Webinar by Chuan Tian: ff19SB - Amino acid specific protein backbone parameters trained against quantum mechanics energies in solution (Sep 12, 2019)
Posted on 3 Sep 2019 by Karmen Condic-Jurkic
Chuan Tian from the Simmerling lab will visit the Chodera lab at MSKCC and give a talk about the latest Amber force field - ff19SB. Join the seminar via Zoom in real time on Sep 12 at 1 pm (EDT), or watch it later on our YouTube channel.
**Abstract:** Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the classical force field (FF), a set of functions with adjustable parameters to compute the potential energy from atomic positions. The relatively simple terms in most of the current force fields are computationally advantageous which enable the simulation of biologically important macromolecules at biologically relevant timescale. However, the overall quality of the FF, including our previously published ff14SB, is limited by the assumptions that were made years ago. (1) An overly symmetric φ/ψ dihedral energy map arises from the uncoupled cosine functions used to model these two degrees of freedom in the protein backbone. (2) The model does not show sufficient dependence of the backbone energetics on the amino acids, probably because the parameters developed for the simple amino acid Ala were applied to all other amino acids without checking the quality of the transferability. (3) The fixed partial charges were trained for aqueous solution, but the dihedral parameters were all fit to gas-phase QM, thus the resulting dihedral parameters actually counteract the intended polarization effect and introduce significant internal inconsistency in the model. In our new force field ff19SB, we have significantly improved the backbone profiles for all 20 amino acids. We have developed an amino-acid specific CMAP term by fitting against in-solution QM data. Our results show that the new FF not only better reproduces various types of experimental data on amino-acid specific properties such as NMR scalar coupling and helical propensity, but it also improves secondary structure content in small peptides and maintains reasonably accurate protein NMR order parameters.