November 2010 IN SILICO DRUG DESIGN USING HPC

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1 November 2010 IN SILICO DRUG DESIGN USING HPC

2 Table of Contents Introduction 2 Computer/ homology modeling 3 virtual screening and docking 3 de novo drug design 4 Challenge 4 How can we help 4 porting autodock to cuda 4 conclusion 5 references 5 abbreviation 6 About the Author 6 About HCL 7 Introduction Medicinal chemists today are facing many complicated challenges. The most demanding and perhaps the most rewarding one is the rational design of new therapeutic agents for treating human diseases. For many years the strategy for discovering new drugs has been to take a lead structure (from natural sources or toxins) and develop (trial and error method) a chemical program to find analog molecules exhibiting the desired biological properties. The entire process is laborious, expensive and time consuming. The traditional methods of drug discovery are now being supplemented by more direct approaches made possible by understanding the molecular processes involved in the underlying disease (Figure 1). In this perspective, the starting point in drug design is a three dimensional structure (3D) of the molecular target [i.e. receptor, enzyme in the body] instead of the already known lead structure. Discussion of the use of structural biology in drug discovery began over 35 years ago, with the advent of the 3D structures of globins, enzymes and polypeptide hormones. The best examples are the chemical modification of insulin to increase half-life in circulation [1] and the design of inhibitors of serine proteases to control blood clotting [2]. Eventually, AIDS drugs such as Agenerase and Viracept were developed using the crystal structure of HIV protease [3,4]. In early 1990s, computational researchers optimized AIDS drugs using In Silico methods to precisely fit the shape of the enzyme s active site as fitting a key to a lock. In today s scenario Pharma R&D, biotech companies are looking to high performance computing support and solutions to help them to improve process efficiencies.

3 Computer/ Homology Modeling When the 3D structure is not available for the target of interest from X-ray crystallography or NMR, it is possible to construct a homology model based on related known structure using computer software. Based on the structural information of the protein, we can identify its protein family and super families. If the key residues are conserved between the families, it will help us gain knowledge about ligand binding site (active site) and molecular function. The topographies of the complementary surfaces of ligand and their protein target could be exploited to optimize potency and selectivity. Increase in processor power and improved software is now allowing complex 3D modeling of the cell membrane components including receptors and ion channels. Identification of Potential Target Target Verification & Selection Determine Structure & Analyze Possible Binding Sites Secondary Assay Effectiveness of binding in solution Database mining for Compounds against Binding site Leads Cytotoxicity to disease Pharmacokinetics study DRUG DESIGN Toxicology and Side Effects In Silico In Silico & Experiment Experiment Figure 1 Schematic flow diagram of drug design and screening process. Virtual Screening and Docking The process of combinatorial chemistry is mimicked by virtual explosion of individual compounds leading to virtual drug libraries. The interaction of the active site of the target with these virtual drugs can be explored in simulation. During virtual screening, a large number of commercially available (or synthetically accessible) compounds are docked and ranked by In Silico methods. Selecting (or designing) a compound based on In Silico analysis that bind to a protein active site is difficult. First, the In Silico method must solve the docking problem by finding the optimum binding orientation for the compound in the active site of the protein. Then the highest ranking compounds are selected for acquisition and experimentally tested for activity against the target protein.

4 De novo Drug Design Existing compound collections might not always contain molecules with an optimal fit for a given target protein or the compounds themselves could be of limited novelty. In silico methods that can design new ligands are therefore, potentially useful. De novo design methods position the fragments in the binding cleft of protein targets and then grow them to fill the space available by optimizing the electrostatic, van der Waals and hydrogen bonding interactions. A related concept is the use of structural knowledge to guide the design of target specific combinatorial libraries. Challenge? About organic and metal-organic 3D structure solved by X- ray crystallography are available in Cambridge Structural Database [CSD]. Nearly, 68,150 protein 3D structures are available in protein data bank solved by X-ray Crystallography and NMR. Designing a new drug can be faster and more economical when researchers incorporate computational tools. How can we help? The potential for a docking algorithm to be used as a virtual screening tool is based on both speed and accuracy. Speedup of the programs available for drug design using recent advances in HPC technology is one of the important applications in this space. The HPC Lab at HCL specializes on technologies like Multi-core, GPGPU and Cluster Computing. In the following section, we will talk about how we increased the speed of Autodock, an In Silico docking tool, using CUDA, a GPGPU technology. Porting Autodock to CUDA Autodock is a Drug Discovery Tool that uses a Genetic Algorithm to find the optimal docking position of a ligand to a protein. Autodock software was originally developed by TSRI (The Scripps Research Institute). More information about Autodock can be found at Our HPC lab ported Autodock to NVIDIA CUDA demonstrating speedup of ~27x. A paper discussing the same has been presented at CIGPU session of IEEE CEC 2010 held at Barcelona on July Our approach differed considerably from previous attempts to parallelize Autodock. Instead of porting only the fitness function, we ported the entire genetic algorithm to the GPU. All the genetic operators were run on the GPU. The CPU merely assisted the GPU in carrying out the task. Three different parallel design alternatives for the fitness function were considered before choosing the best. We also designed a CGPU memory manager to manage memory buffers and their transfers between CPU and GPU. Our implementation features ~50x speedup of the fitness function and an overall speedup of 27x for the entire application. More details can be seen in (Figure 2). The X-axis in Figure 2 represents the population size. The most typical population size used in Autodock is 150.

