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AlphaFold2 Update and Perspectives

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AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility

Martin, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01805 · Cited by 19 · see citing works

COLLAPSE : A representation learning framework for identification and characterization of protein structural sites

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KinFams: De-Novo Classification of Protein Kinases Using CATH Functional Units

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Generative artificial intelligence performs rudimentary structural biology modeling

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Structure-enabled enzyme function prediction unveils elusive terpenoid biosynthesis in archaeaPreprint

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Everything AlphaFold tells us about protein knots

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DeepPath: overcoming data scarcity for protein transition pathway prediction using physics-based deep learning

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Alternative Reading Frames are an Underappreciated Source of Protein Sequence Novelty

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Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics

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Comprehensive applications of the artificial intelligence technology in new drug research and development

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Inventing Novel Protein Folds

Koga, N., & Koga, R. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168791 · Cited by 9 · see citing works

Calcium modulates growth and biofilm formation of Lactobacillus acidophilus ATCC 4356 and Lactiplantibacillus plantarum ATCC 14917

Huynh, U., King, J., & Zastrow, M. L. (2025) · Scientific Reports · doi:10.1038/s41598-025-98577-w · Cited by 9 · see citing works

Fast protein structure searching using structure graph embeddings

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Atlas of predicted protein complex structures across kingdoms

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Large-scale clustering of AlphaFold2 3D models shines light on the structure and function of proteins

Bordin, N., Lau, A. M. C., & Orengo, C. A. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.10.039 · Cited by 8 · see citing works

Why do some predicted protein structures fold poorly? Benchmarking AlphaFold, ESMFold, and Boltz in maizePreprint

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What is hidden in the darkness? Deep-learning assisted large-scale protein family curation uncovers novel protein families and foldsPreprint

Durairaj, J., Waterhouse, A. M., Mets, T., Brodiazhenko, T., Abdullah, M., Studer, G., Akdel, M., Andreeva, A. E., Bateman, A. G., Tenson, T., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.14.532539 · Cited by 7 · see citing works

The topological properties of the protein universe

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Large protein databases reveal structural complementarity and functional locality

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Investigating the ability of deep learning-based structure prediction to extrapolate and/or enrich the set of antibody CDR canonical forms

Greenshields-Watson, A., Abanades, B., & Deane, C. M. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1352703 · Cited by 7 · see citing works

Next-generation direct reprogramming

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Regularly updated benchmark sets for statistically correct evaluations of AlphaFold applications

Dobson, L., Tusnády, G. E., & Tompa, P. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf104 · Cited by 6 · see citing works

A conserved terpene cyclase gene in Sanghuangporus for abscisic acid-related sesquiterpenoid biosynthesis

Cho, Y., Seo, C. W., Cho, H. J., Jin, Y., Lupala, A. S., Shim, S. H., & Lim, Y. W. (2025) · BMC Genomics · doi:10.1186/s12864-025-11542-9 · Cited by 7 · see citing works

Computational studies reveal structural characterization and novel families of Puccinia striiformis f. sp. tritici effectors

Asghar, R., Wu, N., Ali, N., Wang, Y., & Akkaya, M. S. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012503 · Cited by 6 · see citing works

Predicting estrogen receptor agonists from plastic additives across various aquatic-related species using machine learning and AlphaFold2

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FoldExplorer: Fast and Accurate Protein Structure Search with Sequence-Enhanced Graph Embedding

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Dual‐wield NTPases : A novel protein family mined from AlphaFold DB

Sakuma, K., Koike, R., & Ota, M. (2024) · Protein Science · doi:10.1002/pro.4934 · Cited by 4 · see citing works

Genomic hotspots: localized chromosome gene expansions identify lineage-specific innovations as targets for functional biodiversity and predictions of stress resilience

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Generation of a high confidence set of domain–domain interface types to guide protein complex structure predictions by AlphaFold

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AlphaFold2 predicts interactions amidst confounding structural compatibilityPreprint

Martin, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.25.554771 · Cited by 4 · see citing works

Artificial Intelligence Learns Protein PredictionPreprint

Heinzinger, M., & Rost, B. (2024) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041458 · Cited by 4 · see citing works

Understanding the structural and functional diversity of ATP-PPases using protein domains and functional families in the CATH database

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Unsupervised Domain Classification of AlphaFold2-Predicted Protein Structures

