# AI Protein Design & Structure Prediction Domain: Biotechnology & Life Sciences · 323 papers indexed · newest 2026-10-27 AlphaFold and successors, diffusion-based protein design, inverse folding and de novo binders and enzymes. Key concepts: protein design, protein structure prediction, AlphaFold, inverse folding, protein language model, de novo binder ## Most influential - **[Highly accurate protein structure prediction with AlphaFold](https://space-kitty.com/papers/doi:10.1038/s41586-021-03819-2)**. John Jumper, Richard Evans, Alexander Pritzel et al. · 2021 · Nature (doi:10.1038/s41586-021-03819-2 · 43,700 citations). Source: > 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. Predicti… > _Abstract excerpt by John Jumper et al., CC-BY, https://doi.org/10.1038/s41586-021-03819-2; truncated._ - **[Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://space-kitty.com/papers/doi:10.1038/s41586-024-07487-w)**. Josh Abramson, Jonas Adler, Jack Dunger et al. · 2024 · Nature (doi:10.1038/s41586-024-07487-w · 15,805 citations). Source: > 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 intera… > _Abstract excerpt by Josh Abramson et al., CC-BY, https://doi.org/10.1038/s41586-024-07487-w; truncated._ - **[UniProt: the Universal Protein Knowledgebase in 2023](https://space-kitty.com/papers/doi:10.1093/nar/gkac1052)**. 2023 · Nucleic acids research (doi:10.1093/nar/gkac1052 · 6,891 citations). Source: > 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… > _Abstract excerpt by the authors, CC-BY, https://doi.org/10.1093/nar/gkac1052; truncated._ - **[AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models](https://space-kitty.com/papers/doi:10.1093/nar/gkab1061)**. Mihály Váradi, Stephen Anyango, Mandar Deshpande et al. · 2021 · Nucleic Acids Research (doi:10.1093/nar/gkab1061 · 7,841 citations). Source: > 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… > _Abstract excerpt by Mihály Váradi et al., CC-BY, https://doi.org/10.1093/nar/gkab1061; truncated._ - **[Protein complex prediction with AlphaFold-Multimer](https://space-kitty.com/papers/doi:10.1101/2021.10.04.463034)**. Richard Evans, Michael O’Neill, Alexander Pritzel et al. · 2021 · bioRxiv (Cold Spring Harbor Laboratory) (doi:10.1101/2021.10.04.463034 · 3,795 citations). Source: - **[InterPro: the protein sequence classification resource in 2025](https://space-kitty.com/papers/doi:10.1093/nar/gkae1082)**. Blum M, Andreeva A, Florentino LC et al. · 2025 · Nucleic acids research (doi:10.1093/nar/gkae1082 · 1,063 citations). Source: > 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 t… > _Abstract excerpt by Blum M et al., CC-BY, https://doi.org/10.1093/nar/gkae1082; truncated._ - **[AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences](https://space-kitty.com/papers/doi:10.1093/nar/gkad1011)**. Mihály Váradi, Damian Bertoni, Paulyna Magaña et al. · 2023 · Nucleic Acids Research (doi:10.1093/nar/gkad1011 · 2,049 citations). Source: > 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 organi… > _Abstract excerpt by Mihály Váradi et al., CC-BY, https://doi.org/10.1093/nar/gkad1011; truncated._ - **[De novo design of protein structure and function with RFdiffusion](https://space-kitty.com/papers/doi:10.1038/s41586-023-06415-8)**. Watson JL, Juergens D, Bennett NR et al. · 2023 · Nature (doi:10.1038/s41586-023-06415-8 · 1,906 citations). Source: > 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. He… > _Abstract excerpt by Watson JL et al., CC-BY, https://doi.org/10.1038/s41586-023-06415-8; truncated._ - **[Improved protein structure prediction using potentials from deep learning](https://space-kitty.com/papers/doi:10.1038/s41586-019-1923-7)**. Andrew Senior, Richard Evans, John Jumper et al. · 2020 · Nature (doi:10.1038/s41586-019-1923-7 · 3,381 citations). Source: - **[Highly accurate protein structure prediction for the human proteome](https://space-kitty.com/papers/doi:10.1038/s41586-021-03828-1)**. Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu et al. · 2021 · Nature (doi:10.1038/s41586-021-03828-1 · 3,023 citations). Source: > 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 re… > _Abstract excerpt by Kathryn Tunyasuvunakool et al., CC-BY, https://doi.org/10.1038/s41586-021-03828-1; truncated._ - **[Robust deep learning–based protein sequence design using ProteinMPNN](https://space-kitty.com/papers/doi:10.1126/science.add2187)**. Justas Dauparas, Ivan V. Anishchenko, Nathaniel R. Bennett et al. · 2022 · Science (doi:10.1126/science.add2187 · 2,102 citations). Source: > 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 wid… > _Abstract excerpt by Justas Dauparas et al., CC-BY, https://doi.org/10.1126/science.add2187; truncated._ - **[Ensembl 2023](https://space-kitty.com/papers/doi:10.1093/nar/gkac958)**. Fergal J. Martin, M Ridwan Amode, Alisha Aneja et al. · 2022 · Nucleic Acids Research (doi:10.1093/nar/gkac958 · 1,028 citations). Source: > 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 toward… > _Abstract excerpt by Fergal J. Martin et al., CC-BY, https://doi.org/10.1093/nar/gkac958; truncated._ - **[ProtGPT2 is a deep unsupervised language model for protein design](https://space-kitty.com/papers/doi:10.1038/s41467-022-32007-7)**. Noelia Ferruz, Steffen Schmidt, Birte Höcker · 2022 · Nature Communications (doi:10.1038/s41467-022-32007-7 · 795 citations). Source: > 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 GP… > _Abstract excerpt by Noelia Ferruz et al., CC-BY, https://doi.org/10.1038/s41467-022-32007-7; truncated._ - **[Dali server: structural unification of protein families](https://space-kitty.com/papers/doi:10.1093/nar/gkac387)**. Holm L · 2022 · Nucleic acids research (doi:10.1093/nar/gkac387 · 890 citations). Source: > 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 u… > _Abstract excerpt by Holm L, CC-BY, https://doi.org/10.1093/nar/gkac387; truncated._ - **[The Pfam protein families database: embracing AI/ML](https://space-kitty.com/papers/doi:10.1093/nar/gkae997)**. Typhaine Paysan‐Lafosse, Antonina E. Andreeva, Matthias Blum et al. · 2024 · Nucleic Acids Research (doi:10.1093/nar/gkae997 · 362 citations). Source: > 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. N… > _Abstract excerpt by Typhaine Paysan‐Lafosse et al., CC-BY, https://doi.org/10.1093/nar/gkae997; truncated._ ## Newest - **[Accurate structural modelling of protein-glycan recognition with GlycoWeave](https://space-kitty.com/papers/arxiv:2610.37410)**. Huifeng Zhao, Jintu Zhang, Yifei Xiao et al. · 2026 (arXiv:2610.37410). Source: - **[Attention as Lattice Gauge Theory: Relations Interact Only Through Wilson Loops](https://space-kitty.com/papers/arxiv:2610.37656)**. Aakash Sarkar, Ananya Mukherjee, Motoshige Sato · 2026 (arXiv:2610.37656). Source: - **[Computational De Novo Design of Miniprotein Candidate Neutralizers Targeting the IL-23 Cytokine Subunit p19](https://space-kitty.com/papers/arxiv:2610.32779)**. Raghav Paladugu, Fardina Fathmiul Alam · 2026 (arXiv:2610.32779). Source: - **[Agent-guided epitope excision enables economical de novo binder design](https://space-kitty.com/papers/arxiv:2610.33195)**. Jun W. Kim, Artiom Myaskovsky, Oscar Donosa et al. · 2026 (arXiv:2610.33195). Source: - **[Challenges in AI-based structural modeling of host-pathogen protein interactions](https://space-kitty.com/papers/doi:10.1016/j.coviro.2026.101587)**. Gilep K, Kosinski J · 2026 · Current opinion in virology (doi:10.1016/j.coviro.2026.101587). Source: - **[ProtGlycanDock: Towards Accurate Protein-Glycan Docking with Tailored Dataset, Benchmark and Model](https://space-kitty.com/papers/arxiv:2610.26009)**. Minghao Xu, Jiaze Song, Jinyu Wang et al. · 2026 (arXiv:2610.26009). Source: - **[The Effectiveness and Durability of Pretraining Data Filtering in Protein Language Models](https://space-kitty.com/papers/arxiv:2610.26651)**. Aaron Maiwald, Roderick McNeill, Jaeyoung Lee et al. · 2026 (arXiv:2610.26651). Source: - **[recAMPLIFY: Large-Scale Pretraining of Recursive Protein Language Models](https://space-kitty.com/papers/arxiv:2610.26747)**. Saurav Jha, Qianli Liao, Robert Vernon et al. · 2026 (arXiv:2610.26747). Source: - **[PDKE-G: A Bio-Inspired Contributory Group Authenticated Key Exchange Grounded in Per-Member AlphaFold2 Protein Geometry](https://space-kitty.com/papers/doi:10.3390/biomimetics11100753)**. Alabdulatif A · 2026 · Biomimetics (Basel, Switzerland) (doi:10.3390/biomimetics11100753). Source: - **[Generative Retrieval for Reverse Target Fishing with a Proteome-Scale Benchmark](https://space-kitty.com/papers/arxiv:2610.24458)**. Zhichao Deng, Haichuan Tan, Xin Hong et al. · 2026 (arXiv:2610.24458). 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