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 aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms 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 for 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. Of the explicit water models tested here, we recommend use of OPC with ff19SB.

<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>

We applied density functional theory to investigate the mixed aldol condensation of acetone and formaldehyde in acid zeolites HZSM-5 and HY, as a prototypical bond-forming reaction in biofuel production. We modeled the acid-catalyzed reaction in HZSM-5 and HY in two steps: keto− enol tautomerization of acetone and bimolecular condensation between formaldehyde and the acetone enol. For both acid zeolites, the keto−enol tautomerization of acetone was found to be the rate-determining step, consistent with the accepted mechanism in homogeneous acid-catalysis. Convergence studies of the activation energy for keto−enol tautomerization, with respect to cluster sizes of HZSM-5 and HY, exhibit rather different convergence properties for the two zeolites. The keto−enol activation energy was found to converge in HY to ∼20 kcal/mol for a cluster with 11 tetrahedral atoms (11T cluster), which does not complete the HY supercage. In contrast, the activation energy for HZSM-5 reaches an initial plateau at a value of ∼28 kcal/mol for clusters smaller than 20T and then converges to ∼20 kcal/mol for clusters of size 26T or greater, well beyond the completion of the HZSM-5 pore. As such, completing a zeolite pore surrounding a Brønsted acid site may be insufficient to converge activation energies; instead, we recommend an approach based on converging active-site charge.

Maximum pore size (pore size + vibrational amplitude), which is roughly independent of temperature, predicts zeolite pore size for bulky molecules.

We have applied density functional theory calculations to systematically investigate zeolite cluster-size convergence for two acid-zeolite-catalyzed processes related to the conversion of biomass: (1) the keto–enol tautomerization of acetone in HZSM-5 and HY and (2) the protonation and ring opening of furan in HZSM-5. We have used these reactions as platforms to study two different approaches for constructing successively larger cluster models of zeolites, with the aim of determining a protocol that converges the energy differences with minimal system size. One approach for cluster design involves counting framework bonds from the Brønsted acid-site aluminum atom. Another approach involves applying multicentered spherical cutoffs based on geometries of the zeolite active site, the adsorbed reactant, and the adsorbed product. We have investigated the convergence of reaction energies using single-point calculations on clusters containing as many as 166 tetrahedral (T) atoms and geometry optimizations on clusters with as many as 78 T atoms. For all reactions studied, convergence rates of single-point reaction energies agree well with those from geometry-optimized clusters. In addition, converged and optimized reaction energies agree well with previously published values for all reactions. Our central finding is that clusters generated with multicentered spherical cutoffs yield converged reaction energies with smaller system sizes than clusters generated by counting framework bonds. This method, employing a single length scale (5 Å), converges reaction energies with respect to system size to within chemical accuracy (±1 kcal/mol), and it includes between 15 and 34 T atoms in the cluster depending on the process and zeolite framework under investigation. We suggest a general protocol for generating such clusters for subsequent use in computational studies of zeolites and other heterogeneous catalysts.