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

By Jianzhu Ma, Sheng Wang, Zhiyong Wang, Jinbo Xu. 2013

112 citations recorded by the source corpus.

MOTIVATION Protein contact prediction is important for protein structure and functional study. Both evolutionary coupling (EC) analysis and supervised machine learning methods have been developed, making use of different information sources. However, contact prediction is still challenging especially for proteins without a large number of sequence homologs. RESULTS This article presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning to improve accuracy on proteins without many sequence homologs. Different from existing single-family EC analysis that uses residue coevolution information in only the target protein family, our joint EC analysis uses residue coevolution in both the target family and its related families, which may have divergent sequences but similar folds. To implement this, we model a set of related protein families using Gaussian graphical models and then coestimate their parameters by maximum-likelihood, subject to the constraint that these parameters shall be similar to some degree. Our GGL method can also integrate supervised learning methods to further improve accuracy. Experiments show that our method outperforms existing methods on proteins without thousands of sequence homologs, and that our method performs better on both conserved and family-specific contacts. AVAILABILITY AND IMPLEMENTATION See http://raptorx.uchicago.edu/ContactMap/ for a web server implementing the method. CONTACT j3xu@ttic.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “By combining co-evolution information with a few other protein features, shallow neural network methods such as MetaPSICOV [188] and CoinDCA-NN [195] have shown some advantage over pure co-evolution analysis for proteins with few sequence homologs, but their accuracy is still far from satisfactory.”
Evaluating Protein Transfer Learning with TAPE (2019, methodology): “The original RaptorX method has a more complex evolutionary featurization: they construct a Multiple Sequence Alignment for each protein, then pass it through CCMpred [60] - a Markov Random Field based contact prediction method.”
Folding membrane proteins by deep transfer learning (2017, background): “…co-evolved residues, such as EVfold (Marks et al., 2011), PSICOV (Jones et al., 2012), CCMpred (Seemayer et al., 2014), Gremlin (Kamisetty et al., 2013) and others(Weigt et al., 2009), but it needs a large number of sequence homologs to be effective (Skwark et al., 2014, Ma et al., 2015).”
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks (2020, background): “Early attempts utilized support vector machines (SVMs) [17,18], random forests (RFs) [13,19], artificial neural networks (NNs) [20–23] etc.”
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning (2017, background): “We say a contact is short-, medium- and long-range when the sequence distance of two residues in a contact falls into [6, 11], [12, 23], and ≥24, respectively.”
Analysis of deep learning methods for blind protein contact prediction in CASP12 (2017, methodology): “For proteins with few sequence homologs, pure co-evolution methods such as CCMpred[4], PSICOV[5], Evfold[6], Gremlin[7], and CoinDCA[8] do not fare well and their predictions are not very helpful to ab initio folding.”
De novo Protein Structure Prediction by Coupling Contact With Distance Profile (2020, methodology): “In RaptorX [18], [26], [27], [28], predicted contacts are utilized as distance constraints to predict tertiary structural models.”