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Tharanga S, Ünlü ES, Hu Y, Sjaugi MF, Çelik MA, Hekimoğlu H, Miotto O, Öncel MM, Khan AM. DiMA: sequence diversity dynamics analyser for viruses. Brief Bioinform 2024; 26:bbae607. [PMID: 39592151 PMCID: PMC11596295 DOI: 10.1093/bib/bbae607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 09/22/2024] [Accepted: 11/13/2024] [Indexed: 11/28/2024] Open
Abstract
Sequence diversity is one of the major challenges in the design of diagnostic, prophylactic, and therapeutic interventions against viruses. DiMA is a novel tool that is big data-ready and designed to facilitate the dissection of sequence diversity dynamics for viruses. DiMA stands out from other diversity analysis tools by offering various unique features. DiMA provides a quantitative overview of sequence (DNA/RNA/protein) diversity by use of Shannon's entropy corrected for size bias, applied via a user-defined k-mer sliding window to an input alignment file, and each k-mer position is dissected to various diversity motifs. The motifs are defined based on the probability of distinct sequences at a given k-mer alignment position, whereby an index is the predominant sequence, while all the others are (total) variants to the index. The total variants are sub-classified into the major (most common) variant, minor variants (occurring more than once and of incidence lower than the major), and the unique (singleton) variants. DiMA allows user-defined, sequence metadata enrichment for analyses of the motifs. The application of DiMA was demonstrated for the alignment data of the relatively conserved Spike protein (2,106,985 sequences) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the relatively highly diverse pol gene (2637) of the human immunodeficiency virus-1 (HIV-1). The tool is publicly available as a web server (https://dima.bezmialem.edu.tr), as a Python library (via PyPi) and as a command line client (via GitHub).
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Affiliation(s)
- Shan Tharanga
- Centre for Bioinformatics, School of Data Sciences, Perdana University, MAEPS Building, Jalan MAEPS Perdana, Serdang, Kuala Lumpur 50490, Malaysia
| | - Eyyüb Selim Ünlü
- Istanbul Faculty of Medicine, Istanbul University, Turgut Özal Millet St, Topkapi, Istanbul 34093, Türkiye
- Genome Surveillance Unit, Wellcome Sanger Institute, Mill Ln, Hinxton, Saffron Walden CB10 1SA, United Kingdom
| | - Yongli Hu
- Centre for Bioinformatics, School of Data Sciences, Perdana University, MAEPS Building, Jalan MAEPS Perdana, Serdang, Kuala Lumpur 50490, Malaysia
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
| | - Muhammad Farhan Sjaugi
- Centre for Bioinformatics, School of Data Sciences, Perdana University, MAEPS Building, Jalan MAEPS Perdana, Serdang, Kuala Lumpur 50490, Malaysia
| | - Muhammet A Çelik
- Celik Sarayı, Yeni Elektrik Santral St. No:29/2, Meram, Konya 42090, Türkiye
| | - Hilal Hekimoğlu
- Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Ali Ihsan Kalmaz St., No.10 Beykoz, Istanbul 34820, Türkiye
| | - Olivo Miotto
- Nuffield Department of Clinical Medicine, University of Oxford, Old Road, Old Road Campus, Oxford OX3 7LF, United Kingdom
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, 420/6 Ratchawithi Rd., Ratchathewi District, Bangkok 10400, Thailand
| | - Muhammed Miran Öncel
- Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Ali Ihsan Kalmaz St., No.10 Beykoz, Istanbul 34820, Türkiye
| | - Asif M Khan
- Centre for Bioinformatics, School of Data Sciences, Perdana University, MAEPS Building, Jalan MAEPS Perdana, Serdang, Kuala Lumpur 50490, Malaysia
- Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Ali Ihsan Kalmaz St., No.10 Beykoz, Istanbul 34820, Türkiye
- College of Computing and Information Technology, University of Doha for Science and Technology, Jelaiah Street, Duhail North, Doha, Qatar
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Xu J, Ye S, Guan F. A computational strategy to improve the activity of tyrosine phenol-lyase for the synthesis of L-DOPA. Sci Rep 2024; 14:25329. [PMID: 39455666 PMCID: PMC11512013 DOI: 10.1038/s41598-024-76111-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Accepted: 10/10/2024] [Indexed: 10/28/2024] Open
Abstract
Enzymes with high catalytic activity and stability are essential for industrial production, yet most natural enzymes do not meet these requirements. Therefore, efficient strategies for enzyme engineering are crucial. In this study, we developed a cost-effective computational design strategy to enhance the activity of tyrosine phenol-lyase (TPL) for the production of L-DOPA. By integrating structural analysis with computational design, and guided by our understanding of conformational flexibility of TPL, we identified a region where enhanced stability is most likely to facilitate enzyme activity. We screened stabilizing mutations by Cartesian_ddg in Rosetta. After identifying single stabilizing mutations, we grouped the nearby positions harboring multiple stabilizing mutations and calculated the energy of combinatorial variants. We found two promising groups where most variants exhibited lower calculated energy than the wild-type. Experimental validation showed five variants in these groups exhibit increased activity, with the two best variants showing catalytic activity enhancements of 1.8-fold and 1.6-fold compared to the wild-type enzyme.
