ABSTRACT

Professor Michael Schnieders of the University of Iowa is supported by an award from the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry to develop new theoretical approaches to predict crystal structures. Organic molecular crystals play an important role in a range of fields including chemistry, biochemistry, materials science, pharmacology, and engineering. One everyday example of organic molecular crystals are pharmaceutical tablets, which are typically formulated to optimize properties such as shelf-life (i.e. thermal stability) and solubility (i.e. dissolution upon ingestion). A perhaps less appreciated role of organic crystals has been their pivotal impact in understanding the structure and function of biomolecules (i.e. proteins) via X-ray crystallography experiments. Whereas drug molecules typically consist of only a few dozen atoms, proteins generally consist of thousands of atoms whose packing (i.e. 3-dimensional arrangement) is described by a process called "protein folding". A driving force behind the folding of proteins is the hydrophobic effect, which is also responsible for the commonly observed tendency of oil and water to separate. The work in Dr. Schnieder's group focuses on the rigorous incorporation of all forces that contribute to protein folding into efficient algorithms for the computational prediction of peptide and protein crystal structures (polymorphs). The approach combines advanced models of molecular interactions commonly used to predict small molecule crystal polymorphs with sophisticated molecular dynamics sampling algorithms needed to describe protein folding. The impact of this project is to expand the boundaries of the crystal structure prediction (CSP) field beyond small organic molecules (i.e. dozens of atoms) to include peptides and proteins (i.e. hundreds or thousands of atoms). Dr. Schnieder's research is fully integrated with a three-pronged strategy for educational outreach that strengthens and further diversifies training in Simulation Based Engineering & Science (SBE&S). The project's educational plan includes: outreach to underrepresented high school students to help make computational science fair projects and creation of a modern Computational Biochemistry course to train (under)graduates in applying SBE&S methods to fundamental problems in computational (bio)chemistry. The third aim is the continued dissemination of open source Force Field X software (http://ffx.biochem.uiowa.edu). Leadership in SBE&S and high-performance computing (HPC) is of critical importance to the global competitiveness of the United States.

Physics-based protein folding via molecular dynamics (MD) inherently accounts for temperature, pressure, solvent environment and entropic contributions such as the hydrophobic effect. On the other hand, nearly all current crystal structure predication (CSP) approaches perform either a systematic or stochastic search of a potential energy surface, rather than a free energy surface, followed in limited cases by approximate inclusion of entropic considerations. The premise of this project is that a generally applicable solution to the "protein crystal folding problem" requires efficient inclusion of temperature, pressure and solvent environment (hydrophobic effect, pH, etc.) during polymorph discovery simulations. Due to the slow nucleation kinetics of crystallization, ordinary unbiased MD is not efficient for CSP. To overcome this, a novel family of algorithms are being developed to help open the door to polymer crystal property prediction. The first objective focuses on two novel alchemical thermodynamic paths, which do not require a priori knowledge of the crystalline state and that dramatically accelerate phase transitions 1) between vacuum and crystalline states (i.e. sublimation/deposition) and 2) between solvated and crystalline states (i.e. solubility). Both paths efficiently include the influence of temperature and pressure, while the latter path additionally includes the influence of the solvent environment. The second objective focuses on the first constant pH MD (CpHMD) algorithms for a polarizable force field (e.g. AMOEBA) to account for protonation changes as a polymer (e.g. a protein or nucleic acid) with numerous titratable residues folds and/or undergoes a crystalline phase transition. Beyond the focus of this project on protein crystals, the sampling algorithms and CpHMD theories are broadly applicable to a range of simulation applications, including protein-ligand binding, molecular design and refinement of structural models against experiment (i.e. X-ray and neutron crystallography, CryoEM, NMR, etc). The project's educational plan includes: 1) outreach to underrepresented high school students to facilitate computational science fair projects, 2) creation of a modern Computational Biochemistry course to train (under)graduates in applying SBE&S methods to fundamental problems in computational (bio)chemistry, and 3) continued dissemination of the open source Force Field X software (http://ffx.biochem.uiowa.edu).

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.

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