High-throughput prediction of protein conformational distributions with subsampled AlphaFold2

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

NMRPipe: A multidimensional spectral processing system based on UNIX pipes

Delaglio, F., Grzesiek, S., Vuister, G. W., Zhu, G., Pfeifer, J., & Bax, A. (1995) · Journal of Biomolecular NMR · doi:10.1007/bf00197809 · Cited by 15,994 · see citing works

ColabFold - Making protein folding accessible to all

Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · see citing works

HMMER web server: interactive sequence similarity searching

Finn, R. D., Clements, J., & Eddy, S. R. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr367 · Cited by 5,952 · see citing works

Evolutionary-scale prediction of atomic level protein structure with a language model

Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works

The Nose–Hoover thermostat

Evans, D. J., & Holian, B. L. (1985) · The Journal of Chemical Physics · doi:10.1063/1.449071 · Cited by 3,644 · see citing works

Highly accurate protein structure prediction for the human proteome

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 1× · see citing works

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005659 · Cited by 2,748 · see citing works

Pure absorption gradient enhanced heteronuclear single quantum correlation spectroscopy with improved sensitivity

Kay, L., Keifer, P. A., & Saarinen, T. (1992) · Journal of the American Chemical Society · doi:10.1021/ja00052a088 · Cited by 2,268 · see citing works

NMRFAM-SPARKY: enhanced software for biomolecular NMR spectroscopy

Lee, W., Tonelli, M., & Markley, J. L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu830 · Cited by 2,076 · see citing works

UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., & the UniProt Consortium (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu739 · Cited by 1,928 · see citing works

How cryo-EM is revolutionizing structural biology

Bai, X., McMullan, G., & Scheres, S. H. W. (2014) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2014.10.005 · Cited by 945 · see citing works

The importance of not saturating water in protein NMR. Application to sensitivity enhancement and NOE measurements

Grzesiek, S., & Bax, A. (1993) · Journal of the American Chemical Society · doi:10.1021/ja00079a052 · Cited by 892 · see citing works

Clustering huge protein sequence sets in linear time

Steinegger, M., & Söding, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04964-5 · Cited by 875 · see citing works

A general enhancement scheme in heteronuclear multidimensional NMR employing pulsed field gradients

Schleucher, J., Schwendinger, M. G., Sattler, M., Schmidt, P. M., Schedletzky, O., Glaser, S. J., S�rensen, O., & Griesinger, C. (1994) · Journal of Biomolecular NMR · doi:10.1007/bf00175254 · Cited by 808 · see citing works

Protein Structure and the Energetics of Protein Stability

Robertson, A. D., & Murphy, K. P. (1997) · Chemical Reviews · doi:10.1021/cr960383c · Cited by 680 · see citing works

Predicting multiple conformations via sequence clustering and AlphaFold2

Wayment-Steele, H. K., Ojoawo, A., Otten, R., Apitz, J. M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L., & Kern, D. (2024) · Nature · doi:10.1038/s41586-023-06832-9 · Cited by 545 · cited 6× · see citing works

Sampling alternative conformational states of transporters and receptors with AlphaFold2

del Alamo, D., Sala, D., Mchaourab, H. S., & Meiler, J. (2022) · eLife · doi:10.7554/elife.75751 · Cited by 511 · see citing works

Imatinib: A Breakthrough of Targeted Therapy in Cancer

Iqbal, N., & Iqbal, N. (2014) · Chemotherapy Research and Practice · doi:10.1155/2014/357027 · Cited by 481 · see citing works

MGnify: the microbiome sequence data analysis resource in 2023

Richardson, L., Allen, B., Baldi, G., Beracochea, M., Bileschi, M. L., Burdett, T., Burgin, J., Caballero-Pérez, J., Cochrane, G., Colwell, L. J., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1080 · Cited by 442 · see citing works

Protein structure determination in solution by NMR spectroscopy.

