Simultaneous Optimization of Biomolecular Energy Functions on Features From Small Molecules and Macromolecules

J Chem Theory Comput. 2016 Dec 13;12(12):6201-6212. doi: 10.1021/acs.jctc.6b00819. Epub 2016 Nov 7.


Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking have been parametrized using existing macromolecular structural data; this contrasts molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein-protein and protein-ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties.

MeSH terms

  • Hydrogen Bonding
  • Ligands
  • Molecular Docking Simulation
  • Protein Binding
  • Protein Structure, Tertiary
  • Proteins / chemistry*
  • Proteins / metabolism
  • Static Electricity
  • Thermodynamics


  • Ligands
  • Proteins