Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement

By Andrew Leaver-Fay, Matthew J. O’Meara, Michael D. Tyka, Ron Jacak, Yifan Song, Elizabeth H. Kellogg, James M. Thompson, Ian W. Davis, Roland A. Pache, Sergey Lyskov and 7 more. 2013

199 citations recorded by the source corpus.

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 improvement of the Rosetta energy function. 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 and Dunbrack, 2011).

Subfields: Materials Chemistry, Molecular Biology, Evaluation & Benchmarks, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. David Baker
2. Jeffrey J. Gray
3. Tanja Kortemme
4. Yang Zhang
5. Xingjie Pan
6. Yaoqi Zhou
7. Frank DiMaio
8. Yuedong Yang

In Molecular Biology

1. David Baker
2. Jeffrey J. Gray
3. Tanja Kortemme
4. Yang Zhang
5. Xingjie Pan
6. Brian D. Weitzner
7. Yaoqi Zhou
8. Frank DiMaio

In Evaluation & Benchmarks

1. Jeffrey J. Gray
2. Yilun Du
3. Frank DiMaio
4. David Baker
5. Rob Fergus
6. Jerry Ma
7. Ivan V. Anishchenko
8. Yang Zhang

In Computational Chemistry & Drug Discovery

1. David Baker
2. Jeffrey J. Gray
3. Ivan V. Anishchenko
4. Tanja Kortemme
5. Xingjie Pan
6. Yang Zhang
7. Frank DiMaio
8. Yuedong Yang

Citations in the corpus

• Robust deep learning based protein sequence design using ProteinMPNN (2022, methodology): “Nature biotechnology, 35(11), 1026-1028.”
• Energy-based models for atomic-resolution protein conformations (2020, methodology): “Leaver-Fay et al. (2013) proposed optimizing the feature weights and parameters of the terms of an energy function for protein design; however their method used physical features designed with expert knowledge and data analysis.”
• Using the RosettaSurface Algorithm to Predict Protein Structure at Mineral Surfaces (2013, methodology): “Rosetta’s accuracy in modeling the intraprotein component of protein–surface interactions is the main advantage of using score12 as a starting point (Leaver-Fay et al., 2013).”
• Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment‐based local and energy‐based nonlocal profiles (2014, methodology): “The average 30% sequence identity for 50 proteins achieved by RosettaDesign is significantly lower than 37.0% reported by Leaver-Fay et al 40 despite the same scoring function and procedures were employed.”
• Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane. (2020, methodology): “To perform redesign, we used a Monte Carlo fixed-backbone design protocol which samples possible sequences using a full protein rotamer-and-sequence optimization and a multicool annealer-simulated annealing protocol (59).”