Structure guided forcefield optimization

By Yifan Song, Michael D. Tyka, Andrew Leaver-Fay, James M. Thompson, David Baker. 2011

48 citations recorded by the source corpus.

Accurate modeling of biomolecular systems requires accurate forcefields. Widely used molecular mechanics (MM) forcefields obtain parameters from experimental data and quantum chemistry calculations on small molecules but do not have a clear way to take advantage of the information in high‐resolution macromolecular structures. In contrast, knowledge‐based methods largely ignore the physical chemistry of interatomic interactions, and instead derive parameters almost exclusively from macromolecular structures. This can involve considerable double counting of the same physical interactions. Here, we describe a method for forcefield improvement that combines the strengths of the two approaches. We use this method to improve the Rosetta all‐atom forcefield, in which the total energy is expressed as the sum of terms representing different physical interactions as in MM forcefields and the parameters are tuned to reproduce the properties of macromolecular structures. To resolve inaccuracies resulting from possible double counting of interactions, we compare distribution functions from low‐energy modeled structures to those from crystal structures. The structural and physical bases of the deviations between the modeled and reference structures are identified and used to guide forcefield improvements. We describe improvements resolving double counting between backbone hydrogen bond interactions and Lennard‐Jones interactions in helices; between sidechain‐backbone hydrogen bonds and the backbone torsion potential; and between the sidechain torsion potential and Lennard‐Jones interactions. Discrepancies between computed and observed distributions are also used to guide the incorporation of an explicit Cα‐hydrogen bond in β sheets. The method can be used generally to integrate different sources of information for forcefield improvement. Proteins 2011; © 2011 Wiley‐Liss, Inc.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

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. Frank DiMaio
5. Hahnbeom Park
6. Brian Kuhlman
7. Roland L. Dunbrack
8. Rhiju Das

In Molecular Biology

1. David Baker
2. Jeffrey J. Gray
3. Tanja Kortemme
4. Frank DiMaio
5. Rhiju Das
6. Hahnbeom Park
7. Jens Meiler
8. Brian Kuhlman

In Spectroscopy

1. David Baker
2. Frank DiMaio
3. Hahnbeom Park
4. Jeffrey J. Gray
5. Tanja Kortemme
6. Richard Bonneau
7. Brian Kuhlman
8. Roland L. Dunbrack

Citations in the corpus

• Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, background): “To improve the potential, Song et al. (2011) iteratively adjusted the parameters to minimize the difference between the observed and predicted structures.”
• The Rosetta all-atom energy function for macromolecular modeling and design (2017, background): “In addition, and more recently, Song et al ., 94 Conway et al .”
• AbDesign: an algorithm for combinatorial backbone design guided by natural conformations and sequences (2015, background): “…at wileyonlinelibrary.com.] solutions to binding the target epitope; indeed, different natural antibodies are known to bind the same epitope.113 In particular, our results on the anti-lysozyme antibody suggest that AbDesign could propose antibodies that share large regions with natural…”
• Automating human intuition for protein design (2014, methodology): “These were tested by adding the modifications to the standard Rosetta energy terms one at a time.12 ,13 Evolutionary information from a Multiple Sequence Alignment (MSA) was introduced via a position-specific scoring matrix (PSSM) to give a likelihood score to each residue at each position.”
• A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions (2011, unknown): “Song et al. (2011) have recently reported an optimization of Rosetta’s energy function for an earlier version of the rotamer libraries described here.”
• A Combined Covalent-Electrostatic Model of Hydrogen Bonding Improves Structure Prediction with Rosetta (2015, unknown): “43 Other work with Rosetta suggests that adding electrostatics to HBv1 can improve performance in large-scale scienti fi c tests, such as decoy discrimination.”