Wüthrich, K. (1990) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(18)45665-7 · Cited by 384 · see citing works

Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA

Baek, M., McHugh, R., Anishchenko, I., Jiang, H., Baker, D., & DiMaio, F. (2024) · Nature Methods · doi:10.1038/s41592-023-02086-5 · Cited by 372 · see citing works

Using AlphaFold to predict the impact of single mutations on protein stability and function

Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2023) · PLOS One · doi:10.1371/journal.pone.0282689 · Cited by 322 · see citing works

Deep learning methods in protein structure prediction

Torrisi, M., Pollastri, G., & Le, Q. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.12.011 · Cited by 289 · see citing works

State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold

Roney, J. P., & Ovchinnikov, S. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.238101 · Cited by 281 · see citing works

Conformational states dynamically populated by a kinase determine its function

Xie, T., Saleh, T., Rossi, P., & Kalodimos, C. G. (2020) · Science · doi:10.1126/science.abc2754 · Cited by 266 · see citing works

SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

Stein, R. A., & Mchaourab, H. S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010483 · Cited by 258 · see citing works

Machine learning in protein structure prediction

AlQuraishi, M. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.005 · Cited by 252 · see citing works

Extant fold-switching proteins are widespread

Porter, L. L., & Looger, L. L. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800168115 · Cited by 199 · see citing works

<scp>AlphaFold2</scp> fails to predict protein fold switching

Chakravarty, D., & Porter, L. L. (2022) · Protein Science · doi:10.1002/pro.4353 · Cited by 193 · see citing works

Proteins that switch folds

Bryan, P. N., & Orban, J. P. (2010) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2010.06.002 · Cited by 192 · see citing works

AFsample: improving multimer prediction with AlphaFold using massive sampling

Wallner, B. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad573 · Cited by 164 · see citing works

The challenge of protein structure determination—lessons from structural genomics

Slabinski, L., Jaroszewski, L., Rodrigues, A. P. C., Rychlewski, L., Wilson, I. A., Lesley, S. A., & Godzik, A. (2007) · Protein Science · doi:10.1110/ps.073037907 · Cited by 161 · see citing works

Using ancient protein kinases to unravel a modern cancer drug’s mechanism

Wilson, C., Agafonov, R. V., Hoemberger, M., Kutter, S., Zorba, A., Halpin, J. C., Buosi, V., Otten, R., Waterman, D. P., Theobald, D. L., et al. (2015) · Science · doi:10.1126/science.aaa1823 · Cited by 155 · see citing works

Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: Assessment in two blind tests

Ołdziej, S., Czaplewski, C. R., Liwo, A., Chinchio, M., Nanias, M., Vila, J. A., Khalili, M., Arnautova, Y. A., Jagielska, A., Makowski, M., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0502655102 · Cited by 149 · see citing works

GM-CSF: A Promising Target in Inflammation and Autoimmunity

Lee, M.-C., Achuthan, A. A., & Hamilton, J. A. (2020) · ImmunoTargets and Therapy · doi:10.2147/itt.s262566 · Cited by 146 · see citing works

What's next for AlphaFold and the AI protein-folding revolution

Callaway, E. (2022) · Nature · doi:10.1038/d41586-022-00997-5 · Cited by 146 · see citing works

Three-dimensional structure of recombinant human granulocyte-macrophage colony-stimulating factor

Walter, M. R., Cook, W. J., Ealick, S. E., Nagabhushan, T. L., Trotta, P. P., & Bugg, C. E. (1992) · Journal of Molecular Biology · doi:10.1016/0022-2836(92)90470-5 · Cited by 138 · see citing works

AlphaFold2 protein structure prediction: Implications for drug discovery

Borkakoti, N., & Thornton, J. M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102526 · Cited by 137 · see citing works

Kincore: a web resource for structural classification of protein kinases and their inhibitors

Modi, V., & Dunbrack, R. L. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab920 · Cited by 133 · see citing works

Accelerating Cryptic Pocket Discovery Using AlphaFold

Meller, A., Bhakat, S., Solieva, S. O., & Bowman, G. R. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01189 · Cited by 132 · see citing works

AlphaFold2-RAVE: From Sequence to Boltzmann Ranking

Vani, B. P., Aranganathan, A., Wang, D., & Tiwary, P. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00290 · Cited by 120 · see citing works

Functional and Regulatory Roles of Fold-Switching Proteins

Kim, A. K., & Porter, L. L. (2020) · Structure · doi:10.1016/j.str.2020.10.006 · Cited by 83 · see citing works

Cumulative mechanism of several major imatinib-resistant mutations in Abl kinase

Hoemberger, M., Pitsawong, W., & Kern, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919221117 · Cited by 50 · see citing works

Protein crystallography from the perspective of technology developments

Su, X., Zhang, H., Terwilliger, T. C., Liljas, A., Xiao, J., & Dong, Y. (2014) · Crystallography Reviews · doi:10.1080/0889311x.2014.973868 · Cited by 46 · see citing works