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Affiliation(s)
- Jiayu Xu
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Tianjin University, Tianjin, 300072, China
| | - Sheng Ye
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Tianjin University, Tianjin, 300072, China.
| | - Fenghui Guan
- Hangzhou Institute of Medicine, Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, 310000, China.
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3
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Karas M, Vešelényiová D, Boszorádová E, Nemeček P, Gerši Z, Moravčíková J. Comparative Analysis of Dehydrins from Woody Plant Species. Biomolecules 2024; 14:250. [PMID: 38540671 PMCID: PMC10967807 DOI: 10.3390/biom14030250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/26/2024] [Accepted: 02/18/2024] [Indexed: 11/11/2024] Open
Abstract
We conducted analyses on 253 protein sequences (Pfam00257) derived from 25 woody plant species, including trees, shrubs, and vines. Our goal was to gain insights into their architectural types, biochemical characteristics, and potential involvement in mitigating abiotic stresses, such as drought, cold, or salinity. The investigated protein sequences (253) comprised 221 angiosperms (85 trees/shrubs and 36 vines) and 32 gymnosperms. Our sequence analyses revealed the presence of seven architectural types: Kn, KnS, SKn, YnKn, YnSKn, FSKn, and FnKn. The FSKn type predominated in tree and shrub dehydrins of both gymnosperms and angiosperms, while the YnSKn type was more prevalent in vine dehydrins. The YnSKn and YnKn types were absent in gymnosperms. Gymnosperm dehydrins exhibited a shift towards more negative GRAVY scores and Fold Indexes. Additionally, they demonstrated a higher Lys content and lower His content. By analyzing promoter sequences in the angiosperm species, including trees, shrubs, and vines, we found that these dehydrins are induced by the ABA-dependent and light-responsive pathways. The presence of stress- and hormone-related cis-elements suggests a protective effect against dehydration, cold, or salinity. These findings could serve as a foundation for future studies on woody dehydrins, especially in the context of biotechnological applications.
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Affiliation(s)
- Milan Karas
- Institute of Biology and Biotechnology, Faculty of Natural Sciences, University of Ss. Cyril and Methodius in Trnava, Nám. J. Herdu 2, 917 01 Trnava, Slovakia; (M.K.); (D.V.); (Z.G.)
| | - Dominika Vešelényiová
- Institute of Biology and Biotechnology, Faculty of Natural Sciences, University of Ss. Cyril and Methodius in Trnava, Nám. J. Herdu 2, 917 01 Trnava, Slovakia; (M.K.); (D.V.); (Z.G.)
| | - Eva Boszorádová
- Institute of Plant Genetics and Biotechnology, Plant Science and Biodiversity Center Slovak Academy of Sciences, Akademická 2, P.O. Box 39A, 950 07 Nitra, Slovakia;
| | - Peter Nemeček
- Institute of Chemistry and Environmental Sciences, Faculty of Natural Sciences, University of Ss. Cyril and Methodius in Trnava, Nám. J. Herdu 2, 917 01 Trnava, Slovakia;
| | - Zuzana Gerši
- Institute of Biology and Biotechnology, Faculty of Natural Sciences, University of Ss. Cyril and Methodius in Trnava, Nám. J. Herdu 2, 917 01 Trnava, Slovakia; (M.K.); (D.V.); (Z.G.)
| | - Jana Moravčíková
- Institute of Biology and Biotechnology, Faculty of Natural Sciences, University of Ss. Cyril and Methodius in Trnava, Nám. J. Herdu 2, 917 01 Trnava, Slovakia; (M.K.); (D.V.); (Z.G.)
