# Protein Structure Prediction — science-database.com > Computational protein biology. AlphaFold, protein design, structure prediction, drug discovery through molecular simulation, and de novo protein engineering. - Discipline: Biochemistry / AI - URL: https://science-database.com/technology/protein-structure - API: https://science-database.com/api/v1/technology/protein-structure - Last Updated: 2026-09-08T05:37:12.720Z - Articles Indexed: 27 ## Top Publications ### Highly accurate protein structure prediction with AlphaFold - Authors: J. Jumper, Richard Evans, A. Pritzel, Tim Green, Michael Figurnov, O. Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Andy Ballard, A. Cowie, B. Romera-Paredes, Stanislav Nikolov, Rishub Jain, J. Adler, T. Back, Stig Petersen, D. Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, O. Vinyals, A. Senior, K. Kavukcuoglu, Pushmeet Kohli, D. Hassabis - Journal: Nature - Date: 2021 - DOI: https://doi.org/10.1038/s41586-021-03819-2 - Citations: 38861 - Source: Semantic Scholar - llms.txt: https://science-database.com/technology/protein-structure/paper/s2-dc32a984b651256a8ec282be52310e6bd33d9815/llms.txt - TL;DR: This work validated an entirely redesigned version of the 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. ### Accurate structure prediction of biomolecular interactions with AlphaFold 3 - Authors: Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, A. Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, Sebastian Bodenstein, David A Evans, Chia-Chun Hung, Michael O’Neill, D. Reiman, Kathryn Tunyasuvunakool, Zachary Wu, Akvile Zemgulyte, Eirini Arvaniti, Charles Beattie, Ottavia Bertolli, Alex Bridgland, Alexey Cherepanov, Miles Congreve, A. Cowen-Rivers, Andrew Cowie, Michael Figurnov, Fabian B Fuchs, Hannah Gladman, Rishub Jain, Yousuf A. Khan, Caroline M R Low, Kuba Perlin, Anna Potapenko, Pascal Savy, Sukhdeep Singh, A. Stecula, Ashok Thillaisundaram, Catherine Tong, Sergei Yakneen, Ellen D. Zhong, Michal Zielinski, Augustin Žídek, V. Bapst, Pushmeet Kohli, Max Jaderberg, D. Hassabis, J. Jumper - Journal: Nature - Date: 2024 - DOI: https://doi.org/10.1038/s41586-024-07487-w - Citations: 13574 - Source: Semantic Scholar - llms.txt: https://science-database.com/technology/protein-structure/paper/s2-7572ba7f604ef95d7acdd657ebac458106bd35df/llms.txt - TL;DR: 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. ### ColabFold: making protein folding accessible to all - Authors: Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, Martin Steinegger - Journal: Nature Methods - Date: 2022-05-30 - DOI: https://doi.org/10.1038/s41592-022-01488-1 - Citations: 10045 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W4281790889/llms.txt - Abstract: 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 o... ### The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest - Authors: Damian Szklarczyk, Rebecca Kirsch, Mikaela Koutrouli, Katerina Nastou, Farrokh Mehryary, Radja Hachilif, Annika L. Gable, Tao Fang, Nadezhda T. Doncheva, Sampo Pyysalo, Peer Bork, Lars Juhl Jensen, Christian von Mering - Journal: Nucleic Acids Research - Date: 2022-10-19 - DOI: https://doi.org/10.1093/nar/gkac1000 - Citations: 9568 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W4308834893/llms.txt - Abstract: Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modal... ### 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 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 Green, Ankur Vora, Mira Lutfi, Michael Figurnov, Andrew Cowie, Nicole Hobbs, Pushmeet Kohli, Gerard J. Kleywegt, Ewan Birney, Demis Hassabis, Sameer Velankar - Journal: Nucleic Acids Research - Date: 2021-10-19 - DOI: https://doi.org/10.1093/nar/gkab1061 - Citations: 8528 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W3211795435/llms.txt - 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 pr... ### Accurate prediction of protein structures and interactions using a three-track neural network - Authors: Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy DeGiovanni, J.H. Pereira, Andria V. Rodrigues, Alberdina A. van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister, Christoph