Professor Emilio Gallicchio of Brooklyn College of the City University of New York is supported by an award from the Chemical Theory, Models, and Computational Methods and Chemistry of Life Processes programs in the Division of Chemistry to develop theoretical models, computational algorithms, and simulation software to study molecular recognition processes. Molecular recognition---the ability of molecules to recognize and bind specifically to other molecules---is a fundamental aspect of all physicochemical processes, and plays a central role in cellular interactions and biomolecular function. Among the many examples in nature are the self-assembly of viruses in infected cells preparing to attack other cells; chemical signals directing embryos to form specific organs and limbs; and the mediation of thought and memory in the brain through chemical messenger exchange. In industrial settings, the principles of molecular recognition guide the development of new drugs, advanced materials, catalysts and chemical sensors. However, quantitative models do not yet reach the level of atomic resolution and reliability necessary for designing molecules that target specific partners. Molecules recognize other molecules based on static properties such as shape and charge. However, they are also dynamical entities that perform recognition through the ability of binding partners to change shape and assume complementary conformations. Dr. Gallicchio is developing advanced techniques and software to target these complex features and capture the influence of molecular motion and flexibility on molecular recognition processes. The research is being carried out with active participation from Chemistry undergraduate students who, working with partnering experimental laboratories, interactively test computational predictions against measurements. The software and method development efforts involve Physics, Math, and Computer Science students. An important goal of the project is to help students from challenging socio-economic backgrounds to become the next generation of broad-thinking scientists, capable of tackling complex problems from multiple, convergent perspectives.
The likelihood of interaction between two molecular species is related to the standard free energy of binding, or, equivalently, the equilibrium constant for bimolecular association. The goal of this project is to develop theoretical models and computational algorithms to accurately and efficiently compute binding free energies from first principles, and to understand how free energies are influenced by molecular properties. A statistical analytic theory of binding relates physical quantities such as the geometry of the binding site, the size of the ligand, and the strength of their interactions, to dynamical data produced from binding free energy simulations. This theory is being used to improve binding free energy methods through the use of massively-parallel non-equilibrium protocols, and to build an automated classification procedure for molecular complexes based on the parameters estimated from the statistical model. The code developed for this project is being disseminated as high-performance, automated, freely available, and well-documented software, supporting a variety of platforms and operating systems, including computational grids and heterogeneous high-performance computing systems. The ability to treat dynamical aspects of binding has application to novel chemical synthesis, mitigation of chemical hazards, drug discovery, and materials design. Research and educational activities are closely integrated within the project and are providing deserving students from challenging socio-economic backgrounds with the scientific and technological skills to enter the modern workforce, while planting the seeds for growing future generations of research scientists.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
