New Generation DelPhi: large systems and beyond electrostatics

Clemson University, Clemson SC

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Abstract

? DESCRIPTION (provided by applicant): 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 Å, 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.