scholarly journals PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

2011 ◽  
Vol 28 (2) ◽  
pp. 184-190 ◽  
Author(s):  
David T. Jones ◽  
Daniel W. A. Buchan ◽  
Domenico Cozzetto ◽  
Massimiliano Pontil
Keyword(s):  
Covariance Estimation ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Multiple Sequence Alignments ◽  
Sparse Inverse Covariance Estimation

scholarly journals AttentiveDist: Protein Inter-Residue Distance Prediction Using Deep Learning with Attention on Quadruple Multiple Sequence Alignments

2020 ◽  
Author(s):  
Aashish Jain ◽  
Genki Terashi ◽  
Yuki Kagaya ◽  
Sai Raghavendra Maddhuri Venkata Subramaniya ◽  
Charles Christoffer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure Prediction ◽  
Prediction Models ◽  
3D Structure ◽  
Evolutionary Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Multiple Sequence Alignments ◽  
Distance Prediction

ABSTRACTProtein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. The model is trained in a multi-task fashion to also predict backbone and orientation angles further improving the inter-residue distance prediction. We show that AttentiveDist outperforms the top methods for contact prediction in the CASP13 structure prediction competition. To aid in structure modeling we also developed two new deep learning-based sidechain center distance and peptide-bond nitrogen-oxygen distance prediction models. Together these led to a 12% increase in TM-score from the best server method in CASP13 for structure prediction.

scholarly journals The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing

2019 ◽  
Author(s):  
Mark Chonofsky ◽  
Saulo H. P. de Oliveira ◽  
Konrad Krawczyk ◽  
Charlotte M. Deane
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Amino Acid ◽  
Structure Prediction ◽  
Prediction Methods ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Multiple Sequence Alignments ◽  
Physico Chemical

AbstractOver the last few years, the field of protein structure prediction has been transformed by increasingly-accurate contact prediction software. These methods are based on the detection of coevolutionary relationships between residues from multiple sequence alignments. However, despite speculation, there is little evidence of a link between contact prediction and the physico-chemical interactions which drive amino-acid coevolution. Furthermore, existing protocols predict only a fraction of all protein contacts and it is not clear why some contacts are favoured over others.Using a dataset of 863 protein domains, we assessed the physico-chemical interactions of contacts predicted by CCMpred, MetaPSICOV, and DNCON2, as examples of direct coupling analysis, meta-prediction, and deep learning, respectively. To further investigate what sets these predicted contacts apart, we considered correctly-predicted contacts and compared their properties against the protein contacts that were not predicted.We found that predicted contacts tend to form more bonds than non-predicted contacts, which suggests these contacts may be more important. Comparing the contacts predicted by each method, we found that metaPSICOV and DNCON2 favour accuracy whereas CCMPred detects contacts with more bonds. This suggests that the push for higher accuracy may lead to a loss of physico-chemically important contacts.These results underscore the connection between protein physico-chemistry and the coevolutionary couplings that can be derived from multiple sequence alignments. This relationship is likely to be relevant to protein structure prediction and functional analysis of protein structure and may be key to understanding their utility for different problems in structural biology.Author summaryAccurate contact prediction has allowed scientists to predict protein structures with unprecedented levels of accuracy. The success of contact prediction methods, which are based on inferring correlations between amino acids in protein multiple sequence alignments, has prompted a great deal of work to improve the quality of contact prediction, leading to the development of several different methods for detecting amino acids in proximity.In this paper, we investigate the properties of these contact prediction methods. We find that contacts which are predicted differ from the other contacts in the protein, in particular they have more physico-chemical bonds, and the predicted contacts are more strongly conserved than other contacts across protein families. We also compared the properties of different contact prediction methods and found that the characteristics of the predicted sets depend on the prediction method used.Our results point to a link between physico-chemical bonding interactions and the evolutionary history of proteins, a connection which is reflected in their amino acid sequences.

scholarly journals The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing

2019 ◽  
Author(s):  
Mark Chonofsky ◽  
Saulo H P de Oliveira ◽  
Konrad Krawczyk ◽  
Charlotte M Deane
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Supplementary Information ◽  
Chemical Interactions ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Multiple Sequence Alignments ◽  
Physico Chemical

