The main objects in molecular biology are proteins, DNAs and RNAs, along with various small molecules and large macromolecular assemblages. These objects are frequently involved in various phenomena in nano-science together with nano-particles. With the progress of both experimental and computational approaches, nowadays researchers are expanding the repertoire by investigating biological characteristics of systems like microtubules, viruses and cellular organelles. Such systems are posing two major challenges: (a) frequently their atomic structures are not experimentally available and have to be modeled; and (b) they have large dimensions above 1,000 , which cannot be handled by most of the existing modeling packages. With this proposal we plan to address these challenges: (a) further expand the capabilities of Protein-Nano Object Integrator (ProNOI) which allows for atomic style modeling of objects traced from experimental images (as Cryo-EM image); (b) expand DelPhi capabilities, in terms of RAM usage and speed of calculations, to allow systems with large dimensions to be modeled routinely. Furthermore, the work from the previous funding period resulted in object-oriented C++ DelPhi code and one of the core component is the finite-difference (FD) algorithm. Since the FD is one of the most universal numerical technique of solving differential equations (DE), we will develop plugins to solve DE describing quantities different from electrostatics (such as temperature, heat, ion density) and will enable community-driven research to include modeling of other quantities of interest for the biophysical community. Furthermore, frequently researchers want to model systems comprised of a macromolecule interacting with large cellular component (such as microtubule), while exploring different protein orientations and binding modes of the protein binding to it. To facilitate such a research, we will expand the capability of existing DelPhi focusing technique to allow for parent-son focusing runs with off-grid centering and nonreciprocal scale. We will complement the code with a module capable of automatically generating alternative positions and orientations of the domain/protein of interest, computing their energies and utilizing Monte Carlo simulation procedure to assess their probabilities.
Electrostatic interactions and the corresponding electrostatic energy components are essential for function, stability and interactions of virtually all biologica macromolecules, nanoparticles and substrates. The significant role of electrostatics is due to the fact that the atoms within macromolecules are charged and situated at very short distances of several Angstroms. It is well documented that many biologically important effects such as pH and salt dependence effects are primarily electrostatic in nature as well. Therefore an accurate modeling of electrostatic potential and the corresponding energies is critical for successful modeling of biological processes at molecular level. These processes include receptor-drug interactions, nanoparticle-membrane binding, and the effects of amino acid substitutions on protein stability and affinity. Thus, further development and maintenance of DelPhi package will be beneficial for biomedical community in their efforts of addressing the effects of nsSNPs on human health, disease risk and in developing therapeutic solutions. In addition, the proposed development will allow DelPhi to model non- electrostatic quantities as well and this will further extend the scope of biomedical problems that can be investigated with DelPhi.
|Tajielyato, Nayere; Li, Lin; Peng, Yunhui et al. (2018) E-hooks provide guidance and a soft landing for the microtubule binding domain of dynein. Sci Rep 8:13266|
|Chakravorty, Arghya; Jia, Zhe; Peng, Yunhui et al. (2018) Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents. Front Mol Biosci 5:25|
|Peng, Yunhui; Sun, Lexuan; Jia, Zhe et al. (2018) Predicting protein-DNA binding free energy change upon missense mutations using modified MM/PBSA approach: SAMPDI webserver. Bioinformatics 34:779-786|
|Pahari, Swagata; Sun, Lexuan; Basu, Sankar et al. (2018) DelPhiPKa: Including salt in the calculations and enabling polar residues to titrate. Proteins 86:1277-1283|
|Spellicy, Catherine J; Norris, Joy; Bend, Renee et al. (2018) Key apoptotic genes APAF1 and CASP9 implicated in recurrent folate-resistant neural tube defects. Eur J Hum Genet 26:420-427|
|Li, Lin; Jia, Zhe; Peng, Yunhui et al. (2017) DelPhiForce web server: electrostatic forces and energy calculations and visualization. Bioinformatics 33:3661-3663|
|Chakravorty, Arghya; Jia, Zhe; Li, Lin et al. (2017) A New DelPhi Feature for Modeling Electrostatic Potential around Proteins: Role of Bound Ions and Implications for Zeta-Potential. Langmuir 33:2283-2295|
|Li, Lin; Jia, Zhe; Peng, Yunhui et al. (2017) Forces and Disease: Electrostatic force differences caused by mutations in kinesin motor domains can distinguish between disease-causing and non-disease-causing mutations. Sci Rep 7:8237|
|Jia, Zhe; Li, Lin; Chakravorty, Arghya et al. (2017) Treating ion distribution with Gaussian-based smooth dielectric function in DelPhi. J Comput Chem 38:1974-1979|
|Li, Lin; Chakravorty, Arghya; Alexov, Emil (2017) DelPhiForce, a tool for electrostatic force calculations: Applications to macromolecular binding. J Comput Chem 38:584-593|
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