GPU optimizations for a production molecular docking code

Thesis (M.Sc.Eng.) -- Boston UniversityScientists have always felt the desire to perform computationally intensive tasks that surpass the capabilities of conventional single core computers. As a result of this trend, Graphics Processing Units (GPUs) have come to be increasingly used for general computation in scientific research. This field of GPU acceleration is now a vast and mature discipline. Molecular docking, the modeling of the interactions between two molecules, is a particularly computationally intensive task that has been the subject of research for many years. It is a critical simulation tool used for the screening of protein compounds for drug design and in research of the nature of life itself. The PIPER molecular docking program was previously accelerated using GPUs, achieving a notable speedup over conventional single core implementation. Since its original release the development of the CPU based PIPER has not ceased, and it is now a mature and fast parallel code. The GPU version, however, still contains many potential points for optimization. In the current work, we present a new version of GPU PIPER that attains a 3.3x speedup over a parallel MPI version of PIPER running on an 8 core machine and using the optimized Intel Math Kernel Library. We achieve this speedup by optimizing existing kernels for modern GPU architectures and migrating critical code segments to the GPU. In particular, we both improve the runtime of the filtering and scoring stages by more than an order of magnitude, and move all molecular data permanently to the GPU to improve data locality. This new speedup is obtained while retaining a computational accuracy virtually identical to the CPU based version. We also demonstrate that, due to the algorithmic dependencies of the PIPER algorithm on the 3D Fast Fourier Transform, our GPU PIPER will likely remain proportionally faster than equivalent CPU based implementations, and with little room for further optimizations. This new GPU accelerated version of PIPER is integrated as part of the ClusPro molecular docking and analysis server at Boston University. ClusPro has over 4000 registered users and more than 50000 jobs run over the past 4 years

GPU acceleration of a production molecular docking code

Abstract: Modeling the interactions of biological molecules, or docking, is critical to both understand-ing basic life processes and to designing new drugs. Here we describe the GPU-based acceleration of a recently developed, complex, production docking code. We show how the various functions can be mapped to the GPU and present numerous optimizations. We find which parts of the problem domain are best suited to the different correlation methods. The GPU-accelerated system achieves a speedup of at least 16x for all likely problems sizes. This makes it competitive with FPGA-based systems for small molecule docking, and superior for protein-protein docking.

MDM2 Case Study: Computational Protocol Utilizing Protein Flexibility Improves Ligand Binding Mode Predictions

Recovery of the P53 tumor suppressor pathway via small molecule inhibitors of onco-protein MDM2 highlights the critical role of computational methodologies in targeted cancer therapies. Molecular docking programs in particular, provide a quantitative ranking of predicted binding geometries based on binding free energy allowing for the screening of large chemical libraries in search of lead compounds for cancer therapeutics. This study found improved binding mode predictions of medicinal compounds to MDM2 using the popular docking programs AutoDock and AutoDock Vina, while adopting a rigid-ligand/flexible-receptor protocol. Crystal structures representing small molecule inhibitors bound to MDM2 were selected and a total of 12 rotatable bonds was supplied to each complex and distributed systematically between the ligand and binding site residues. Docking results were evaluated in terms of the top ranked binding free energy and corresponding RMSD values from the experimentally known binding site. Results show lowest RMSD values coincide with a rigid ligand, while the protein retained the majority of flexibility. This study suggests the future implementation of a rigid-ligand/flexible-receptor protocol may improve accuracy of high throughput screenings of potential cancer drugs targeting the MDM2 protein, while maintaining manageable computational costs

In silico & In vitro study to estimate Plasma Protein Binding of anti-parasitic compounds for Sleeping sickness (Human African trypanosomiasis)

Human African trypanosomiasis (HAT), also known as sleeping sickness, is a disease caused by a group of parasites called Trypanosoma brucei (Tb). The two main types causing HAT are T. brucei gambiense and T. brucei rhodesiense. T. brucei gambiense is the most common form of HAT, accounting for ninety seven percent of all reported cases of sleeping sickness. According to WHO, HAT is endemic in 36 sub-Saharan African countries. The disease can lead to death during the second stage if left untreated. Several drugs have been developed for the first stage such as pentamidine and suramin, and for the second stage such as melarsoprol, nifurtimox-eflornithine combination therapy (NECT). In 2019, fexinidazole was introduced as an oral treatment for the first stage and non-severe second stage of HAT. Several antiparasitic compounds prepared by our collaborator’s research group at the University of Graz, Austria showed varying levels of activity against Tb in vitro, whereas the compounds had only a moderate in vivo effect if at all. The suggested reason for the poor in vivo activities is that the compounds may bind tightly to plasma proteins, or they are metabolized before reaching the target sites for therapeutic effect. The prediction of plasma protein binding is of paramount importance in the pharmacokinetics characterization of drugs, as it causes significant changes in volume of distribution, clearance and drug half-life. Human serum albumin (HSA), an abundant plasma protein, can bind a remarkable variety of drugs impacting their delivery and efficacy and ultimately altering the drug’s pharmacokinetic and pharmacodynamic properties. In this current investigation, the overall aim was to investigate whether a strong HSA binding could be a probable reason for the poor in vivo activity of the provided antiparasitic compounds. The interaction of the antiparasitic compounds with HSA was studied computationally by docking them in the HSA drug binding site I and II. The compounds with the highest docking score were additionally studied using molecular dynamics simulations to evaluate the stability of the binding interactions. Moreover, the HSA binding affinity of the compounds was estimated by calculating the binding free energies using the MM-GBSA approach. In addition, experimental HSA binding studies using Microscale thermophoresis (MST) were conducted for some of the compounds. The results of the in silico studies suggest that majority of the investigated compounds may bind to HSA with varying affinity whereas a few of them did not show favorable binding interactions with HSA. Further, none of the compounds studied in vitro by MST showed HSA binding. In sum, plasma protein binding may be the reason for the in vivo inactivity for some of the investigated antiparasitic compounds

Exploration of Reaction Pathways and Chemical Transformation Networks

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and automatedly. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure

GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis

Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns

Morphing and docking visualisation of biomolecular structures using multi-dimensional scaling

Protein structures are often solved at atomic resolution in two states defining a functional movement but intervening conformations are usually unknown. Morphing methods generate intervening conformations between two known structures. When viewed as an animation using molecular graphics, a smooth, direct morph enables the eye to track changes in structure that might be otherwise missed. We present a morphing method that aims to linearly interpolate interatomic distances and which uses SMACOF (Scaling by MAjorisation of COmplicated Function) and multigrid techniques with a cut-off distance based weighting that optimizes the MolProbity score of intervening structures. The all-atom morphs are smooth, move directly between the two structures, and are shown, in general, to pass closer to a set of known intermediates than those generated using other methods. The techniques are also used for docking by putting the unbound structures in a “near-approach pose” and then morphing to the bound complex. The resulting GPU-accelerated tools are available on a webserver, Morphit_Pro, at http://morphit-pro.cmp.uea.ac.uk/ and more than 5000 domains movements available at the DynDom website can now be viewed as morphs http://morphit-pro.cmp.uea.ac.uk/dyndom/