Direct Coupling Analysis Methods Research Articles

A direct coupling analysis method and its application to the Scientific Research and Demonstration Platform

Design of a very large floating structure (VLFS) deployed near islands and reefs, different from those in the open sea, inevitably faces new technical challenges including numerical analysis methods. In this paper, a direct coupling analysis method (DCAM) has been established based on the Boussinesq equations and the three-dimensional hydroelasisity theory with Rankine source method to analyze the responses of a VLFS in shallow sea with complicated geographical environment. Model tests have been carried out to validate the DCAM. To further verify the numerical methods and investigate the performance of such a VLFS, a “Scientific Research and Demonstration Platform (SRDP)” was built and deployed in 2019 at the site about 1 000 m off an island with water depth around 40m in South China Sea. It is a simplified small model of a two-module semi-submersible-type VLFS. The numerical simulation of its responses on severe waves with focus on motions and connector forces is conduct by DCAM, and compared with the on-site measurements. Good agreement has been achieved. This approves the DCAM as a feasible tool for design and safety assessment of a VLFS deployed near islands and reefs.

Numerical and experimental investigation on hydroelastic responses of an 8-module VLFS near a typical island

The very large floating structure (VLFS) deployed near seashore, islands and reefs are always exposed to the extremely complex encountering waves. Traditional methods with homogeneous wave inputs are not applicable for the hydroelastic analysis of this type VLFS. In this paper, the authors carry out a numerical and experimental investigation on hydroelastic responses of an 8-module VLFS near a typical island. A direct coupling analysis method is established on the basis of the Boussinesq equation and RANKINE source method. The detailed theoretical derivation, numerical method and relevant calculation program THAFTS-BR are presented. Wave propagation near the island, motions and connector loads of the VLFS deployed a typical island are investigated based on the numerical method and model tests. Several useful conclusions are obtained, which can provide a reference for the analysis of dynamic responses, design and construction of the floating structures near islands and seashores.

Assessing the accuracy of direct-coupling analysis for RNA contact prediction.

Many noncoding RNAs are known to play a role in the cell directly linked to their structure. Structure prediction based on the sole sequence is, however, a challenging task. On the other hand, thanks to the low cost of sequencing technologies, a very large number of homologous sequences are becoming available for many RNA families. In the protein community, the idea of exploiting the covariance of mutations within a family to predict the protein structure using the direct-coupling-analysis (DCA) method has emerged in the last decade. The application of DCA to RNA systems has been limited so far. We here perform an assessment of the DCA method on 17 riboswitch families, comparing it with the commonly used mutual information analysis and with state-of-the-art R-scape covariance method. We also compare different flavors of DCA, including mean-field, pseudolikelihood, and a proposed stochastic procedure (Boltzmann learning) for solving exactly the DCA inverse problem. Boltzmann learning outperforms the other methods in predicting contacts observed in high-resolution crystal structures.

Phylogenetic correlations can suffice to infer protein partners from sequences.

Determining which proteins interact together is crucial to a systems-level understanding of the cell. Recently, algorithms based on Direct Coupling Analysis (DCA) pairwise maximum-entropy models have allowed to identify interaction partners among paralogous proteins from sequence data. This success of DCA at predicting protein-protein interactions could be mainly based on its known ability to identify pairs of residues that are in contact in the three-dimensional structure of protein complexes and that coevolve to remain physicochemically complementary. However, interacting proteins possess similar evolutionary histories. What is the role of purely phylogenetic correlations in the performance of DCA-based methods to infer interaction partners? To address this question, we employ controlled synthetic data that only involve phylogeny and no interactions or contacts. We find that DCA accurately identifies the pairs of synthetic sequences that share evolutionary history. While phylogenetic correlations confound the identification of contacting residues by DCA, they are thus useful to predict interacting partners among paralogs. We find that DCA performs as well as phylogenetic methods to this end, and slightly better than them with large and accurate training sets. Employing DCA or phylogenetic methods within an Iterative Pairing Algorithm (IPA) allows to predict pairs of evolutionary partners without a training set. We further demonstrate the ability of these various methods to correctly predict pairings among real paralogous proteins with genome proximity but no known direct physical interaction, illustrating the importance of phylogenetic correlations in natural data. However, for physically interacting and strongly coevolving proteins, DCA and mutual information outperform phylogenetic methods. We finally discuss how to distinguish physically interacting proteins from proteins that only share a common evolutionary history.