Principles, Challenges and Advances in ab initio Protein Structure Prediction

Jothi, A. (2012) · Protein and Peptide Letters · doi:10.2174/092986612803217015 · Cited by 25 · see citing works

Petascale Homology Search for Structure PredictionPreprint

Lee, S., Kim, G., Karin, E. L., Mirdita, M., Park, S., Chikhi, R., Babaian, A., Kryshtafovych, A., & Steinegger, M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.10.548308 · Cited by 16 · see citing works

Binding Affinities Controlled by Shifting Conformational Equilibria: Opportunities and Limitations

Michielssens, S., de Groot, B. L., & Grubmüller, H. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2015.04.012 · Cited by 14 · see citing works

A Suite of Tutorials for the WESTPA 2.0 Rare-Events Sampling Software [Article v2.0]

Bogetti, A. T., Leung, J. M. G., Russo, J. D., Zhang, S., Thompson, J. P., Saglam, A. S., Ray, D., Mostofian, B., Pratt, A. J., Abraham, R. C., et al. (2023) · Living Journal of Computational Molecular Science · doi:10.33011/livecoms.5.1.1655 · Cited by 14 · see citing works

Mapping the Structural and Dynamic Determinants of pH-Sensitive Heparin Binding to Granulocyte Macrophage Colony Stimulating Factor

Cui, J. Y., Zhang, F., Nierzwicki, Ł., Palermo, G., Linhardt, R. J., & Lisi, G. P. (2020) · Biochemistry · doi:10.1021/acs.biochem.0c00538 · Cited by 6 · see citing works

Protein Structure Prediction and Homology Modeling

Leach, A. R., & Thomas, P. J. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.12350-9 · Cited by 3 · see citing works

GMdSilva/gms_natcomms_1705932980_data: added zenodo link

GMdSilva (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.10621195 · Cited by 1 · see citing works

Easy and accurate protein structure prediction using ColabFold

Kim, G., Lee, S., Karin, E. L., Kim, H., Moriwaki, Y., Ovchinnikov, S., Steinegger, M., & Mirdita, M. (2024) · Nature Protocols · doi:10.1038/s41596-024-01060-5 · Cited by 167 · see citing works

AlphaFold predictions of fold-switched conformations are driven by structure memorization

Chakravarty, D., Schafer, J. W., Chen, E. A., Thole, J. F., Ronish, L. A., Lee, M., & Porter, L. L. (2024) · Nature Communications · doi:10.1038/s41467-024-51801-z · Cited by 159 · see citing works

Molecular principles underlying aggressive cancers

Nussinov, R., Yavuz, B. R., & Jang, H. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02129-7 · Cited by 86 · see citing works

Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction

Chakravarty, D., Lee, M., & Porter, L. L. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102973 · Cited by 74 · see citing works

Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era

Cui, X., Ge, L., Chen, X., Lv, Z., Wang, S., Zhou, X., & Zhang, G. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf340 · Cited by 67 · see citing works

Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

Guan, X., Tang, Q.-Y., Ren, W., Chen, M., Wang, W., Wolynes, P. G., & Li, W. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2410662121 · Cited by 59 · see citing works

Bacterial Metallostasis: Metal Sensing, Metalloproteome Remodeling, and Metal Trafficking

Capdevila, D. A., Rondón, J. J., Edmonds, K. A., Rocchio, J. S., Dujovne, M. V., & Giedroc, D. P. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00264 · Cited by 59 · see citing works

Large-scale predictions of alternative protein conformations by AlphaFold2-based sequence association

Lee, M., Schafer, J. W., Prabakaran, J., Chakravarty, D., Clore, M. F., & Porter, L. L. (2025) · Nature Communications · doi:10.1038/s41467-025-60759-5 · Cited by 35 · see citing works

Predicting multiple conformations of ligand binding sites in proteins suggests that AlphaFold2 may remember too much

Lazou, M., Khan, O., Nguyen, T., Padhorny, D., Kozakov, D., Joseph‐McCarthy, D., & Vajda, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412719121 · Cited by 31 · see citing works

Artificial intelligence for medicine 2025: Navigating the endless frontier

Dai, J., Xu, H., Chen, T., Huang, T., Liang, W., Zhang, R., Xu, G.-X., Zhang, Z., Xue, L., Gao, Y. Q., et al. (2025) · The Innovation Medicine · doi:10.59717/j.xinn-med.2025.100120 · Cited by 35 · see citing works

Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects

Elgawish, M. S., Almatary, A. M., Zaitone, S. A., & Salem, M. S. H. (2025) · RSC Medicinal Chemistry · doi:10.1039/d5md00494b · Cited by 28 · see citing works

Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE

Gu, X., Aranganathan, A., & Tiwary, P. (2024) · eLife · doi:10.7554/elife.99702 · Cited by 25 · see citing works

Computational modeling of protein–ligand interactions: From binding site identification to pose prediction and beyond

Tran‐Nguyen, V., & Camproux, A. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103152 · Cited by 25 · see citing works

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites

Olanders, G., Testa, G., Tibo, A., Nittinger, E., & Tyrchan, C. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01475 · Cited by 26 · see citing works

AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network

Fadini, A., Li, M., McCoy, A. J., Banjara, S., Okumura, H., Napier, E., Fontana, P., Khan, A. R., Jovine, L., Terwilliger, T. C., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03047-4 · Cited by 22 · see citing works

A Computational Perspective to Intermolecular Interactions and the Role of the Solvent on Regulating Protein Properties

Mieres‐Pérez, J., Almeida‐Hernández, Y., Sander, W., & Sánchez‐García, E. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00807 · Cited by 21 · see citing works

Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase

Raisinghani, N., Alshahrani, M., Gupta, G., Tian, H., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00222 · Cited by 17 · see citing works

Exploring the conformational landscape of protein kinases

Gough, N. R., & Kalodimos, C. G. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102890 · Cited by 22 · see citing works

Multimeric protein interaction and complex prediction: Structure, dynamics and function

Lu, D., Yu, S., Huang, Y., & Gong, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.009 · Cited by 20 · see citing works

Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints

Zhang, Y., Zhang, Z., Kagaya, Y., Terashi, G., Zhao, B., Xiong, Y., & Kihara, D. (2025) · Communications Biology · doi:10.1038/s42003-025-08783-5 · Cited by 19 · see citing works

NMR-driven structure-based drug discovery by unveiling molecular interactions

Platzer, G., Mayer, M., McConnell, D. B., & Konrat, R. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01542-x · Cited by 19 · see citing works

Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales

Russo, C. J., Husain, K. B., & Murugan, A. (2025) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-081624-030543 · Cited by 22 · see citing works

Generation of protein dynamics by machine learning

Janson, G., & Feig, M. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103115 · Cited by 18 · see citing works

Improving AlphaFold 3 structural modeling by incorporating explicit crosslinksPreprint

Gilep, K., Obarska-Kosińska, A., & Kosiński, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.03.626671 · Cited by 19 · see citing works

AlphaPulldown2—a general pipeline for high-throughput structural modeling

Molodenskiy, D., Maurer, V. J., Yu, D., Chojnowski, G., Bienert, S., Tauriello, G., Gilep, K., Schwede, T., & Kosinski, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf115 · Cited by 15 · see citing works

Receptor Binding for the Entry Mechanisms of SARS-CoV-2: Insights from the Original Strain and Emerging Variants

Mahdi, M., Kiarie, I. W., Mótyán, J. A., Hoffka, G., Al-Muffti, A. S., Tóth, A., & Tőzsér, J. (2025) · Viruses · doi:10.3390/v17050691 · Cited by 15 · see citing works

Evaluating zero‐shot prediction of monomeric protein design success by AlphaFold , ESMFold , and ProteinMPNN

Garcia, M., Dixit, S. M., & Rocklin, G. J. (2026) · Protein Science · doi:10.1002/pro.70453 · Cited by 25 · see citing works

Scaling down protein language modeling with MSA PairformerPreprint

Akiyama, Y., Zhang, Z., Mirdita, M., Steinegger, M., & Ovchinnikov, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.02.668173 · Cited by 16 · see citing works

Challenging AlphaFold in predicting proteins with large-scale allosteric transitions

Perkins-Jechow, B. H., Ahualli, J. P. I., Nhu, H. T., Omidi, A., Li, C., Holguin-Cruz, J. A., Cho, D., Na, D., Malhis, N., Bui, J. M., et al. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01763-0 · Cited by 13 · see citing works

AlphaFold2 's training set powers its predictions of some fold‐switched conformations

Schafer, J. W., & Porter, L. L. (2025) · Protein Science · doi:10.1002/pro.70105 · Cited by 17 · see citing works

Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?

