Enabling ab initio molecular dynamics for large biological molecules
Abstract/Contents
- Abstract
- The role of atomistic modeling of molecules and organic compounds in biology and pharmaceutical research is constantly increasing, providing insights on chemical and biological phenomena at the highest resolution. To achieve relevant results, however, computational biology has to deal with systems containing at least 1000 atoms. Such big molecules cause large computational demands and impose limitations on the level of theory used to describe molecular interactions. Classical molecular mechanics based on various empirical relationships has become a workhorse of computational biology, as a practical compromise between accuracy and computational cost. Several decades of classical force field development have seen many successes. Nevertheless, more accurate treatment of bio-molecules from first principles is highly desirable. Hartree-Fock (HF) and density functional theory (DFT) are two low-level ab initio methods that provide sufficient accuracy to interpret experimental data. They are therefore the methods of choice to study large biological systems. Recently DFT has been applied to calculate single point energy of a solvated Rubredoxin protein. The system contained 2825 atoms and required more than two hours on a supercomputer with 8196 parallel cores. This study clearly demonstrates the scale of problems one has to tackle in first principles calculations of biologically relevant systems. Dynamical simulations requiring thousands of single point energy and force evaluations therefore appear to be completely out of reach. This fact has essentially prohibited the use of first principles methods for many important biological systems. Fortunately, the computer industry is evolving quickly and novel computing architectures such as graphical processing units (GPUs) are emerging. The GPU is an indispensable part any modern desktop computer. It is special purpose hardware responsible for graphics processing. Most problems in computer graphics are embarrassingly parallel, meaning they can be split into a large number of smaller subproblems that can be solved in parallel. This fact has guided GPU development for more than a decade; and modern GPUs evolved into a massively parallel computing v architecture containing hundreds of basic computational units, which all together can perform trillions of arithmetic operations per second. The large computational performance and low price of consumer graphics cards makes it tempting to consider using them for computationally intensive general purpose computing. This fact was recognized long ago and several groups of enthusiasts attempted to use GPUs for non-graphics computing in the early 2000's. One of the few successes from these attempts is now known as Folding@Home. These early attempts were primarily stymied by three major problems: lack of adequate development frameworks, limited precision available on GPUs, and the difficulty of mapping existing algorithms onto the new architecture. The two former impediments have been recently alleviated by the introduction of efficient GPU programming toolkits such as CUDA and the latest generation of graphics cards supporting full double precision arithmetic operations in hardware. These advances led to an explosion of interest in general purpose GPU computing and led to the development of many GPU-based high performance applications in various fields such as classical molecular dynamics, magnetic resonance imaging, and computational fluid dynamics. Most of the projects, however, lie far outside of quantum chemistry which is likely caused by the complexity of quantum chemistry algorithms and the associated difficulty of mapping them onto the GPU architecture. Various specific features of the hardware require complete redesign of conventional HF and DFT algorithms in order to fully benefit from the large computational performance of GPUs. We have successfully solved this problem and implemented the new algorithms in TeraChem, a high performance general purpose quantum chemistry package designed for graphical processing units from the ground up. Using TeraChem, we performed the first ab initio molecular dynamics simulation of an entire Bovine pancreatic trypsin inhibitor (BPTI) protein for tens of picoseconds on a desktop workstation with eight GPUs operating in parallel. Coincidently, this was also the first protein ever simulated on a computer using the classical molecular mechanics approach. BPTI binds to trypsin with a binding free energy of approximately 20 kcal/mol, making BPTI one of the strongest non-covalent binders. It vi is even more remarkable that a single BPTI amino acid LYS15 contributes half of the binding free energy by forming a salt bridge with one of the trypsin's negatively charged residues inside the binding pocket. In fact, the LYS15's contribution to the overall binding energy is approximately twice as large as what would be expected based on experimental measurements of salt bridge interactions in other proteins. Our simulation of BPTI demonstrated that substantial charge transfer occurs at the proteinwater interface, where between 2.0 and 3.5 electrons are transferred from the interfacial water to the protein. This effect decreases the net protein charge from +6e as observed in gas-phase experiments to +4e or less. We demonstrate how this effect may explain the unusual binding affinity of the LYS15 amino acid.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2011 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Ufimtsev, Ivan |
|---|---|
| Associated with | Stanford University, Department of Chemistry |
| Primary advisor | Martinez, Todd J. (Todd Joseph), 1968- |
| Thesis advisor | Martinez, Todd J. (Todd Joseph), 1968- |
| Thesis advisor | Markland, Thomas E |
| Thesis advisor | Pande, Vijay |
| Advisor | Markland, Thomas E |
| Advisor | Pande, Vijay |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Ivan S. Ufimtsev. |
|---|---|
| Note | Submitted to the Department of Chemistry. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2011. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2011 by Ivan Ufimtsev
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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