5 Figure 2. Shows the net speedup of the Energy evaluation and Genetic Algorithm with respect to the population size. Conclusion In Silico analysis has saved greater amount of time and money in R & D projects. Docking method has the ability to predict binding conformation and affinities of millions of molecules during virtual screening. Additionally, structural biology plays an important role in increasing the affinity and selectivity of lead molecule. Due to rapid advances in structural biology and computer technology, In Silico drug design has become a powerful tool in the multi-step process of drug discovery. Despite these trends, the main challenge in this field is to use the vast amount of information and technique for accelerating drug discovery process. We have contributed to solving one of these problems by speeding up the docking program Autodock using CUDA technology. References 1. Blundell, T. L., et al. (1972) The structure, chemistry and biological activity of insulin. Adv. Protein Chem. 26, Davie, E. W. et al. (1991) The coagulation cascade: initiation, maintenance and regulation. Biochemistry 30, Miller, M. et al. (1989) Structure of complex of synthetic HIV-1 protease with a substrate-based inhibitor at 2.3 Å. Resolution. Science 246, Lapatto, R. et al. (1989) X-ray analysis of HIV-1 proteinase at 2.7 Å resolution confirms structural homology among retroviral enzymes. Nature 342, Jürgen Drews. (2000) Drug Discovery: A Historical perspective. Science 287, Porting Autodock to CUDA - a c. u k / s t a f f / W. L a n g d o n / c i g p u / p a p e r s / c p d f

6 Abbreviation HPC High Performance Computing CUDA Compute Unified Device Architecture GPGPU General Purpose Computing on Graphics Processing Unit AIDS Acquired Immunodeficiency Syndrome HIV Human Immunodeficiency Virus NMR Nuclear Magnetic Resonance About the Author Dr. Ompraba has a master s in Biochemistry and post graduate diploma in Bioinformatics. She acquired a doctorate degree in the field of Biochemistry, Crystallography and Biophysics from University of Madras. She has eight years of research experience in the field of Molecular Modeling, Molecular Biology/Biotechnology and Crystallography. She has published 10 papers in the reputed National and International Journals and gave oral presentation in 3 International conferences. She has extensive research experience in the area of Computer Aided Drug Design. She is the key member in the prediction of the additional inhibitor binding site in the case of anti-inflammatory drug target Phospholipase A2 and the prediction of novel gene Ryncolin from snake venom gland. She is currently the Consultant with Life Science Vertical at HCL technologies, Chennai and she can be reached at Sarnath has completed 10 years with HCL Tech and is currently leading the HPC Lab as Senior Technical Manager. He has a B.Tech degree in Computer Science & Engg from Pondicherry University. The HPC team works on various domains including life sciences, FEA, compiler technologies and so on. The HPC team specializes on all kinds of HPC technologies, including Multi-core, SSE, GPGPU, IBM Cell, Cluster compute technologies like MS HPC Server 2008 and so on.

7 About HCL HCL Technologies HCL Technologies is a leading global IT services company, working with clients in the areas that impact and redefine the core of their businesses. Since its inception into the global landscape after its IPO in 1999, HCL focuses on transformational outsourcing, underlined by innovation and value creation, and offers integrated portfolio of services including software-led IT solutions, remote infrastructure management, engineering and R&D services and BPO. HCL leverages its extensive global offshore infrastructure and network of offices in 26 countries to provide holistic, multi-service delivery in key industry verticals including Financial Services, Manufacturing, Consumer Services, Public Services and Healthcare. HCL takes pride in its philosophy of Employees First which empowers our 70,218 transformers to create a real value for the customers. HCL Technologies, along with its subsidiaries, had consolidated revenues of US$ 2.9 billion (Rs. 13,145 crores), as on 30th September 2010 (on LTM basis). For more information, please visit About HCL Enterprise HCL is a $5.5 billion leading global technology and IT enterprise comprising two companies listed in India - HCL Technologies and HCL Infosystems. Founded in 1976, HCL is one of India s original IT garage start-ups. A pioneer of modern computing, HCL is a global transformational enterprise today. Its range of offerings includes product engineering, custom & package applications, BPO, IT infrastructure services, IT hardware, systems integration, and distribution of information and communications technology (ICT) products across a wide range of focused industry verticals. The HCL team consists of over 77,000 professionals of diverse nationalities, who operate from 29 countries including over 500 points of presence in India. HCL has partnerships with several leading Global 1000 firms, including leading IT and technology firms. For more information, please visit

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