Generating Protein Structures for Pathway Discovery Using Deep Learning.

ABSTRACT: Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be a

SUBMITTER: Georgouli K 

PROVIDER: S-EPMC11500303 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature