Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …

By averaging over atomic details, coarse-grained (CG) models provide profound
computational and conceptual advantages for studying soft materials. In particular, bottom …

In Dec 2020, the results of AlphaFold version 2 were presented at CASP14, sparking a
revolution in the field of protein structure predictions. For the first time, a purely …

A generalized understanding of protein dynamics is an unsolved scientific problem, the
solution of which is critical to the interpretation of the structure-function relationships that …

The molecular details involved in the folding, dynamics, organization, and interaction of
proteins with other molecules are often difficult to assess by experimental techniques …

Coarse-grained molecular models of proteins permit access to length and time scales
unattainable by all-atom models and the simulation of processes that occur on long time …

Machine-learned coarse-grained (CG) models have the potential for simulating large
molecular complexes beyond what is possible with atomistic molecular dynamics. However …

Implicit solvent models are essential for molecular dynamics simulations of biomolecules,
striking a balance between computational efficiency and biological realism. Efforts are …

Coarse-grained models allow computational investigation of biomolecular processes
occurring on long time and length scales, intractable with atomistic simulation. Traditionally …

Low-resolution coarse-grained (CG) models provide remarkable computational and
conceptual advantages for simulating soft materials. In principle, bottom-up CG models can …