Abstract Motivation Over the last few years, the field of protein structure prediction has been transformed by increasingly-accurate contact prediction software. These methods are based on the detection of coevolutionary relationships between residues from multiple sequence alignments. However, despite speculation, there is little evidence of a link between contact prediction and the physico-chemical interactions which drive amino-acid coevolution. Furthermore, existing protocols predict only a fraction of all protein contacts and it is not clear why some contacts are favoured over others. Using a dataset of 863 protein domains, we assessed the physico-chemical interactions of contacts predicted by CCMpred, MetaPSICOV, and DNCON2, as examples of direct coupling analysis, meta-prediction, and deep learning. Results We considered correctly-predicted contacts and compared their properties against the protein contacts that were not predicted. Predicted contacts tend to form more bonds than non-predicted contacts, which suggests these contacts may be more important than contacts that were not predicted. Comparing the contacts predicted by each method, we found that metaPSICOV and DNCON2 favour accuracy whereas CCMPred detects contacts with more bonds. This suggests that the push for higher accuracy may lead to a loss of physico-chemically important contacts. These results underscore the connection between protein physico-chemistry and the coevolutionary couplings that can be derived from multiple sequence alignments. This relationship is likely to be relevant to protein structure prediction and functional analysis of protein structure and may be key to understanding their utility for different problems in structural biology. Availability We use publicly-available databases. Our code is available for download at http://opig.stats.ox.ac.uk/. Supplementary information Supplementary information is available at Bioinformatics online.

scholarly journals End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman

2021 ◽  
Author(s):  
Samantha Petti ◽  
Nicholas Bhattacharya ◽  
Roshan Rao ◽  
Justas Dauparas ◽  
Neil Thomas ◽  
...  
Keyword(s):  
Random Field ◽  
Structure Prediction ◽  
Pairwise Alignment ◽  
Learning System ◽  
Alignment Algorithm ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Multiple Sequence Alignments ◽  
End To End

Multiple Sequence Alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for. Here, we implement a smooth and differentiable version of the Smith-Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF mildly improves contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold2 and maximizing the predicted confidence metric, we can learn MSAs that improve structure predictions over the initial MSAs. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment.

scholarly journals Positive natural selection in primate genes of the type I interferon response

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Elena N. Judd ◽  
Alison R. Gilchrist ◽  
Nicholas R. Meyerson ◽  
Sara L. Sawyer
Keyword(s):  
Natural Selection ◽  
Positive Selection ◽  
Type I Interferon ◽  
Interferon Response ◽  
Type I ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Interferon Stimulated Genes ◽  
Interferon Induction

Abstract Background The Type I interferon response is an important first-line defense against viruses. In turn, viruses antagonize (i.e., degrade, mis-localize, etc.) many proteins in interferon pathways. Thus, hosts and viruses are locked in an evolutionary arms race for dominance of the Type I interferon pathway. As a result, many genes in interferon pathways have experienced positive natural selection in favor of new allelic forms that can better recognize viruses or escape viral antagonists. Here, we performed a holistic analysis of selective pressures acting on genes in the Type I interferon family. We initially hypothesized that the genes responsible for inducing the production of interferon would be antagonized more heavily by viruses than genes that are turned on as a result of interferon. Our logic was that viruses would have greater effect if they worked upstream of the production of interferon molecules because, once interferon is produced, hundreds of interferon-stimulated proteins would activate and the virus would need to counteract them one-by-one. Results We curated multiple sequence alignments of primate orthologs for 131 genes active in interferon production and signaling (herein, “induction” genes), 100 interferon-stimulated genes, and 100 randomly chosen genes. We analyzed each multiple sequence alignment for the signatures of recurrent positive selection. Counter to our hypothesis, we found the interferon-stimulated genes, and not interferon induction genes, are evolving significantly more rapidly than a random set of genes. Interferon induction genes evolve in a way that is indistinguishable from a matched set of random genes (22% and 18% of genes bear signatures of positive selection, respectively). In contrast, interferon-stimulated genes evolve differently, with 33% of genes evolving under positive selection and containing a significantly higher fraction of codons that have experienced selection for recurrent replacement of the encoded amino acid. Conclusion Viruses may antagonize individual products of the interferon response more often than trying to neutralize the system altogether.

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