{"topic":"protein-design","sort":"top","limit":25,"offset":0,"count":25,"papers":[{"id":"doi:10.1038/s41586-021-03819-2","kind":"article","title":"Highly accurate protein structure prediction with AlphaFold","authors":["John Jumper","Richard Evans","Alexander Pritzel","Tim Green","Michael Figurnov","Olaf Ronneberger","Kathryn Tunyasuvunakool","Russ Bates","Augustin Žídek","Anna Potapenko","Alex Bridgland","Clemens Meyer","Simon Köhl","Andrew J. Ballard","Andrew Cowie","Bernardino Romera‐Paredes","Stanislav Nikolov","Rishub Jain","Jonas Adler","Trevor Back","Stig Petersen","David Reiman","Ellen Clancy","Michał Zieliński","Martin Steinegger"],"author_count":34,"year":2021,"published":"2021-07-15","venue":"Nature","cited_by":43700,"doi":"10.1038/s41586-021-03819-2","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-021-03819-2","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-021-03819-2","updated":null,"pmid":"34265844","pmcid":"PMC8371605","openalex_id":"W3177828909","pdf_url":"https://www.nature.com/articles/s41586-021-03819-2.pdf","abstract":"Abstract 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.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Highly accurate protein structure prediction with AlphaFold\" by John Jumper, Richard Evans, Alexander Pritzel et al., https://doi.org/10.1038/s41586-021-03819-2, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-024-07487-w","kind":"article","title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","authors":["Josh Abramson","Jonas Adler","Jack Dunger","Richard Evans","Tim Green","Alexander Pritzel","Olaf Ronneberger","Lindsay Willmore","Andrew J. Ballard","Joshua Bambrick","Sebastian W. Bodenstein","David A. Evans","Chia-Chun Hung","Michael O’Neill","David Reiman","Kathryn Tunyasuvunakool","Zachary Wu","Akvilė Žemgulytė","Eirini Arvaniti","Charles Beattie","Ottavia Bertolli","Alex Bridgland","Alexey Cherepanov","Miles Congreve","Alexander I. Cowen-Rivers"],"author_count":48,"year":2024,"published":"2024-05-08","venue":"Nature","cited_by":15805,"doi":"10.1038/s41586-024-07487-w","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-024-07487-w","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-024-07487-w","updated":null,"pmid":"38718835","pmcid":"PMC11168924","openalex_id":"W4396721167","pdf_url":"https://europepmc.org/articles/PMC11168924/pdf","abstract":"Abstract The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2–6 . Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein–ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein–nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody–antigen prediction accuracy compared with AlphaFold-Multimer v.2.3 7,8 . Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Accurate structure prediction of biomolecular interactions with AlphaFold 3\" by Josh Abramson, Jonas Adler, Jack Dunger et al., https://doi.org/10.1038/s41586-024-07487-w, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1093/nar/gkac1052","kind":"article","title":"UniProt: the Universal Protein Knowledgebase in 2023","authors":[],"author_count":0,"year":2023,"published":"2023-01-01","venue":"Nucleic acids research","cited_by":6891,"doi":"10.1093/nar/gkac1052","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/nar/gkac1052","archive_url":"https://space-kitty.com/papers/doi:10.1093/nar/gkac1052","updated":null,"pmid":"36408920","pmcid":"PMC9825514","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC9825514/pdf","abstract":"The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication we describe enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"UniProt: the Universal Protein Knowledgebase in 2023\" by the authors, https://doi.org/10.1093/nar/gkac1052, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1093/nar/gkab1061","kind":"article","title":"AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models","authors":["Mihály Váradi","Stephen Anyango","Mandar Deshpande","Sreenath Sasidharan Nair","Cindy Natassia","Galabina Yordanova","David Yu Yuan","Oana Stroe","Gemma Wood","Agata Laydon","Augustin Žídek","Tim Green","Kathryn Tunyasuvunakool","Stig Petersen","John Jumper","Ellen Clancy","Richard R. Green","Ankur Vora","Mira Lutfi","Michael Figurnov","Andrew Cowie","Nicole Hobbs","Pushmeet Kohli","Gerard J. Kleywegt","Ewan Birney"],"author_count":27,"year":2021,"published":"2021-10-19","venue":"Nucleic Acids Research","cited_by":7841,"doi":"10.1093/nar/gkab1061","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/nar/gkab1061","archive_url":"https://space-kitty.com/papers/doi:10.1093/nar/gkab1061","updated":null,"pmid":"34791371","pmcid":"PMC8728224","openalex_id":"W3211795435","pdf_url":"https://europepmc.org/articles/PMC8728224/pdf","abstract":"The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models\" by Mihály Váradi, Stephen Anyango, Mandar Deshpande et al., https://doi.org/10.1093/nar/gkab1061, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1101/2021.10.04.463034","kind":"article","title":"Protein complex prediction with AlphaFold-Multimer","authors":["Richard Evans","Michael O’Neill","Alexander Pritzel","Natasha Antropova","Andrew Senior","Tim Green","Augustin Žídek","Russ Bates","Sam