Buhlheller, Tea Pavkov‐Keller, Manoj Kumar Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Christopher García, Nick V. Grishin, Paul D. Adams, Randy J. Read, David Baker - Journal: Science - Date: 2021-07-15 - DOI: https://doi.org/10.1126/science.abj8754 - Citations: 5913 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W3186179742/llms.txt - Abstract: DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensiona... ### Protein complex prediction with AlphaFold-Multimer - Authors: Richard Evans, M. E. O’Neill, Alexander Pritzel, Н. В. Антропова, Andrew Senior, Tim Green, Augustin Žídek, Russ Bates, Sam Blackwell, Jason Yim, Olaf Ronneberger, Sebastian W. Bodenstein, Michał Zieliński, Alex Bridgland, Anna Potapenko, Andrew Cowie, Kathryn Tunyasuvunakool, Rishub Jain, Ellen Clancy, Pushmeet Kohli, John Jumper, Demis Hassabis - Journal: bioRxiv (Cold Spring Harbor Laboratory) - Date: 2021-10-04 - DOI: https://doi.org/10.1101/2021.10.04.463034 - Citations: 4167 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W3202105508/llms.txt - Abstract: While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically f... ### 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 Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis - Journal: Nature - Date: 2020-01-15 - DOI: https://doi.org/10.1038/s41586-019-1923-7 - Citations: 3601 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W2999044305/llms.txt ### Highly accurate protein structure prediction for the human proteome - Authors: Kathryn Tunyasuvunakool, J. Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, A. Cowie, Clemens Meyer, Agata Laydon, S. Velankar, G. Kleywegt, A. Bateman, R. Evans, A. Pritzel, Michael Figurnov, O. Ronneberger, Russ Bates, Simon A A Kohl, Anna Potapenko, A. Ballard, B. Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, D. Reiman, Stig Petersen, A. Senior, K. Kavukcuoglu, E. Birney, Pushmeet Kohli, J. Jumper, D. Hassabis - Journal: Nature - Date: 2021 - DOI: https://doi.org/10.1038/s41586-021-03828-1 - Citations: 2584 - Source: Semantic Scholar - llms.txt: https://science-database.com/technology/protein-structure/paper/s2-1fb3ad9969245795f268636eff9a145337144718/llms.txt - TL;DR: The state-of-the-art machine learning method, AlphaFold, is applied at a scale that covers almost the entire human proteome, and the resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. ### 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 Tsenkov, Sreenath Nair, Milot Mirdita, Jingi Yeo, Oleg Kovalevskiy, Kathryn Tunyasuvunakool, Agata Laydon, Augustin Žídek, Hamish Tomlinson, Dhavanthi Hariharan, Josh Abrahamson, Tim Green, John Jumper, Ewan Birney, Martin Steinegger, Demis Hassabis, Sameer Velankar - Journal: Nucleic Acids Research - Date: 2023-10-18 - DOI: https://doi.org/10.1093/nar/gkad1011 - Citations: 2096 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W4388464011/llms.txt - 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... ### Robust deep learning–based protein sequence design using ProteinMPNN - Authors: Justas Dauparas, Ivan 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 Baker - Journal: Science - Date: 2022-09-15 - DOI: https://doi.org/10.1126/science.add2187 - Citations: 2057 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W4296032638/llms.txt - 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... ### AlphaFold and Implications for Intrinsically Disordered Proteins - Authors: Kiersten M. Ruff, Rohit V. Pappu - Journal: Journal of Molecular Biology - Date: 2021-08-18 - DOI: https://doi.org/10.1016/j.jmb.2021.167208 - Citations: 686 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W3195375135/llms.txt - Abstract: Accurate predictions of the three-dimensional structures of proteins from their amino acid sequences have come of age. AlphaFold, a deep learning-based approach to protein structure prediction, shows remarkable success in independent assessments of prediction accuracy. A significant epoch in structu... ### Protein structure predictions to atomic accuracy with AlphaFold - Authors: John Jumper, Demis Hassabis - Journal: Nature Methods - Date: 2022-01-01 - DOI: