The development of smart peptide binders requires an understanding of the fundamental mechanisms of recognition which has remained an elusive grail of the research community for decades. Recent advances in automated discovery and synthetic library science show great promise for the development of recognition elements with improved stability, affinity and specificity. In addition, these advances provide a wealth of information to probe these fundamental details and develop improved models for a priori prediction of affinity and specificity. Here we present the modeling portion of an iterative experimental/computational study to produce high affinity peptide binders to the Protective Antigen (PA) of Bacillus anthracis. The result is XPairIt�a general usage, HPC-oriented, Python-based toolkit connected to powerful third-party freeware.  It is designed to provide a better understanding of peptide-protein interactions, and ultimately predict and measure new smart peptide binder candidates. We present XPairIt's simulation protocol for improved flexible peptide and protein docking, including coarse grain and atomistic models, on-the-fly molecular dynamics, and explicit water representation.  Candidate peptides are docked with the Anthrax Protective Antigen, and binding locations and affinities are reported.  Verification of quality docking procedures and results are shown with 20mer and 16mer peptides docked with the Lethal Factor (LF) of Bacillus anthracis, and then compared to experimental bound crystal structures of the respective peptide-LF pairs (PDBs: 1JKY, 1PWV).

XPairIt: A software toolkit for smart peptide reagent design

243rd National Meeting (2012)

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