Blackwell","Jason Yim","Olaf Ronneberger","Sebastian W. Bodenstein","Michal Zielinski","Alex Bridgland","Anna Potapenko","Andrew Cowie","Kathryn Tunyasuvunakool","Rishub Jain","Ellen Clancy","Pushmeet Kohli","John Jumper","Demis Hassabis"],"author_count":22,"year":2021,"published":"2021-10-04","venue":"bioRxiv (Cold Spring Harbor Laboratory)","cited_by":3795,"doi":"10.1101/2021.10.04.463034","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1101/2021.10.04.463034","archive_url":"https://space-kitty.com/papers/doi:10.1101/2021.10.04.463034","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3202105508","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1093/nar/gkae1082","kind":"article","title":"InterPro: the protein sequence classification resource in 2025","authors":["Blum M","Andreeva A","Florentino LC","Chuguransky SR","Grego T","Hobbs E","Pinto BL","Orr A","Paysan-Lafosse T","Ponamareva I","Salazar GA","Bordin N","Bork P","Bridge A","Colwell L","Gough J","Haft DH","Letunic I","Llinares-López F","Marchler-Bauer A","Meng-Papaxanthos L","Mi H","Natale DA","Orengo CA","Pandurangan AP"],"author_count":34,"year":2025,"published":"2025-01-01","venue":"Nucleic acids research","cited_by":1063,"doi":"10.1093/nar/gkae1082","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/nar/gkae1082","archive_url":"https://space-kitty.com/papers/doi:10.1093/nar/gkae1082","updated":null,"pmid":"39565202","pmcid":"PMC11701551","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC11701551/pdf","abstract":"InterPro (https://www.ebi.ac.uk/interpro) is a freely accessible resource for the classification of protein sequences into families. It integrates predictive models, known as signatures, from multiple member databases to classify sequences into families and predict the presence of domains and significant sites. The InterPro database provides annotations for over 200 million sequences, ensuring extensive coverage of UniProtKB, the standard repository of protein sequences, and includes mappings to several other major resources, such as Gene Ontology (GO), Protein Data Bank in Europe (PDBe) and the AlphaFold Protein Structure Database. In this publication, we report on the status of InterPro (version 101.0), detailing new developments in the database, associated web interface and software. Notable updates include the increased integration of structures predicted by AlphaFold and the enhanced description of protein families using artificial intelligence. Over the past two years, more than 5000 new InterPro entries have been created. The InterPro website now offers access to 85 000 protein families and domains from its member databases and serves as a long-term archive for retired databases. InterPro data, software and tools are freely available.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"InterPro: the protein sequence classification resource in 2025\" by Blum M, Andreeva A, Florentino LC et al., https://doi.org/10.1093/nar/gkae1082, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1093/nar/gkad1011","kind":"article","title":"AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences","authors":["Mihály Váradi","Damian Bertoni","Paulyna Magaña","Urmila Paramval","Ivanna Pidruchna","Malarvizhi Radhakrishnan","Maxim I. Tsenkov","Sreenath Sasidharan Nair","Milot Mirdita","Jingi Yeo","Oleg V. Kovalevskiy","Kathryn Tunyasuvunakool","Agata Laydon","Augustin Žídek","Hamish Tomlinson","Dhavanthi Hariharan","Josh Abrahamson","Tim Green","John Jumper","Ewan Birney","Martin Steinegger","Demis Hassabis","Sameer S Velankar"],"author_count":23,"year":2023,"published":"2023-10-18","venue":"Nucleic Acids Research","cited_by":2049,"doi":"10.1093/nar/gkad1011","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/nar/gkad1011","archive_url":"https://space-kitty.com/papers/doi:10.1093/nar/gkad1011","updated":null,"pmid":"37933859","pmcid":"PMC10767828","openalex_id":"W4388464011","pdf_url":"https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkad1011/52777135/gkad1011.pdf","abstract":"The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences\" by Mihály Váradi, Damian Bertoni, Paulyna Magaña et al., https://doi.org/10.1093/nar/gkad1011, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-023-06415-8","kind":"article","title":"De novo design of protein structure and function with RFdiffusion","authors":["Watson JL","Juergens D","Bennett NR","Trippe BL","Yim J","Eisenach HE","Ahern W","Borst AJ","Ragotte RJ","Milles LF","Wicky BIM","Hanikel N","Pellock SJ","Courbet A","Sheffler W","Wang J","Venkatesh P","Sappington I","Torres SV","Lauko A","De Bortoli V","Mathieu E","Ovchinnikov S","Barzilay R","Jaakkola TS"],"author_count":28,"year":2023,"published":"2023-07-11","venue":"Nature","cited_by":1906,"doi":"10.1038/s41586-023-06415-8","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-023-06415-8","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-023-06415-8","updated":null,"pmid":"37433327","pmcid":"PMC10468394","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC10468394/pdf","abstract":"There has been considerable recent progress in designing new proteins using deep-learning methods 1-9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"De