A Web-Based Protocol for Interprotein Contact Prediction by Deep Learning.

Identifying residue-residue contacts in protein-protein interactions or complex is crucial for understanding protein and cell functions. DCA (direct-coupling analysis) methods shed some light on this, but they need many sequence homologs to yield accurate prediction. Inspired by the success of our deep-learning method for intraprotein contact prediction, we have developed RaptorX-ComplexContact, a web server for interprotein residue-residue contact prediction. Given a pair of interacting protein sequences, RaptorX-ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA) based on genomic distance and phylogeny information, respectively. Then, RaptorX-ComplexContact uses two deep convolutional residual neural networks (ResNet) to predict interprotein contacts from sequential features and coevolution information of paired MSAs. RaptorX-ComplexContact shall be useful for protein docking, protein-protein interaction prediction, and protein interaction network construction.

A direct coupling analysis method of hydroelastic responses of VLFS in complicated ocean geographical environment

In this paper a direct coupling analysis method (DCAM) of hydroelastic responses of a very large floating structures (VLFS) in complicated geographical environment is presented. In this method the three-dimensional hydroelasticity theory of floating bodies is combined with the shallow water wave theory, to allow for proper description of the influence of uneven seabed and sheltering effect of islands on the hydroelastic responses of a VLFS deployed near island and reefs in shallow sea. This method and the numerical procedures were verified and validated by comparison between the predictions and the model test results of a 3-module VLFS and an 8-module VLFS in two simulated shallow sea regions with different seabed topography.

Predicting protein residue-residue contacts using random forests and deep networks

BackgroundThe ability to predict which pairs of amino acid residues in a protein are in contact with each other offers many advantages for various areas of research that focus on proteins. For example, contact prediction can be used to reduce the computational complexity of predicting the structure of proteins and even to help identify functionally important regions of proteins. These predictions are becoming especially important given the relatively low number of experimentally determined protein structures compared to the amount of available protein sequence data.ResultsHere we have developed and benchmarked a set of machine learning methods for performing residue-residue contact prediction, including random forests, direct-coupling analysis, support vector machines, and deep networks (stacked denoising autoencoders). These methods are able to predict contacting residue pairs given only the amino acid sequence of a protein. According to our own evaluations performed at a resolution of +/− two residues, the predictors we trained with the random forest algorithm were our top performing methods with average top 10 prediction accuracy scores of 85.13% (short range), 74.49% (medium range), and 54.49% (long range). Our ensemble models (stacked denoising autoencoders combined with support vector machines) were our best performing deep network predictors and achieved top 10 prediction accuracy scores of 75.51% (short range), 60.26% (medium range), and 43.85% (long range) using the same evaluation. These tests were blindly performed on targets from the CASP11 dataset; and the results suggested that our models achieved comparable performance to contact predictors developed by groups that participated in CASP11.ConclusionsDue to the challenging nature of contact prediction, it is beneficial to develop and benchmark a variety of different prediction methods. Our work has produced useful tools with a simple interface that can provide contact predictions to users without requiring a lengthy installation process. In addition to this, we have released our C++ implementation of the direct-coupling analysis method as a standalone software package. Both this tool and our RFcon web server are freely available to the public at http://dna.cs.miami.edu/RFcon/.

Statistical investigations of protein residue direct couplings.

Protein Direct Coupling Analysis (DCA), which predicts residue-residue contacts based on covarying positions within a multiple sequence alignment, has been remarkably effective. This suggests that there is more to learn from sequence correlations than is generally assumed, and calls for deeper investigations into DCA and perhaps into other types of correlations. Here we describe an approach that enables such investigations by measuring, as an estimated p-value, the statistical significance of the association between residue-residue covariance and structural interactions, either internal or homodimeric. Its application to thirty protein superfamilies confirms that direct coupling (DC) scores correlate with 3D pairwise contacts with very high significance. This method also permits quantitative assessment of the relative performance of alternative DCA methods, and of the degree to which they detect direct versus indirect couplings. We illustrate its use to assess, for a given protein, the biological relevance of alternative conformational states, to investigate the possible mechanistic implications of differences between these states, and to characterize subtle aspects of direct couplings. Our analysis indicates that direct pairwise correlations may be largely distinct from correlated patterns associated with functional specialization, and that the joint analysis of both types of correlations can yield greater power. Data, programs, and source code are freely available at http://evaldca.igs.umaryland.edu.

PconsC4: fast, accurate and hassle-free contact predictions.

Residue 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. Here, 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. PconsC4 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 GCC compiler. It does not require a GPU nor special hardware. Supplementary data are available at Bioinformatics online.

SuperDCA for genome-wide epistasis analysis.

The potential for genome-wide modelling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has previously been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 104–105 polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here, we introduce a novel inference method (SuperDCA) that employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 105 polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA, thus, holds considerable potential in building understanding about numerous organisms at a systems biological level.

Forecasting residue-residue contact prediction accuracy.

Apart from meta-predictors, most of today's methods for residue-residue contact prediction are based entirely on Direct Coupling Analysis (DCA) of correlated mutations in multiple sequence alignments (MSAs). These methods are on average ∼40% correct for the 100 strongest predicted contacts in each protein. The end-user who works on a single protein of interest will not know if predictions are either much more or much less correct than 40%, which is especially a problem if contacts are predicted to steer experimental research on that protein. We designed a regression model that forecasts the accuracy of residue-residue contact prediction for individual proteins with an average error of 7 percentage points. Contacts were predicted with two DCA methods (gplmDCA and PSICOV). The models were built on parameters that describe the MSA, the predicted secondary structure, the predicted solvent accessibility and the contact prediction scores for the target protein. Results show that our models can be also applied to the meta-methods, which was tested on RaptorX. All data and scripts are available from http://comprec-lin.iiar.pwr.edu.pl/dcaQ/. malgorzata.kotulska@pwr.edu.pl. Supplementary data are available at Bioinformatics online.

Residual Stress Study of Autofrettaged Barrel under Impulsive Loading

An analysis model of barrel was established for rapid-firing gun by using the thermal structure direct coupling analysis method. As a solving tactics, finite element method was employed to analyze the influence of degree of autofrettage on the residual stress of the tube under impact load caused by high temperature and high pressure powder gases during continuous shots process. The results indicated that there is a significant effect of degree of autofrettage on the residual stress distribution of barrel. As the autofrettage level increases, the radius of the compressive tangential plastic strain zone on the inner wall and the maximal residual stress of the base near chromium-steel interface become larger. This is the cause that the tensile tangential plastic strain in the self-reinforced barrel easily occurred in the process of continuous shots after cooling. This goes against stability of the stress condition of the gun tube.

Evaluation of residue–residue contact prediction in CASP10

ABSTRACTWe present the results of the assessment of the intramolecular residue‐residue contact predictions from 26 prediction groups participating in the 10th round of the CASP experiment. The most recently developed direct coupling analysis methods did not take part in the experiment likely because they require a very deep sequence alignment not available for any of the 114 CASP10 targets. The performance of contact prediction methods was evaluated with the measures used in previous CASPs (i.e., prediction accuracy and the difference between the distribution of the predicted contacts and that of all pairs of residues in the target protein), as well as new measures, such as the Matthews correlation coefficient, the area under the precision‐recall curve and the ranks of the first correctly and incorrectly predicted contact. We also evaluated the ability to detect interdomain contacts and tested whether the difficulty of predicting contacts depends upon the protein length and the depth of the family sequence alignment. The analyses were carried out on the target domains for which structural homologs did not exist or were difficult to identify. The evaluation was performed for all types of contacts (short, medium, and long‐range), with emphasis placed on long‐range contacts, i.e. those involving residues separated by at least 24 residues along the sequence. The assessment suggests that the best CASP10 contact prediction methods perform at approximately the same level, and comparably to those participating in CASP9. Proteins 2014; 82(Suppl 2):138–153. © 2013 Wiley Periodicals, Inc.