Antibody Docking on the Amazon Cloud

Today an article I wrote for Bio-IT World was published describing Antibody docking experiments that are running on Amazon EC2. Since my final edits didn’t make the deadline I wanted to post the entire article here with some inline links.

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It was 18 months ago in this column that Mike Cariaso proclaimed, “Buying CPUs by the hour is back” in reference to our work with Amazon’s Elastic Compute Cloud (EC2). Back then, we were perhaps a bit far ahead of the hype vs. performance curve of cloud computing. A handful of forward-thinking companies were finding ways to scale out web services. Few research groups were putting EC2 instances to work for real number crunching in the life sciences. In the last two years, utility computing has begun to make an impact on real world problems (and budgets) in many industries. For researchers starved for computing power, the flexibility of the pay-as-you-go access model is compelling. The Amazon EC2 process makes the grant process used by national Supercomputing centers look arcane and downright stifling. Innovative and ‘bursty‘ research requires dynamic access to a large pool of CPU and storage. Computational drug design is a great place to begin to clear the air about the reality of this emerging technology.

Accelerating the creation of novel therapeutics is priority one for the research side of the pharmaceutical industry. Much time is spent optimizing the later phases of clinical trials in many pipelines. However, IT and infrastructure decisions made much earlier in the process can have a profound impact on the momentum and direction of the entire endeavor. For protein engineers at Pfizer’s Bioinnovation and Biotherapeutics Center, the challenging task of Antibody docking presents computational roadblocks. All-atom refinement is the major high performance computing challenge in this area.

Respectable models of a protein’s three-dimensional structure can usually be generated on a single workstation in a matter hours. After building multiple models, a refinement step typically produces the most accurate models. Atomic detail is necessary to validate whether newly modeled antibodies will bind their target epitopes and to get a clear picture of the protein-protein interactions and binding interfaces of these immunogenic molecules.

One of the most successful frameworks for studying protein structures at this scale is Rosetta++, developed by David Baker at the University of Washington. Baker describes Rosetta as “a unified kinematic and energetic framework… (that) allows a wide-range of molecular modeling problems … to be readily investigated.” Refinement of antibody docking involves small local perturbations around the binding site followed by evaluation with Rosetta’s energy function. It’s an iterative process that requires a massive amount of computing based on a small amount of input data. The mix of computational complexity with a pleasantly parallel nature makes the task suitable for both high-end supercomputers and Internet-scale grids.