novo design of protein structure and function with RFdiffusion\" by Watson JL, Juergens D, Bennett NR et al., https://doi.org/10.1038/s41586-023-06415-8, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-019-1923-7","kind":"article","title":"Improved protein structure prediction using potentials from deep learning","authors":["Andrew Senior","Richard Evans","John Jumper","James Kirkpatrick","Laurent Sifre","Tim Green","Chongli Qin","Augustin Žídek","Alexander W. R. Nelson","Alex Bridgland","Hugo Penedones","Stig Petersen","Karen Simonyan","Steve Crossan","Pushmeet Kohli","David T. Jones","David Silver","Koray Kavukcuoglu","Demis Hassabis"],"author_count":19,"year":2020,"published":"2020-01-15","venue":"Nature","cited_by":3381,"doi":"10.1038/s41586-019-1923-7","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-019-1923-7","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-019-1923-7","updated":null,"pmid":"31942072","pmcid":null,"openalex_id":"W2999044305","pdf_url":"https://discovery.ucl.ac.uk/10089234/1/343019_3_art_0_py4t4l_convrt.pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41586-021-03828-1","kind":"article","title":"Highly accurate protein structure prediction for the human proteome","authors":["Kathryn Tunyasuvunakool","Jonas Adler","Zachary Wu","Tim Green","Michal Zielinski","Augustin Žídek","Alex Bridgland","Andrew Cowie","Clemens Meyer","Agata Laydon","Sameer S Velankar","Gerard J. Kleywegt","Alex G. Bateman","Richard Evans","Alexander Pritzel","Michael Figurnov","Olaf Ronneberger","Russ Bates","Simon Köhl","Anna Potapenko","Andrew J. Ballard","Bernardino Romera‐Paredes","Stanislav Nikolov","Rishub Jain","Ellen Clancy"],"author_count":33,"year":2021,"published":"2021-07-22","venue":"Nature","cited_by":3023,"doi":"10.1038/s41586-021-03828-1","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-021-03828-1","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-021-03828-1","updated":null,"pmid":"34293799","pmcid":"PMC8387240","openalex_id":"W3183475563","pdf_url":"https://www.nature.com/articles/s41586-021-03828-1.pdf","abstract":"Abstract Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure 1 . Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold 2 , at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Highly accurate protein structure prediction for the human proteome\" by Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu et al., https://doi.org/10.1038/s41586-021-03828-1, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1126/science.add2187","kind":"article","title":"Robust deep learning–based protein sequence design using ProteinMPNN","authors":["Justas Dauparas","Ivan V. Anishchenko","Nathaniel R. Bennett","Hua Bai","Robert J. Ragotte","Lukas F. Milles","Basile I. M. Wicky","Alexis Courbet","Robbert J. de Haas","Neville P. Bethel","Philip J. Y. Leung","Timothy F. Huddy","Samuel J. Pellock","Doug Tischer","F. Chan","Brian Koepnick","Hannah Nguyen","Alex Kang","Banumathi Sankaran","Asim K. Bera","Neil P. King","David A. Baker"],"author_count":22,"year":2022,"published":"2022-09-15","venue":"Science","cited_by":2102,"doi":"10.1126/science.add2187","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1126/science.add2187","archive_url":"https://space-kitty.com/papers/doi:10.1126/science.add2187","updated":null,"pmid":"36108050","pmcid":"PMC9997061","openalex_id":"W4296032638","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2022/06/04/2022.06.03.494563.full.pdf","abstract":"Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo-electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"cc-by-nc-nd","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Robust deep learning–based protein sequence design using ProteinMPNN\" by Justas Dauparas, Ivan V. Anishchenko, Nathaniel R. Bennett et al., https://doi.org/10.1126/science.add2187, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1093/nar/gkac958","kind":"article","title":"Ensembl 2023","authors":["Fergal J. Martin","M Ridwan Amode","Alisha Aneja","Olanrewaju Austine-Orimoloye","Andrey G Azov","If Barnes","Arne Becker","Ruth Bennett","Andrew E. Berry","Jyothish N Nair Thulasee Bhai","Simarpreet Kaur Bhurji","Alexandra Bignell","Sanjay Boddu","Paulo R. Branco Lins","Lucy Brooks","Shashank Budhanuru Ramaraju","Mehrnaz Charkhchi","Alexander Cockburn","Luca Da Rin Fioretto","Claire Davidson","Kamalkumar Dodiya","Sarah Donaldson","Bilal El Houdaigui","Tamara El Naboulsi","Reham Fatima"],"author_count":96,"year":2022,"published":"2022-10-14","venue":"Nucleic Acids Research","cited_by":1028,"doi":"10.1093/nar/gkac958","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/nar/gkac958","archive_url":"https://space-kitty.com/papers/doi:10.1093/nar/gkac958","updated":null,"pmid":"36318249","pmcid":"PMC9825606","openalex_id":"W4307843054","pdf_url":"https://academic.oup.com/nar/article-pdf/51/D1/D933/48440737/gkac958.pdf","abstract":"Ensembl (https://www.ensembl.org) has produced high-quality genomic resources for vertebrates and model organisms for more than twenty years. During that time, our resources, services and tools have continually evolved in line with both the publicly available genome