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Deep learning augmented protein mapping software to screen large compound libraries

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R43GM144992-01
Agency Tracking Number: R43GM144992
Amount: $173,006.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 400
Solicitation Number: PA20-260
Solicitation Year: 2020
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-02-01
Award End Date (Contract End Date): 2023-01-31
Small Business Information
Holliston, MA 01746-1042
United States
DUNS: 830023755
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 (774) 217-8850
Business Contact
Phone: (508) 893-0667
Research Institution

Fragment based drug discovery starts with screening libraries of fragment-sized organic
molecules for binding to the target protein. The fragments cluster at binding hot spots,
the most important regions for drug discovery, and can be extended into larger and
higher affinity ligands. The protein-mapping program FTMap is a computational
analogue of fragment screening experiments. Acpharis has licensed the docking engine
of FTMap and developed the ATLAS software as an updated version of the FTMap
program. While ATLAS is a useful tool for identifying binding sites and predicting
druggability, with proper development it can provide much more valuable
characterization of both the binding site and the preferred fragments. The major goal of
this proposal is to develop a software package based on ATLAS that, starting from the
structure of a target protein, will be able to reliably screen very large virtual compound
libraries for potential hits. To achieve this major goal we propose the following
developments. Our first goal is to identify regions on the target protein that have
preferences for binding specific functional groups and to identify a set of bound
fragments that can be used as seeds for 2D and 3D screening. This will involve four
steps. (1) Developing a higher accuracy scoring function to enable discrimination among
different functional groups. (2) Obtaining generalized pharmacophore information by
iterative mapping, where the initial mapping, indicating preferences for certain functional
groups, will be followed by more focused mapping using probes containing similar
functional groups; (3) designing basic and extended fragment libraries for the two steps
of mapping; and (4) improving the functional characterization of the site by adding
binding information from the PDB using a novel pocket similarity algorithm. Once
extended pharmacophores are established, we plan to use ensembles of binding
fragments as pseudo-compounds to seed a ligand-based shape-matching search
method to screen large libraries of compounds based on molecular similarity. The
traditional 2D similarity search will be modified to account for the additional 3D
information provided by the mapping. This will enable screening larger libraries and will
yield more specific results than the existing 2D ligand based tools. Once we have a set
of potential ligand hits, we will perform template based ligand placement to produce a
variety of possible poses, and to score the refined poses.

* Information listed above is at the time of submission. *

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