Atomic-Level Characterization of the Structural Dynamics of Proteins

By R. Buller, M. Peterson, O. Almarsson, L. Leiserowitz. 2010

591 citations recorded by the source corpus.

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Citations in the corpus

State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022, neutral): “Nevertheless, the computational modelling of protein–ligand complexes that are coupled to substantial changes in receptor conformation is hampered by the prohibitive cost of simulating slow protein state transitions 20,21 .”
Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, neutral): “such as BPTI and WW domains [29,30].”
Protein sequence design by conformational landscape optimization (2021, background): “…folding energy landscape characterization using molecular dynamics methods even for small proteins is a resource-intensive computational task (18, 19), and even Rosetta ab initio folding simulations are extremely computationally intensive, requiring thousands of CPU hours to generate enough…”
Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer (2014, background): “Studies enabled by this new capability—including several based on millisecond-scale all-atom MD simulations [32, 37, 38, 46, 47]—have led to a number of scientific discoveries (e.”
Parallel random numbers: As easy as 1, 2, 3 (2011, unknown): “We originally used this approach in biomolecular simulations on Anton [40, 41], a massively parallel special-purpose machine, but the approach is far more general.”
rawMSA: End-to-end Deep Learning Makes Protein Sequence Profiles and Feature Extraction obsolete (2018, background): “Unfortunately, proteins are very large molecules, and the huge amount of available conformations, even for relatively small proteins, makes it prohibitively expensive to fold them even on customized computer hardware [8].”
Systematic Validation of Protein Force Fields against Experimental Data (2012, methodology): “We have recently described the construction and initial applications of a specialized computer for MD simulations called Anton [2,8].”
Molecular dynamics simulations and drug discovery (2011, background): “With some important exceptions [31], simulations are currently limited to at most millionths of a second; indeed, most simulations are measured in billionths of a second.”
Weighted Ensemble Simulation: Review of Methodology, Applications, and Software (2017, unknown): “In addition to record-setting molecular dynamics (MD) simulations in terms of both length (57) and system size (71), it has become routine to carry out simulations on the microsecond timescale, a critical regime for biological processes such as protein conformational changes and binding processes.”
Kinetic distance and kinetic maps from molecular dynamics simulation. (2015, methodology): “Qualitatively, this analysis agrees with previous analyses of that system [33, 16], but the relaxation timescale found here are larger than previously estimated.”
Methods in Enzymology: “Flexible backbone sampling methods to model and design protein alternative conformations” (2013, unknown): “However, other than in exceptional cases (Kelley, Vishal, Krafft, & Pande, 2008; Shaw et al., 2010), the timescales accessible to molecular dynamics often preclude sampling functional motions.”