Chen, E. A., & Zhang, Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00331 · Cited by 13 · see citing works

Unfreezing structural biology for drug discovery

Stachowski, T. R., & Fischer, M. M. (2025) · Nature Chemical Biology · doi:10.1038/s41589-025-02047-3 · Cited by 12 · see citing works

The physics-AI dialogue in drug design

Vargas-Rosales, P. A., & Caflisch, A. (2025) · RSC Medicinal Chemistry · doi:10.1039/d4md00869c · Cited by 13 · see citing works

Emerging approaches to investigating functional protein dynamics in modular redox enzymes: Nitric oxide synthase as a model system

Jiang, T., Thielges, M. C., & Feng, C. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.108282 · Cited by 11 · see citing works

The Ensemble Basis of Allostery and Function: Insights from Models of Local Unfolding

Millard, C. E., Wrabl, J. O., Brantley, S. J., Grasso, E. M., Schmitz, A., White, J. T., & Hilser, V. J. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169287 · Cited by 11 · see citing works

Non-standard proteins in the lens of AlphaFold 3: a case study of amyloids

Wojciechowska, A. W., Wojciechowski, J. W., Vriend, G., & Kotulska, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-68041-4 · Cited by 10 · see citing works

AlphaFold2-Based Characterization of Apo and Holo Protein Structures and Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics and Ligand-Induced Conformational Changes

Raisinghani, N., Parikh, V., Foley, B., & Verkhivker, G. M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252312968 · Cited by 10 · see citing works

af2rave: protein ensemble generation with physics-based sampling

Teng, D., Meraz, V. J., Aranganathan, A., Gu, X., & Tiwary, P. (2025) · Digital Discovery · doi:10.1039/d5dd00201j · Cited by 10 · see citing works

Hidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMRPreprint

Huang, Y. J., Ramelot, T. A., Spaman, L. E., Kobayashi, N., & Montelione, G. T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.26.600902 · Cited by 10 · see citing works

Resolving the conformational ensemble of a membrane protein by integrating small-angle scattering with AlphaFold

Lidbrink, S. E., Howard, R. J., Haloi, N., & Lindahl, E. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013187 · Cited by 9 · see citing works

Generating Multistate Conformations of P-type ATPases with a Conditional Diffusion Model

Xu, J., & Wang, Y. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01519 · Cited by 9 · see citing works

MSA clustering enhances AF-Multimer’s ability to predict conformational landscapes of protein–protein interactions

Rustamov, K., & Baev, A. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae197 · Cited by 13 · see citing works

Adaptive sampling–based structural prediction reveals opening of a GABA A receptor through the αβ interface

Haloi, N., Lidbrink, S. E., Howard, R. J., & Lindahl, E. R. (2025) · Science Advances · doi:10.1126/sciadv.adq3788 · Cited by 9 · see citing works

Statistical Thermodynamics of the Protein Ensemble: Mediating Function and Evolution

Hilser, V. J., Wrabl, J. O., Millard, C. E., Schmitz, A., Brantley, S. J., Pearce, M., Rehfus, J., Russo, M. M., & Voortman‐Sheetz, K. (2025) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-061824-104900 · Cited by 9 · see citing works

Computational methods for modeling protein–protein interactions in the AI era: Current status and future directions

Li, H., Nithin, C., Kmiecik, S., & Huang, S. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104382 · Cited by 9 · see citing works

Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design

Vost, L., Ziv, Y., & Deane, C. M. (2025) · Chemical Science · doi:10.1039/d5sc05748e · Cited by 9 · see citing works

AI-based methods for simulating, sampling, and predicting protein ensembles

Jing, B., Berger, B., & Jaakkola, T. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2026.103251 · Cited by 9 · see citing works

Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVEPreprint

Gu, X., Aranganathan, A., & Tiwary, P. (2024) · eLife · doi:10.7554/elife.99702.2 · Cited by 9 · see citing works

Physics-Grounded Evaluation to Guide Accurate Biomolecular PredictionPreprint

Lyu, N., Du, S., Shao, Q., Yang, Z. J., Ma, J., & Herschlag, D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.30.662466 · Cited by 10 · see citing works

Does Sequence Clustering Confound AlphaFold2?

