Deep Networks

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

PLoS Computational Biology ◽

10.1371/journal.pcbi.1006526 ◽

2018 ◽

Vol 14 (11) ◽

pp. e1006526 ◽

Cited By ~ 5

Author(s):

Susann Vorberg ◽

Stefan Seemayer ◽

Johannes Söding

Keyword(s):

Contact Prediction ◽

Residue Contact ◽

Synthetic Protein ◽

Protein Alignments

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PconsC4: fast, free, easy, and accurate contact predictions

10.1101/383133 ◽

2018 ◽

Cited By ~ 2

Author(s):

Mirco Michel ◽

David Menéndez Hurtado ◽

Arne Elofsson

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Prediction Methods ◽

Coupling Analysis ◽

Learning Methods ◽

Contact Prediction ◽

Residue Contact ◽

Direct Coupling Analysis ◽

Computationally Expensive ◽

Contact Predictions

AbstractMotivationResidue contact prediction was revolutionized recently by the introduction of direct coupling analysis (DCA). Further improvements, in particular for small families, have been obtained by the combination of DCA and deep learning methods. However, existing deep learning contact prediction methods often rely on a number of external programs and are therefore computationally expensive.ResultsHere, we introduce a novel contact predictor, PconsC4, which performs on par with state of the art methods. PconsC4 is heavily optimized, does not use any external programs and therefore is significantly faster and easier to use than other methods.AvailabilityPconsC4 is freely available under the GPL license from https://github.com/ElofssonLab/PconsC4. Installation is easy using the pip command and works on any system with Python 3.5 or later and a modern GCC [email protected]

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