Discovering optimal kinetic pathways for self-assembly using automatic differentiation.

ABSTRACT: During self-assembly of macromolecules ranging from ribosomes to viral capsids, the formation of long-lived intermediates or kinetic traps can dramatically reduce yield of the functional products. Understanding biological mechanisms for avoiding traps and efficiently assembling is essential for designing synthetic assembly systems, but learning optimal solutions requires numerical searches in high-dimensional parameter spaces. Here, we exploit powerful automatic differentiation algorithms commonly employed by deep learning frameworks to optimize physical models of reversible self-assembly, discovering diverse solutions in the space of rate constants for 3-7 subunit complexes. We define two biologically-inspired protocols that prevent kinetic trapping through either internal design of subun

SUBMITTER: Jhaveri A 

PROVIDER: S-EPMC10491160 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature