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. 2013:523:109-43.
doi: 10.1016/B978-0-12-394292-0.00006-0.

Scientific benchmarks for guiding macromolecular energy function improvement

Andrew Leaver-Fay  1 , Matthew J O'Meara, Mike Tyka, Ron Jacak, Yifan Song, Elizabeth H Kellogg, James Thompson, Ian W Davis, Roland A Pache, Sergey Lyskov, Jeffrey J Gray, Tanja Kortemme, Jane S Richardson, James J Havranek, Jack Snoeyink, David Baker, Brian Kuhlman
Affiliations

Scientific benchmarks for guiding macromolecular energy function improvement

Andrew Leaver-Fay et al. Methods Enzymol. 2013.

Abstract

Accurate energy functions are critical to macromolecular modeling and design. We describe new tools for identifying inaccuracies in energy functions and guiding their improvement, and illustrate the application of these tools to the improvement of the Rosetta energy function VSports手机版. The feature analysis tool identifies discrepancies between structures deposited in the PDB and low-energy structures generated by Rosetta; these likely arise from inaccuracies in the energy function. The optE tool optimizes the weights on the different components of the energy function by maximizing the recapitulation of a wide range of experimental observations. We use the tools to examine three proposed modifications to the Rosetta energy function: improving the unfolded state energy model (reference energies), using bicubic spline interpolation to generate knowledge-based torisonal potentials, and incorporating the recently developed Dunbrack 2010 rotamer library (Shapovalov & Dunbrack, 2011). .

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Figures

Figure 1
Figure 1. HBondFeatures Database Schema
Boxes represent tables in the database and arrows represent foreign-key dependencies. The HBondFeatures class populates these tables with hydrogen bond data. For each hydrogen bond site (an acceptor atom, or a polar hydrogen), atomic coordinates, experimental data and solvent environment are reported. For each hydrogen bond that forms between two hydrogen bond sites, the geometric properties (i.e., distances and angles) and the sum of the Lennard-Jones energies for the atoms involved are also reported.
Figure 2
Figure 2. Workflow for feature analysis
Example usage workflow for the feature analysis tool. Layer 1: Each sample source consists of a batch of molecular conformations, e.g., experimentally determined or predicted conformations. Layer 2: Features from each sample source are extracted into a relational database. Layer 3: Conditional feature distributions are estimated from feature instances queried from the database. Layer 4: The distributions are compared graphically. Layer 5: The results of the comparisons are used as scientific benchmarks or to inform modifications to the structure prediction protocol and energy function, where the cycle can begin again.
Figure 3
Figure 3. Hydrogen bond A–H Distance Distributions for Hydroxyl Donors (SER/THR) to Backbone Oyxgens
The thick curves are kernel density estimations from observed data normalized for equal volume per unit distance. The black curve in the background of each panel represents the Native sample source. (A) Boltzmann distribution for the A–H distance term in the Rosetta H-bond model with the Score12 and NewHB parameterizations. (B) Relaxed Natives with the Score12 energy function. The excessive peakiness is due to a discontinuity in the Score12 parametrization of the H-bond model. (C) Relaxed Natives with the NewHB energy function. (D) Relaxed Natives with the NewHB energy function and the Lennard-Jones minima between the acceptor and hydroxyl heavy atoms adjusted from 3.0 Å to 2.6 Å, and between the acceptor and the hydrogen atoms adjusted from 1.95 Å to 1.75 Å.
Figure 4
Figure 4. Backbone Torsion Angles in the Beta-Region with B-Factors less than 30
A. The distribution for the top8000. B. In Score12. density accumulates on the 5° bins due to derivative discontinuities caused by bilinear interpolation. C. Score12Bicubic has only a few remaining artifacts on the 10° bin boundaries due to the continued use of bilinear interpolation for parts of the Dunbrack energy. D. Score12Dun10 has very few remaining artifacts.
Figure 5
Figure 5. ASN/ASP χ2 distribution by χ1 bin
Comparison of χ2 distributions for the semirotameric amino acids ASN and ASP, broken down by χ1 rotamer. Rosetta’s implementation of the 2002 library produces Gaussian-like distributions for χ2 in relaxed natives (green), though the native distributions do not resemble Gaussians (red). Using the 2010 library (blue), the distributions improve considerably though they remain too peaky in places.
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References

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