https://doi.org/10.1038/s41592-021-01362-6 - Citations: 294 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W4206563428/llms.txt ### De novo protein design by inversion of the AlphaFold structure prediction network - Authors: Casper A. Goverde, Benedict Wolf, Hamed Khakzad, Stéphane Rosset, Bruno E. Correia - Journal: Protein Science - Date: 2023-05-11 - DOI: https://doi.org/10.1002/pro.4653 - Citations: 97 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W4376131109/llms.txt - Abstract: De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in computational design strategies, de novo design ... ### Protein structure prediction beyond AlphaFold - Authors: Guo‐Wei Wei - Journal: Nature Machine Intelligence - Date: 2019-08-09 - DOI: https://doi.org/10.1038/s42256-019-0086-4 - Citations: 84 - Source: OpenAlex - llms.txt: https://science-database.com/technology/protein-structure/paper/oa-W2967175367/llms.txt ### Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction - Authors: Devlina Chakravarty, Myeongsang Lee, Lauren L. Porter - Journal: Current opinion in structural biology - Date: 2025 - DOI: https://doi.org/10.1016/j.sbi.2024.102973 - Citations: 42 - Source: Semantic Scholar - llms.txt: https://science-database.com/technology/protein-structure/paper/s2-78c0348f9dd5117b8494dceca73d7a7ad9252cde/llms.txt - TL;DR: Three blind spots that alternative conformations reveal about AF-based protein structure prediction are reviewed and suggested approaches to predict alternative folds more reliably are suggested. ### Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives - Authors: Yuktika Malhotra, Jerry John, Deepika Yadav, D. Sharma, Vanshika, Kamal Rawal, Vaibhav Mishra, Navaneet Chaturvedi - Journal: Computers in biology and medicine - Date: 2025 - DOI: https://doi.org/10.1016/j.compbiomed.2025.109842 - Citations: 33 - Source: Semantic Scholar - llms.txt: https://science-database.com/technology/protein-structure/paper/s2-956a714e0cd9b6ddf7c0bb35d50c3dc8a4c6e6b6/llms.txt - TL;DR: This review provides a comprehensive analysis of AlphaFold and its derivatives (AF2 and AF3) in protein structure prediction, which enables groundbreaking advancements in protein design, disease research and discusses future integration with experimental techniques. ### Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models. - Authors: Jue Wang, Joseph L. Watson, S. Lisanza - Journal: Cold Spring Harbor perspectives in biology - Date: 2024 - DOI: https://doi.org/10.1101/cshperspect.a041472 - Citations: 32 - Source: Semantic Scholar - llms.txt: https://science-database.com/technology/protein-structure/paper/s2-327990f4276d1d533214c8d2673a1ea97968353a/llms.txt - TL;DR: This work reviews recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion, and suggests that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design. ### Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction - Authors: Devlina Chakravarty, Myeongsang Lee, Lauren L. Porter - Journal: ArXiv - Date: 2024 - Citations: 31 - Source: Semantic Scholar - llms.txt: https://science-database.com/technology/protein-structure/paper/s2-a5f27b945ba3236d1b2d3896f8a47deb793551b4/llms.txt - TL;DR: Three blind spots that alternative conformations reveal about AF-based protein structure prediction are reviewed and suggested approaches to predict alternative folds more reliably are suggested. ### Emerging frontiers in protein structure prediction following the AlphaFold revolution - Authors: M. Rennie, Michael R. Oliver - Journal: Journal of the Royal Society Interface - Date: 2025 - DOI: https://doi.org/10.1098/rsif.2024.0886 - Citations: 21 - Source: Semantic Scholar - llms.txt: https://science-database.com/technology/protein-structure/paper/s2-021a5bdde19ddeb7eaa9458ed5cb7d041a5d4903/llms.txt - TL;DR: This review focuses on the application of state-of-the-art protein structure prediction to these advanced applications of deep learning, and suggests a set of guidelines for reporting AlphaFold predictions. --- Generated by science-database.com — The Knowledge Interface Full data available at: https://science-database.com/api/v1/technology/protein-structure