data and the downstream research and applications that utilise the Ensembl platform. In recent years we have witnessed a dramatic shift in the genomic landscape. There has been a large increase in the number of high-quality reference genomes through global biodiversity initiatives. In parallel, there have been major advances towards pangenome representations of higher species, where many alternative genome assemblies representing different breeds, cultivars, strains and haplotypes are now available. In order to support these efforts and accelerate downstream research, it is our goal at Ensembl to create high-quality annotations, tools and services for species across the tree of life. Here, we report our resources for popular reference genomes, the dramatic growth of our annotations (including haplotypes from the first human pangenome graphs), updates to the Ensembl Variant Effect Predictor (VEP), interactive protein structure predictions from AlphaFold DB, and the beta release of our new website.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Genomics and Phylogenetic Studies"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Ensembl 2023\" by Fergal J. Martin, M Ridwan Amode, Alisha Aneja et al., https://doi.org/10.1093/nar/gkac958, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41467-022-32007-7","kind":"article","title":"ProtGPT2 is a deep unsupervised language model for protein design","authors":["Noelia Ferruz","Steffen Schmidt","Birte Höcker"],"author_count":3,"year":2022,"published":"2022-07-27","venue":"Nature Communications","cited_by":795,"doi":"10.1038/s41467-022-32007-7","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41467-022-32007-7","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41467-022-32007-7","updated":null,"pmid":"35896542","pmcid":"PMC9329459","openalex_id":"W4288066876","pdf_url":"https://www.nature.com/articles/s41467-022-32007-7.pdf","abstract":"Abstract Protein design aims to build new proteins from scratch thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in the field of natural language processing (NLP) has enabled the implementation of ever-growing language models capable of understanding and generating text with human-like capabilities. Given the many similarities between human languages and protein sequences, the use of NLP models offers itself for predictive tasks in protein research. Motivated by the evident success of generative Transformer-based language models such as the GPT-x series, we developed ProtGPT2, a language model trained on protein space that generates de novo protein sequences that follow the principles of natural ones. In particular, the generated proteins display amino acid propensities which resemble natural proteins. Disorder and secondary structure prediction indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yielded well-folded non-idealized structures with embodiments as well as large loops and revealed new topologies not captured in current structure databases. ProtGPT2 has learned to speak the protein language. It has the potential to generate de novo proteins in a high throughput fashion in a matter of seconds. The model is easy-to-use and freely available.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"ProtGPT2 is a deep unsupervised language model for protein design\" by Noelia Ferruz, Steffen Schmidt, Birte Höcker, https://doi.org/10.1038/s41467-022-32007-7, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1093/nar/gkac387","kind":"article","title":"Dali server: structural unification of protein families","authors":["Holm L"],"author_count":1,"year":2022,"published":"2022-07-01","venue":"Nucleic acids research","cited_by":890,"doi":"10.1093/nar/gkac387","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/nar/gkac387","archive_url":"https://space-kitty.com/papers/doi:10.1093/nar/gkac387","updated":null,"pmid":"35610055","pmcid":"PMC9252788","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC9252788/pdf","abstract":"Protein structure is key to understanding biological function. Structure comparison deciphers deep phylogenies, providing insight into functional conservation and functional shifts during evolution. Until recently, structural coverage of the protein universe was limited by the cost and labour involved in experimental structure determination. Recent breakthroughs in deep learning revolutionized structural bioinformatics by providing accurate structural models of numerous protein families for which no structural information existed. The Dali server for 3D protein structure comparison is widely used by crystallographers to relate new structures to pre-existing ones. Here, we report two most recent upgrades to the web server: (i) the foldomes of key organisms in the AlphaFold Database (version 1) are searchable by Dali, (ii) structural alignments are annotated with protein families. Using these new features, we discovered a novel functionally diverse subgroup within the WRKY/GCM1 clan. This was accomplished by linking the structurally characterized SWI/SNF and NAM families as well as the structural models of the CG-1 family and uncharacterized proteins to the structure of Gti1/Pac2, a previously known member of the WRKY/GCM1 clan. The Dali server is available at http://ekhidna2.biocenter.helsinki.fi/dali. This website is free and open to all users and there is no login requirement.