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

Abstract

Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking, have been parameterized 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.

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

“…The atomic radii and well depths are derived from small molecule liquid phase data optimized in context of the energy model. 50 …”

“…To evaluate these interactions, Rosetta uses Coulomb’s Law with partial charges originally taken from CHARMM and adjusted via a group optimization scheme (Table S3). 50 Coulomb’s law is a pairwise term commonly expressed in terms of the distance between atoms i and j ( d i,j ), dielectric constant ε , partial atomic charges for each atom q i and q j , and Coulomb’s constant, C 0 = 322 Å kcal/mol e −2 (with e being the elementary charge) (Eq. 6).…”

“…50,51 During design, this energy term helps normalize the observed frequencies of the different amino acids. When design is turned off, the term contributes a constant offset for a fixed sequence.…”

“…92 More recently, we fix physics-based terms with weights of 1.0 and perturb other weights and atomic-level parameters using a Nelder-Mead 93 scheme to optimize agreement of Rosetta calculations with small-molecule thermodynamic data and high-resolutions structural features. 50 The energy function parameters have evolved over the years by optimizing the performance of multiple scientific benchmarks (Table 2). 50,51,94 These benchmarks were chosen to test recovery of native-like structural features, ranging from individual hydrogen bond geometries to thermodynamic properties and interface conformations.…”

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

“…The effect was corrected for later targets and in other Rosetta RNA applications (Chou et al 2016). Independently, protein modeling work in Rosetta discovered a similar issue (Park et al 2016). …”