High-quality and universal empirical atomic charges for chemoinformatics applications.

BackgroundPartial atomic charges describe the distribution of electron density in a molecule and therefore provide clues to the chemical behaviour of molecules. Recently, these charges have become popular in chemoinformatics, as they are informative descriptors that can be utilised in pharmacophore design, virtual screening, similarity searches etc. Especially conformationally-dependent charges perform very successfully. In particular, their fast and accurate calculation via the Electronegativity Equalization Method (EEM) seems very promising for chemoinformatics applications. Unfortunately, published EEM parameter sets include only parameters for basic atom types and they often miss parameters for halogens, phosphorus, sulphur, triple bonded carbon etc. Therefore their applicability for drug-like molecules is limited.ResultsWe have prepared six EEM parameter sets which enable the user to calculate EEM charges in a quality comparable to quantum mechanics (QM) charges based on the most common charge calculation schemes (i.e., MPA, NPA and AIM) and a robust QM approach (HF/6-311G, B3LYP/6-311G). The calculated EEM parameters exhibited very good quality on a training set ([Formula: see text]) and also on a test set ([Formula: see text]). They are applicable for at least 95 % of molecules in key drug databases (DrugBank, ChEMBL, Pubchem and ZINC) compared to less than 60 % of the molecules from these databases for which currently used EEM parameters are applicable.ConclusionsWe developed EEM parameters enabling the fast calculation of high-quality partial atomic charges for almost all drug-like molecules. In parallel, we provide a software solution for their easy computation (http://ncbr.muni.cz/eem_parameters). It enables the direct application of EEM in chemoinformatics

High-dimension profiling data generate a multifunctional peptide-mimic chemo-structure by connecting conserved fragments based on the neutrophil immune defense CAP37 protein as an in-silico antibacterial and woundhealing candidate agent

CAP37, a protein constitutively EXPRESSED in human neutrophils and induced in responseto infection in corneal epithelial cells, plays a significant role in host defense against infection. Initiallyidentified through its potent bactericidal activity for Gram-negative bacteria, it is now known that CAP37regulates numerous host cell functions, including corneal epithelial cell chemotaxis. Delineation of thedomains of CAP37 that define these functions and synthesize bioactive peptides for therapeutic use have alsobeen explored. Novel findings of a multifunctional domain between a 120 and 146 have also been reported.Here, in Biogenea Pharmaceuticals Ltd we for the first time generated a multifunctional peptide-mimicchemo-structure by connecting conserved fragments based on the neutrophil immune defense CAP37 proteinas an in-silico antibacterial and wound-healing canditate agent. This in silico effort was accomplished byutilizing various generated descriptors of proteins, compounds and their interactions resulting in aperformance/cost evaluation study for a GPU-based drug discovery application on volunteer computingapproaches based on Automated Structure-Activity Relationship Minings in Connecting Chemical Structureto Biological Profiles for the generation of novel Computational biomodeling of 3D drug-protein binding freeenergy evaluation

PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations

Real-world observable physical and chemical characteristics are increasingly being calculated from the 3D structures of biomolecules. Methods for calculating pKa values, binding constants of ligands, and changes in protein stability are readily available, but often the limiting step in computational biology is the conversion of PDB structures into formats ready for use with biomolecular simulation software. The continued sophistication and integration of biomolecular simulation methods for systems- and genome-wide studies requires a fast, robust, physically realistic and standardized protocol for preparing macromolecular structures for biophysical algorithms. As described previously, the PDB2PQR web server addresses this need for electrostatic field calculations (Dolinsky et al., Nucleic Acids Research, 32, W665–W667, 2004). Here we report the significantly expanded PDB2PQR that includes the following features: robust standalone command line support, improved pKa estimation via the PROPKA framework, ligand parameterization via PEOE_PB charge methodology, expanded set of force fields and easily incorporated user-defined parameters via XML input files, and improvement of atom addition and optimization code. These features are available through a new web interface (http://pdb2pqr.sourceforge.net/), which offers users a wide range of options for PDB file conversion, modification and parameterization

Stochastic Constrained Extended System Dynamics for Solving Charge Equilibration Models