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Dali server: structural unification of protein families\" by Holm L, https://doi.org/10.1093/nar/gkac387, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1093/nar/gkae997","kind":"article","title":"The Pfam protein families database: embracing AI/ML","authors":["Typhaine Paysan‐Lafosse","Antonina E. Andreeva","Matthias Blum","Sara Chuguransky","Tiago Grego","Beatriz Lazaro Pinto","Gustavo A Salazar","Maxwell L. Bileschi","Felipe Llinares-López","Laetitia Meng-Papaxanthos","Lucy J. Colwell","Nick V. Grishin","Richard Dustin Schaeffer","Damiano Clementel","Silvio C. E. Tosatto","Erik L. L. Sonnhammer","Valerie Wood","Alex G. Bateman"],"author_count":18,"year":2024,"published":"2024-10-16","venue":"Nucleic Acids Research","cited_by":362,"doi":"10.1093/nar/gkae997","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/nar/gkae997","archive_url":"https://space-kitty.com/papers/doi:10.1093/nar/gkae997","updated":null,"pmid":"39540428","pmcid":"PMC11701544","openalex_id":"W4404343451","pdf_url":"https://europepmc.org/articles/PMC11701544/pdf","abstract":"The Pfam protein families database is a comprehensive collection of protein domains and families used for genome annotation and protein structure and function analysis (https://www.ebi.ac.uk/interpro/). This update describes major developments in Pfam since 2020, including decommissioning the Pfam website and integration with InterPro, harmonization with the ECOD structural classification, and expanded curation of metagenomic, microprotein and repeat-containing families. We highlight how AlphaFold structure predictions are being leveraged to refine domain boundaries and identify new domains. New families discovered through large-scale sequence similarity analysis of AlphaFold models are described. We also detail the development of Pfam-N, which uses deep learning to expand family coverage, achieving an 8.8% increase in UniProtKB coverage compared to standard Pfam. We discuss plans for more frequent Pfam releases integrated with InterPro and the potential for artificial intelligence to further assist curation. Despite recent advances, many protein families remain to be classified, and Pfam continues working toward comprehensive coverage of the protein universe.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Genomics and Phylogenetic Studies"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"The Pfam protein families database: embracing AI/ML\" by Typhaine Paysan‐Lafosse, Antonina E. Andreeva, Matthias Blum et al., https://doi.org/10.1093/nar/gkae997, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.jmb.2021.167208","kind":"review","title":"AlphaFold and Implications for Intrinsically Disordered Proteins","authors":["Kiersten M. Ruff","Rohit V. Pappu"],"author_count":2,"year":2021,"published":"2021-08-18","venue":"Journal of Molecular Biology","cited_by":633,"doi":"10.1016/j.jmb.2021.167208","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.jmb.2021.167208","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.jmb.2021.167208","updated":null,"pmid":"34418423","pmcid":null,"openalex_id":"W3195375135","pdf_url":"https://www.sciencedirect.com/science/article/pii/S0022283621004411/pdf","abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1093/nar/gkad1083","kind":"article","title":"MODOMICS: a database of RNA modifications and related information. 2023 update","authors":["Cappannini A","Ray A","Purta E","Mukherjee S","Boccaletto P","Moafinejad SN","Lechner A","Barchet C","Klaholz BP","Stefaniak F","Bujnicki JM"],"author_count":11,"year":2024,"published":"2024-01-01","venue":"Nucleic acids research","cited_by":404,"doi":"10.1093/nar/gkad1083","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/nar/gkad1083","archive_url":"https://space-kitty.com/papers/doi:10.1093/nar/gkad1083","updated":null,"pmid":"38015436","pmcid":"PMC10767930","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC10767930/pdf","abstract":"The MODOMICS database was updated with recent data and now includes new data types related to RNA modifications. Changes to the database include an expanded modification catalog, encompassing both natural and synthetic residues identified in RNA structures. This addition aids in representing RNA sequences from the RCSB PDB database more effectively. To manage the increased number of modifications, adjustments to the nomenclature system were made. Updates in the RNA sequences section include the addition of new sequences and the reintroduction of sequence alignments for tRNAs and rRNAs. The protein section was updated and connected to structures from the RCSB PDB database and predictions by AlphaFold. MODOMICS now includes a data annotation system, with 'Evidence' and 'Estimated Reliability' features, offering clarity on data support and accuracy. This system is open to all MODOMICS entries, enhancing the accuracy of RNA modification data representation. MODOMICS is available at https://iimcb.genesilico.pl/modomics/.