Current approaches to drug discovery are yielding diminishing returns as costs, failure rate, and drug resistance all increase. Meanwhile, novel targets and drug candidates are not keeping up with demand across the disease spectrum. This work seeks to address several of these areas. It seeks to lower cost, increase success rate, and address drug resistance while increasing novel targets and potential drug candidates. While most drugs are found by trial-and-error or designed for specific structured protein pockets, it turns out that many diseases and drug resistance occur at interfaces involving disordered protein regions. So, while most informatics for drug design has focused on structured protein pockets, an area with tremendous potential lies in disordered proteins and their interfaces. To do so effectively, and at a large-scale, an informatics framework is needed that effectively uses information across genomic, proteomic, structural, chemical, pathway, ontological, interaction modeling, and evolutionary space. Here, we present such a generalized, informatics framework that creates: 1) disordered target libraries and corresponding small molecules to interact them and 2) small molecules that can mimic disordered regions and thus interact with the usual partners of the disordered protein regions. We will first create a disordered target library across several organisms. Then, through a Bayesian framework, we will integrate expert knowledge, sequence information/statistics, and interaction modeling to predict drugs that can: 1) target these regions and 2) mimic these regions in interactions. Finally, we will focus on drug resistant pathogens to validate predicted drugs experimentally.

Public Health Relevance

Relevance Increased drug development costs, resistance to existing drugs, and a lack of new drug targets necessitate a new paradigm. This work will create the generalized, informatics- based framework needed for large-scale drug discovery and testing in a new category of drug targets found across many common diseases, thus enabling novel therapeutics as well as potential synergies with existing drugs. As a proof of concept, new drugs will be analyzed across multiple organisms and tested against resistant pathogens.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM118467-04
Application #
9773139
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Ravichandran, Veerasamy
Project Start
2016-09-01
Project End
2019-12-31
Budget Start
2019-09-01
Budget End
2019-12-31
Support Year
4
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Boston Children's Hospital
Department
Type
DUNS #
076593722
City
Boston
State
MA
Country
United States
Zip Code
02115