A Combined Covalent-Electrostatic Model of Hydrogen Bonding Improves Structure Prediction with Rosetta

By Matthew J. O’Meara, Andrew Leaver-Fay, Michael D. Tyka, Amelie Stein, Kevin Houlihan, Frank DiMaio, Philip Bradley, Tanja Kortemme, David Baker, Jack Snoeyink and 1 more. 2015

233 citations recorded by the source corpus.

Interactions between polar atoms are challenging to model because at very short ranges they form hydrogen bonds (H-bonds) that are partially covalent in character and exhibit strong orientation preferences; at longer ranges the orientation preferences are lost, but significant electrostatic interactions between charged and partially charged atoms remain. To simultaneously model these two types of behavior, we refined an orientation dependent model of hydrogen bonds [Kortemme et al. 2003] used by the molecular modeling program Rosetta and then combined it with a distance-dependent Coulomb model of electrostatics. The functional form of the H-bond potential is physically motivated and parameters are fit so that H-bond geometries that Rosetta generates closely resemble H-bond geometries in high-resolution crystal structures. The combined potentials improve performance in a variety of scientific benchmarks including decoy discrimination, side chain prediction, and native sequence recovery in protein design simulations, and establishes a new standard energy function for Rosetta.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design (2015, methodology): “Key results: Recognition specificity performance with Talaris is comparable to the originally published performance of the protocol with Score12 [39,40].”
SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding (2021, methodology): “Most methods predict protein structures by minimizing statistics energy functions [23, 24] and/or performing comparative modeling from structural templates [37].”
Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane. (2020, background): “This approach contrasts soluble protein energy function evaluation, in which there is an abundance of thermodynamic and spectroscopic measurements of small molecules (81) and high-resolution protein structures (82).”
Tertiary alphabet for the observable protein structural universe (2016, methodology): “For the combined Rosetta-TERMs design procedure, we limited the amino acids that Rosetta could use at position r to the smallest set that accounted for at least 80% cumulative probability according to TERM-based self-energy (i.e., f a 1 , ...”