Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network.

ABSTRACT: Rational protein design aims at the targeted modification of existing proteins. To reach this goal, software suites like Rosetta propose sequences to introduce the desired properties. Challenging design problems necessitate the representation of a protein by means of a structural ensemble. Thus, Rosetta multi-state design (MSD) protocols have been developed wherein each state represents one protein conformation. Computational demands of MSD protocols are high, because for each of the candidate sequences a costly three-dimensional (3D) model has to be created and assessed for all states. Each of these scores contributes one data point to a complex, design-specific energy landscape. As neural networks (NN) proved well-suited to learn such solution spaces, we integrated one into the framework

SUBMITTER: Nazet J 

PROVIDER: S-EPMC8389498 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature