PconsFold: improved contact predictions improve protein models

Michel, Mirco; Hayat, Sikander; Skwark, Marcin J.; Sander, Chris; Marks, Debora S.; Elofsson, Arne

doi:10.1093/bioinformatics/btu458

Cited by 95 publications

(104 citation statements)

References 36 publications

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“…In contrast to more traditional approaches based on homology detection and sequence conservation, contact prediction supported by residue coevolution (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) makes use of sequence variability as an alternative source of information (26). The analysis of residue coevolution has been successfully applied to contact prediction at the interface of protein dimers (27)(28)(29)(30)(31)(32)(33), eventually leading to de novo prediction of protein complexes assisted by coevolution (29,30).…”

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confidence: 99%

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Rodriguez-Rivas

Marsili

Juan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Protein-protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein-protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein-protein interfaces. We show that sequencebased coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein-protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequencebased accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.coevolution | protein-protein interaction | protein complex | homology modeling | contact prediction C ells function as a remarkably synchronized orchestra of finely tuned molecular interactions, and establishing this molecular network has become a major goal of molecular biology. Important methodological and technical advances have led to the identification of a large number of novel protein-protein interactions and to major contributions to our understanding of the functioning of cells and organisms (1, 2). In contrast, and despite relevant advances in EM (3) and crystallography (4), the molecular details of a large number of interactions remain unknown.When experimental structural data are absent or incomplete, template-based homology modeling of protein complexes represents the most reliable option (5, 6). Similarly to modeling of tertiary structure for single-chain proteins, homology modeling of protein-protein interactions follows a conservation-based approach, in which the quaternary structure of one or more experimentally solved complexes with enough sequence similarity to a target complex (the templates) is projected onto the target. Templatebased techniques have provided models for a large number of protein complexes with structurally solved homologous complexes (7-10). Unfortunately, proteins involved in homologous protein dimers tend to systema...

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confidence: 99%

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Rodriguez-Rivas

Marsili

Juan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…The average PPV for a 100 effective sequence protein family is 0.47, and increases to 0.60 for a 1000 member family. We have noted that on average a PPV of 0.5 is needed for accurate modeling using the PconsFold [22] pipeline. This average precision is never reached for PhyCMAP, for plmDCA and GaussDCA more than 1700 effective sequences are needed, for PconsC2 314 and for PconsC3 only 115.…”

Section: Resultsmentioning

confidence: 99%

“…When ensuring proteins are mostly covered by at least half of all sequences (black dots) r increases to 0.83 (r covered ) showing that the average PconsC3 score is an excellent estimator of the contact map quality in single domain proteins. servations [22], Fig. 5 (a).…”

Section: Resultsmentioning

confidence: 99%

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Accurate contact predictions for thousands of protein families using PconsC3

Skwark

Michel

et al. 2016

Preprint

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Protein structure prediction was for decades one of the grand unsolved challenges in bioinformatics. A few years ago it was shown that by using a maximum entropy approach to describe couplings between columns in a multiple sequence alignment it was possible to significantly increase the accuracy of residue contact predictions. For very large protein families with more than 1000 effective sequences the accuracy is sufficient to produce accurate models of proteins as well as complexes. Today, for about half of all Pfam domain families no structure is known, but unfortunately most of these families have at most a few hundred members, i.e. are too small for existing contact prediction methods. To extend accurate contact predictions to the thousands of smaller protein families we present PconsC3, an improved method for protein contact predictions that can be used for families with as little as 100 effective sequence members. We estimate that PconsC3 provides accurate contact predictions for up to 4646 Pfam domain families. In addition, PconsC3 outperforms previous methods significantly independent on family size, secondary structure content, contact range, or the number of selected contacts. This improvement translates into improved de-novo prediction of three-dimensional structures. PconsC3 is available as a web server and downloadable version at http://c3.pcons.net. The downloadable version is free for all to use and licensed under the GNU General Public License, version 2.protein structure prediction | contact prediction | direct coupling analysis | protein family I n recent years great progress has been made in the area of residue contact prediction. The vast amount of available sequence data is utilized by direct coupling analysis (DCA) methods to predict contacts between residues with unprecedented quality [1,2]. This has enabled accurate blind predictions of the structure of soluble proteins [3][4][5], membrane proteins [6][7][8], and protein complexes [9,10]. However, the widespread use of such methods has been limited to protein families with more than 1000 members [11,12]. Unfortunately, the structure of at least one member of most large families is known (see Fig. S1). This limits the practical usefulness of DCA methods [13] and strongly suggests that methods that accurately predict residue contacts for smaller protein families would be of much greater utility.Before the advent of DCA methods there has been a longstanding effort in using machine learning techniques to predict residue contacts [14][15][16]. These methods utilize covariancebased evolutionary information (e.g. mutual information), as well as knowledge based constraints as inputs to a machine learning algoritm. The best non-DCA methods are less dependent on the size of the protein family and although their predictive quality is easily outperformed by DCA on large families, they perform significantly better on smaller families, Figure 1 (a). We have earlier used an iterative machine learning approach, built on the observation that contacts are ...

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“…Arguably, structural models of mainly β proteins are reported to be less accurate than those of mainly α proteins, when constructed from residue contact information with comparable levels of accuracies [24].…”

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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PconsFold: improved contact predictions improve protein models

Cited by 95 publications

References 36 publications

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Accurate contact predictions for thousands of protein families using PconsC3

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

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