SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

Highly accurate protein structure prediction with AlphaFold

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The PyMOL Molecular Graphics System

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A method and server for predicting damaging missense mutations.

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Database resources of the National Center for Biotechnology Information: update

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ColabFold - Making protein folding accessible to all

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Principles that Govern the Folding of Protein Chains

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SIFT: predicting amino acid changes that affect protein function

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Database resources of the National Center for Biotechnology Information

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A general framework for estimating the relative pathogenicity of human genetic variants

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MolProbity: More and better reference data for improved all‐atom structure validation

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TM-align: a protein structure alignment algorithm based on the TM-score

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Protein 3D Structure Computed from Evolutionary Sequence Variation

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ProDy : Protein Dynamics Inferred from Theory and Experiments

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Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies

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Intrinsic motions along an enzymatic reaction trajectory

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M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity

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Uniclust databases of clustered and deeply annotated protein sequences and alignments

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MMseqs2 desktop and local web server app for fast, interactive sequence searches

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MGnify: the microbiome analysis resource in 2020

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Structure of the complex between adenylate kinase from Escherichia coli and the inhibitor Ap5A refined at 1.9 Å resolution

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Sampling alternative conformational states of transporters and receptors with AlphaFold2

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Sampling the conformational landscapes of transporters and receptors with AlphaFold2

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Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp>

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Adenylate kinase motions during catalysis: an energetic counterweight balancing substrate binding

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ColabFold - Making protein folding accessible to allPreprint

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Applying and improving AlphaFold at CASP14

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Structure and Molecular Mechanism of a Nucleobase–Cation–Symport-1 Family Transporter

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State-of-the-art estimation of protein model accuracy using AlphaFold

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

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Molecular Basis of Alternating Access Membrane Transport by the Sodium-Hydantoin Transporter Mhp1

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Machine learning in protein structure prediction

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Structure and mechanism of the mammalian fructose transporter GLUT5

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Impact of protein conformational diversity on AlphaFold predictions

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Impact of protein conformational diversity on AlphaFold predictions

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

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Multiple open forms of ribose-binding protein trace the path of its conformational change

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Structural basis for substrate transport in the GLUT-homology family of monosaccharide transporters

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A structural biology community assessment of AlphaFold 2 applications

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Lipids modulate the conformational dynamics of a secondary multidrug transporter

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Probing protein-protein interactions. The ribose-binding protein in bacterial transport and chemotaxis.

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Conformational cycle and ion-coupling mechanism of the Na+/hydantoin transporter Mhp1

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PROBING PROTEIN-PROTEIN INTERACTIONS: THE RIBOSE-BINDING PROTEIN IN BACTERIAL TRANSPORT AND CHEMOTAXIS

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Current structure predictors are not learning the physics of protein folding

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Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations

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Protonation drives the conformational switch in the multidrug transporter LmrP

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Structure and mutagenic analysis of the lipid II flippase MurJ from Escherichia coli

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Moving beyond static snapshots: Protein dynamics and the Protein Data Bank

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An embedded lipid in the multidrug transporter LmrP suggests a mechanism for polyspecificity

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Molecular mechanism of ligand recognition by membrane transport protein, Mhp1

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End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman

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End-to-end learning of multiple sequence alignments with differentiable Smith–Waterman

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Sequence and structural determinants of ligand-dependent alternating access of a MATE transporter

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Engineered XylE as a tool for mechanistic investigation and ligand discovery of the glucose transporters GLUTs

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Applications of AlphaFold beyond Protein Structure PredictionPreprint

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Co-evolutionary distance predictions contain flexibility information

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Ligand-induced structural changes analysis of ribose-binding protein as studied by molecular dynamics simulations

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From nature to industry: Harnessing enzymes for biocatalysis

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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 · see citing works

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

Terwilliger, T. C., Liebschner, D., Croll, T. I., Williams, C. J., McCoy, A. J., Poon, B. K., Afonine, P. V., Oeffner, R. D., Richardson, J. S., Read, R. J., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02087-4 · Cited by 411 · see citing works

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

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Modeling conformational states of proteins with AlphaFold

Sala, D., Engelberger, F., Mchaourab, H. S., & Meiler, J. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102645 · Cited by 182 · see citing works

High-throughput prediction of protein conformational distributions with subsampled AlphaFold2

Monteiro da Silva, G., Cui, J. Y., Dalgarno, D. C., Lisi, G. P., & Rubenstein, B. M. (2024) · Nature Communications · doi:10.1038/s41467-024-46715-9 · Cited by 196 · see citing works

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

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Improved AlphaFold modeling with implicit experimental information

Terwilliger, T. C., Poon, B. K., Afonine, P. V., Schlicksup, C. J., Croll, T. I., Millán, C., Richardson, J. S., Read, R. J., & Adams, P. D. (2022) · Nature Methods · doi:10.1038/s41592-022-01645-6 · Cited by 140 · 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

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

AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges

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From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2

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AlphaFold prediction of structural ensembles of disordered proteins

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AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103551 · Cited by 96 · see citing works

AFsample2 predicts multiple conformations and ensembles with AlphaFold2

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Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions

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Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties

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Generative artificial intelligence for de novo protein design

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PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins

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

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Deep learning for protein structure prediction and design—progress and applications

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Prediction of multiple conformational states by combining sequence clustering with AlphaFold2Preprint

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AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design

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From byte to bench to bedside: molecular dynamics simulations and drug discovery

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

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Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning

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Computational drug development for membrane protein targets

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Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

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Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

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Computational advances in discovering cryptic pockets for drug discovery

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Evolutionary selection of proteins with two folds

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Modeling protein–ligand interactions for drug discovery in the era of deep learning

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Recent Advances in NMR Protein Structure Prediction with ROSETTA

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Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

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Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations

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Challenges in bridging the gap between protein structure prediction and functional interpretation

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Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence–based methods

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

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A Perspective on the Prospective Use of AI in Protein Structure Prediction

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

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Representing structures of the multiple conformational states of proteins

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Machine learning in RNA structure prediction: Advances and challenges

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P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching

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Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

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Artificial intelligence for medicine 2025: Navigating the endless frontier

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AlphaFold2 Modeling and Molecular Dynamics Simulations of the Conformational Ensembles for the SARS-CoV-2 Spike Omicron JN.1, KP.2 and KP.3 Variants: Mutational Profiling of Binding Energetics Reveals Epistatic Drivers of the ACE2 Affinity and Escape Hotspots of Antibody Resistance

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Modeling protein conformational ensembles by guiding AlphaFold2 with Double Electron Electron Resonance (DEER) distance distributions

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Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies

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Is the Functional Response of a Receptor Determined by the Thermodynamics of Ligand Binding?

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AFsample2: Predicting multiple conformations and ensembles with AlphaFold2Preprint

Kalakoti, Y., & Wallner, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596195 · Cited by 26 · see citing works

AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics

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Frameshift mutation S368fs in the gene encoding cytoskeletal β-actin leads to ACTB-associated syndromic thrombocytopenia by impairing actin dynamics

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Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?

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Applicability of <scp>AlphaFold2</scp> in the modeling of dimeric, trimeric, and tetrameric coiled‐coil domains

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Emerging frontiers in protein structure prediction following the AlphaFold revolution

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Artificial intelligence in pharmaceutical sciences: A comprehensive review

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NetTCR-struc, a structure driven approach for prediction of TCR-pMHC interactions

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Generative AI for computational chemistry: A roadmap to predicting emergent phenomena

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Estimating conformational heterogeneity of tryptophan synthase with a template‐based Alphafold2 approach

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AlphaFold Prediction of Structural Ensembles of Disordered Proteins

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Protein structure and dynamics in the era of integrative structural biology

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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

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Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction

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Distinguishing features of fold‐switching proteins

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Harnessing conformational dynamics in enzyme catalysis to achieve nature-like catalytic efficiencies: the shortest path map tool for computational enzyme redesign

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Predicting the conformational flexibility of antibody and T-cell receptor CDRs

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ExEnDiff: An Experiment-Guided Diffusion Model for Protein Conformational Ensemble Generation

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

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

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Targeting in silico GPCR conformations with ultra-large library screening for hit discovery

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AlphaFold2 captures the conformational landscape of the HAMP signaling domain

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A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

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A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures

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Predicting the conformational flexibility of antibody and T cell receptor complementarity-determining regions

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Advancing protein structure prediction beyond AlphaFold2

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Rational design of small‐molecule responsive protein switches

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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

AlphaFold2 Predictions of Conformational Ensembles and Atomistic Simulations of the SARS-CoV-2 Spike XBB Lineages Reveal Epistatic Couplings between Convergent Mutational Hotspots that Control ACE2 Affinity

Raisinghani, N., Alshahrani, M., Gupta, G., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c01341 · Cited by 17 · see citing works

An outlook on structural biology after A lpha F old: tools, limits and perspectives

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Assessing AF2’s ability to predict structural ensembles of proteins

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Integration of AlphaFold with Molecular Dynamics for Efficient Conformational Sampling of Transporter Protein NarK

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Confrontation of AlphaFold models with experimental structures enlightens conformational dynamics supporting CYP102A1 functions

Urban, P., & Pompon, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-20390-6 · Cited by 16 · see citing works

Do Deep Learning Models for Co-Folding Learn the Physics of Protein-Ligand Interactions?Preprint

Masters, M., Mahmoud, A., & Lill, M. A. (2024) · bioRxiv · doi:10.1101/2024.06.03.597219 · Cited by 11 · see citing works

Conditioned Protein Structure Prediction

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Hybrid computational methods combining experimental information with molecular dynamics

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Accelerated Molecular Dynamics and AlphaFold Uncover a Missing Conformational State of Transporter Protein OxlT

Ohnuki, J., Jaunet-Lahary, T., Yamashita, A., & Okazaki, K.-I. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c03052 · Cited by 14 · see citing works

Utilization of AlphaFold2 to Predict MFS Protein Conformations after Selective Mutation

Xiao, Q., Xu, M., Wu, T., zhang, W., xu, Q., li, L., Qin, W., & Sun, B. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23137235 · Cited by 14 · see citing works

Challenging AlphaFold in predicting proteins with large-scale allosteric transitions

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Metastable alpha‐rich and beta‐rich conformations of small Aβ42 peptide oligomers

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NMR tools to detect protein allostery

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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

Analog epigenetic memory revealed by targeted chromatin editing

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Analog epigenetic memory revealed by targeted chromatin editing

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

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Predicted models and CCP 4

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Exploring conformational landscapes and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variant complexes with the ACE2 receptor using AlphaFold2-based structural ensembles and molecular dynamics simulations

Raisinghani, N., Alshahrani, M., Gupta, G., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp01372g · Cited by 12 · see citing works

The physics-AI dialogue in drug design

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Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures

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An agnostic analysis of the human AlphaFold2 proteome using local protein conformations

de Brevern, A. G. (2022) · Biochimie · doi:10.1016/j.biochi.2022.11.009 · Cited by 11 · see citing works

Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology

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

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AFsample2: Predicting multiple conformations and ensembles with AlphaFold2Preprint

Kalakoti, Y., & Wallner, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596195 · Cited by 26 · see citing works

Predicting Alternative Protein Conformations with AlphaFold2

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Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm

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AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold

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