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 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 DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Hiroyuki Fukuda ◽  
Kentaro Tomii
Keyword(s):  
Structure Prediction ◽  
Prediction Accuracy ◽  
Protein Sequences ◽  
Secondary Structures ◽  
Target Protein ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Learning Framework ◽  
End To End

Abstract Background Recently developed methods of protein contact prediction, a crucially important step for protein structure prediction, depend heavily on deep neural networks (DNNs) and multiple sequence alignments (MSAs) of target proteins. Protein sequences are accumulating to an increasing degree such that abundant sequences to construct an MSA of a target protein are readily obtainable. Nevertheless, many cases present different ends of the number of sequences that can be included in an MSA used for contact prediction. The abundant sequences might degrade prediction results, but opportunities remain for a limited number of sequences to construct an MSA. To resolve these persistent issues, we strove to develop a novel framework using DNNs in an end-to-end manner for contact prediction. Results We developed neural network models to improve precision of both deep and shallow MSAs. Results show that higher prediction accuracy was achieved by assigning weights to sequences in a deep MSA. Moreover, for shallow MSAs, adding a few sequential features was useful to increase the prediction accuracy of long-range contacts in our model. Based on these models, we expanded our model to a multi-task model to achieve higher accuracy by incorporating predictions of secondary structures and solvent-accessible surface areas. Moreover, we demonstrated that ensemble averaging of our models can raise accuracy. Using past CASP target protein domains, we tested our models and demonstrated that our final model is superior to or equivalent to existing meta-predictors. Conclusions The end-to-end learning framework we built can use information derived from either deep or shallow MSAs for contact prediction. Recently, an increasing number of protein sequences have become accessible, including metagenomic sequences, which might degrade contact prediction results. Under such circumstances, our model can provide a means to reduce noise automatically. According to results of tertiary structure prediction based on contacts and secondary structures predicted by our model, more accurate three-dimensional models of a target protein are obtainable than those from existing ECA methods, starting from its MSA. DeepECA is available from https://github.com/tomiilab/DeepECA.

QuanTest2: benchmarking multiple sequence alignments using secondary structure prediction

2019 ◽  
Author(s):  
Fabian Sievers ◽  
Desmond G Higgins
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Reference Sequence ◽  
Supplementary Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Reference Sequences ◽  
Selection Of

Abstract Motivation Secondary structure prediction accuracy (SSPA) in the QuanTest benchmark can be used to measure accuracy of a multiple sequence alignment. SSPA correlates well with the sum-of-pairs score, if the results are averaged over many alignments but not on an alignment-by-alignment basis. This is due to a sub-optimal selection of reference and non-reference sequences in QuanTest. Results We develop an improved strategy for selecting reference and non-reference sequences for a new benchmark, QuanTest2. In QuanTest2, SSPA and SP correlate better on an alignment-by-alignment basis than in QuanTest. Guide-trees for QuanTest2 are more balanced with respect to reference sequences than in QuanTest. QuanTest2 scores correlate well with other well-established benchmarks. Availability and implementation QuanTest2 is available at http://bioinf.ucd.ie/quantest2.tar, comprises of reference and non-reference sequence sets and a scoring script. Supplementary information Supplementary data are available at Bioinformatics online

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