The goal of this project is to maintain and further develop the existing software DelPhi ( DelPhi provides numerical solutions to the Poisson-Boltzmann Equation (PBE) (both linear and non-linear forms) and calculates the corresponding energies for molecules and geometric objects immersed in water and salt phase or another continuum medium. Electrostatic forces are essential for the function, stability and interactions of virtually all biological macromolecules because most biological macromolecules, especially DNA and RNA, are highly charged. The role of electrostatics is two fold: providing long-range interactions steering biological molecules toward their pre-binding orientations and contributing to the specificity by strong short-range direct interactions. In addition, many biologically important effects such as pH and salt dependence effects are primarily electrostatic in nature. Moreover, the constant progress of nanotechnology requires modeling of systems made of biological molecules and charged metal/dielectric surfaces and objects. Thus, accurate calculations of electrostatic fields and energies are crucial for successful modeling of virtually all biological processes and many other phenomena occurring in nanosystems and nanodevices. We propose to maintain and further develop the DelPhi, the first PB solver used by many researchers as is shown in the main body of the proposal. In addition to the existing features such as assigning different dielectric constants to different regions of space, modeling geometrical objects and charge distributions, treating systems containing mixed salt solutions, we plan to develop new options as modeling implicit/explicit membrane, predicting explicit ion binding and new geometrical objects. In parallel we will modernize the code and the corresponding algorithms and will facilitate interactions with our users.

Public Health Relevance

Electrostatics is essential for function, stability and interactions of virtually all biological macromolecules, including receptor-drug recognition. The central role of electrostatics is due to the fact that most biological macromolecules are highly charged, because they contain many charged amino acids which in turn are essential for structure, function and interactions of variety of biomolecules. Many biologically important effects such as pH and salt dependence effects are primarily electrostatic in nature. Therefore an accurate modeling of electrostatic potential and the corresponding energies is critical for successful drug discovery and optimization.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Brazhnik, Paul
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Clemson University
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Wang, Lin; Zhang, Min; Alexov, Emil (2016) DelPhiPKa web server: predicting pKa of proteins, RNAs and DNAs. Bioinformatics 32:614-5
Chakavorty, Arghya; Li, Lin; Alexov, Emil (2016) Electrostatic component of binding energy: Interpreting predictions from poisson-boltzmann equation and modeling protocols. J Comput Chem 37:2495-507
Peng, Yunhui; Alexov, Emil (2016) Investigating the linkage between disease-causing amino acid variants and their effect on protein stability and binding. Proteins 84:232-9
Li, Lin; Alper, Joshua; Alexov, Emil (2016) Cytoplasmic dynein binding, run length, and velocity are guided by long-range electrostatic interactions. Sci Rep 6:31523
Peng, Yunhui; Alexov, Emil (2016) Cofactors-loaded quaternary structure of lysine-specific demethylase 5C (KDM5C) protein: Computational model. Proteins 84:1797-1809
Getov, Ivan; Petukh, Marharyta; Alexov, Emil (2016) SAAFEC: Predicting the Effect of Single Point Mutations on Protein Folding Free Energy Using a Knowledge-Modified MM/PBSA Approach. Int J Mol Sci 17:512
Peng, Yunhui; Norris, Joy; Schwartz, Charles et al. (2016) Revealing the Effects of Missense Mutations Causing Snyder-Robinson Syndrome on the Stability and Dimerization of Spermine Synthase. Int J Mol Sci 17:
Petukh, Marharyta; Dai, Luogeng; Alexov, Emil (2016) SAAMBE: Webserver to Predict the Charge of Binding Free Energy Caused by Amino Acids Mutations. Int J Mol Sci 17:547
Li, Lin; Alper, Joshua; Alexov, Emil (2016) Multiscale method for modeling binding phenomena involving large objects: application to kinesin motor domains motion along microtubules. Sci Rep 6:23249
Decherchi, Sergio; Berteotti, Anna; Bottegoni, Giovanni et al. (2015) The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning. Nat Commun 6:6155

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