(2025) · doi:10.1016/j.jmb.2025.169376 · Cited by 8 · see citing works

AlphaFold modeling uncovers global structural features of class I and class II fungal hydrophobins

Yang, L., Hicks, D. J., Russo, P. S., & McShan, A. C. (2025) · Protein Science · doi:10.1002/pro.70279 · Cited by 8 · see citing works

Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery

Mateos, D. L., Harris, B. J., González, A. H., Narang, K., & Yarov-Yarovoy, V. M. (2024) · Physiology · doi:10.1152/physiol.00029.2024 · Cited by 8 · see citing works

Predicting Mutation-Induced Allosteric Changes in Structures and Conformational Ensembles of the ABL Kinase Using AlphaFold2 Adaptations with Alanine Sequence Scanning

Raisinghani, N., Alshahrani, M., Gupta, G., & Verkhivker, G. M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251810082 · Cited by 8 · see citing works

Recent Advances in Expression Screening and Sample Evaluation for Structural Studies of Membrane Proteins

Huang, Y., Zhang, Z., & Hattori, M. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168809 · Cited by 7 · see citing works

Predicting Resistance to Small Molecule Kinase Inhibitors

Nagarajan, A., Amberg-Johnson, K., Paull, E., Huang, K., Ghanakota, P., Chandrasinghe, A., Elk, J. C., Sampson, J. M., Wang, L., Abel, R., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02313 · Cited by 8 · see citing works

Evaluating zero-shot prediction of protein design success by AlphaFold, ESMFold, and ProteinMPNNPreprint

Garcia, M., Dixit, S. M., & Rocklin, G. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.29.667290 · Cited by 8 · see citing works

A Deep Learning Approach to Uncover Voltage-Gated Ion Channels’ Intermediate States

Kacher, J., Sokolova, O. S., & Tarek, M. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c03182 · Cited by 7 · see citing works

From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning

Ille, A. M., Anas, E., Mathews, M. B., & Burley, S. K. (2025) · Structural Dynamics · doi:10.1063/4.0000765 · Cited by 7 · see citing works

Structure Prediction of Alternate Frame Folding Systems with AlphaFold3

Jiménez‐Osés, G., & Peccati, F. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00906 · Cited by 7 · see citing works

ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences

Kleiman, D. E., Feng, J., Xue, Z., & Shukla, D. (2026) · Nature Communications · doi:10.1038/s41467-026-76361-2 · Cited by 7 · see citing works

Evolutionary Constraints Guide AlphaFold2 in Predicting Alternative Conformations and Inform Rational Mutation Design

Piomponi, V., Cazzaniga, A., & Cuturello, F. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01090 · Cited by 7 · see citing works

TMEM63 proteins act as mechanically activated cholesterol modulated lipid scramblases contributing to membrane mechano-resilience

Lin, Y., Zhou, Z., Han, Y., Cheng, D., Wang, H. J., Ju, L. A., Zhang, Y., Cox, C. D., & Corry, B. (2026) · Nature Communications · doi:10.1038/s41467-026-68919-x · Cited by 7 · see citing works

Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence

Frasnetti, E., Magni, A., Castelli, M., Serapian, S. A., Moroni, E., & Colombo, G. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102835 · Cited by 7 · see citing works

Unveiling the evolution of policies for enhancing protein structure predictions: A comprehensive analysis

Rahimzadeh, F., Khanli, L. M., Salehpoor, P., Golabi, F., & Pourbahrami, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108815 · Cited by 7 · see citing works

AF2χ: Predicting protein side-chain rotamer distributions with AlphaFold2Preprint

Cagiada, M., Thomasen, F. E., Ovchinnikov, S., Deane, C. M., & Lindorff‐Larsen, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.16.649219 · Cited by 7 · see citing works

Sampling and Ranking of Protein Conformations Using Machine Learning Techniques Do Not Improve the Quality of Rigid Protein–Protein Docking

Stratiichuk, R., Kyrylenko, R., Koleiev, I., Savchenko, I., Voitsitskyi, T., Husak, V., Yesylevskyy, S., Starosyla, S., & Nafiiev, A. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01765 · Cited by 6 · see citing works

Beyond evolution: De novo designed protein toolkit rewriting the rules of synthetic biology

Zhang, G., Liu, C., Li, W., Lu, J., Li, A., & Zhu, L. (2025) · Biosafety and Health · doi:10.1016/j.bsheal.2025.09.004 · Cited by 6 · see citing works

Illuminating an invisible state of the HIV-1 capsid protein CTD dimer using 19 F NMR and weighted ensemble simulations

Yang, D. T., Chong, L. T., & Gronenborn, A. M. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2420371122 · Cited by 6 · see citing works

Conditional diffusion with locality-aware modal alignment for generating diverse protein conformational ensembles

Wang, B., Wang, C., Chen, J., Liu, D., Sun, C., Zhang, J., Zhang, K., & Li, H. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01198-9 · Cited by 5 · see citing works