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"MODOMICS: a database of RNA modifications and related information. 2023 update\" by Cappannini A, Ray A, Purta E et al., https://doi.org/10.1093/nar/gkad1083, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1002/pro.4582","kind":"article","title":"Using evolutionary data to make sense of macromolecules with a \"face-lifted\" ConSurf","authors":["Yariv B","Yariv E","Kessel A","Masrati G","Chorin AB","Martz E","Mayrose I","Pupko T","Ben-Tal N"],"author_count":9,"year":2023,"published":"2023-03-01","venue":"Protein science : a publication of the Protein Society","cited_by":461,"doi":"10.1002/pro.4582","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1002/pro.4582","archive_url":"https://space-kitty.com/papers/doi:10.1002/pro.4582","updated":null,"pmid":"36718848","pmcid":"PMC9942591","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC9942591/pdf","abstract":"The ConSurf web-sever for the analysis of proteins, RNA, and DNA provides a quick and accurate estimate of the per-site evolutionary rate among homologues. The analysis reveals functionally important regions, such as catalytic and ligand-binding sites, which often evolve slowly. Since the last report in 2016, ConSurf has been improved in multiple ways. It now has a user-friendly interface that makes it easier to perform the analysis and to visualize the results. Evolutionary rates are calculated based on a set of homologous sequences, collected using hidden Markov model-based search tools, recently embedded in the pipeline. Using these, and following the removal of redundancy, ConSurf assembles a representative set of effective homologues for protein and nucleic acid queries to enable informative analysis of the evolutionary patterns. The analysis is particularly insightful when the evolutionary rates are mapped on the macromolecule structure. In this respect, the availability of AlphaFold model structures of essentially all UniProt proteins makes ConSurf particularly relevant to the research community. The UniProt ID of a query protein with an available AlphaFold model can now be used to start a calculation. Another important improvement is the Python re-implementation of the entire computational pipeline, making it easier to maintain. This Python pipeline is now available for download as a standalone version. We demonstrate some of ConSurf's key capabilities by the analysis of caveolin-1, the main protein of membrane invaginations called caveolae.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Using evolutionary data to make sense of macromolecules with a \"face-lifted\" ConSurf\" by Yariv B, Yariv E, Kessel A et al., https://doi.org/10.1002/pro.4582, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41592-022-01685-y","kind":"article","title":"AlphaFill: enriching AlphaFold models with ligands and cofactors","authors":["Maarten L. Hekkelman","Ida de Vries","Robbie P. Joosten","Anastassis Perrakis A"],"author_count":4,"year":2022,"published":"2022-11-24","venue":"Nature Methods","cited_by":511,"doi":"10.1038/s41592-022-01685-y","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41592-022-01685-y","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41592-022-01685-y","updated":null,"pmid":"36424442","pmcid":"PMC9911346","openalex_id":"W4310058955","pdf_url":"https://www.nature.com/articles/s41592-022-01685-y.pdf","abstract":"Artificial intelligence-based protein structure prediction approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the AlphaFold protein structure database, however, all lack coordinates for small molecules, essential for molecular structure or function: hemoglobin lacks bound heme; zinc-finger motifs lack zinc ions essential for structural integrity and metalloproteases lack metal ions needed for catalysis. Ligands important for biological function are absent too; no ADP or ATP is bound to any of the ATPases or kinases. Here we present AlphaFill, an algorithm that uses sequence and structure similarity to 'transplant' such 'missing' small molecules and ions from experimentally determined structures to predicted protein models. The algorithm was successfully validated against experimental structures. A total of 12,029,789 transplants were performed on 995,411 AlphaFold models and are available together with associated validation metrics in the alphafill.eu databank, a resource to help scientists make new hypotheses and design targeted experiments.