Sharply Tuned pH Response of Genetic Competence Regulation in Streptococcus mutans: a Microfluidic Study of the Environmental Sensitivity of comX.

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 (PcomX) 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 PcomX response from a subpopulation of cells, whereas outside this range the proportion of cells expressing comX declines sharply. Likewise, PcomX 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.

Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions.

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 (Schrödinger-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.

OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space.

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 sulfur-containing 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.

Advancing Free-Energy Calculations of Metalloenzymes in Drug Discovery via Implementation of LFMM Potentials.

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.

Fast Implementation of the Nudged Elastic Band Method in AMBER.

We present a fast implementation of the nudged elastic band (NEB) method into the particle mesh Ewald molecular dynamics module of the Amber software package for both 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 ß-sheet transition in polyalanine, and a large conformational transition in the human 8-oxoguanine-DNA glycosylase with DNA complex (OGG1-DNA). Timing benchmark tests were performed on the explicitly solvated OGG1-DNA system containing ∼50 000 atoms. The GPU-optimized implementation of NEB achieves a more than two orders of magnitude speedup compared with 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.