PhD student, University of Cambridge
Patterns of coevolving amino acids can be used to predict protein structure and function
In recent years, there has been remarkable progress in the development of computational methods for detecting evolutionary couplings between residues in proteins from multiple sequence alignments (MSA) of protein families. These methods have been successfully applied in predicting three-dimensional structures from amino acid sequences, as well as in identifying functionally important residues.
Abstract: Genomic sequences contain rich evolutionary information about functional constraints on macromolecules such as proteins. This information can be efficiently mined to detect evolutionary couplings between residues in proteins and address the long-standing challenge to compute protein three-dimensional structures from amino acid sequences. Substantial progress has recently been made on this problem owing to the explosive growth in available sequences and the application of global statistical methods. In addition to three-dimensional structure, the improved understanding of covariation may help identify functional residues involved in ligand binding, protein-complex formation and conformational changes. We expect computation of covariation patterns to complement experimental structural biology in elucidating the full spectrum of protein structures, their functional interactions and evolutionary dynamics.
Pub.: 10 Nov '12, Pinned: 17 Jun '17
Abstract: Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.
Pub.: 01 Aug '15, Pinned: 04 Jun '17
Abstract: 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.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.See http://raptorx.uchicago.edu/ContactMap/ for a web server implementing the email@example.comSupplementary data are available at Bioinformatics online.
Pub.: 16 Aug '15, Pinned: 04 Jun '17
Abstract: Recently, several new contact prediction methods have been published. They use (i) large sets of multiple aligned sequences and (ii) assume that correlations between columns in these alignments can be the results of indirect interaction. These methods are clearly superior to earlier methods when it comes to predicting contacts in proteins. Here, we demonstrate that combining predictions from two prediction methods, PSICOV and plmDCA, and two alignment methods, HHblits and jackhmmer at four different e-value cut-offs, provides a relative improvement of 20% in comparison with the best single method, exceeding 70% correct predictions for one contact prediction per residue.The source code for PconsC along with supplementary data is freely available at http://firstname.lastname@example.orgSupplementary data are available at Bioinformatics online.
Pub.: 10 May '13, Pinned: 17 Jun '17
Abstract: In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation) have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids), exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i) the prediction of residue-residue contacts in proteins, and (ii) the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code.
Pub.: 26 Mar '14, Pinned: 17 Jun '17
Abstract: Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used.In a small benchmark of 15 proteins, we show that the TM-scores of top-ranked models are improved by on average 33% using PconsFold compared with the original version of EVfold. In a larger benchmark, we find that the quality is improved with 15-30% when using PconsC in comparison with earlier contact prediction methods. Further, using Rosetta instead of CNS does not significantly improve global model accuracy, but the chemistry of models generated with Rosetta is improved.PconsFold is a fully automated pipeline for ab initio protein structure prediction based on evolutionary information. PconsFold is based on PconsC contact prediction and uses the Rosetta folding protocol. Due to its modularity, the contact prediction tool can be easily exchanged. The source code of PconsFold is available on GitHub at https://www.github.com/ElofssonLab/pcons-fold under the MIT license. PconsC is available from http://c.pcons.net/.Supplementary data are available at Bioinformatics online.
Pub.: 28 Aug '14, Pinned: 17 Jun '17
Abstract: Strategies for correlation analysis in protein contact prediction often encounter two challenges, namely, the indirect coupling among residues, and the background correlations mainly caused by phylogenetic biases. While various studies have been conducted on how to disentangle indirect coupling, the removal of background correlations still remains unresolved. Here, we present an approach for removing background correlations via low-rank and sparse decomposition (LRS) of a residue correlation matrix. The correlation matrix can be constructed using either local inference strategies (e.g., mutual information, or MI) or global inference strategies (e.g., direct coupling analysis, or DCA). In our approach, a correlation matrix was decomposed into two components, i.e., a low-rank component representing background correlations, and a sparse component representing true correlations. Finally the residue contacts were inferred from the sparse component of correlation matrix.
Pub.: 23 Feb '16, Pinned: 04 Jun '17
Abstract: CoinFold (http://raptorx2.uchicago.edu/ContactMap/) is a web server for protein contact prediction and contact-assisted de novo structure prediction. CoinFold predicts contacts by integrating joint multi-family evolutionary coupling (EC) analysis and supervised machine learning. This joint EC analysis is unique in that it not only uses residue coevolution information in the target protein family, but also that in the related families which may have divergent sequences but similar folds. The supervised learning further improves contact prediction accuracy by making use of sequence profile, contact (distance) potential and other information. Finally, this server predicts tertiary structure of a sequence by feeding its predicted contacts and secondary structure to the CNS suite. Tested on the CASP and CAMEO targets, this server shows significant advantages over existing ones of similar category in both contact and tertiary structure prediction.
