ff19SB: Amino-Acid Specific Protein Backbone Parameters Trained Against Quantum Mechanics Energy Surfaces in Solution

Abstract

<p>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 molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled ϕ/ψ parameters using 2D ϕ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, we have performed a total of ~5 milliseconds MD simulations in explicit solvent. Our results show that after amino-acid specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino acid specific Protein Data Bank (PDB) Ramachandran maps better, but also shows significantly improved capability to differentiate amino acid dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. </p>

The Mycobacterium lipid transporter MmpL3 is dimeric in detergent solution, SMALPs and reconstituted nanodiscs

“…All hydrogens were then removed using pdb4amber , for the file to be correctly read by the preparatory program tLeap 35 . Force field ff19SB, lipid17 and TIP3P were sourced into tLeap to define protein, lipid and water counterparts respectively 37,38 . Finally, topology and coordinate files were generated.…”

TopEC: Improved classification of enzyme function by a localized 3D protein descriptor and 3D Graph Neural Networks

“…To distinguish the chemical space among atom and residue types, respectively, we annotate each atom or residue based on the ff19SB forcefield from AMBER 68 . We furthermore extend this for heavy atoms and hydrogens into 21 annotations for residues, 31 annotations for heavy atoms, and 20 annotations for hydrogens.…”

IDRWalker: A Random Walk based Modeling Tool for Disordered Regions in Proteins

“…The modeling process of random walk is independent across different chains, so when reading input files, it is processed chain by chain. For each chain, the program, based on the read sequence, creates a chain containing the complete sequence by copying the amino acid residue templates which are prepared in advance according to the AMBER ff19SB force field 25 . Subsequently, according to the residue numbers, the 3D coordinates in the input PDB file are written to the newly created chain.…”

“…In addition to the known residue, the formula also requires bond lengths, angles, and dihedrals. In protein structures, the bond lengths and bond angles are less variable, which can therefore be given directly, for example, using reference values in the AMBER ff19SB force field 25 . In contrast, the dihedrals can be rotated relatively free but with some regularities that can be depicted by the Ramachandran plot 26 .…”