The newest AI breakthroughs in structural biology: protein binder design and conformational state prediction

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  • Jumper, J. et al. Highly correct protein construction prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chica, R. A. & Ferruz, N. What does it take for an ‘AlphaFold Moment’in purposeful protein engineering and design? Nat. Biotechnol. 42, 173–174 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Schneider, B. et al. When will RNA get its AlphaFold second? Nucleic Acids Res. 51, 9522–9532 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kinch, L. N., Pei, J., Kryshtafovych, A., Schaeffer, R. D. & Grishin, N. V. Topology analysis of fashions for tough targets within the 14th spherical of the important evaluation of protein construction prediction (CASP14). Proteins Struct. Funct. Bioinform. 89, 1673–1686 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Kryshtafovych, Schwede, A., Topf, T., Fidelis, M. & Moult, Okay. J. Critical evaluation of strategies of protein construction prediction (CASP)—Round XIV. Proteins Struct. Funct. Bioinform. 89, 1607–1617 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Abriata, L. A. The nobel prize in chemistry: previous, current, and way forward for AI in biology. Commun. Biol. 7, 1–3 (2024).

    Article 

    Google Scholar
     

  • Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science https://www.science.org/doi/abs/10.1126/science.adl2528.

  • Abramson, J. et al. Accurate construction prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Discovery (Chai), C. et al. Chai-1: Decoding the molecular interactions of life. 2024.10.10.615955 Preprint at (2024).

  • Passaro, S. et al. Boltz-2: in the direction of correct and environment friendly binding affinity prediction. Preprint at (2025).

  • Ahdritz, G. et al. OpenFold: retraining AlphaFold2 yields new insights into its studying mechanisms and capability for generalization. Nat. Methods 21, 1514–1524 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Naddaf, M. Open-source protein construction AI goals to match AlphaFold. Nature (2025).

  • Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, Okay. & Moult, J. Progress and bottlenecks for deep studying in computational construction biology: CASP spherical XVI. Proteins Struct. Funct. Bioinform. 94, 5–14 (2026).

  • Abriata, L. A. & Dal Peraro, M. Practical outcomes from CASP16 for customers in want of biomolecular construction prediction. Proteins Struct. Funct. Bioinform. 94, 435–446 (2026).

    Article 
    CAS 

    Google Scholar
     

  • Kretsch, R. C. et al. Assessment of nucleic acid construction prediction in CASP16. Preprint at (2025).

  • Gilson, M. et al. Assessment of pharmaceutical protein-ligand pose and affinity predictions in CASP16. https://www.authorea.com/users/917462/articles/1290108-assessment-of-pharmaceutical-protein-ligand-pose-and-affinity-predictions-in-casp16?commit=497136dba6cfb4039aa88e1e3d4e71f9bb30a1c0.

  • Škrinjar, P., Eberhardt, J., Durairaj, J. & Schwede, T. Have protein-ligand co-folding strategies moved past memorisation? Preprint at (2025).

  • Masters, M. R., Mahmoud, A. H. & Lill, M. A. Investigating whether or not deep studying fashions for co-folding study the physics of protein-ligand interactions. Nat. Commun. 16, 8854 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lazou, M. et al. Predicting a number of conformations of ligand binding websites in proteins means that AlphaFold2 could bear in mind an excessive amount of. Proc. Natl. Acad. Sci. USA 121, e2412719121 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chakravarty, D. et al. AlphaFold predictions of fold-switched conformations are pushed by construction memorization. Nat. Commun. 15, 7296 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bryant, P. & Noé, F. Structure prediction of other protein conformations. Nat. Commun. 15, 7328 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dube, N. et al. Modeling different conformational states in CASP16. Proteins Struct. Funct. Bioinform. 94, 330–347 (2026).

    Article 
    CAS 

    Google Scholar
     

  • Swapna, G. V. T., Dube, N., Roth, M. J. & Montelione, G. T. Memorization bias impacts modeling of other conformational states of solute service membrane proteins with strategies from deep studying. PLOS Comput. Biol. 21, e1013590 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, J. et al. Assessment of protein complicated predictions in CASP16: are we making progress? Proteins: Structure, Function, and Bioinformatics. 94, 106–130 (2026).

