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The biological functions of proteins often depend on dynamic structural ensembles.
Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential
Bahar, I., Atilgan, A. R., and Erman, B · 1997
Earlier work this paper cites.
Anisotropy of fluctuation dynamics of proteins with an elastic network model
Atilgan, A. R., Durell, S., Jernigan, R. L., Demirel, M. C., Keskin, O., and Bahar, I · 2001
Earlier work this paper cites.
Clustal w and clustal x version 2.0
Larkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., McGettigan, P. A., McWilliam, H., Valentin, F., Wallace, I. M., Wilm, A., Lopez, R., et al · 2007
Earlier work this paper cites.
Atomic-level characterization of the structural dynamics of proteins
Shaw, D. E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R. O., Eastwood, M. P., Bank, J. A., Jumper, J. M., Salmon, J. K., Shan, Y., et al · 2010
Earlier work this paper cites.
Prody: protein dynamics inferred from theory and experiments
Bakan, A., Meireles, L. M., and Bahar, I · 2011
Earlier work this paper cites.
The resolution revolution
Kühlbrandt, W · 2014
Earlier work this paper cites.
Mdtraj: a modern open library for the analysis of molecular dynamics trajectories
McGibbon, R. T., Beauchamp, K. A., Harrigan, M. P., Klein, C., Swails, J. M., Hernández, C. X., Schwantes, C. R., Wang, L.-P., Lane, T. J., and Pande, V. S · 2015
Earlier work this paper cites.
Ecod: new developments in the evolutionary classification of domains
Schaeffer, R. D., Liao, Y., Cheng, H., and Grishin, N. V · 2017
Earlier work this paper cites.
Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M. and Söding, J · 2017
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Molecular dynamics simulation for all
Hollingsworth, S. A. and Dror, R. O · 2018
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Sifts: updated structure integration with function, taxonomy and sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins
Dana, J. M., Gutmanas, A., Tyagi, N., Qi, G., O’Donovan, C., Martin, M., and Velankar, S · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., and Wu, H · 2019
Earlier work this paper cites.
Cooperative changes in solvent exposure identify cryptic pockets, switches, and allosteric coupling
Porter, J. R., Moeder, K. E., Sibbald, C. A., Zimmerman, M. I., Hart, K. M., Greenberg, M. J., and Bowman, G. R · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Earlier work this paper cites.
Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M., Srinivasan, P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J., and Ng, R · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Smooth normalizing flows
Köhler, J., Krämer, A., and Noé, F · 2021
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
Earlier work this paper cites.
Cryodrgn: reconstruction of heterogeneous cryo-em structures using neural networks
Zhong, E. D., Bepler, T., Berger, B., and Davis, J. H · 2021
Earlier work this paper cites.
Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization
Ahdritz, G., Bouatta, N., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., et al · 2022
Earlier work this paper cites.
Building normalizing flows with stochastic interpolants
Albergo, M. S. and Vanden-Eijnden, E · 2022
Earlier work this paper cites.
Alphafold2 fails to predict protein fold switching
Chakravarty, D. and Porter, L. L · 2022
Earlier work this paper cites.
Analog bits: Generating discrete data using diffusion models with self-conditioning
Chen, T., Zhang, R., and Hinton, G · 2022
Earlier work this paper cites.
Sampling alternative conformational states of transporters and receptors with alphafold2
Del Alamo, D., Sala, D., Mchaourab, H. S., and Meiler, J · 2022
Cited alongside, same era.
Flow matching for generative modeling
Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., and Le, M · 2022
Cited alongside, same era.
Flow straight and fast: Learning to generate and transfer data with rectified flow
Liu, X., Gong, C., and Liu, Q · 2022
Cited alongside, same era.
Flow annealed importance sampling bootstrap
Midgley, L. I., Stimper, V., Simm, G. N., Schölkopf, B., and Hernández-Lobato, J. M · 2022
Cited alongside, same era.
Structural biology is solved—now what?
Ourmazd, A., Moffat, K., and Lattman, E. E · 2022
Cited alongside, same era.
