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Lee S, Kim G, Karin EL, Mirdita M, Park S, Chikhi R, Babaian A, Kryshtafovych A, Steinegger M. Petabase-Scale Homology Search for Structure Prediction. Cold Spring Harb Perspect Biol 2024; 16:a041465. [PMID: 38316555 PMCID: PMC11065157 DOI: 10.1101/cshperspect.a041465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too. We analyze these in the context of existing strategies to improve prediction.
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Affiliation(s)
- Sewon Lee
- School of Biological Sciences, Seoul National University, Gwanak-gu, Seoul 08826, South Korea
| | - Gyuri Kim
- School of Biological Sciences, Seoul National University, Gwanak-gu, Seoul 08826, South Korea
| | | | - Milot Mirdita
- School of Biological Sciences, Seoul National University, Gwanak-gu, Seoul 08826, South Korea
| | - Sukhwan Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, South Korea
| | - Rayan Chikhi
- Institut Pasteur, Université Paris Cité, G5 Sequence Bioinformatics, 75015 Paris, France
| | - Artem Babaian
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | | | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Gwanak-gu, Seoul 08826, South Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul 08826, South Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, South Korea
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Lee S, Kim G, Karin EL, Mirdita M, Park S, Chikhi R, Babaian A, Kryshtafovych A, Steinegger M. Petascale Homology Search for Structure Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.548308. [PMID: 37503235 PMCID: PMC10369885 DOI: 10.1101/2023.07.10.548308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too. We analyze these in the context of existing strategies to improve prediction.
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Affiliation(s)
- Sewon Lee
- School of Biological Sciences, Seoul National University, Seoul 08826, South Korea
| | - Gyuri Kim
- School of Biological Sciences, Seoul National University, Seoul 08826, South Korea
| | | | - Milot Mirdita
- School of Biological Sciences, Seoul National University, Seoul 08826, South Korea
| | - Sukhwan Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, South Korea
| | - Rayan Chikhi
- Institut Pasteur, Université Paris Cité, G5 Sequence Bioinformatics, 75015 Paris, France
| | - Artem Babaian
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | | | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul 08826, South Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul 08826, South Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, South Korea
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Cai P, Liu S, Zhang D, Xing H, Han M, Liu D, Gong L, Hu QN. SynBioTools: a one-stop facility for searching and selecting synthetic biology tools. BMC Bioinformatics 2023; 24:152. [PMID: 37069545 PMCID: PMC10111727 DOI: 10.1186/s12859-023-05281-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/11/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. RESULTS We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. CONCLUSIONS SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.
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Affiliation(s)
- Pengli Cai
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Sheng Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Dachuan Zhang
- Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland
| | - Huadong Xing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mengying Han
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Dongliang Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Linlin Gong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
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Pereira JM, Vieira M, Santos SM. Step-by-step design of proteins for small molecule interaction: A review on recent milestones. Protein Sci 2021; 30:1502-1520. [PMID: 33934427 PMCID: PMC8284594 DOI: 10.1002/pro.4098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/21/2021] [Accepted: 04/23/2021] [Indexed: 01/01/2023]
Abstract
Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.
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Affiliation(s)
- José M. Pereira
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
| | - Maria Vieira
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
| | - Sérgio M. Santos
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
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