--- title: "Protein Structure Prediction" slug: "protein-structure" discipline: "Biochemistry / AI" description: "Computational protein biology. AlphaFold, protein design, structure prediction, drug discovery through molecular simulation, and de novo protein engineering." icon: "🔬" url: "https://science-database.com/technology/protein-structure" api: "https://science-database.com/api/v1/technology/protein-structure" llms_txt: "https://science-database.com/technology/protein-structure/llms.txt" articles_indexed: 15 last_updated: "2026-04-11T06:58:06.186Z" search_terms: - "protein structure prediction AlphaFold" - "de novo protein design" - "molecular dynamics drug discovery" source: "science-database.com" license: "metadata CC0, abstracts belong to respective publishers" --- # Protein Structure Prediction Computational protein biology. AlphaFold, protein design, structure prediction, drug discovery through molecular simulation, and de novo protein engineering. **Discipline:** Biochemistry / AI **Indexed Papers:** 15 **Last Updated:** 2026-04-11 ## Top Publications Ranked by citation impact across Semantic Scholar, OpenAlex & arXiv. ### 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, Michalina Pacholska, Tamas Berghammer, Sebastian W. Bodenstein, David Silver, Oriol Vinyals, Andrew Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis - **Journal:** Nature - **Published:** 2021-07-15 - **DOI:** [10.1038/s41586-021-03819-2](https://doi.org/10.1038/s41586-021-03819-2) - **Citations:** 43,225 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://www.nature.com/articles/s41586-021-03819-2.pdf) - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W3177828909/llms.txt) > Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1-4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by ... ### 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 V. Cherepanov, Miles Congreve, Alexander I. 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, Adrian Stecuła, Ashok Thillaisundaram, Catherine Tong, Sergei Yakneen, Ellen D. Zhong, Michał Zieliński, Augustin Žídek, Victor Bapst, Pushmeet Kohli, Max Jaderberg, Demis Hassabis, John Jumper - **Journal:** Nature - **Published:** 2024-05-08 - **DOI:** [10.1038/s41586-024-07487-w](https://doi.org/10.1038/s41586-024-07487-w) - **Citations:** 11,938 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://www.nature.com/articles/s41586-024-07487-w_reference.pdf) - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4396721167/llms.txt) > The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. 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, nuc... ### 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 - **Published:** 2021-10-19 - **DOI:** [10.1093/nar/gkab1061](https://doi.org/10.1093/nar/gkab1061) - **Citations:** 8,008 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://doi.org/10.1093/nar/gkab1061) - **llms.txt:** [View](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 protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualiz... ### 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) - **Published:** 2021-10-04 - **DOI:** [10.1101/2021.10.04.463034](https://doi.org/10.1101/2021.10.04.463034) - **Citations:** 3,937 - **Source:** OpenAlex - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W3202105508/llms.txt) > 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 for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increas... ### 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 - **Published:** 2020-01-15 - **DOI:** [10.1038/s41586-019-1923-7](https://doi.org/10.1038/s41586-019-1923-7) - **Citations:** 3,451 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://discovery.ucl.ac.uk/10089234/1/343019_3_art_0_py4t4l_convrt.pdf) - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W2999044305/llms.txt) ### Highly accurate protein structure prediction for the human proteome - **Authors:** Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michał Zieliński, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J. Kleywegt, Alex 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, David Reiman, Stig Petersen, Andrew Senior, Koray Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper, Demis Hassabis - **Journal:** Nature - **Published:** 2021-07-22 - **DOI:** [10.1038/s41586-021-03828-1](https://doi.org/10.1038/s41586-021-03828-1) - **Citations:** 3,143 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://www.nature.com/articles/s41586-021-03828-1.pdf) - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W3183475563/llms.txt) > 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 structure1. Here we markedly expand the structural coverage of the pr... ### 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 - **Published:** 2023-11-02 - **DOI:** [10.1093/nar/gkad1011](https://doi.org/10.1093/nar/gkad1011) - **Citations:** 1,749 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkad1011/52777135/gkad1011.pdf) - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4388464011/llms.txt) > 