pH-dependent mechanism of nitric oxide release in nitrophorins 2 and 4

Nitrophorins are NO carrier proteins that transport and release NO through a pH-dependent conformational change. They bind NO tightly in a low pH environment and release it in a higher pH environment. Experimental evidence shows that the increase in the NO dissociation equilibrium constant, K d, is due mainly to an increase in the NO release rate. Structural and kinetic data strongly suggest that NPs control NO escape by modulating its migration from the active site to the solvent through a pH-dependent conformational change. NP2 and NP4 are two representative proteins of the family displaying a 39% overall sequence identity, and interestingly, NP2 releases NO slower than NP4. The proposal that NPs' NO release relies mainly on the NO escape rate makes NPs a very peculiar case among typical heme proteins. The connection between the pH-dependent conformational change and ligand release mechanism is not fully understood and the structural basis for the pH induced structural transition and the different NO release patterns in NPs are unresolved, yet interesting issues. In this work, we have used state of the art molecular dynamics simulations to study the NO escape process in NP2 and NP4 in both the low and high pH states. Our results show that both NPs modulate NO release by switching between a "closed" conformation in a low pH environment and an "open" conformation at higher pH. In both proteins, the change is caused by the differential protonation of a common residue Asp30 in NP4 and Asp29 in NP2, and the NO escape route is conserved. Finally, our results show that, in NP2, the conformational change to the "open" conformation is smaller than that for NP4 which results in a higher barrier for NO release.Fil: Swails, Jason M.. University of Florida; Estados UnidosFil: Meng, Yilin. University of Florida; Estados UnidosFil: Walker, F. Ann. University of Arizona; Estados UnidosFil: Marti, Marcelo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Estrin, Dario Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Roitberg, Adrián. University of Florida; Estados Unido

Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix.

The refinement of biomolecular crystallographic models relies on geometric restraints to help to address the paucity of experimental data typical in these experiments. Limitations in these restraints can degrade the quality of the resulting atomic models. Here, an integration of the full all-atom Amber molecular-dynamics force field into Phenix crystallographic refinement is presented, which enables more complete modeling of biomolecular chemistry. The advantages of the force field include a carefully derived set of torsion-angle potentials, an extensive and flexible set of atom types, Lennard-Jones treatment of nonbonded interactions and a full treatment of crystalline electrostatics. The new combined method was tested against conventional geometry restraints for over 22 000 protein structures. Structures refined with the new method show substantially improved model quality. On average, Ramachandran and rotamer scores are somewhat better, clashscores and MolProbity scores are significantly improved, and the modeling of electrostatics leads to structures that exhibit more, and more correct, hydrogen bonds than those refined using traditional geometry restraints. In general it is found that model improvements are greatest at lower resolutions, prompting plans to add the Amber target function to real-space refinement for use in electron cryo-microscopy. This work opens the door to the future development of more advanced applications such as Amber-based ensemble refinement, quantum-mechanical representation of active sites and improved geometric restraints for simulated annealing

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.Comment: 16 pages, 5 figure

OpenMM 8:Molecular Dynamics Simulation with Machine Learning Potentials

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.</p

pytraj: v1.0.6

Interactive data analysis for molecular dynamics simulation

Amber-MD/pytraj: v2.0.0

Same as the version in AmberTools 1

Improved Accuracy for Constant pH-REMD Simulations through Modification of Carboxylate Effective Radii

The accuracy of computational models for simulating biomolecules under specific solution pH conditions is critical for properly representing the effect of pH in biological processes. Constant pH (CpH) simulations involving implicit solvent using the AMBER software often incorrectly estimate p<i>K</i>_a values of aspartate and glutamate residues due to large effective radii stemming from the presence of dummy protons. These inaccuracies stem from problems in the sampled ensembles of titratable residues that can influence other observable pH-dependent behavior, such as conformational change. We investigate new radii assignments for atoms in titratable residues with carboxylate groups to mitigate the systematic overestimation in the current method. We find that decreased carboxylate radii correspond with increased agreement with experimentally derived p<i>K</i>_a values for residues in hen egg-white lysozyme and Δ+PHS variants of staphylococcal nuclease (SNase) and improved conformation state sampling compared to experimentally described expectations of native-like structure. Our CpH simulations suggest that decreasing the effective radii of these carboxylate groups is essential for eliminating a significant source of systematic error that hurts the accuracy of both conformational and protonation state sampling with implicit solvent

Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix

Abstract The refinement of biomolecular crystallographic models relies on geometric restraints to help address the paucity of experimental data typical in these experiments. Limitations in these restraints can degrade the quality of the resulting atomic models. Here we present an integration of the full all-atom Amber molecular dynamics force field into Phenix crystallographic refinement, which enables a more complete modeling of biomolecular chemistry. The advantages of the force field include a carefully derived set of torsion angle potentials, an extensive and flexible set of atom types, Lennard-Jones treatment of non-bonded interactions and a full treatment of crystalline electrostatics. The new combined method was tested against conventional geometry restraints for over twenty-two thousand protein structures. Structures refined with the new method show substantially improved model quality. On average, Ramachandran and rotamer scores are somewhat better; clash scores and MolProbity scores are significantly improved; and the modelling of electrostatics leads to structures that exhibit more, and more correct, hydrogen bonds than those refined with traditional geometry restraints. We find in general that model improvements are greatest at lower resolutions, prompting plans to add the Amber target function to real-space refinement for use in electron cryo-microscopy. This work opens the door to the future development of more advanced applications such as Amber-based ensemble refinement, quantum mechanical representation of active sites and improved geometric restraints for simulated annealing. IMPORTANT this document contains embedded data - to preserve data integrity, please ensure where possible that the IUCr Word tools (available from http://journals.iucr.org/services/docxtemplate/ ) are installed when editing this document. Synopsis The full Amber force field has been integrated into Phenix as an alternative refinement target. With a slight loss in speed, it achieves improved stereochemistry, fewer steric clashes and better hydrogen bonds

A Coupled Ionization-Conformational Equilibrium Is Required To Understand the Properties of Ionizable Residues in the Hydrophobic Interior of Staphylococcal Nuclease

Ionizable residues in the interior of proteins play essential roles, especially in biological energy transduction, but are relatively rare and seem incompatible with the complex and polar environment. We perform a comprehensive study of the internal ionizable residues on 21 variants of staphylococcal nuclease with internal Lys, Glu, or Asp residues. Using pH replica exchange molecular dynamics simulations, we find that, in most cases, the p<i>K</i>_a values of these internal ionizable residues are shifted significantly from their values in solution. Our calculated results are in excellent agreement with the experimental observations of the Garcia-Moreno group. We show that the interpretation of the experimental p<i>K</i>_a values requires the study of not only protonation changes but also conformational changes. The coupling between the protonation and conformational equilibria suggests a mechanism for efficient pH-sensing and regulation in proteins. This study provides new physical insights into how internal ionizable residues behave in the hydrophobic interior of proteins

Interpretation of pH–Activity Profiles for Acid–Base Catalysis from Molecular Simulations

The measurement of reaction rate as a function of pH provides essential information about mechanism. These rates are sensitive to the p<i>K</i>_a values of amino acids directly involved in catalysis that are often shifted by the enzyme active site environment. Experimentally observed pH–rate profiles are usually interpreted using simple kinetic models that allow estimation of “apparent p<i>K</i>_a” values of presumed general acid and base catalysts. One of the underlying assumptions in these models is that the protonation states are uncorrelated. In this work, we introduce the use of constant pH molecular dynamics simulations in explicit solvent (CpHMD) with replica exchange in the pH-dimension (pH-REMD) as a tool to aid in the interpretation of pH–activity data of enzymes and to test the validity of different kinetic models. We apply the methods to RNase A, a prototype acid–base catalyst, to predict the macroscopic and microscopic p<i>K</i>_a values, as well as the shape of the pH–rate profile. Results for apo and cCMP-bound RNase A agree well with available experimental data and suggest that deprotonation of the general acid and protonation of the general base are not strongly coupled in transphosphorylation and hydrolysis steps. Stronger coupling, however, is predicted for the Lys41 and His119 protonation states in apo RNase A, leading to the requirement for a microscopic kinetic model. This type of analysis may be important for other catalytic systems where the active forms of the implicated general acid and base are oppositely charged and more highly correlated. These results suggest a new way for CpHMD/pH-REMD simulations to bridge the gap with experiments to provide a molecular-level interpretation of pH–activity data in studies of enzyme mechanisms