Integrating linear optimization with structural modeling to increase HIV neutralization breadth.

ABSTRACT: Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-

SUBMITTER: Sevy AM 

PROVIDER: S-EPMC5833279 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature