scholarly journals fastMSA: Accelerating Multiple Sequence Alignment with Dense Retrieval on Protein Language

2021 ◽  
Author(s):  
Liang Hong ◽  
Siqi Sun ◽  
Liangzhen Zheng ◽  
Qingxiong Tan ◽  
Yu Li
Keyword(s):  
Protein Structure ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Structure Prediction ◽  
Structure And Function ◽  
Sequence Alignments ◽  
Protein Structure And Function ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
And Function

Evolutionarily related sequences provide information for the protein structure and function. Multiple sequence alignment, which includes homolog searching from large databases and sequence alignment, is efficient to dig out the information and assist protein structure and function prediction, whose efficiency has been proved by AlphaFold. Despite the existing tools for multiple sequence alignment, searching homologs from the entire UniProt is still time-consuming. Considering the success of AlphaFold, foreseeably, large- scale multiple sequence alignments against massive databases will be a trend in the field. It is very desirable to accelerate this step. Here, we propose a novel method, fastMSA, to improve the speed significantly. Our idea is orthogonal to all the previous accelerating methods. Taking advantage of the protein language model based on BERT, we propose a novel dual encoder architecture that can embed the protein sequences into a low-dimension space and filter the unrelated sequences efficiently before running BLAST. Extensive experimental results suggest that we can recall most of the homologs with a 34-fold speed-up. Moreover, our method is compatible with the downstream tasks, such as structure prediction using AlphaFold. Using multiple sequence alignments generated from our method, we have little performance compromise on the protein structure prediction with much less running time. fastMSA will effectively assist protein sequence, structure, and function analysis based on homologs and multiple sequence alignment.

scholarly journals CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction

2020 ◽  
Author(s):  
Fusong Ju ◽  
Jianwei Zhu ◽  
Bin Shao ◽  
Lupeng Kong ◽  
Tie-Yan Liu ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Query Protein ◽  
Spatial Proximity ◽  
Multiple Sequence ◽  
Variance Matrix

Protein functions are largely determined by the final details of their tertiary structures, and the structures could be accurately reconstructed based on inter-residue distances. Residue co-evolution has become the primary principle for estimating inter-residue distances since the residues in close spatial proximity tend to co-evolve. The widely-used approaches infer residue co-evolution using an indirect strategy, i.e., they first extract from the multiple sequence alignment (MSA) of query protein some handcrafted features, say, co-variance matrix, and then infer residue co-evolution using these features rather than the raw information carried by MSA. This indirect strategy always leads to considerable information loss and inaccurate estimation of inter-residue distances. Here, we report a deep neural network framework (called CopulaNet) to learn residue co-evolution directly from MSA without any handcrafted features. The CopulaNet consists of two key elements: i) an encoder to model context-specific mutation for each residue, and ii) an aggregator to model correlations among residues and thereafter infer residue co-evolutions. Using the CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrated the successful application of CopulaNet for estimating inter-residue distances and further predicting protein tertiary structure with improved accuracy and efficiency. Head-to-head comparison suggested that for 24 out of the 31 free modeling CASP13 domains, ProFOLD outperformed AlphaFold, one of the state-of-the-art prediction approaches.

scholarly journals MSAC: Compression of multiple sequence alignment files

2017 ◽  
Author(s):  
Sebastian Deorowicz ◽  
Joanna Walczyszyn ◽  
Agnieszka Debudaj-Grabysz
Keyword(s):  
Sequence Alignment ◽  
Compression Ratio ◽  
Multiple Sequence Alignment ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Link Type ◽  
Bioinformatics Databases ◽  
Supplementary Material ◽  
Burrows Wheeler Transform

AbstractMotivationBioinformatics databases grow rapidly and achieve values hardly to imagine a decade ago. Among numerous bioinformatics processes generating hundreds of GB is multiple sequence alignments of protein families. Its largest database, i.e., Pfam, consumes 40–230 GB, depending of the variant. Storage and transfer of such massive data has become a challenge.ResultsWe propose a novel compression algorithm, MSAC (Multiple Sequence Alignment Compressor), designed especially for aligned data. It is based on a generalisation of the positional Burrows–Wheeler transform for non-binary alphabets. MSAC handles FASTA, as well as Stockholm files. It offers up to six times better compression ratio than other commonly used compressors, i.e., gzip. Performed experiments resulted in an analysis of the influence of a protein family size on the compression ratio.AvailabilityMSAC is available for free at https://github.com/refresh-bio/msac and http://sun.aei.polsl.pl/REFRESH/[email protected] materialSupplementary data are available at the publisher Web site.

scholarly journals SequenceBouncer: A method to remove outlier entries from a multiple sequence alignment

2020 ◽  
Author(s):  
Cory D. Dunn
Keyword(s):  
Nucleic Acid ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Analyses ◽  
Protein Sequences ◽  
Mitochondrial Genomes ◽  
Dna Barcodes ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

AbstractPhylogenetic analyses can take advantage of multiple sequence alignments as input. These alignments typically consist of homologous nucleic acid or protein sequences, and the inclusion of outlier or aberrant sequences can compromise downstream analyses. Here, I describe a program, SequenceBouncer, that uses the Shannon entropy values of alignment columns to identify outlier alignment sequences in a manner responsive to overall alignment context. I demonstrate the utility of this software using alignments of available mammalian mitochondrial genomes, bird cytochrome c oxidase-derived DNA barcodes, and COVID-19 sequences.

scholarly journals Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yan Wang ◽  
Qiang Shi ◽  
Pengshuo Yang ◽  
Chengxin Zhang ◽  
S. M. Mortuza ◽  
...  
Keyword(s):  
Protein Structure ◽  
Ab Initio ◽  
Structure Prediction ◽  
Primary Energy ◽  
Structure And Function ◽  
Protein Structure And Function ◽  
New Protein Families ◽  
Highly Correlated ◽  
Folding Simulations ◽  
And Function

Abstract Introduction The ocean microbiome represents one of the largest microbiomes and produces nearly half of the primary energy on the planet through photosynthesis or chemosynthesis. Using recent advances in marine genomics, we explore new applications of oceanic metagenomes for protein structure and function prediction. Results By processing 1.3 TB of high-quality reads from the Tara Oceans data, we obtain 97 million non-redundant genes. Of the 5721 Pfam families that lack experimental structures, 2801 have at least one member associated with the oceanic metagenomics dataset. We apply C-QUARK, a deep-learning contact-guided ab initio structure prediction pipeline, to model 27 families, where 20 are predicted to have a reliable fold with estimated template modeling score (TM-score) at least 0.5. Detailed analyses reveal that the abundance of microbial genera in the ocean is highly correlated to the frequency of occurrence in the modeled Pfam families, suggesting the significant role of the Tara Oceans genomes in the contact-map prediction and subsequent ab initio folding simulations. Of interesting note, PF15461, which has a majority of members coming from ocean-related bacteria, is identified as an important photosynthetic protein by structure-based function annotations. The pipeline is extended to a set of 417 Pfam families, built on the combination of Tara with other metagenomics datasets, which results in 235 families with an estimated TM-score over 0.5. Conclusions These results demonstrate a new avenue to improve the capacity of protein structure and function modeling through marine metagenomics, especially for difficult proteins with few homologous sequences.

scholarly journals ClipKIT: a multiple sequence alignment-trimming algorithm for accurate phylogenomic inference

2020 ◽  
Author(s):  
Jacob L. Steenwyk ◽  
Thomas J. Buida ◽  
Yuanning Li ◽  
Xing-Xing Shen ◽  
Antonis Rokas
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Phylogenetic Inference ◽  
Recent Analysis ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Time Saving ◽  
Multiple Sequence Alignments

AbstractHighly divergent sites in multiple sequence alignments, which stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Trimming methods aim to remove these sites before phylogenetic inference, but recent analysis suggests that doing so can worsen inference. We introduce ClipKIT, a trimming method that instead aims to retain phylogenetically-informative sites; phylogenetic inference using ClipKIT-trimmed alignments is accurate, robust, and time-saving.