Pub.: 27 Apr '16, Pinned: 04 Jun '17
Abstract: Protein contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method for contact prediction that predicts contacts by integrating both evolutionary coupling (EC) information and sequence conservation information through an ultra-deep neural network consisting of two deep residual neural networks. The two residual networks conduct a series of convolutional transformation of protein features including sequence profile, EC information and pairwise potential. This neural network allows us to model very complex relationship between sequence and contact map as well as long-range interdependency between contacts and thus, obtain high-quality contact prediction. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. For example, on the 105 CASP11 test proteins, the L/10 long-range accuracy obtained by our method is 83.3% while that by CCMpred and MetaPSICOV (the CASP11 winner) is 43.4% and 60.2%, respectively. On the 398 membrane proteins, the L/10 long-range accuracy obtained by our method is 77.3% while that by CCMpred and MetaPSICOV is 51.8% and 61.2%, respectively. Ab initio folding guided by our predicted contacts can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Further, our contact-assisted models also have much better quality (especially for membrane proteins) than template-based models.
Pub.: 05 Sep '16, Pinned: 04 Jun '17
Abstract: Understanding protein−protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databases to characterize protein−protein interactions are urgently needed. Such methods should connect multiple scales from evolutionary conserved interactions between families of homologous proteins, over the identification of specifically interacting proteins in the case of multiple paralogs inside a species, down to the prediction of residues being in physical contact across interaction interfaces. Statistical inference methods detecting residue−residue coevolution have recently triggered considerable progress in using sequence data for quaternary protein structure prediction; they require, however, large joint alignments of homologous protein pairs known to interact. The generation of such alignments is a complex computational task on its own; application of coevolutionary modeling has, in turn, been restricted to proteins without paralogs, or to bacterial systems with the corresponding coding genes being colocalized in operons. Here we show that the direct coupling analysis of residue coevolution can be extended to connect the different scales, and simultaneously to match interacting paralogs, to identify interprotein residue−residue contacts and to discriminate interacting from noninteracting families in a multiprotein system. Our results extend the potential applications of coevolutionary analysis far beyond cases treatable so far.
Pub.: 11 Oct '16, Pinned: 17 Jun '17
Abstract: The 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.Using 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.We 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.
Pub.: 17 Dec '16, Pinned: 04 Jun '17
Abstract: Computational prediction of membrane protein (MP) structures is very challenging partially due to lack of sufficient solved structures for homology modeling. Recently direct evolutionary coupling analysis (DCA) sheds some light on protein contact prediction and accordingly, contact-assisted folding, but DCA is effective only on some very large-sized families since it uses information only in a single protein family. This paper presents a deep transfer learning method that can significantly improve MP contact prediction by learning contact patterns and complex sequence-contact relationship from thousands of non-membrane proteins (non-MPs). Tested on 510 non-redundant MPs, our deep model (learned from only non-MPs) has top L/10 long-range contact prediction accuracy 0.69, better than our deep model trained by only MPs (0.63) and much better than a representative DCA method CCMpred (0.47) and the CASP11 winner MetaPSICOV (0.55). The accuracy of our deep model can be further improved to 0.72 when trained by a mix of non-MPs and MPs. When only contacts in transmembrane regions are evaluated, our method has top L/10 long-range accuracy 0.62, 0.57, and 0.53 when trained by a mix of non-MPs and MPs, by non-MPs only, and by MPs only, respectively, still much better than MetaPSICOV (0.45) and CCMpred (0.40). All these results suggest that sequence-structure relationship learned by our deep model from non-MPs generalizes well to MP contact prediction. Improved contact prediction also leads to better contact-assisted folding. Using only top predicted contacts as restraints, our deep learning method can fold 160 and 200 of 510 MPs with TMscore>0.6 when trained by non-MPs only and by a mix of non-MPs and MPs, respectively, while CCMpred and MetaPSICOV can do so for only 56 and 77 MPs, respectively. Our contact-assisted folding also greatly outperforms homology modeling.
Pub.: 24 Apr '17, Pinned: 04 Jun '17
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