We present a new stochastic extended Lagrangian solution to charge equilibration that eliminates self-consistent field (SCF) calculations, eliminating the computational bottleneck in solving the many-body solution with standard SCF solvers. By formulating both charges and chemical potential as latent variables, and introducing a holonomic constraint that satisfies charge conservation, the SC-XLMD method accurately reproduces structural, thermodynamic, and dynamics properties using ReaxFF, and shows excellent weak- and strong-scaling performance in the LAMMPS molecular simulation package

Computational Investigation of Ionic Diffusion in Polymer Electrolytes for Lithium-Ion Batteries

Energy storage is a critical problem in the 21st century and improvements in battery technology are required for the next generation of electric cars and electronic devices. Solid polymer electrolytes show promise as a material for use in long-lifetime, high energy density lithium-ion batteries. Improvements in ionic conductivity, however, for the development of commercially viable materials, and, to this end, a series of computational studies of ionic diffusion were performed. First, pulsed charging is examined as a technique for inhibiting the growth of potentially dangerous lithium dendrites. The effective timescale for pulse lengths is determined as a function of cell geometry. Next, the atomistic diffusion mechanism in the leading polymer electrolyte, PEO-LiTFSI, is characterized as a function of temperature, molecular weight, and ionic concentration using molecular dynamics simulations. A novel model for describing coordination of lithium to the polymer structure is developed which describes two types of interchain motion "hops" and "shifts," the former of which is shown to contribute significantly to ionic diffusion. The methodology developed in this study is then applied to a new problem – the adsorption of CO2 at the surface of semi-permeable polymer membranes. Finally, a new method, PQEq, is developed, which provides an improved description of electrostatic interactions with the inclusion of explicit polarization, Gaussian shielding, and charge equilibration. The dipole interaction energies obtained from PQEq are shown to be in excellent agreement with QM and a preliminary application of PQEq to a polymer electrolyte suggest that it can provide an improved description of ionic diffusion. Taken as a whole, these techniques show promise as tools to explore and characterize novel materials for lithium-ion batteries

Atomic radius and charge parameter uncertainty in biomolecular solvation energy calculations

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a new method for quantifying this uncertainty in implicit solvation calculations of small molecules using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many more types of atomic charges; therefore, construction of surrogate models for the charge parameter space requires compressed sensing combined with an iterative rotation method to enhance problem sparsity. We demonstrate the application of the method by presenting results for the uncertainties in small molecule solvation energies based on these approaches. The method presented in this paper is a promising approach for efficiently quantifying uncertainty in a wide range of force field parameterization problems, including those beyond continuum solvation calculations.The intent of this study is to provide a way for developers of implicit solvent model parameter sets to understand the sensitivity of their target properties (solvation energy) on underlying choices for solute radius and charge parameters

Molecular Dynamics Simulations of Biological Molecules on the Natively Oxidized Titanium Surface

In order to investigate the surface properties of metals in a realistic fashion it is crucial to take into account the thin oxide layer that forms spontaneously when the surface is exposed to an oxidising environment. Starting from reference oxide layer structures obtained in extensive first-principles molecular dynamics simulations, we have developed a novel classical potential which is able to reproduce the topological binding features of the amorphous oxide network on Ti as well as the interfacial behaviour of the TiOx/water interface. By combination of this specific potential with well-established biomolecular force fields, we have performed classical simulations of small organic molecules on the oxide surface and successfully compared their results to DFT calculations. The final model is applied to elucidate the microscopic mechanisms that take place at experimentally relevant bio-interfaces. In particular, we focus on the titanium-binding peptide motif minTBP-1. By using advanced simulation techniques, such as metadynamics, replica exchange molecular dynamics, as well as steered molecular dynamics, we have quantified the adhesion strength to the oxidized titanium surface and to the oxidized silicon surface in excellent agreement with experimental results. A microscopical analysis of the simulations reveals that the stronger adhesion to titanium compared to silicon is primarily caused by differences in the interfacial water structure. Furthermore, we have employed the model to calculate the contact forces between two water-covered titania nanoparticles and compared the results to the findings from AFM experiments