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McWhite CD, Armour-Garb I, Singh M. Leveraging protein language models for accurate multiple sequence alignments. Genome Res 2023; 33:1145-1153. [PMID: 37414576 PMCID: PMC10538487 DOI: 10.1101/gr.277675.123] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 06/29/2023] [Indexed: 07/08/2023]
Abstract
Multiple sequence alignment (MSA) is a critical step in the study of protein sequence and function. Typically, MSA algorithms progressively align pairs of sequences and combine these alignments with the aid of a guide tree. These alignment algorithms use scoring systems based on substitution matrices to measure amino acid similarities. Although successful, standard methods struggle on sets of proteins with low sequence identity: the so-called twilight zone of protein alignment. For these difficult cases, another source of information is needed. Protein language models are a powerful new approach that leverages massive sequence data sets to produce high-dimensional contextual embeddings for each amino acid in a sequence. These embeddings have been shown to reflect physicochemical and higher-order structural and functional attributes of amino acids within proteins. Here, we present a novel approach to MSA, based on clustering and ordering amino acid contextual embeddings. Our method for aligning semantically consistent groups of proteins circumvents the need for many standard components of MSA algorithms, avoiding initial guide tree construction, intermediate pairwise alignments, gap penalties, and substitution matrices. The added information from contextual embeddings leads to higher accuracy alignments for structurally similar proteins with low amino-acid similarity. We anticipate that protein language models will become a fundamental component of the next generation of algorithms for generating MSAs.
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Affiliation(s)
- Claire D McWhite
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA;
| | - Isabel Armour-Garb
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
- Department of Computer Science, Princeton University, Princeton, New Jersey 08544, USA
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA;
- Department of Computer Science, Princeton University, Princeton, New Jersey 08544, USA
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Santus L, Garriga E, Deorowicz S, Gudyś A, Notredame C. Towards the accurate alignment of over a million protein sequences: Current state of the art. Curr Opin Struct Biol 2023; 80:102577. [PMID: 37012200 DOI: 10.1016/j.sbi.2023.102577] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/21/2023] [Accepted: 02/27/2023] [Indexed: 04/04/2023]
Abstract
Large-scale genomics requires highly scalable and accurate multiple sequence alignment methods. Results collected over this last decade suggest accuracy loss when scaling up over a few thousand sequences. This issue has been actively addressed with a number of innovative algorithmic solutions that combine low-level hardware optimization with novel higher-level heuristics. This review provides an extensive critical overview of these recent methods. Using established reference datasets we conclude that albeit significant progress has been achieved, a unified framework able to consistently and efficiently produce high-accuracy large-scale multiple alignments is still lacking.
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Sharkey RE, Herbert JB, McGaha DA, Nguyen V, Schoeffler AJ, Dunkle JA. Three critical regions of the erythromycin resistance methyltransferase, ErmE, are required for function supporting a model for the interaction of Erm family enzymes with substrate rRNA. RNA (NEW YORK, N.Y.) 2022; 28:210-226. [PMID: 34795028 PMCID: PMC8906542 DOI: 10.1261/rna.078946.121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/23/2021] [Indexed: 06/13/2023]
Abstract
6-Methyladenosine modification of DNA and RNA is widespread throughout the three domains of life and often accomplished by a Rossmann-fold methyltransferase domain which contains conserved sequence elements directing S-adenosylmethionine cofactor binding and placement of the target adenosine residue into the active site. Elaborations to the conserved Rossman-fold and appended domains direct methylation to diverse DNA and RNA sequences and structures. Recently, the first atomic-resolution structure of a ribosomal RNA adenine dimethylase (RRAD) family member bound to rRNA was solved, TFB1M bound to helix 45 of 12S rRNA. Since erythromycin resistance methyltransferases are also members of the RRAD family, and understanding how these enzymes recognize rRNA could be used to combat their role in antibiotic resistance, we constructed a model of ErmE bound to a 23S rRNA fragment based on the TFB1M-rRNA structure. We designed site-directed mutants of ErmE based on this model and assayed the mutants by in vivo phenotypic assays and in vitro assays with purified protein. Our results and additional bioinformatic analyses suggest our structural model captures key ErmE-rRNA interactions and indicate three regions of Erm proteins play a critical role in methylation: the target adenosine binding pocket, the basic ridge, and the α4-cleft.
