Protein structure prediction using Rosetta in CASP12

By Sergey Ovchinnikov, Hahnbeom Park, David E. Kim, Frank DiMaio, David Baker. 2017

92 citations recorded by the source corpus.

We describe several notable aspects of our structure predictions using Rosetta in CASP12 in the free modeling (FM) and refinement (TR) categories. First, we had previously generated (and published) models for most large protein families lacking experimentally determined structures using Rosetta guided by co‐evolution based contact predictions, and for several targets these models proved better starting points for comparative modeling than any known crystal structure—our model database thus starts to fulfill one of the goals of the original protein structure initiative. Second, while our “human” group simply submitted ROBETTA models for most targets, for six targets expert intervention improved predictions considerably; the largest improvement was for T0886 where we correctly parsed two discontinuous domains guided by predicted contact maps to accurately identify a structural homolog of the same fold. Third, Rosetta all atom refinement followed by MD simulations led to consistent but small improvements when starting models were close to the native structure, and larger but less consistent improvements when starting models were further away.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Assessment of hard target modeling in CASP12 reveals an emerging role of alignment‐based contact prediction methods (2018, methodology): “Another possible strategy, exploited by the Baker group for some targets, 20 is to use contact predictions to identify structurally similar templates in the PDB, especially helpful to find templates of very different sequence and to map discontinuous domains of the target to continuous templates.”
Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, background): “17,18 This approach improved for a few years due to advances in force fields and benefited from an ability to carry out longer simulations.”
High-accuracy refinement using Rosetta in CASP13 (2019, methodology): “Overall, the framework is similar to that in our previous studies 1,2 ; here we provide a brief description.”
Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021, methodology): “Both QUARK and Rosetta have demonstrated excellent performance in the FM section of the CASP experiment by successfully folding protein targets that lack identiﬁable homology templates [16 ,17,18].”
Blind prediction of noncanonical RNA structure at atomic accuracy (2017, background): “Therefore, a critical open question is whether residual free energy function problemsmight be corrected by improved RNA torsional potentials, treatment of electrostatic effects, or use of energy functions independently developed for biomolecular mechanics and refinement (5, 30, 31).”
Assessment of the model refinement category in CASP12 (2017, methodology): “According to the authors, the method was in part limited by insufficient sampling and by failure of correcting sequence alignment errors, 47 showing a clear potential to emerge as a very robust and reliable refinement method across all categories.”
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints (2018, methodology): “Fragment-based methods have performed well in the biennial Critical Assessment of protein Structure Prediction (CASP) experiments [6] but take a large amount of computing power, produce a variable fraction of native-like models and are dependent on native-like fragments being available.”