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>

Water-Mediated Electronic Structure of Oligopeptides Probed by Their UV Circular Dichroism, Absorption Spectra, and Time-Dependent DFT Calculations

“…In this context, the question arises whether force fields with force constants obtained from DFT-calculations on model peptides in the gas phase or even in implicit water can sufficiently describe the energetics and dynamics of peptide/protein backbones. 20 It is obvious that a more complete picture than that reported in this paper could be gained by TDDFT calculations with a larger number of hydration water molecules. DFT-based geometry optimizations of tripeptides have recently been achieved with up to 30 water molecules.…”

“…[9][10][11][12][13][14][15][16][17] Recent results from MD and DFT computations have pointed to the same direction. [18][19][20][21][22][23][24] All residues predominantly sample the upper left quadrant of the Ramachandran plot, and they differ mostly in terms of their population of the polyproline II (pPII) (φ > -100 0 , ψ > 100 0 ) and β-strand region (φ ≤ -100 0 , ψ > 100 0 ). The former is stabilized enthalpically while the latter is favored entropically.…”

Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical Shifts

“…Other properties, such as the radius of gyration ( R g ), are sensitive to the global mass distribution of the conformations. For expanded ensembles, an increase of helicity leads to more collapsed ensembles, as helical structures are compact [ 66 , 67 ]. For the a03ws, reweighting leads to an overall decrease of helicity ( Figure S10 ), and therefore to a larger R g .…”

Computational Prediction of Mutational Effects on the SARS-CoV-2 Binding by Relative Free Energy Calculations

“…First, MD simulations were necessary to relax the undesired contacts both in the homology model and in the crystal structure, and thus the quality of the protein force field was heavily relied on to produce conformations close to the native states. A more accurate general force field and water model could be developed and used to improve on predicting binding properties [48][49][50] . Secondly, our single-point alaninescanning calculations only yielded the contribution of the individual residue without taking the coupling effect caused by double or multiple mutations into account.…”