A Pareto-Optimal Refinement Method for Protein Design Scaffolds

Abstract

Computational design of protein function involves a search for amino acids with the lowest energy subject to a set of constraints specifying function. In many cases a set of natural protein backbone structures, or ''scaffolds'', are searched to find regions where functional sites (an enzyme active site, ligand binding pocket, protein -protein interaction region, etc.) can be placed, and the identities of the surrounding amino acids are optimized to satisfy functional constraints. Input native protein structures almost invariably have regions that score very poorly with the design force field, and any design based on these unmodified structures may result in mutations away from the native sequence solely as a result of the energetic strain. Because the input structure is already a stable protein, it is desirable to keep the total number of mutations to a minimum and to avoid mutations resulting from poorly-scoring input structures. Here we describe a protocol using cycles of minimization with combined backbone/sidechain restraints that is Pareto-optimal with respect to RMSD to the native structure and energetic strain reduction. The protocol should be broadly useful in the preparation of scaffold libraries for functional site design.

Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein

“…21 Recently, Arpino et al [16] experimentally determined the functional consequences 22 of single deletions in enhanced green fluorescent protein (eGFP) by systematically 23 deleting individual residues from eGFP and then assaying for function. Similar to 24 observations from computational studies, most functional deletion mutants were located 25 in unstructured loop regions, as opposed to in highly structured regions comprised of 26 β-sheets and α-helices. In addition, Arpino et al observed that non-functional mutants 27 were more likely to occur in buried regions than were functional mutants [16].…”

Structural basis for the regulatory interaction of the methylglyoxal synthase MgsA with the carbon flux regulator Crh in

“…The substrate molecule as well as side chains forming the active site were kept flexible during the docking trials: Ile-6, His-8, Asp-9, Lys-12, Thr-34, Thr-36, Thr-37, Ser-54, Asp-60, Phe-77, His-87 and Val-91. Interestingly, successful docking results were obtained only for the receptor model which was relaxed using Rosetta application (72). Relax protocol in Rosetta performs a simple all-atom model refinement in the Rosetta force-field which searches the local conformational space around the starting structure (RMSD calculated for C atoms forming the MgsA hexamer amounted to 0.06 Å).…”

Searching for the Pareto frontier in multi-objective protein design

“…2 The Pareto optimal set is depicted for a bi-objective problem f 1 , f 2 , where non-dominated solutions (red) cannot be further improved for f 1 without degrading f 2 or vice versa. Other solutions (gray) may be improved along either objective without compromising the other applied to the design of stabilizing mutations to proteins that minimally disrupt the native structure, concurrently optimizing energy and RMSD from the initial structure (Nivón et al 2013). State-of-the-art multi-objective optimization evolutionary algorithms such as SMS-EMOA have been applied to the design of peptide ligands that bind with reasonable affinity and selectivity for a specific isoform of 14-3-3 proteins (Sanchez-Faddeev et al 2012).…”