CONIFER POINT PHARMACEUTICALS LLC — Department of Health and Human Services SBIR Phase I: 400

Description

DESCRIPTION provided by applicant Maps of where small chemical fragments bind to proteins are proving to be an important source of information for life sciences research most particularly in drug discovery For example such maps are used to discover binding andquot hot spotsandquot by fragment clustering identify how fragment binding patterns differ among protein structure variations critical for achieving selectivity mutation avoidance or multi targeting ad find starting points for difficult targets The best computationally generated fragment maps are produced by Grand Canonical Monte Carlo fragment protein simulations using simulated annealing of chemical potential GC MC SACP These maps the subject of numerous publications allow predictive free energy ranking of binding sites incorporate comprehensive sampling of binding configurations and describe rigorous Boltzmann distributions Our primary assertion is that making these fragment and water binding maps and tools widely available at low cost will have a transformational impact on the productivity of researchers and drug designers Foundations Historically these simulations have been considered expensive but we have developed a highly optimized GC MC SACP simulator to dramatically reduce the cost Over the last years we have computed an unprecedented GC MC SACP fragment and water maps GMAPandapos s We have used these maps in identifying and optimizing drug lead compounds in over a dozen projects This includes several protein protein interaction inhibitors targeting PCSK cholesterol lowering and RecA bacterial DNA repair Goal We will make GMAPandapos s and fragment based design tools available to the broadest possible research community at low cost using recently available Web and Cloud computing technologies GMAPandapos s are currently not available to most researchers Our tools currently require optimized software on high end workstations Innovation GMAPandapos s consist of andapos s GB per protein We will use matching pursuit algorithms for statistical modeling to derive compact representations of fragment distributions This is novel and a publishable result The computational demands of interactive fragment based design can now be met by employing the WebGL D graphics and WebCL physics computation APIandapos s in browsers allowing compute intensive functions to be run on the userandapos s graphics hardware GPU This is the future of computational chemistry tools Aim Use matching pursuit algorithms to achieve x reduction in the size of GMAPandapos s compress our existing maps for several hundred therapeutically relevant proteins and demonstrate practical delivery of GMAPandapos s over the Internet Aim Implement a preliminary Web service for fragment visualization for searching GMAPandapos s for bondable fragment pairs and for clustering fragments for hot spot analysis Overall Impact The full program will deliver a tiered Web Cloud service Researchers need alternative approaches to drug lead development Mining GMAP data is a fertile source of new chemistry ideas When widely adopted the comprehensive service would provide the platform to significantly advance drug discovery PUBLIC HEALTH RELEVANCE Maps of where small chemical fragments bind to proteins are proving to be an important source of information for life sciences research for example these maps are used to discover binding andquot hot spotsandquot by fragment clustering identify how fragment binding patterns differ among protein structure variations critical for achieving selectivity mutation avoidance or multi targeting and find starting points for difficult tarets The goal of the proposed work is to now make these maps and associated drug design tools available to a broad community of researchers Using recently emerging Web and Cloud computing technologies we will deliver maps and tools through a Web based service with free subscription and contract tiers and we will develop innovative solutions to the technical challenges in exploiting GB maps for computer aided drug design