Predicting the Tolerated Sequences for Proteins and Protein Interfaces Using RosettaBackrub Flexible Backbone Design

By Colin A. Smith, Tanja Kortemme. 2011

104 citations recorded by the source corpus.

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.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020, background): “Flexible backbone design methods that use pre-generated ensembles as a starting point for design 92,115 perform poorly in benchmarks, likely because the ensemble does not accurately describe the unbound-to-bound conformational changes.”
Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences (2015, background): “It is well known that many proteins tolerate a wider variety of sequences than simply the native sequence [13,29,32,33]— therefore a major goal of multi-specificity design is to recover not only the native sequence of a protein, but also sequence variations that are tolerated by all binding partners”
Protein sequence design with a learned potential (2020, unknown): “In practice, the generation of diverse sequences with Rosetta often requires manually manipulating amino acid identities at key positions [7, 9], adjusting the energy function [43, 44], or explicitly modeling perturbations of the protein backbone [45, 46, 47, 48].”
Protein Engineering by Combined Computational and In Vitro Evolution Approaches. (2016, unknown): “Several studies have utilized slightly different CPD approaches to simulate observed sequence profiles obtained in PD binding selections [67–70].”
Web‐accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE) (2018, unknown): “47,48 VIP—Core redesign to eliminate voids.”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “Incorporating such back-rub moves into design simulations has led to considerable improvements in modeling structural changes in point mutants (49, 50, 52), protein dynamics on fast timescales (53, 54), prediction of molecular recognition specificity (55), and the sequence design (56).”
Methods in Enzymology: “Flexible backbone sampling methods to model and design protein alternative conformations” (2013, unknown): “In this protocol , the interface energy is given greater weight (Smith & Kortemme, 2010, 2011).”
Achievements and Challenges in Computational Protein Design. (2017, methodology): “Flexible backbone CPD used RosettaBackrub [157] and the robotics-inspired local loop reconstruction method for peptide Achievements and Challenges in Computational Protein Design 71”
Protein engineering by highly parallel screening of computationally designed variants (2016, background): “Alternatively, increasing the score contribution of residues within the interface was found to enhance the recovery of experimentally determined variants (29).”
Improved Modeling of Side-Chain–Base Interactions and Plasticity in Protein–DNA Interface Design (2012, methodology): “47,48 Flexible loop regions of protein – DNA interfaces could bene ﬁ t from combining the motif-based approach described here with the previously published method that rebuilds protein backbones to accommodate rotamers that can form motif interactions.”
Multi-state design of flexible proteins predicts sequences optimal for conformational change (2019, methodology): “The structural ensembles used for this approach were generated by reverting all structural side chains to a consensus sequence and employing ROSETTA Backrub to introduce small local rotation about the Cα-Cα axis of each of three-residue segments while maintaining ideal bond length, angle, and the st”
Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity (2015, background): “Previous work in modeling peptide-binding specificity found that up-weighting intermolecular interactions relative to intra-molecular interactions improved performance [28].”
A structural biology community assessment of AlphaFold2 applications (2021, methodology): “We then used 3 structure-based variant effect predictors (FoldX (Delgado et al., 2019), Rosetta (Smith and Kortemme, 2011) and DynaMut2 (Rodrigues et al., 2021)) to compare the DMS measurements with predicted impacts using either AF2 or experimentally derived structures.”
Control of protein signaling using a computationally designed GTPase/GEF orthogonal pair (2012, methodology): “Although the flexible backbone design method has been benchmarked on existing data (23, 24), this application represents the first forward engineering test of the method’s efficacy (see Methods).”