Montgomery Pettitt of The University of Texas Medical Branch (UTMB) is supported by an award from the Chemistry of Life Processes Program in the Chemistry Division to create a new set of computational tools for simulating and understanding the fundamental properties of proteins in solution, with application to protein design and the development of new materials and biotechnologies. Some of the most challenging systems in biotechnology applications are those involving liquids, particularly mixtures of biopolymers in water. Professor Pettitt is developing a new computational framework for the molecular design of proteins based on current theories of how liquids behave. Solutions of proteins, which serve as structural components and perform catalytic chemistry, are central to much of biotechnology. This study will enable the rapid screening of proposed protein modifications computationally with greatly improved accuracy compared to existing methods. Codes, scripts, and documentation developed in this project are being made freely-available to the simulation community, and can be interfaced to widely-used molecular dynamics simulation codes. The project is recruiting several students each year for a 10-week internship in computational biology in Professor Pettitt's laboratory, through outreach efforts at local HBCU and Hispanic-serving institutions and the ACS SEED and R. A. Welch Summer Scholar high school programs. There is close cooperation with the UTMB Sealy Center for Structural Biology, which provides a venue for student research presentations at research symposia and conferences, and opportunities for research collaborations using the new methodology and to provide feedback on future enhancements.
The mechanisms governing recognition between proteins, ligands and the transition of proteins from their unfolded state to their native state remain as fundamental chemical questions. Those equilibria are governed by free energy differences in solution. The calculation of solvation free energies by explicit computer simulations can be accurate, but is computationally expensive and essentially prohibitive for entire proteins, even on large supercomputing systems. This project is developing precomputed solvent distributions ("proximal (radial) distribution functions", or pDFs) extracted from benchmark all-atom molecular dynamics simulations to produce a near neighbor approximation to the solvation density profile about arbitrary protein solutes. The initial libraries of pDFs are being developed for the widely-used CHARMM force field. The principal goals of the research are to (1) generate pDF libraries for all naturally-occurring amino acids and additional important functional groups, (2) develop efficient methods based on the pDFs to compute total and relative solvation free energies, and (3) extend the method to multicomponent solutions. The third goal includes experimental collaboration to test the methodology against solution thermodynamic measurements. Given the precomputed nature of the pDFs, the evaluation of thermodynamic averages as integrals over the precomputed distribution is computationally very efficient. These fast and accurate methods can be generally applied to protein systems where the understanding of the recognition, self-recognition, or folding process is essential. This results of this project have far-reaching applications to the fields of computer-aided molecular design, structural analysis, and biotechnology. The code and scripts developed for the research are being disseminated as open source and can be readily extended to interface to widely-used molecular dynamics simulation codes and force fields.