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 ... ### Protein structure predictions to atomic accuracy with AlphaFold - **Authors:** John Jumper, Demis Hassabis - **Journal:** Nature Methods - **Published:** 2022-01-01 - **DOI:** [10.1038/s41592-021-01362-6](https://doi.org/10.1038/s41592-021-01362-6) - **Citations:** 285 - **Source:** OpenAlex - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4206563428/llms.txt) ### The case for post-predictional modifications in the AlphaFold Protein Structure Database - **Authors:** Haroldas Bagdonas, Carl A. Fogarty, Elisa Fadda, Jon Agirre - **Journal:** Nature Structural & Molecular Biology - **Published:** 2021-10-29 - **DOI:** [10.1038/s41594-021-00680-9](https://doi.org/10.1038/s41594-021-00680-9) - **Citations:** 84 - **Source:** OpenAlex - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W3208408872/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 - **Published:** 2023-05-11 - **DOI:** [10.1002/pro.4653](https://doi.org/10.1002/pro.4653) - **Citations:** 84 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pro.4653) - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4376131109/llms.txt) > 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 of protein structures remains challenging, given the vast size of the sequence-structure space. Alph... ### Protein structure prediction beyond AlphaFold - **Authors:** Guo‐Wei Wei - **Journal:** Nature Machine Intelligence - **Published:** 2019-08-09 - **DOI:** [10.1038/s42256-019-0086-4](https://doi.org/10.1038/s42256-019-0086-4) - **Citations:** 83 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://pmc.ncbi.nlm.nih.gov/articles/PMC10956386/pdf/nihms-1972073.pdf) - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W2967175367/llms.txt) ### Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15 - **Authors:** Jian Liu, Zhiye Guo, Tianqi Wu, Raj S. Roy, Farhan Quadir, Chen Chen, Jianlin Cheng - **Journal:** Communications Biology - **Published:** 2023-11-10 - **DOI:** [10.1038/s42003-023-05525-3](https://doi.org/10.1038/s42003-023-05525-3) - **Citations:** 72 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://www.nature.com/articles/s42003-023-05525-3.pdf) - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4388571183/llms.txt) > To enhance the AlphaFold-Multimer-based protein complex structure prediction, we developed a quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine its outputs. MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural predictions by using both traditional sequence align... ### 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 - **Published:** 2025-01-05 - **DOI:** [10.1016/j.sbi.2024.102973](https://doi.org/10.1016/j.sbi.2024.102973) - **Citations:** 60 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://doi.org/10.1016/j.sbi.2024.102973) - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4406063185/llms.txt) > In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind ... ### Enhancing Protein Function Prediction Performance by Utilizing AlphaFold-Predicted Protein Structures - **Authors:** Wenjian Ma, Shugang Zhang, Zhen Li, Mingjian Jiang, Shuang Wang, Weigang Lu, Xiangpeng Bi, Huasen Jiang, Henggui Zhang, Zhiqiang Wei - **Journal:** Journal of Chemical Information and Modeling - **Published:** 2022-08-25 - **DOI:** [10.1021/acs.jcim.2c00885](https://doi.org/10.1021/acs.jcim.2c00885) - **Citations:** 59 - **Source:** OpenAlex - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4293046261/llms.txt) > The structure of a protein is of great importance in determining its functionality, and this characteristic can be leveraged to train data-driven prediction models. However, the limited number of available protein structures severely limits the performance of these models. AlphaFold2 and its open-source data set of predicted protein structures have provided a promising solution to this problem, an... ### Deep learning for protein secondary structure prediction: Pre and post-AlphaFold - **Authors:** Dewi Pramudi Ismi, Reza Pulungan, Afiahayati - **Journal:** Computational and Structural Biotechnology Journal - **Published:** 2022-01-01 - **DOI:** [10.1016/j.csbj.2022.11.012](https://doi.org/10.1016/j.csbj.2022.11.012) - **Citations:** 44 - **Source:** OpenAlex - **Access:** Open Access - **PDF:** [Download](https://doi.org/10.1016/j.csbj.2022.11.012) - **llms.txt:** [View](https://science-database.com/technology/protein-structure/paper/oa-W4308930019/llms.txt) > This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure prediction. Previous studies showed that deep neural networks had uplifted the accuracy of three-state secondary structure prediction to more tha... --- *Generated by [science-database.com](https://science-database.com) — The Knowledge Interface* *Full data available via [JSON API](https://science-database.com/api/v1/technology/protein-structure)*