Modeling structurally variable regions in homologous proteins with rosetta

Abstract

A major limitation of current comparative modeling methods is the accuracy with which regions that are structurally divergent from homologues of known structure can be modeled. Because structural differences between homologous proteins are responsible for variations in protein function and specificity, the ability to model these differences has important functional consequences. Although existing methods can provide reasonably accurate models of short loop regions, modeling longer structurally divergent regions is an unsolved problem. Here we describe a method based on the de novo structure prediction algorithm, Rosetta, for predicting conformations of structurally divergent regions in comparative models. Initial conformations for short segments are selected from the protein structure database, whereas longer segments are built up by using three- and nine-residue fragments drawn from the database and combined by using the Rosetta algorithm. A gap closure term in the potential in combination with modified Newton's method for gradient descent minimization is used to ensure continuity of the peptide backbone. Conformations of variable regions are refined in the context of a fixed template structure using Monte Carlo minimization together with rapid repacking of side-chains to iteratively optimize backbone torsion angles and side-chain rotamers. For short loops, mean accuracies of 0.69, 1.45, and 3.62 A are obtained for 4, 8, and 12 residue loops, respectively. In addition, the method can provide reasonable models of conformations of longer protein segments: predicted conformations of 3A root-mean-square deviation or better were obtained for 5 of 10 examples of segments ranging from 13 to 34 residues. In combination with a sequence alignment algorithm, this method generates complete, ungapped models of protein structures, including regions both similar to and divergent from a homologous structure. This combined method was used to make predictions for 28 protein domains in the Critical Assessment of Protein Structure 4 (CASP 4) and 59 domains in CASP 5, where the method ranked highly among comparative modeling and fold recognition methods. Model accuracy in these blind predictions is dominated by alignment quality, but in the context of accurate alignments, long protein segments can be accurately modeled. Notably, the method correctly predicted the local structure of a 39-residue insertion into a TIM barrel in CASP 5 target T0186.

Physically realistic homology models built with rosetta can be more accurate than their templates

“…The final all-atom models were analyzed for atomic clashes by using the program MOLPROBITY (7), and the results were compared with those obtained for the native query structures, the template-based model generated by using ROSETTASVR modeling (12), and the model built with MODELLER (13). Only aligned regions were included in the calculation.…”

“…We generated one model for each query sequence by using the ROSETTASVR modeling protocol (12). The MODELLER model used for analysis was the best-scoring model, using the MODELLER score, out of 100 models produced initially.…”

“…Atomic clashes for native structures, ROSETTA models produced by folding with constraints, models produced with the ROSETTASVR fixed-template method (12), or models produced with MODELLER (13) were calculated by using MOLPROBITY (7). Before calculating clashes, hydrogen atoms were optimized and added according to suggestions from the ''Reduce'' utility incorporated with MOLPROBITY.…”

“…We modified the ROSETTA ab initio folding protocol (8)(9)(10) to incorporate interatomic distance information from homologous structures and applied the revised protocol to a test set of query sequences. We compared models generated with this method to models generated with a fixed template method, ROSETTA Structurally Variable Region (ROSETTASVR) modeling (11,12). To assess the accuracy of side-chain modeling, we compared the ROSETTA models folded with constraints to those generated with MODELLER (13).…”

The Role of the Conformational Dynamics of Glutathione S-Transferase Epsilon Class on Insecticide Resistance inAnopheles gambiae

“…[31][32][33][34][35] The SWISS-MODEL workspace has been used to construct the structural models of the target sequences. 36 Template structures are superposed by the means of a least squares algorithm and a structural alignment is generated after removing template structures with high C α root mean square deviations to the highest homology template.…”