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"AlphaFill: enriching AlphaFold models with ligands and cofactors\" by Maarten L. Hekkelman, Ida de Vries, Robbie P. Joosten et al., https://doi.org/10.1038/s41592-022-01685-y, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-024-08416-7","kind":"article","title":"Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3","authors":["Josh Abramson","Jonas Adler","Jack Dunger","Richard Evans","Tim Green","Alexander Pritzel","Olaf Ronneberger","Lindsay Willmore","Andrew J. Ballard","Joshua Bambrick","Sebastian W. Bodenstein","David A. Evans","Chia-Chun Hung","Michael O’Neill","David Reiman","Kathryn Tunyasuvunakool","Zachary Wu","Akvilė Žemgulytė","Eirini Arvaniti","Charles Beattie","Ottavia Bertolli","Alex Bridgland","Alexey Cherepanov","Miles Congreve","Alexander I. Cowen-Rivers"],"author_count":48,"year":2024,"published":"2024-11-27","venue":"Nature","cited_by":350,"doi":"10.1038/s41586-024-08416-7","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-024-08416-7","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-024-08416-7","updated":null,"pmid":"39604737","pmcid":"PMC11634763","openalex_id":"W4404755712","pdf_url":"https://www.nature.com/articles/s41586-024-08416-7.pdf","abstract":"In our original article, we provided an extensive description in the Supplementary Information of pseudocode underlying the functioning of our AlphaFold3 model, which allows prediction of the structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. Accompanying release of the paper we provided free non-commercial access to the AlphaFold3 server ( https://alphafoldserver.com/welcome ). Google DeepMind has now released the underlying inference code, which can be accessed here: https://github.com/google-deepmind/alphafold3 .","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3\" by Josh Abramson, Jonas Adler, Jack Dunger et al., https://doi.org/10.1038/s41586-024-08416-7, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41592-023-02087-4","kind":"article","title":"AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination","authors":["Thomas C. Terwilliger","Dorothee Claudia Liebschner","Tristan Ian Croll","Christopher J. Williams","Airlie J. McCoy","Billy K. Poon","Pavel V. Afonine","Robert D. Oeffner","Jane S. Richardson","Randy J. Read","Paul D. Adams"],"author_count":11,"year":2023,"published":"2023-11-30","venue":"Nature Methods","cited_by":410,"doi":"10.1038/s41592-023-02087-4","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41592-023-02087-4","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41592-023-02087-4","updated":null,"pmid":"38036854","pmcid":"PMC10776388","openalex_id":"W4389174567","pdf_url":"https://www.nature.com/articles/s41592-023-02087-4.pdf","abstract":"Abstract AI-based methods such as AlphaFold have revolutionized structural biology, often making it possible to predict protein structures with high accuracy. The accuracies of these predictions vary, however, and they do not include ligands, covalent modifications or other environmental factors. Here we focus on very-high-confidence parts of AlphaFold predictions, evaluating how well they can be expected to describe the structure of a protein in a particular environment. We compare predictions with experimental crystallographic maps of the same proteins for 102 crystal structures. In many cases, those parts of AlphaFold predictions that were predicted with very high confidence matched experimental maps remarkably closely. In other cases, these predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. Overall, C α atoms in very-high-confidence parts of AlphaFold predictions differed from corresponding crystal structures by a median of 0.6 Å, and about 10% of these differed by more than 2 Å, each about twice the values found for pairs of crystal structures containing the same components but determined in different space groups. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination\" by Thomas C. Terwilliger, Dorothee Claudia Liebschner, Tristan Ian Croll et al., https://doi.org/10.1038/s41592-023-02087-4, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.jmb.2025.168967","kind":"article","title":"AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities","authors":["Jennifer Fleming","Paulyna Magaña","Sreenath Sasidharan Nair","Maxim I. Tsenkov","Damian Bertoni","Ivanna Pidruchna","Marcelo Querino Lima Afonso","Adam Midlik","Urmila Paramval","Augustin Žídek","Agata Laydon","Oleg V. Kovalevskiy","Elisa Wong","Joshua Pan","Jun Cheng","Žiga Avsec","Clare Bycroft","Lai Hong Won","Meera Last","Milot Mirdita","Martin Steinegger","Mihály Váradi","Pushmeet Kohli","Sameer Velnankar"],"author_count":24,"year":2025,"published":"2025-01-29","venue":"Journal of Molecular Biology","cited_by":284,"doi":"10.1016/j.jmb.2025.168967","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.jmb.2025.168967","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.jmb.2025.168967","updated":null,"pmid":"40133787","pmcid":null,"openalex_id":"W4406944029","pdf_url":null,"abstract":"The AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/) has made significant strides in enhancing its utility and accessibility for the life science research community. The recent integration of AlphaMissense predictions enables access to the pathogenicity of human protein missense variants, with an innovative and interactive heatmap and 3D visualisation that display variant data at the residue level. Users can now toggle between structure model quality (pLDDT) and average pathogenicity scores, providing insights into the implications of specific residue changes. The Foldseek integration offers a rapid and accurate method for protein structure searches and comparisons. Bulk data download options further facilitate comprehensive data analysis and integration with other computational tools. The 3D-Beacons framework (https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/) has also been enhanced with detailed annotation endpoints (such as AlphaMissense data) and integrates LevyLab’s dataset of homomeric AlphaFold 2 models. These advancements, plus the release of the training module \"AlphaFold: A Practical Guide\", significantly improve the functionality and accessibility of these resources, enabling discoveries using structure data.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities\" by Jennifer Fleming, Paulyna Magaña, Sreenath Sasidharan Nair et al., https://doi.org/10.1016/j.jmb.2025.168967, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1126/science.abm4805","kind":"article","title":"Computed structures of core eukaryotic protein complexes","authors":["Ian R. Humphreys","Jimin Pei","Minkyung Baek","Aditya Krishnakumar","Ivan V. Anishchenko","Sergey Ovchinnikov","Jing Zhang","Travis J. Ness","Sudeep Banjade","Saket Rahul Bagde","Viktoriya G. Stancheva","Xiaohan Li","Kaixian Liu","Zhi Zheng","Daniel J. Barrero","Upasana Roy","Jochen Kuper","Israel S. Fernández","Barnabás Szakál","Dana Branzei","Josep Rizo","Caroline Kisker","Eric C. Greene","Sue Biggins","Scott Keeney"],"author_count":30,"year":2021,"published":"2021-11-11","venue":"Science","cited_by":541,"doi":"10.1126/science.abm4805","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1126/science.abm4805","archive_url":"https://space-kitty.com/papers/doi:10.1126/science.abm4805","updated":null,"pmid":"34762488","pmcid":null,"openalex_id":"W3212912034","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2021/09/30/2021.09.30.462231.full.pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1126/science.abn2100","kind":"article","title":"Scaffolding protein functional sites using deep learning","authors":["Jue Wang","Sidney Lyayuga Lisanza","David Juergens","Doug Tischer","Joseph L. Watson","Karla M. Castro","Robert J. Ragotte","Amijai Saragovi","Lukas F. Milles","Minkyung Baek","Ivan V. Anishchenko","Wei Yang","Derrick R. Hicks","Marc Expòsit","Thomas Schlichthaerle","Jung-Ho Chun","Justas Dauparas","Nathaniel R. Bennett","Basile I. M. Wicky","Andrew Muenks","Frank DiMaio","Bruno E. Correia","Sergey Ovchinnikov","David A. Baker"],"author_count":24,"year":2022,"published":"2022-07-21","venue":"Science","cited_by":487,"doi":"10.1126/science.abn2100","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1126/science.abn2100","archive_url":"https://space-kitty.com/papers/doi:10.1126/science.abn2100","updated":null,"pmid":"35862514","pmcid":null,"openalex_id":"W4286488966","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2021/11/15/2021.11.10.468128.full.pdf","abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1093/nar/gkac1046","kind":"article","title":"The next-generation Open Targets Platform: reimagined, redesigned, rebuilt","authors":["Ochoa D","Hercules A","Carmona M","Suveges D","Baker J","Malangone C","Lopez I","Miranda A","Cruz-Castillo C","Fumis L","Bernal-Llinares M","Tsukanov K","Cornu H","Tsirigos K","Razuvayevskaya O","Buniello A","Schwartzentruber J","Karim M","Ariano B","Martinez Osorio RE","Ferrer J","Ge X","Machlitt-Northen S","Gonzalez-Uriarte A","Saha S"],"author_count":34,"year":2023,"published":"2023-01-01","venue":"Nucleic acids research","cited_by":428,"doi":"10.1093/nar/gkac1046","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/nar/gkac1046","archive_url":"https://space-kitty.com/papers/doi:10.1093/nar/gkac1046","updated":null,"pmid":"36399499","pmcid":"PMC9825572","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC9825572/pdf","abstract":"The Open Targets Platform (https://platform.opentargets.org/) is an open source resource to systematically assist drug target identification and prioritisation using publicly available data. Since our last update, we have reimagined, redesigned, and rebuilt the Platform in order to streamline data integration and harmonisation, expand the ways in which users can explore the data, and improve the user experience. The gene-disease causal evidence has been enhanced and expanded to better capture disease causality across rare, common, and somatic diseases. For target and drug annotations, we have incorporated new features that help assess target safety and tractability, including genetic constraint, PROTACtability assessments, and AlphaFold structure predictions. We have also introduced new machine learning applications for knowledge extraction from the published literature, clinical trial information, and drug labels. The new technologies and frameworks introduced since the last update will ease the introduction of new features and the creation of separate instances of the Platform adapted to user requirements. Our new Community forum, expanded training materials, and outreach programme support our users in a range of use cases.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"The next-generation Open Targets Platform: reimagined, redesigned, rebuilt\" by Ochoa D, Hercules A, Carmona M et al., https://doi.org/10.1093/nar/gkac1046, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."}],"next":"https://space-kitty.com/api/v1/topics/protein-design/papers?sort=top&limit=25&offset=25"}