Rational Design of Temperature-Sensitive Alleles Using Computational Structure Prediction

Abstract

Temperature-sensitive (ts) mutations are mutations that exhibit a mutant phenotype at high or low temperatures and a wild-type phenotype at normal temperature. Temperature-sensitive mutants are valuable tools for geneticists, particularly in the study of essential genes. However, finding ts mutations typically relies on generating and screening many thousands of mutations, which is an expensive and labor-intensive process. Here we describe an in silico method that uses Rosetta and machine learning techniques to predict a highly accurate “top 5” list of ts mutations given the structure of a protein of interest. Rosetta is a protein structure prediction and design code, used here to model and score how proteins accommodate point mutations with side-chain and backbone movements. We show that integrating Rosetta relax-derived features with sequence-based features results in accurate temperature-sensitive mutation predictions.

Predicting Protein Thermostability Upon Mutation Using Molecular Dynamics Timeseries Data

“…However, we can utilize physicochemical data on the change of amino acids at a particular site ("residue change in charge, hydrophobicity, volume, molecular weight") as a means to approximate this. To compliment these differences in physicochemical empirical data, we utilize a qualitative residue swap similarity metric that is 0 when both the unmutated and mutated amino acids belong to the same class (small nonpolar, small polar, negative charge, large nonpolar, bad behaved, positive charge, side chain amide) and 1 otherwise, as defined by Poultney et al [58]. Additionally, a static structural feature of potentially high descriptive value is "residue mean mutual information" which is the average value in bits at a particular residue in a mutual information matrix computed using MDEntropy.…”

Robust Classification of Protein Variation Using Structural Modeling and Large-Scale Data Integration

“…Both protocols 1) substitute the native residue for the variant amino acid, 2) refine the variant structure, including protein backbone movements, to accommodate this change, and 3) compare the output structures using the Rosetta score terms (Figure 1, Supplementary Figure S2). To generate features for each variant, we follow Poultney et al (40) and normalize structure-based features by comparing scores for a given variant to scores derived from Rosetta-relaxed ensembles of its native protein. We also include the accessible surface area at the position of variation as a feature, calculated using PROBE (52).…”

“…We include additional features describing the geometric differences between the input and final structure for each trajectory (e.g., RMSD and gdtmm) to detect proteins undergoing large rearrangements, totaling 23 features. To compare the native and variant ensembles and eliminate potential differences in score magnitude across diverse protein folds, we 1) extract the distributions of each Rosetta score term for the native and variant proteins, 2) calculate the quartiles of the variant protein score distributions, and 3) calculate the cumulative density for these quantiles on the corresponding native protein score distribution (40). FastRelax and quartile analysis produce three features per score term for each variant, corresponding to the Q1, Q2, and Q3 quartiles (40), totaling 60 features.…”

“…Each of these methods predicts a label that is designed to correlate with variant deleteriousness and is used to prioritize causal pathogenic variants from large genomic datasets (10). Deleteriousness can be approximated with measures of conservation and molecular functionality but available data on both protein sequence variation and structural energetics are rarely combined (6,40,43). Selection against deleterious variants can be detected by analysis of conservation and other alignment-based methods, although these metrics may not apply to de novo mutations.…”

“…We obtained structural models for these proteins from solved crystal structures and comparative modeling initiatives, such as ModBase (39), taking advantage of reliable homology models freely available for most human proteins. Structural analysis is performed using Rosetta to rigorously sample variant protein conformations, properly accommodating the variant amino acid by moving the protein backbone (20,40,49). We combine sequence-based and structure-based features in a sparse logistic regression framework, leading to a classifier that accurately ranks deleterious variants, with ≥90% precision on the highest scoring 3,800 variants (40% of variants classified) and 0.872 Area Under the Precision-Recall curve (AUPR).…”