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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM093937-08
Application #
9433659
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Lyster, Peter
Project Start
2010-08-10
Project End
2020-02-29
Budget Start
2018-03-01
Budget End
2019-02-28
Support Year
8
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Clemson University
Department
Physics
Type
Schools of Arts and Sciences
DUNS #
042629816
City
Clemson
State
SC
Country
United States
Zip Code
29634
Li, Lin; Jia, Zhe; Peng, Yunhui et al. (2017) DelPhiForce web server: electrostatic forces and energy calculations and visualization. Bioinformatics 33:3661-3663
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
Li, Lin; Chakravorty, Arghya; Alexov, Emil (2017) DelPhiForce, a tool for electrostatic force calculations: Applications to macromolecular binding. J Comput Chem 38:584-593
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
Peng, Yunhui; Sun, Lexuan; Jia, Zhe et al. (2017) Predicting protein-DNA binding free energy change upon missense mutations using modified MM/PBSA approach: SAMPDI webserver. Bioinformatics :
Hoffman, Laurel; Li, Lin; Alexov, Emil et al. (2017) Cytoskeletal-like Filaments of Ca2+-Calmodulin-Dependent Protein Kinase II Are Formed in a Regulated and Zn2+-Dependent Manner. Biochemistry 56:2149-2160
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
Yang, Ye; Kucukkal, Tugba G; Li, Jing et al. (2016) Binding Analysis of Methyl-CpG Binding Domain of MeCP2 and Rett Syndrome Mutations. ACS Chem Biol 11:2706-2715
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
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

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