Deep generative models of protein structure uncover distant relationships across a continuous fold space.

ABSTRACT: Our views of fold space implicitly rest upon many assumptions that impact how we analyze, interpret and understand protein structure, function and evolution. For instance, is there an optimal granularity in viewing protein structural similarities (e.g., architecture, topology or some other level)? Similarly, the discrete/continuous dichotomy of fold space is central, but remains unresolved. Discrete views of fold space bin similar folds into distinct, non-overlapping groups; unfortunately, such binning can miss remote relationships. While hierarchical systems like CATH are indispensable resources, less heuristic and more conceptually flexible approaches could enable more nuanced explorations of fold space. Building upon an Urfold model of protein structure, here we present a deep generativ

SUBMITTER: Draizen EJ 

PROVIDER: S-EPMC11410806 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature