In Silico Design of Smart Binders to Anthrax PA

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 provide a wealth of information to probe fundamental details of binding and facilitate the development of 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 a general usage, HPC-oriented, python-based toolkit based upon powerful third-party freeware, which is designed to provide a better understanding of peptide-protein interactions and ultimately predict and measure new smart peptide binder candidates. We present an improved simulation protocol with flexible peptide docking to the Anthrax Protective Antigen, reported within the context of experimental data presented in a companion work.