Identifying protein conformational states in the pdb and comparison to alphafold2 predictions
Ellaway, J. I., Anyango, S., Nair, S., Zaki, H. A., Nadzirin, N., Powell, H. R., Gutmanas, A., Varadi, M., and Velankar, S · 2023
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Alphafold2 models of the active form of all 437 catalytically-competent typical human kinase domains
Faezov, B. and Dunbrack Jr, R. L · 2023
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Designing losses for data-free training of normalizing flows on boltzmann distributions
Felardos, L., Hénin, J., and Charpiat, G · 2023
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Exploring the druggable conformational space of protein kinases using ai-generated structures
Herrington, N. B., Stein, D., Li, Y. C., Pandey, G., and Schlessinger, A · 2023
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Eigenfold: Generative protein structure prediction with diffusion models
Jing, B., Erives, E., Pao-Huang, P., Corso, G., Berger, B., and Jaakkola, T. S · 2023
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Impact of protein conformational diversity on alphafold predictions
Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., García Melani, A., Novomisky Nechcoff, J., Salas, M. N., et al · 2022
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
Cited alongside, same era.
Speach_af: Sampling protein ensembles and conformational heterogeneity with alphafold2
Stein, R. A. and Mchaourab, H. S · 2022
Cited alongside, same era.
Systematic analysis of biomolecular conformational ensembles with pensa
Vögele, M., Thomson, N. J., Truong, S. T., McAvity, J., Zachariae, U., and Dror, R. O · 2022
Cited alongside, same era.
High-resolution de novo structure prediction from primary sequence
Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al · 2022
Cited alongside, same era.
Pepflow: direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion
Abdin, O. and Kim, P. M · 2023
Cited alongside, same era.
Later among the works it cites.
Protein structure prediction has reached the single-structure frontier
Lane, T. J · 2023
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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 · 2023
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Score-based enhanced sampling for protein molecular dynamics
Lu, J., Zhong, B., and Tang, J · 2023
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Predicting locations of cryptic pockets from single protein structures using the pocketminer graph neural network
Meller, A., Ward, M., Borowsky, J., Kshirsagar, M., Lotthammer, J. M., Oviedo, F., Ferres, J. L., and Bowman, G. R · 2023
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Multisample flow matching: Straightening flows with minibatch couplings
Pooladian, A.-A., Ben-Hamu, H., Domingo-Enrich, C., Amos, B., Lipman, Y., and Chen, R · 2023
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Colabfold predicts alternative protein structures from single sequences, coevolution unnecessary for af-cluster
Porter, L. L., Chakravarty, D., Schafer, J. W., and Chen, E. A · 2023
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Song, Y., Dhariwal, P., Chen, M., and Sutskever, I · 2023
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Harmonic self-conditioned flow matching for multi-ligand docking and binding site design
Stärk, H., Jing, B., Barzilay, R., and Jaakkola, T · 2023
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Rosetta energy analysis of alphafold2 models: Point mutations and conformational ensembles
Stein, R. A. and Mchaourab, H. S · 2023
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Atlas: protein flexibility description from atomistic molecular dynamics simulations
Vander Meersche, Y., Cretin, G., Gheeraert, A., Gelly, J.-C., and Galochkina, T · 2023
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Alphafold2-rave: From sequence to boltzmann ranking
Vani, B. P., Aranganathan, A., Wang, D., and Tiwary, P · 2023
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Is the functional response of a receptor determined by the thermodynamics of ligand binding?
Vögele, M., Zhang, B. W., Kaindl, J., and Wang, L · 2023
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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., and Kern, D · 2023
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Can protein structure prediction methods capture alternative conformations of membrane proteins?
Xie, T. and Huang, J · 2023
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Se (3) diffusion model with application to protein backbone generation
Yim, J., Trippe, B. L., De Bortoli, V., Mathieu, E., Doucet, A., Barzilay, R., and Jaakkola, T · 2023
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One-step diffusion with distribution matching distillation
Yin, T., Gharbi, M., Zhang, R., Shechtman, E., Durand, F., Freeman, W. T., and Park, T · 2023
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Towards predicting equilibrium distributions for molecular systems with deep learning
Zheng, S., He, J., Liu, C., Shi, Y., Lu, Z., Feng, W., Ju, F., Wang, J., Zhu, J., Min, Y., et al · 2023
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