Barone, F., Laio, A., Punta, M., Cozzini, S., Ansuini, A., & Cazzaniga, A. (2025) · PRX Life · doi:10.1103/prxlife.3.023009 · Cited by 3 · see citing works

Role of Chemoinformatics and Machine Learning in Drug Repurposing

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(Re)defining the human chromatome: an integrated meta-analysis of localization, function, abundance, physical properties, and domain composition of chromatin proteins

Gribkova, A. K., Armeev, G. A., Kirpichnikov, M. P., & Shaytan, A. К. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1489 · Cited by 3 · see citing works

Sequence-Structure Analysis Unlocking the Potential Functional Application of the Local 3D Motifs of Plant-Derived Diterpene Synthases

Zhao, Y., Liang, Y., Luo, G., Li, Y., Han, X.-L., & Wen, M. (2024) · Biomolecules · doi:10.3390/biom14010120 · Cited by 2 · see citing works

Computational studies reveal structural characterization and novel families of Puccinia striiformis f. sp. tritici effectorsPreprint

Asghar, R., Wu, N., Ali, N., Wang, Y., & Akkaya, M. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.12.612600 · Cited by 2 · see citing works

What is hidden in the darkness? Characterization of AlphaFold structural spacePreprint

Durairaj, J., Pereira, J., Akdel, M., & Schwede, T. F. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.11.511548 · Cited by 2 · see citing works

Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated function enable RNA virus discovery

Tahzima, R., Charon, J., Díaz, A., Jonghe, K. D., Massart, S., Michon, T., & Vranken, W. (2025) · Frontiers in Virology · doi:10.3389/fviro.2025.1501616 · Cited by 1 · see citing works

CATHe2: Enhanced CATH superfamily detection using ProstT5 and structural alphabets

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Artificial Intelligence in Chemical Engineering: Protein Design from First Principles to Structural Prediction

Bailey, J., Spina, S. C., Hu, A., Phan, N., Getman, R. B., & Kimmel, B. R. (2026) · ACS Engineering Au · doi:10.1021/acsengineeringau.5c00099 · Cited by 3 · see citing works

Artificial intelligence for CRISPR-based biosensing: From molecular design to assay intelligence

Wang, R., Zhang, Q., Gaira, S., Zhao, Y., Lai, J., Li, M., Zhang, C., Wu, L., Deng, F., & Tang, S. (2026) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2026.119061 · Cited by 1 · see citing works

Assessing Structural Classification Using AlphaFold2 Models Through ECOD ‐Based Comparative Analysis

Kawabata, T., & Kinoshita, K. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26828 · Cited by 1 · see citing works

Opinion piece/Book chapter: Artificial Intelligence learns protein predictionPreprint

Heinzinger, M., & Rost, B. (2023) · doi:10.31219/osf.io/n7wfp · Cited by 1 · see citing works

Deep Learning-based structural and functional annotation of Pandoravirus hypothetical proteinsPreprint

Horder, J. L., Connor, A. J., Duggan, A. L., Hale, J. J., McDermott, F. J., Norris, L. E., Whinney, S. J. D., Mesdaghi, S., Murphy, D., Simpkin, A. J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.02.569716 · Cited by 1 · see citing works

Conservation of the hydrogen-bond network in bacterial response regulatorsPreprint

Hamid, M., Jabeen, I., Chaudhary, S. U., & Khan, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.13.674700 · Cited by 1 · see citing works

A Structure-Aware Generative AI Framework for Revealing Functional Relationships in Proteins FamiliesPreprint

Shukla, D., Martin, J., Morcos, F., & Potoyan, D. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.18.676787 · Cited by 1 · see citing works

Understanding structural and functional diversity of ATP-PPases using protein domains and functional families in CATH databasePreprint

Waman, V., Yin, J., Sen, N., Firdaus‐Raih, M., Lam, S. D., & Orengo, C. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.12.562014

In vitro functional analysis and in silico structural modelling of pathogen-secreted polyglycine hydrolases

Naumann, T. A., Dowling, N. V., Price, N. P. J., & Rose, D. R. (2024) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2024.149746

Large scale analysis of predicted protein structures links model features to in vivo behaviourPreprint

Stam, M. J., Oyarzún, D. A., Laohakunakorn, N., & Wood, C. W. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.10.588835

Remote Homology Detection Via 3d Structural Comparison of Alphafold2-Predicted Tertiary StructuresPreprint

Kawabata, T., & Kinoshita, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4906071

Protein Structure Classification in the Era of AlphaFold2

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Algorithms for Structure Comparison and Analysis: Prediction of Structures of Proteins

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Databases of Protein Families and Superfamilies

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Beyond classical domains: The protein unit paradigm - from protein-architecture characterization to downstream applications

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A comprehensive proteome structural analysis suggests undiscovered functional domains in ocean archaea

Zhang, Y., Liu, Y., Zhou, P., Pan, X., Chou, J. J., & Shen, H. (2025) · Protein Science · doi:10.1002/pro.70311

Exploring the structural lexicon of the Proteome via Metric Geometry

Gunther, E., & Cámara, P. G. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014487

Boosting Dataset Distillation With the Assistance of Crucial Samples for Visual Learning

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In Silico Analysis of the Binding Mode of Verteporfin, a YAP–TEAD Interaction Inhibitor

Ikegami, Y., Kudo, G., Hirao, T., Yoshino, R., & Hirokawa, T. (2026) · Chemical and Pharmaceutical Bulletin · doi:10.1248/cpb.c25-00587

Study ot the coevolution between antimicrobial peptides and peptide transporters in legume-rhizobium symbiosis

Boukherissa, A. (2024) · doi:10.70675/e0071d31z973az4bd7z83dez00769e30143d

Metabolomic and proteomic analysis reveals the metabolic changes of alkaloids in Ganoderma lucidum fermented tea

Dai, Y., Yue, R., Chen, J., Liu, Y., Luo, J., Fang, S., Shen, Q., Pan, K., & Zeng, L. (2026) · Food Research International · doi:10.1016/j.foodres.2026.119285

AI-derived Protein Structures Validation: AlphaFold2 Models in the Twilight ZonePreprint

Griffin, P., Deganutti, G., Jadeja, K., Idigbe, C., Pipitò, L., Mejuto, L., Ng, C. P., Peck, S., Greaves, J., & Reynolds, C. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.12.724499

Dynamic proteomics of Nasonia vitripennis pupal development reveals testis-specific regulation of new proteins (orphans) likely related to exo-α-sialidases

Lécureuil, C., Almunia, C., Guérin, V., Armengaud, J., Moreau, T., & Fouchécourt, S. (2026) · BMC Genomics · doi:10.1186/s12864-026-12918-1

One-site polarity switch enhances catalytic efficiency and stability of d-allulose 3-epimerase via flexibility–rigidity rebalancing

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Conserved folds enable immune antagonism across the tree of lifePreprint

Profeta, L., Doherty, E., Ebner, J. M., Deák, G., Arasa-Verge, E. A., Poehlman, L., Shukla, A., Kikugawa, M. S., Barnett, A., Jordan, T. X., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.21.739742

AlphaFold-driven structural proteomics reveals extensive cellulosome machinery in human ruminococcal symbionts

Minor, C. E., Takayesu, A., Arbing, M. A., Ha, S. M., Gunsalus, R. P., Pellegrini, M. A., Sawaya, M. R., & Clubb, R. (2026) · mBio · doi:10.1128/mbio.01295-26

Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation

Li, L., Soriano, L. D., Kedir, W. M., Hoch, F. L., & Loke, D. K. (2026) · Molecular Biomedicine · doi:10.1186/s43556-026-00498-1

What is hidden in the darkness? Deep-learning assisted large-scale protein family curation uncovers novel protein families and foldsPreprint

Durairaj, J., Waterhouse, A. M., Mets, T., Brodiazhenko, T., Abdullah, M., Studer, G., Akdel, M., Andreeva, A. E., Bateman, A. G., Tenson, T., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.14.532539 · Cited by 7 · see citing works

AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function

Skolnick, J., Gao, M., Zhou, H., & Singh, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01114 · Cited by 251 · see citing works

CATH 2024: CATH-AlphaFlow Doubles the Number of Structures in CATH and Reveals Nearly 200 New Folds

Waman, V., Bordin, N., Alcraft, R., Vickerstaff, R. J., Rauer, C., Chan, Q., Sillitoe, I., Yamamori, H., & Orengo, C. A. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168551 · Cited by 44 · see citing works

Deep Learning of Protein Structural Classes: Any Evidence for an 'Urfold'?

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Exploring structural diversity across the protein universe with The Encyclopedia of Domains

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