Interactive drug-design: using advanced computing to evaluate the induced fit effect

This thesis describes the efforts made to provide protein flexibility in a molecular modelling software application, which prior to this work, was operating using rigid proteins and semi flexible ligands. Protein flexibility during molecular modelling simulations is a non-­‐trivial task requiring a great number of floating point operations and it could not be accomplished without the help of supercomputing such as GPGPUs (or possibly Xeon Phi). The thesis is structured as follows. It provides a background section, where the reader can find the necessary context and references in order to be able to understand this report. Next is a state of the art section, which describes what had been done in the fields of molecular dynamics and flexible haptic protein ligand docking prior to this work. An implementation section follows, which lists failed efforts that provided the necessary feedback in order to design efficient algorithms to accomplish this task. Chapter 6 describes in detail an irregular – grid decomposition approach in order to provide fast non-­‐bonded interaction computations for GPGPUs. This technique is also associated with algorithms that provide fast bonded interaction computations and exclusions handling for 1-­‐4 bonded atoms during the non-­‐bonded forces computation part. Performance benchmarks as well as accuracy tables for energy and force computations are provided to demonstrate the efficiency of the methodologies explained in this chapter. Chapter 7 provides an overview of an evolutionary strategy used to overcome the problems associated with the limited capabilities of local search strategies such as steepest descents, which get trapped in the first local minima they find. Our proposed method is able to explore the potential energy landscape in such a way that it can pick competitive uphill solutions to escape local minima in the hope of finding deeper valleys. This methodology is also serving the purpose of providing a good number of conformational updates such that it is able to restore the areas of interaction between the protein and the ligand while searching for optimum global solutions

Simulating molecular docking with haptics

Intermolecular binding underlies various metabolic and regulatory processes of the cell, and the therapeutic and pharmacological properties of drugs. Molecular docking systems model and simulate these interactions in silico and allow the study of the binding process. In molecular docking, haptics enables the user to sense the interaction forces and intervene cognitively in the docking process. Haptics-assisted docking systems provide an immersive virtual docking environment where the user can interact with the molecules, feel the interaction forces using their sense of touch, identify visually the binding site, and guide the molecules to their binding pose. Despite a forty-year research e�ort however, the docking community has been slow to adopt this technology. Proprietary, unreleased software, expensive haptic hardware and limits on processing power are the main reasons for this. Another signi�cant factor is the size of the molecules simulated, limited to small molecules. The focus of the research described in this thesis is the development of an interactive haptics-assisted docking application that addresses the above issues, and enables the rigid docking of very large biomolecules and the study of the underlying interactions. Novel methods for computing the interaction forces of binding on the CPU and GPU, in real-time, have been developed. The force calculation methods proposed here overcome several computational limitations of previous approaches, such as precomputed force grids, and could potentially be used to model molecular exibility at haptic refresh rates. Methods for force scaling, multipoint collision response, and haptic navigation are also reported that address newfound issues, particular to the interactive docking of large systems, e.g. force stability at molecular collision. The i ii result is a haptics-assisted docking application, Haptimol RD, that runs on relatively inexpensive consumer level hardware, (i.e. there is no need for specialized/proprietary hardware)

GPU-accelerated molecular mechanics computations

In this article, we describe an improved cell-list approach designed to match the Kepler architecture of General-purpose graphics processing units (GPGPU). We explain how our approach improves load balancing for the above algorithm and how warp intrinsics are used to implement Newton's third law for the nonbonded force calculations. We also talk through our approach to exclusions handling together with a method to calculate bonded forces and 1–4 electrostatic scaling using a single Cuda kernel. Performance benchmarks are included in the last sections to show the linear scaling of our implementation using a step minimization method. In addition, multiple performance benchmarks demonstrate the contribution of various optimizations we used for our implementations

GPU-accelerated molecular mechanics computations

In this article, we describe an improved cell-list approach designed to match the Kepler architecture of General-purpose graphics processing units (GPGPU). We explain how our approach improves load balancing for the above algorithm and how warp intrinsics are used to implement Newton's third law for the nonbonded force calculations. We also talk through our approach to exclusions handling together with a method to calculate bonded forces and 1–4 electrostatic scaling using a single Cuda kernel. Performance benchmarks are included in the last sections to show the linear scaling of our implementation using a step minimization method. In addition, multiple performance benchmarks demonstrate the contribution of various optimizations we used for our implementations