We report on the development and validation of the OPLS4 force field. OPLS4 builds upon our previous work with OPLS3e to improve model accuracy on challenging regimes of drug-like chemical space that includes molecular ions and sulfurcontaining moieties. A novel parametrization strategy for charged species, which can be extended to other systems, is introduced. OPLS4 leads to improved accuracy on benchmarks that assess small-molecule solvation and protein−ligand binding.

Genetic competence in Streptococcus mutans is a transient state that is regulated in response to multiple environmental inputs. These include extracellular pH and the concentrations of two secreted peptides, designated CSP (competence-stimulating peptide) and XIP (comX-inducing peptide). The role of environmental cues in regulating competence can be difficult to disentangle from the effects of the organism's physiological state and its chemical modification of its environment. We used microfluidics to control the extracellular environment and study the activation of the key competence gene comX. We find that the comX promoter (P comX ) responds to XIP or CSP only when the extracellular pH lies within a narrow window, about 1 pH unit wide, near pH 7. Within this pH range, CSP elicits a strong P comX response from a subpopulation of cells, whereas outside this range the proportion of cells expressing comX declines sharply. Likewise, P comX is most sensitive to XIP only within a narrow pH window. While previous work suggested that comX may become refractory to CSP or XIP stimulus as cells exit early exponential phase, our microfluidic data show that extracellular pH dominates in determining sensitivity to XIP and CSP. The data are most consistent with an effect of pH on the ComR/ComS system, which has direct control over transcription of comX in S. mutans. Genetic competence is a transient physiological state during which a bacterial cell is able to internalize DNA from its environment. Competence occurs in many bacterial species but was first described in the streptococci, where its regulation has been the subject of intensive study (1, 2). In the oral pathogen Streptococcus mutans, competence is important not only because it contributes to genetic diversity but also because its regulation is closely intertwined with the manifestation of virulence-related behaviors, including bacteriocin production, biofilm formation, tolerance of low pH, and carbohydrate catabolism (3-7). S. mutans regulates competence in part through two secreted quorum-sensing peptides, designated competence-stimulating peptide (CSP) and comX-inducing peptide (XIP). Interestingly, the activity of these peptides depends on environmental parameters, including pH, carbohydrate, and media (8-11). Through mechanisms that are not well understood, the competence regulon integrates the peptide signals with environmental and internal parameters (12, 13) to trigger a transient state of competence during early exponential growth phase.The interaction of the extracellular environment with competence and related virulence behaviors is important in the context of oral biofilms. Heterogeneous local environments of pH and oxygen/redox, carbohydrate, and secreted-peptide concentrations in a biofilm could potentially lead to spatial variations in virulence gene expression in S. mutans (14-17). pH is particularly important because the fermentation of carbohydrates by S. mutans generates acids that can rapidly modify the pH of the environment. The pH in a biofilm ...

Transferable high dimensional neural network potentials (HDNNPs) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previously reported such a potential (Schrodinger-ANI) that has broad coverage of druglike molecules. We extend that work here to cover ionic and zwitterionic druglike molecules expected to be relevant to drug discovery research activities. We report a novel HDNNP architecture, which we call QRNN, that predicts atomic charges and uses these charges as descriptors in an energy model that delivers conformational energies within chemical accuracy when measured against the reference theory it is trained to. Further, we find that delta learning based on a semiempirical level of theory approximately halves the errors. We test the models on torsion energy profiles, relative conformational energies, geometric parameters, and relative tautomer errors.

We present a fast implementation of the nudged elastic band (NEB) method into the particle mesh Ewald molecular dynamics (pmemd) module of the Amber software package both for central processing units (CPU) and graphics processing units (GPU). The accuracy of the new implementation has been validated for three cases: a conformational change of alanine dipeptide, the α-helix to β-sheet transition in polyalanine, and a large conformational transition in human 8oxoguanine-DNA glycosylase with DNA complex (OGG1-DNA). Timing benchmark tests were performed on the explicitly solvated OGG1-DNA system containing ~50k atoms. The GPUoptimized implementation of NEB achieves more than two orders of magnitude speedup compared to the previous CPU implementation performed with a two-core CPU processor. The speed and scalable features of this implementation will enable NEB applications on larger and more complex systems.

To address some of the inherent challenges in modeling metalloenzymes, we here report an extension to the functional form of the OPLS3e force field to include terms adopted from the ligand field molecular mechanics (LFMM) model, including the angular overlap and Morse potential terms. The integration of these terms with OPLS3e, herein referred to as OPLS3e+M, improves the description of metal–ligand interactions and provides accurate relative binding energies and geometric preferences of transition-metal complexes by training to gas-phase density functional theory (DFT) energies. For [Cu­(H2O)4]2+, OPLS3e+M significantly improves H2O binding energies and the geometric preference of the tetra-aqua Cu2+ complex. In addition, we conduct free-energy perturbation calculations on two pharmaceutically relevant metalloenzyme targets, which include chemical modifications at varying proximity to the binding-site metals, including changes to the metal-binding moiety of the ligand itself. The extensions made to OPLS3e lead to accurate predicted relative binding free energies for these series (mean unsigned error of 1.29 kcal mol–1). Our results provide evidence that integration of the LFMM model with OPLS3e can be utilized to predict thermodynamic quantities for such systems near chemical accuracy. With these improvements, we anticipate that robust free-energy perturbation calculations can be employed to accelerate the drug development efforts for metalloenzyme targets.