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      Utilizing an artificial intelligence system to build the digital structural proteome of reef-building corals

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          Abstract

          Background

          Reef-building corals play an important role in the marine ecosystem, and analyzing their proteomes from a structural perspective will exert positive effects on exploring their biology. Here we integrated mass spectrometry with newly published ColabFold to obtain digital structural proteomes of dominant reef-building corals.

          Results

          Of the 8,382 homologous proteins in Acropora muricata, Montipora foliosa, and Pocillopora verrucosa identified, 8,166 received predicted structures after about 4,060 GPU hours of computation. The resulting dataset covers 83.6% of residues with a confident prediction, while 25.9% have very high confidence.

          Conclusions

          Our work provides insight-worthy predictions for coral research, confirms the reliability of ColabFold in practice, and is expected to be a reference case in the impending high-throughput era of structural proteomics.

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          Most cited references56

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          Highly accurate protein structure prediction with AlphaFold

          John Jumper, Richard Evans, Alexander Pritzel … (2021)
          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            ColabFold: making protein folding accessible to all

            Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki … (2022)
            ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.
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              Accurate prediction of protein structures and interactions using a 3-track neural network

              Minkyung Baek, Frank DiMaio, Ivan V Anishchenko … (2022)
              DeepMind presented remarkably accurate predictions at the recent CASP14 protein structure prediction assessment conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.
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                Author and article information

                Contributors
                Yunchi Zhu:
                ORCID: https://orcid.org/0000-0003-0818-2585
                Xin Liao
                Tingyu Han
                J-Y Chen
                Chunpeng He:
                ORCID: https://orcid.org/0000-0003-0566-5397
                Zuhong Lu
                Journal
                Journal ID (nlm-ta): Gigascience
                Journal ID (iso-abbrev): Gigascience
                Journal ID (publisher-id): gigascience
                Title: GigaScience
                Publisher: Oxford University Press
                ISSN (Electronic): 2047-217X
                Publication date (Electronic): 18 November 2022
                Publication date Collection: 2022
                Publication date PMC-release: 18 November 2022
                Volume: 11
                Electronic Location Identifier: giac117
                Affiliations
                State Key Laboratory of Bioelectronics, Southeast University , Nanjing, Jiangsu, 210096, China
                Guangxi Key Lab of Mangrove Conservation and Utilization, Guangxi Mangrove Research Center , Guangxi Academy of Sciences, Beihai 536000, China
                State Key Laboratory of Bioelectronics, Southeast University , Nanjing, Jiangsu, 210096, China
                Nanjing Institute of Paleontology and Geology , Chinese Academy of Sciences, Nanjing 210008, China
                State Key Laboratory of Bioelectronics, Southeast University , Nanjing, Jiangsu, 210096, China
                State Key Laboratory of Bioelectronics, Southeast University , Nanjing, Jiangsu, 210096, China
                Author notes
                Correspondence address. Chunpeng He, State Key Lab for Bioelectroincs School of Biological Science and Medical Engineering Southeast University Nanjing 210096, China Tel and Fax 86-25-83793779. E-mail: cphe@ 123456seu.edu.cn
                Correspondence address. Zuhong Lu, E-mail: zhlu@ 123456seu.edu.cn
                Author information
                https://orcid.org/0000-0003-0818-2585
                https://orcid.org/0000-0003-0566-5397
                Article
                Publisher ID: giac117
                DOI: 10.1093/gigascience/giac117
                PMC ID: 9673494
                PubMed ID: 36399057
                SO-VID: d5ff6b08-b81b-4c87-8a9d-25fd54d68988
                Copyright © © The Author(s) 2022. Published by Oxford University Press GigaScience.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 05 July 2022
                Date revision received : 03 October 2022
                Date accepted : 31 October 2022
                Page count
                Pages: 10
                Funding
                Funded by: Southeast University, DOI 10.13039/501100008081;
                Award ID: Sklb2021-k02
                Funded by: Guangxi Key Lab of Mangrove Conservation and Utilization, DOI 10.13039/501100015937;
                Award ID: GKLMC-202002
                Categories
                Subject: Data Note
                Subject: AcademicSubjects/SCI00960
                Subject: AcademicSubjects/SCI02254

                Keywords: reef-building coral,colabfold,structural proteomics
                Data availability:
                Keywords: reef-building coral, colabfold, structural proteomics

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