TY - DATA T1 - Building a More Predictive Protein Force Field: A Systematic and Reproducible Route to AMBER-FB15 PY - 2017/03/17 AU - Lee-Ping Wang AU - Keri A. McKiernan AU - Joseph Gomes AU - Kyle A. Beauchamp AU - Teresa Head-Gordon AU - Julia E. Rice AU - William C. Swope AU - Todd J. Martínez AU - Vijay S. Pande UR - https://acs.figshare.com/articles/journal_contribution/Building_a_More_Predictive_Protein_Force_Field_A_Systematic_and_Reproducible_Route_to_AMBER-FB15/4828321 DO - 10.1021/acs.jpcb.7b02320.s001 L4 - https://ndownloader.figshare.com/files/8009464 KW - simulation results KW - TIP 3P water model KW - AMBER-FB 15 KW - Predictive Protein Force Field KW - accuracy KW - AMBER-FB 15 protein force field KW - protein force field KW - ForceBalance software package KW - quantum chemical data N2 - The increasing availability of high-quality experimental data and first-principles calculations creates opportunities for developing more accurate empirical force fields for simulation of proteins. We developed the AMBER-FB15 protein force field by building a high-quality quantum chemical data set consisting of comprehensive potential energy scans and employing the ForceBalance software package for parameter optimization. The optimized potential surface allows for more significant thermodynamic fluctuations away from local minima. In validation studies where simulation results are compared to experimental measurements, AMBER-FB15 in combination with the updated TIP3P-FB water model predicts equilibrium properties with equivalent accuracy, and temperature dependent properties with significantly improved accuracy, in comparison with published models. We also discuss the effect of changing the protein force field and water model on the simulation results. ER -