Exploring voltage-gated sodium channel conformations and protein-protein interactions using AlphaFold2

Lopez-Mateos, D., Narang, K., & Yarov-Yarovoy, V. (2024) · doi:10.1101/2024.10.15.618559 · Cited by 5 · see citing works

Aromatic–aromatic interactions drive fold switch of GA95 and GB95 with three residue difference

Chen, C., Zhang, Z., Duan, M., Wu, Q., Yang, M., Jiang, L., Liu, M., & Li, C. (2024) · Chemical Science · doi:10.1039/d4sc04951a · Cited by 5 · see citing works

The m⁶A epitranscriptome: A regulatory nexus linking cellular senescence and oncogenesis

Xu, Y., Zhou, X., Su, X., Xie, W., Huang, Z., Yu, S., Tan, J., He, J., & Zhang, J. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103032 · Cited by 5 · see citing works

Protein dynamics prediction by integrating biophysics and artificial intelligence

Huang, H., Guan, X., Li, W., Zhang, J., & Wang, W. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2026.103223 · Cited by 5 · see citing works

Dissecting AlphaFold2’s capabilities with limited sequence information

Gut, J. A., & Lemmin, T. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae187 · Cited by 5 · see citing works

Co2+-boosted catalytic performance of polyhistidine-tagged organophosphate hydrolase locked in cobalt-organic framework

Yang, Q., Shi, Q., Dong, X., Yu, L., & Sun, Y. P. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.138636 · Cited by 5 · see citing works

Characterizing Structural and Kinetic Ensembles of Intrinsically Disordered Proteins Using Writhe

(2025) · doi:10.1021/acs.jctc.5c01133 · Cited by 5 · see citing works

Exploring voltage-gated sodium channel conformations and protein–protein interactions using AlphaFold2

Lopez-Mateos, D., Narang, K., & Yarov-Yarovoy, V. (2026) · Journal of General Physiology · doi:10.1085/jgp.202413705 · Cited by 5 · see citing works

Voltage gating and 4-aminopyridine inhibition in the Shaker Kv channel revealed by a closed-state model

Pinto-Anwandter, B. I. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2025.06.029 · Cited by 5 · see citing works

Probing Functional Allosteric States and Conformational Ensembles of the Allosteric Protein Kinase States and Mutants: Atomistic Modeling and Comparative Analysis of AlphaFold2, OmegaFold, and AlphaFlow Approaches and Adaptations

Raisinghani, N., Alshahrani, M., Gupta, G., Tian, H., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04985 · Cited by 5 · see citing works

Predicting the alternative conformation of a known protein structure based on the distance map of AlphaFold2Preprint

Li, J., Zhu, Z., & Song, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.09.598121 · Cited by 6 · see citing works

Deep learning of protein energy landscape and conformational dynamics from experimental structures in PDBPreprint

Tang, Y., Yu, M., Bai, G., Li, X., Xu, Y., & Ma, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.27.600251 · Cited by 5 · see citing works

AlphaFold2’s training set powers its predictions of fold-switched conformationsPreprint

Schafer, J. W., & Porter, L. L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.11.617857 · Cited by 5 · see citing works

Mapping allosteric rewiring in related protein structures from collections of crystallographic multiconformer modelsPreprint

Raju, A., Sharma, S., Riley, B. T., Djuraev, S., Tan, Y. L. K., Kim, M., Mahmud, T., & Keedy, D. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.23.655529 · Cited by 5 · see citing works

eRMSF: A Python Package for Ensemble-Based RMSF Analysis of Biomolecular Systems

Arantes, P. R., Ligabue-Braun, R., & Pedebos, C. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02413 · Cited by 5 · see citing works

Mobility of four structural regions drives isoform-specific properties of photoenzyme LPOR in plants

Gabruk, M., Łuszczyński, M., Szafran, K., Ogrodzińska, W., Rubenstein, B. M., & da Silva, G. M. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.108261 · Cited by 4 · see citing works

Combining simulations and experiments – a perspective on maximum entropy methods

Stöckelmaier, J., & Oostenbrink, C. (2025) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp01263e · Cited by 4 · see citing works

Deciphering mutational effects on inducible NO synthase conformational dynamics via quantitative cross-linking mass spectrometry and AlphaFold2 subsampling

Jiang, T., Zhang, H., da Silva, G. M., Gyawali, Y. P., & Feng, C. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110673 · Cited by 4 · see citing works

Structural insights into cationic amino acid transport and viral receptor engagement by CAT1

Xia, L., Lin, B., Zou, R., Wu, Y., Xu, J., Pan, Y., Yuan, Y., Li, S., Yang, Y., & Chen, X. (2025) · Nature Communications · doi:10.1038/s41467-025-67704-6 · Cited by 4 · see citing works

Fine-Tuning DiffDock-L for Allosteric Kinase Docking

Chen, E., Green, J., & Zhang, Y. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02846 · Cited by 4 · see citing works

AlphaFlex: Accuracy modeling of protein multiple conformations via predicted flexible residuesPreprint

Ge, L., Cui, X., Zhao, K., Zhou, X., Zhang, Y., & Zhang, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.11.664327 · Cited by 4 · see citing works

ConfRank+: Extending Conformer Ranking to Charged Molecules

Oerder, R., Hölzer, C., & Hamaekers, J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01259 · Cited by 3 · see citing works

ChronoSort: Revealing Hidden Dynamics in AlphaFold3 Structure Predictions

Argyle, M. J., Heaps, W. P., Kubalek, C., Gardiner, S., Bundy, B., & Corte, D. D. (2025) · SynBio · doi:10.3390/synbio3040018 · Cited by 3 · see citing works

Advances and challenges in multiscale biomolecular simulations: artificial intelligence‐driven paradigm shift

Li, W., & Wang, W. (2025) · Quantitative Biology · doi:10.1002/qub2.70024 · Cited by 3 · see citing works

Designing modular genetic sensors with TtgR/AcrR family transcriptional regulators by using a statistical and data-driven approach

Budhathoki, A., Kinshuk, S., Nguyen, N. T., & Chan, C. T. Y. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1336 · Cited by 2 · see citing works

Defining active conformations from substrate-bound structures enables active-state AlphaFold2 modeling of all 437 human catalytic protein kinase domains

Gizzio, J., Faezov, B., Xu, Q., & Dunbrack, R. L. (2026) · Biochemical Journal · doi:10.1042/bcj20260137 · Cited by 3 · see citing works

Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 Ca2+-Binding Proteins

Gu, X., Adury, V. S. S., Aranganathan, A., Zhuang, X., Varney, K. M., Weber, D. J., & Tiwary, P. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c01093 · Cited by 3 · see citing works

AI-driven mechanistic analysis of conformational dynamics in CNNM/CorC Mg2+ transporters

Ma, J., Song, X., Funato, Y., Teng, X., Huang, Y., Miki, H., Wang, W., & Hattori, M. (2024) · Structure · doi:10.1016/j.str.2024.10.021 · Cited by 3 · see citing works

An in silico framework to visualize how cancer‐associated mutations influence structural plasticity of the chemokine receptor CCR3

van Aalst, E. J., & Wylie, B. J. (2024) · Protein Science · doi:10.1002/pro.70013 · Cited by 3 · see citing works

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation DataPreprint

Benavides, T. L., & Montelione, G. T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.19.613999 · Cited by 2 · see citing works

Conformational ensemble-based framework enables rapid development of Lassa virus vaccine candidatesPreprint

Mishra, N., Avillion, G., Callaghan, S., DiBiase, C. N., Hurtado, J., Liendo, N. S., Burbach, S. M., Messmer, T., & Briney, B. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.21.624760 · Cited by 2 · see citing works

Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold2Preprint

da Silva, G. M., Cui, J. Y., Dalgarno, D. C., Lisi, G. P., & Rubenstein, B. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.25.550545 · Cited by 10 · see citing works

AlphaFold2 Predicts Alternative Conformation Populations in Green Fluorescent Protein Variants

Núñez‐Franco, R., Muriel-Olaya, M. M., Jiménez‐Osés, G., & Peccati, F. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01388 · Cited by 2 · see citing works

AlphaConformers: Structure-guided sampling enables prediction of multiple protein conformationsPreprint

Daniel, J., vitoriano, L., & Zea, D. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.18.745512

Predicting Alternative Protein Conformations with AlphaFold2

Wang, M. (2026) · Theoretical and Natural Science · doi:10.54254/2753-8818/2026.hz38002

FastConformation: A Standalone ML-Based Toolkit for Modeling and Analyzing Protein Conformational Ensembles at ScalePreprint

Galeazzi, F. M., da Silva, G. M., Arantes, P. R., Varghese, I., Shukla, A., & Rubenstein, B. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.09.653048 · Cited by 2 · see citing works