title: Predicting the tolerated sequences for proteins and protein interfaces using RosettaBackrub flexible backbone design.
abstract: Predicting the set of sequences that are tolerated by a protein or protein interface, while maintaining a desired function, is useful for characterizing protein interaction specificity and for computationally designing sequence libraries to engineer proteins with new functions. Here we provide a general method, a detailed set of protocols, and several benchmarks and analyses for estimating tolerated sequences using flexible backbone protein design implemented in the Rosetta molecular modeling software suite. The input to the method is at least one experimentally determined three-dimensional protein structure or high-quality model. The starting structure(s) are expanded or refined into a conformational ensemble using Monte Carlo simulations consisting of backrub backbone and side chain moves in Rosetta. The method then uses a combination of simulated annealing and genetic algorithm optimization methods to enrich for low-energy sequences for the individual members of the ensemble. To emphasize certain functional requirements (e.g. forming a binding interface), interactions between and within parts of the structure (e.g. domains) can be reweighted in the scoring function. Results from each backbone structure are merged together to create a single estimate for the tolerated sequence space. We provide an extensive description of the protocol and its parameters, all source code, example analysis scripts and three tests applying this method to finding sequences predicted to stabilize proteins or protein interfaces. The generality of this method makes many other applications possible, for example stabilizing interactions with small molecules, DNA, or RNA. Through the use of within-domain reweighting and/or multistate design, it may also be possible to use this method to find sequences that stabilize particular protein conformations or binding interactions over others.
authors.affiliation: Graduate Program in Biological and Medical Informatics, University of California San Francisco, San Francisco, California, United States of America.
authors.forename: Colin A
authors.initials: CA
authors.lastname: Smith
authors.forename: Tanja
authors.initials: T
authors.lastname: Kortemme
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000737:chemistry / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D019382:Human Growth Hormone / Q000737:chemistry / Q000378:metabolism; D006801:Humans; D008969:Molecular Sequence Data; D054731:PDZ Domains; D010455:Peptides / Q000737:chemistry / Q000378:metabolism; D011485:Protein Binding; D055550:Protein Stability; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D011986:Receptors, Somatotropin / Q000737:chemistry / Q000378:metabolism; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D001426:Bacterial Proteins; C034284:IgG Fc-binding protein, Streptococcus; D010455:Peptides; D011506:Proteins; D011986:Receptors, Somatotropin; D019382:Human Growth Hormone