Supporting Online Material Materials and Methods Figs. S1 to S10 Table S1 References Movies S1 to S3 Atomic-level Characterization of the Structural Dynamics of Proteins

By J. Dolin, L. Asplin, M. Flagel, D. Grasso, Goldfarb, Y Nakagawa, J. Asplin, D. Goldfarb, J. Parks, F. Coe and 36 more

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Subfields: Materials Chemistry, Molecular Biology

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Sampling alternative conformational states of transporters and receptors with AlphaFold2 (2022, background): “Dynamic interconversion between multiple conformations underpins the functions of integral membrane proteins in all domains of life ( Campbell et al., 2016 ; Cournia et al., 2015 ; Shaw et al., 2010 ; Boehr et al., 2009 ).”
Protein structure prediction from sequence variation (2012, methodology): “Not included in our comparison matrix are large specialized hardware computational methods for protein structures such as Anton, which though providing insights into protein dynamics and folding are not yet easily reproducible 59 .”
3D Protein Structure Predicted from Sequence (2011, positive): “In addition, custom-‐designed supercomputers with orders of magnitude higher throughput have allowed insight not only into molecular dynamics of protein function, but also into the folding pathways of smaller ”
Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022, methodology): “…prediction of protein-ligand structures that are coupled to receptor conformational responses is still hampered by the prohibitive cost of physically simulating slow protein state transitions [7, 8], as well as the static nature of existing protein folding prediction algorithms [9, 10].”
Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview (2018, background): “…Shaw et al. to characterize the folding of FiP35 WW domain from a fully extended state in a 100 µ s simulation and, in addition, to reach the millisecond timescale in a single simulation of BPTI in the folded-state (Shaw et al., 2010), followed recently by ubiquitin (Lindorﬀ-Larsen et al., 2016).”
Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis (2012, background): “On the theoretical side, energy landscape theory provides a basis to understand protein folding and function (4), and atomically resolved simulations can describe transitions between multiple (native) conformations covering ms timescales on specialized supercomputers (5).”
Identification of slow molecular order parameters for Markov model construction. (2013, methodology): “…models of molecular kinetics: Generation and validation The Journal of Chemical Physics 134, 174105 (2011); 10.1063/1.3565032 Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: The case of domain motions The Journal of Chemical Physics 134,…”
Scalable emulation of protein equilibrium ensembles with generative deep learning (2025, positive): “…each residue, we obtain a sequence of position-orientation tuples, { } To recover the Cartesian backbone atom positions from the frame representation, we start with a reference backbone heavy-atom frame per residue type, with idealized atom positions, similarly to AlphaFold2 [27] or OpenFold [32].”
Principles of protein structural ensemble determination. (2017, neutral): “10 EOM 2007 x x x [45] 11 ASTEROIDS 2009 x x x [46] 12 SES 2013 x x x x [47] 13 SAS 2007 x x x [48] 14 MaxOcc 2010 x x x [49] 15 MES 2009 x x x [50] 16 BSS-SAXS 2010 x x x [5] 17 Bayesian ensemble refinement 2015 x x x x x x [53] 18 BE-SAXS 2016 x x x x x [54] 19 EISD 2016 x x x x x [55] 20 BELT 201”