scholarly journals Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments

2015 ◽  
Vol 32 (6) ◽  
pp. 814-820 ◽  
Author(s):  
Gearóid Fox ◽  
Fabian Sievers ◽  
Desmond G. Higgins
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
De Novo ◽  
Biological Data ◽  
Supplementary Information ◽  
Test Case ◽  
Sequence Alignments ◽  
Progressive Alignment ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

Abstract Motivation: Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for testing these types of alignments, as test cases either contain a very small number of sequences or are based purely on simulation rather than empirical data. Results: We take advantage of recent developments in protein structure prediction methods to create a benchmark (ContTest) for protein MSAs containing many thousands of sequences in each test case and which is based on empirical biological data. We rank popular MSA methods using this benchmark and verify a recent result showing that chained guide trees increase the accuracy of progressive alignment packages on datasets with thousands of proteins. Availability and implementation: Benchmark data and scripts are available for download at http://www.bioinf.ucd.ie/download/ContTest.tar.gz. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals Benchmarking inverse statistical approaches for protein structure and design with exactly solvable models

2015 ◽  
Author(s):  
Hugo Jacquin ◽  
Amy Gilson ◽  
Eugene Shakhnovich ◽  
Simona Cocco ◽  
Rémi Monasson
Keyword(s):  
Protein Structure ◽  
Structural Information ◽  
Sequence Data ◽  
Careful Analysis ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Pairwise Models ◽  
Statistical Approaches ◽  
And Function

Inverse statistical approaches to determine protein structure and function from Multiple Sequence Alignments (MSA) are emerging as powerful tools in computational biology. However the underlying assumptions of the relationship between the inferred effective Potts Hamiltonian and real protein structure and energetics remain untested so far. Here we use lattice protein model (LP) to benchmark those inverse statistical approaches. We build MSA of highly stable sequences in target LP structures, and infer the effective pairwise Potts Hamiltonians from those MSA. We find that inferred Potts Hamiltonians reproduce many important aspects of `true' LP structures and energetics. Careful analysis reveals that effective pairwise couplings in inferred Potts Hamiltonians depend not only on the energetics of the native structure but also on competing folds; in particular, the coupling values reflect both positive design (stabilization of native conformation) and negative design (destabilization of competing folds). In addition to providing detailed structural information, the inferred Potts models used as protein Hamiltonian for design of new sequences are able to generate with high probability completely new sequences with the desired folds, which is not possible using independent-site models. Those are remarkable results as the effective LP Hamiltonians used to generate MSA are not simple pairwise models due to the competition between the folds. Our findings elucidate the reasons of the power of inverse approaches to the modelling of proteins from sequence data, and their limitations; we show, in particular, that their success crucially depend on the accurate inference of the Potts pairwise couplings.

scholarly journals Decoding microbiome and protein family linkage to improve protein structure prediction

2021 ◽  
Author(s):  
Pengshuo Yang ◽  
Wei Zheng ◽  
Kang Ning ◽  
Yang Zhang
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Structure Prediction ◽  
Large Scale ◽  
Structure And Function ◽  
Protein Family ◽  
Protein Structure And Function ◽  
Model Training ◽  
Low Efficiency ◽  
And Function

Information extracted from microbiome sequences through deep-learning techniques can significantly improve protein structure and function modeling. However, the model training and metagenome search were largely blind with low efficiency. Built on 4.25 billion microbiome sequences from four major biomes (Gut, Lake, Soil and Fermentor), we proposed a MetaSource model to decode the inherent link of microbial niches with protein homologous families. Large-scale protein family folding experiments showed that a targeted approach using predicted biomes significantly outperform combined metagenome datasets in both speed of MSA collection and accuracy of deep-learning structure assembly. These results revealed the important link of biomes with protein families and provided a useful bluebook to guide future microbiome sequence database and modeling development for protein structure and function prediction.

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