Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.

ABSTRACT: The energy landscape that organizes microstates of a molecular system and governs theunderlying molecular dynamics exposes the relationship between molecular form/structure, changesto form, and biological activity or function in the cell. However, several challenges stand in the wayof leveraging energy landscapes for relating structure and structural dynamics to function. Energylandscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins inthem do not always correspond to stable structural states but are instead the result of inherentinaccuracies in semi-empirical molecular energy functions. Due to these challenges, energeticsis typically ignored in computational approaches addressing long-standing central questions incomputational biology, such as protein de

SUBMITTER: Akhter N 

PROVIDER: S-EPMC6843838 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature