The Rosetta Modeling Suite is a powerful computational platform focused primarily on solving protein 3-dimensional structure from sequence, prediction of protein-protein and protein-ligand docking, and functional protein design. The tools which are employed to perform these operations are centered around protein poses, protein movers and scorefunctions. Here we demonstrate that the reweighting of the full-atom energy terms in Rosetta into “Custom Scorefunctions” can tailor outputs to better predict protein properties for specific applications. Custom Scorefunctions based on regressed models can predict physical properties such as solubility, improve the structure prediction of fully disordered and partially disordered proteins, or act as secondary scoring criteria for the design of proteins with functions not obviously tied to 3-dimensional structure. Furthermore, we have employed custom movers alongside custom scorefunctions to improve protein-ligand docking and subsequent prediction of ligand binding affinities through extended sampling and scoring of ligand states within a protein pocket. These modifications of Rosetta increase the amount of information that can be extracted from both the simulations and the experimental data-sets, expanding the diversity of problems which can be addressed within this computational platform.

Custom score functions expand the scope of Rosetta modeling

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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