  • Elofsson, A., Kretsch, R. C., Magnus, M. & Montelione, G. T. Engaging the group: CASP particular curiosity teams. https://doi.org/10.1002/prot.26833.

  • Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, Okay. & Moult, J. Critical evaluation of strategies of protein construction prediction (CASP)—Round XV. Proteins Struct. Funct. Bioinform. 91, 1539–1549 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Kryshtafovych, A. et al. Breaking the conformational ensemble barrier: ensemble construction modeling challenges in CASP15. Proteins Struct. Funct. Bioinform. 91, 1903–1911 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Abriata, L. A. & Dal Peraro, M. Will cryo-electron microscopy shift the present paradigm in protein construction prediction? J. Chem. Inf. Model. 60, 2443–2447 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Tamò, G. E., Abriata, L. A. & Dal Peraro, M. The significance of dynamics in integrative modeling of supramolecular assemblies. Curr. Opin. Struct. Biol. 31, 28–34 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Huang, Y. J., Ramelot, T. A., Spaman, L. E., Kobayashi, N. & Montelione, G. T. Hidden structural states of proteins revealed by conformer choice with AlphaFold-NMR. (2026).

  • Cui, X. et al. Beyond static constructions: protein dynamic conformations modeling within the post-AlphaFold period. Brief. Bioinform. 26, bbaf340 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lewis, S. et al. Scalable emulation of protein equilibrium ensembles with generative deep studying. Science 389, eadv9817 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kalakoti, Y. & Wallner, B. AFsample2 predicts a number of conformations and ensembles with AlphaFold2. Commun. Biol. 8, 373 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gu, X., Aranganathan, A. & Tiwary, P. Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE. eLife 13, RP99702 (2024).

  • Pacesa, M. et al. One-shot design of purposeful protein binders with BindCraft. Nature 646, 483–492 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Adaptyv Foundry – EGFR problem spherical 1. https://foundry.adaptyvbio.com/egfr_design_competition.

  • Adaptyv Foundry – EGFR problem spherical 2. https://foundry.adaptyvbio.com/competition.

  • Nipah Competition Results. Proteinbase https://proteinbase.com/collections/nipah-binder-competition-results.

  • Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, G. et al. Easy and correct protein construction prediction utilizing ColabFold. Nat. Protoc. 20, 620–642 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dauparas, J. et al. Robust deep studying–based mostly protein sequence design utilizing ProteinMPNN. Science 378, 49–56 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lisanza, S. L. et al. Multistate and purposeful protein design utilizing RoseTTAFold sequence house diffusion. Nat. Biotechnol. 1–11, (2024).

  • Corso, G., Stärk, H., Jing, B., Barzilay, R. & Jaakkola, T. DiffDock: diffusion steps, twists, and turns for molecular docking. Preprint at (2023).

  • Brixi, G. et al. Genome modelling and design throughout all domains of life with Evo 2. Nature 1–13, (2026).

  • Koch, M., Ott, F. & Richter, A. The way forward for interactive info radiators for information staff: How will information staff eat ambient consciousness info sooner or later? Comput. Support. Coop. Work 23, 139–153 (2024).


    Google Scholar
     

  • Abriata, L. A., Tamò, G. E. & Dal Peraro, M. An additional leap of enchancment in tertiary construction prediction in CASP13 prompts new routes for future assessments. Proteins Struct. Funct. Bioinform. 87, 1100–1112 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Scutteri, L. et al. De novo design of light-regulated dynamic proteins utilizing deep studying. Preprint at (2025).

  • Buckley, S. et al. De novo design of phosphorylation-induced protein switches for artificial signaling in cells. Preprint at (2025).

  • Avsec, Ž et al. Advancing regulatory variant impact prediction with AlphaGenome. Nature 649, 1206–1218 (2026).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Joveneche, M. G., Gemis-Aebi, X. & Abriata, L. A. PDB Manipulation Suite (PDBMS): a browser-based toolkit for PDB file manipulation, molecule constructing and biophysical evaluation, simply extendable by way of reasoning fashions. Preprint at ChemRxiv (2026).


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