Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning

Ma, Jianzhu; Wang, Sheng; Wang, Zhiyong; Xu, Jinbo

doi:10.1093/bioinformatics/btv472

Cited by 109 publications

(59 citation statements)

References 44 publications

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“…At first, a Monte Carlo approach was used to explore parameter space randomly [7], but this approach is computationally costly. Since then, various entropy-maximization methods have been implemented to approximate and simplify these coupling parameters [8], including susceptibility propagation [9], mean-field approximation (mfDCA and in the original EVFold) [10-12], multivariate Gaussian modeling [13], pseudo-likelihood maximization (implemented in plmDCA, CCMpred, the new EVFold, and GREMLIN) [14-17], Boltzmann network formalism [18], Bayesian network models [19], and sparse inverse covariance estimation (PSICOV) [20,21]. These methods, which we will call direct methods , have greatly increased the accuracy of sequence coevolution analysis in predicting true contacts, opening up this method to predicting protein structures [11,22-24], protein-protein interactions [25,26], and the effects of mutations [27].…”

Section: Introduction To Sequence Coevolution Methodsmentioning

confidence: 99%

Applications of sequence coevolution in membrane protein biochemistry

Nicoludis¹,

Gaudet²

2018

Biochimica et Biophysica Acta (BBA) - Biomembranes

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Recently, protein sequence coevolution analysis has matured into a predictive powerhouse for protein structure and function. Direct methods, which use global statistical models of sequence coevolution, have enabled the prediction of membrane and disordered protein structures, protein complex architectures, and the functional effects of mutations in proteins. The field of membrane protein biochemistry and structural biology has embraced these computational techniques, which provide functional and structural information in an otherwise experimentally-challenging field. Here we review recent applications of protein sequence coevolution analysis to membrane protein structure and function and highlight the promising directions and future obstacles in these fields. We provide insights and guidelines for membrane protein biochemists who wish to apply sequence coevolution analysis to a given experimental system.

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Section: Introduction To Sequence Coevolution Methodsmentioning

confidence: 99%

Applications of sequence coevolution in membrane protein biochemistry

Nicoludis¹,

Gaudet²

2018

Biochimica et Biophysica Acta (BBA) - Biomembranes

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“…In this case, we used the number of effective sequences in a MSA as comparison metric to account for the fact that highly similar homologous do not provide any additional contact information than a single one. Similar to Ma et al [21] we grouped the test set members into five categories by l n ( N eff ): [4,5),[5,6),[6,7),[7,8),[8,10), and calculated the averaged L /10 accuracies for each group. Figure 3 shows clearly that COUSCOus outperforms PSICOV regardless of the l n ( N eff ) on long, medium and short range contacts.…”

Section: Resultsmentioning

confidence: 99%

“…Most recently, new hybrid methods have been developed, amidst many others such as DNCON [19], PConsC [20], CoinDCA [21] or MetaPSICOV [22], where contact detecting methods are combined along with protein physiochemical features to provide more accurate contact predictions.…”

Section: Introductionmentioning

confidence: 99%

COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator

et al. 2016

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BackgroundThe post-genomic era with its wealth of sequences gave rise to a broad range of protein residue-residue contact detecting methods. Although various coevolution methods such as PSICOV, DCA and plmDCA provide correct contact predictions, they do not completely overlap. Hence, new approaches and improvements of existing methods are needed to motivate further development and progress in the field. We present a new contact detecting method, COUSCOus, by combining the best shrinkage approach, the empirical Bayes covariance estimator and GLasso.ResultsUsing the original PSICOV benchmark dataset, COUSCOus achieves mean accuracies of 0.74, 0.62 and 0.55 for the top L/10 predicted long, medium and short range contacts, respectively. In addition, COUSCOus attains mean areas under the precision-recall curves of 0.25, 0.29 and 0.30 for long, medium and short contacts and outperforms PSICOV. We also observed that COUSCOus outperforms PSICOV w.r.t. Matthew’s correlation coefficient criterion on full list of residue contacts. Furthermore, COUSCOus achieves on average 10% more gain in prediction accuracy compared to PSICOV on an independent test set composed of CASP11 protein targets. Finally, we showed that when using a simple random forest meta-classifier, by combining contact detecting techniques and sequence derived features, PSICOV predictions should be replaced by the more accurate COUSCOus predictions.ConclusionWe conclude that the consideration of superior covariance shrinkage approaches will boost several research fields that apply the GLasso procedure, amongst the presented one of residue-residue contact prediction as well as fields such as gene network reconstruction.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1400-3) contains supplementary material, which is available to authorized users.

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“…Protein contact prediction is a crucially important step for protein structure prediction. Many contact prediction methods have been developed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In earlier stages of contact prediction history, most successful prediction methods were based on evolutionary coupling analysis (ECA) of large multiple sequence alignment (MSA) of homologue sequences.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Protein Contact Prediction by Weighting Sequences in a Multiple Sequence Alignment

Fukuda

Tomii

2018

Preprint

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Protein contact prediction is a crucially important step for protein structure prediction. To predict a contact, approaches of two types are used: evolutionary coupling analysis (ECA) and supervised learning. ECA uses a large multiple sequence alignment (MSA) of homologue sequences and extract correlation information between residues. Supervised learning uses ECA analysis results as input features and can produce higher accuracy. As described herein, we present a new approach to contact prediction which can both extract correlation information and predict contacts in a supervised manner directly from MSA using a deep neural network (DNN). Using DNN, we can obtain higher accuracy than with earlier ECA methods. Simultaneously, we can weight each sequence in MSA to eliminate noise sequences automatically in a supervised way. It is expected that the combination of our method and other meta-learning methods can provide much higher accuracy of contact prediction.

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Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 109 publications

References 44 publications

Applications of sequence coevolution in membrane protein biochemistry

Applications of sequence coevolution in membrane protein biochemistry

COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator

Deep Neural Network for Protein Contact Prediction by Weighting Sequences in a Multiple Sequence Alignment

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