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      AlphaPulldown—a python package for protein–protein interaction screens using AlphaFold-Multimer

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      Author(s): , , ,
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      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: Oxford University Press

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          Abstract

          Summary

          The artificial intelligence-based structure prediction program AlphaFold-Multimer enabled structural modelling of protein complexes with unprecedented accuracy. Increasingly, AlphaFold-Multimer is also used to discover new protein–protein interactions (PPIs). Here, we present AlphaPulldown, a Python package that streamlines PPI screens and high-throughput modelling of higher-order oligomers using AlphaFold-Multimer. It provides a convenient command-line interface, a variety of confidence scores and a graphical analysis tool.

          Availability and implementation

          AlphaPulldown is freely available at https://www.embl-hamburg.de/AlphaPulldown.

          Supplementary information

          Supplementary note is available at Bioinformatics online.

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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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              MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

              Martin Steinegger, Johannes Söding (2017)
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                Author and article information

                Contributors
                Dingquan Yu
                Grzegorz Chojnowski
                Maria Rosenthal:
                ORCID: https://orcid.org/0000-0003-2986-936X
                Jan Kosinski:
                ORCID: https://orcid.org/0000-0002-3641-0322
                Lenore Cowen: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date Collection: January 2023
                Publication date (Electronic): 22 November 2022
                Publication date PMC-release: 22 November 2022
                Volume: 39
                Issue: 1
                Electronic Location Identifier: btac749
                Affiliations
                European Molecular Biology Laboratory Hamburg , Hamburg 22607, Germany
                Centre for Structural Systems Biology (CSSB) , Hamburg 22607, Germany
                European Molecular Biology Laboratory Hamburg , Hamburg 22607, Germany
                Bernhard Nocht Institute for Tropical Medicine , Hamburg 20359, Germany
                European Molecular Biology Laboratory Hamburg , Hamburg 22607, Germany
                Centre for Structural Systems Biology (CSSB) , Hamburg 22607, Germany
                Structural and Computational Biology Unit, European Molecular Biology Laboratory , Heidelberg 69117, Germany
                Author notes
                To whom correspondence should be addressed. Email: jan.kosinski@ 123456embl.de
                Author information
                https://orcid.org/0000-0003-2986-936X
                https://orcid.org/0000-0002-3641-0322
                Article
                Publisher ID: btac749
                DOI: 10.1093/bioinformatics/btac749
                PMC ID: 9805587
                PubMed ID: 36413069
                SO-VID: 848e4c20-3fde-4c48-bdf0-ac8d3fab97ba
                Copyright © © The Author(s) 2022. Published by Oxford University Press.
                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 August 2022
                Date revision received : 13 October 2022
                Date: 12 November 2022
                Date accepted : 21 November 2022
                Date: 30 November 2022
                Page count
                Pages: 3
                Funding
                Funded by: German Research Foundation, DOI 10.13039/501100001659;
                Funded by: DFG, DOI 10.13039/100004807;
                Award ID: KO 5979/2-1
                Categories
                Subject: Applications Note
                Subject: Structural Bioinformatics
                Subject: AcademicSubjects/SCI01060

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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