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Affiliation(s)
- Rory E Sharkey
- Department of Chemistry and Biochemistry, University of Alabama, Tuscaloosa, Alabama 35487, USA
| | - Johnny B Herbert
- Department of Chemistry and Biochemistry, University of Alabama, Tuscaloosa, Alabama 35487, USA
| | - Danielle A McGaha
- Department of Chemistry and Biochemistry, University of Alabama, Tuscaloosa, Alabama 35487, USA
| | - Vy Nguyen
- Department of Chemistry and Biochemistry, Loyola University New Orleans, New Orleans, Louisiana 70118, USA
| | - Allyn J Schoeffler
- Department of Chemistry and Biochemistry, Loyola University New Orleans, New Orleans, Louisiana 70118, USA
| | - Jack A Dunkle
- Department of Chemistry and Biochemistry, University of Alabama, Tuscaloosa, Alabama 35487, USA
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7
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Lichman BR. Ancestral Sequence Reconstruction for Exploring Alkaloid Evolution. Methods Mol Biol 2022; 2505:165-179. [PMID: 35732944 DOI: 10.1007/978-1-0716-2349-7_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The complex and bioactive monoterpene indole alkaloids (MIAs) found in Catharanthus roseus and related species are the products of many millions of years of evolution through mutation and natural selection. Ancestral sequence reconstruction (ASR) is a method that combines phylogenetic analysis and experimental biochemistry to infer details about past events in protein evolution. Here, I propose that ASR could be leveraged to understand how enzymes catalyzing the formation of complex alkaloids arose over evolutionary time. I discuss the steps of ASR, including sequence selection, multiple sequence alignment, tree inference, and the generation and characterization of inferred ancestral enzymes.
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Affiliation(s)
- Benjamin R Lichman
- Centre for Novel Agricultural Products, Department of Biology, University of York, York, UK.
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8
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Emms DM, Kelly S. Benchmarking Orthogroup Inference Accuracy: Revisiting Orthobench. Genome Biol Evol 2020; 12:2258-2266. [PMID: 33022036 PMCID: PMC7738749 DOI: 10.1093/gbe/evaa211] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/29/2020] [Indexed: 01/24/2023] Open
Abstract
Orthobench is the standard benchmark to assess the accuracy of orthogroup inference methods. It contains 70 expert-curated reference orthogroups (RefOGs) that span the Bilateria and cover a range of different challenges for orthogroup inference. Here, we leveraged improvements in tree inference algorithms and computational resources to reinterrogate these RefOGs and carry out an extensive phylogenetic delineation of their composition. This phylogenetic revision altered the membership of 31 of the 70 RefOGs, with 24 subject to extensive revision and 7 that required minor changes. We further used these revised and updated RefOGs to provide an assessment of the orthogroup inference accuracy of widely used orthogroup inference methods. Finally, we provide an open-source benchmarking suite to support the future development and use of the Orthobench benchmark.
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Affiliation(s)
- David M Emms
- Department of Plant Sciences, University of Oxford, United Kingdom
| | - Steven Kelly
- Department of Plant Sciences, University of Oxford, United Kingdom
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9
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Wright ES. RNAconTest: comparing tools for noncoding RNA multiple sequence alignment based on structural consistency. RNA (NEW YORK, N.Y.) 2020; 26:531-540. [PMID: 32005745 PMCID: PMC7161358 DOI: 10.1261/rna.073015.119] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 01/28/2020] [Indexed: 05/05/2023]
Abstract
The importance of noncoding RNA sequences has become increasingly clear over the past decade. New RNA families are often detected and analyzed using comparative methods based on multiple sequence alignments. Accordingly, a number of programs have been developed for aligning and deriving secondary structures from sets of RNA sequences. Yet, the best tools for these tasks remain unclear because existing benchmarks contain too few sequences belonging to only a small number of RNA families. RNAconTest (RNA consistency test) is a new benchmarking approach relying on the observation that secondary structure is often conserved across highly divergent RNA sequences from the same family. RNAconTest scores multiple sequence alignments based on the level of consistency among known secondary structures belonging to reference sequences in their output alignment. Similarly, consensus secondary structure predictions are scored according to their agreement with one or more known structures in a family. Comparing the performance of 10 popular alignment programs using RNAconTest revealed that DAFS, DECIPHER, LocARNA, and MAFFT created the most structurally consistent alignments. The best consensus secondary structure predictions were generated by DAFS and LocARNA (via RNAalifold). Many of the methods specific to noncoding RNAs exhibited poor scalability as the number or length of input sequences increased, and several programs displayed substantial declines in score as more sequences were aligned. Overall, RNAconTest provides a means of testing and improving tools for comparative RNA analysis, as well as highlighting the best available approaches. RNAconTest is available from the DECIPHER website (http://DECIPHER.codes/Downloads.html).
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Affiliation(s)
- Erik S Wright
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania 15219, USA
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10
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Torrisi M, Pollastri G, Le Q. Deep learning methods in protein structure prediction. Comput Struct Biotechnol J 2020; 18:1301-1310. [PMID: 32612753 PMCID: PMC7305407 DOI: 10.1016/j.csbj.2019.12.011] [Citation(s) in RCA: 132] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/19/2019] [Accepted: 12/20/2019] [Indexed: 01/01/2023] Open
Abstract
Protein Structure Prediction is a central topic in Structural Bioinformatics. Since the '60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural information at various levels of detail. In this review, we briefly introduce the problem of protein structure prediction and essential elements of Deep Learning (such as Convolutional Neural Networks, Recurrent Neural Networks and basic feed-forward Neural Networks they are founded on), after which we discuss the evolution of predictive methods for one-dimensional and two-dimensional Protein Structure Annotations, from the simple statistical methods of the early days, to the computationally intensive highly-sophisticated Deep Learning algorithms of the last decade. In the process, we review the growth of the databases these algorithms are based on, and how this has impacted our ability to leverage knowledge about evolution and co-evolution to achieve improved predictions. We conclude this review outlining the current role of Deep Learning techniques within the wider pipelines to predict protein structures and trying to anticipate what challenges and opportunities may arise next.
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Affiliation(s)
- Mirko Torrisi
- School of Computer Science, University College Dublin, Ireland
| | | | - Quan Le
- Centre for Applied Data Analytics Research, University College Dublin, Ireland
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Lassmann T. Kalign 3: multiple sequence alignment of large data sets. Bioinformatics 2019; 36:btz795. [PMID: 31665271 PMCID: PMC7703769 DOI: 10.1093/bioinformatics/btz795] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 10/12/2019] [Accepted: 10/14/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Kalign is an efficient multiple sequence alignment (MSA) program capable of aligning thousands of protein or nucleotide sequences. However, current alignment problems involving large numbers of sequences are exceeding Kalign's original design specifications. Here we present a completely re-written and updated version to meet current and future alignment challenges. RESULTS Kalign now uses a SIMD accelerated version of the bit-parallel Gene Myers algorithm to estimate pariwise distances, adopts a sequence embedding strategy and the bi-secting K-means algorithm to rapidly construct guide trees for thousands of sequences. The new version maintains high alignment accuracy on both protein and nucleotide alignments and scales better than other MSA tools. AVAILABILITY The source code of Kalign and code to reproduce the results are found here: https://github.com/timolassmann/kalign.
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Affiliation(s)
- Timo Lassmann
- Telethon Kids Institute, University of Western Australia, Nedlands, WA, Australia
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