Project Grant R43GM144992

Award Date 2/1/22
Completion Date 1/31/24
Dollars Obligated $346K
Federal Grant Program
93.859
Assistance Type
Project Grant
Place of Performance
Holliston, MA 01746, USA
Similar Awards
This Project Grant award of $306,872.00 from the National Institute of General Medical Sciences (NIGMS), under the Biomedical Research and Research Training Program (CFDA 93.859), aims to develop a new 3D shape matching methodology that accounts for water molecules when comparing ligands during drug discovery. The key products and services to be delivered include: Adapting an existing algorithm (WATGEN) to predict water positions in unbound proteins and protein-ligand complexes, and calculate...
The National Institute of General Medical Sciences (NIGMS) awarded a $360,100 Project Grant (CFDA 93.859 - Biomedical Research and Research Training) to The Regents of the University of California, San Francisco (UCSF) to develop and test algorithms for iteratively docking subsets of ultra-large small molecule libraries against molecular targets. The project aims to accelerate ligand discovery by overcoming the computational challenges posed by the rapid expansion of chemical space. The research...
This Project Grant award of $275,000 from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) supports the development of a novel platform for massive, empirical, and scalable generation of small molecule and protein interaction data. The goal is to enable more efficient discovery of new medicines using machine learning. The key products and services to be delivered under this SBIR Phase I award include: Demonstrating the ability to identify...
This Project Grant award from the National Institute of General Medical Sciences (NIGMS), under the Biomedical Research and Research Training program (CFDA 93.859), provides $202,274 to develop an easy-to-use computer program for predicting the structure and degrading efficiency of proteolysis targeting chimeras (PROTACs). PROTACs are heterobifunctional molecules that induce the degradation of target proteins by recruiting them to E3 ubiquitin ligases. The awarded project aims to implement a...
This Project Grant award from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training Program (CFDA 93.859) will support the development of two digital applications to provide immediate feedback and assessment for student-folded protein models using 3D Molecular Designs' Mini-Toobers modeling tool. The $591,842 award will fund the creation of a Student Training App that uses augmented reality to give students real-time feedback on modeling alpha...
This Project Grant award from the National Institute of General Medical Sciences (NIGMS), under the Biomedical Research and Research Training (CFDA 93.859) program, provides $1,203,701 to The Research Foundation For The State University Of New York to evaluate aspects of drug development and offer new methods to accelerate the delivery of new therapies. The award supports three interconnected program areas: Developing ATP-allosteric bivalent inhibitors (AABIs) to selectively target the...
This Project Grant award of $350,000.00 from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training Program (CFDA 93.859) aims to develop an artificial intelligence-based platform for optimizing peptide-based drug candidates. The key products or services to be delivered include: Methods for encoding non-canonical amino acids and cyclic peptides to enable high-performance machine learning on commercial peptide sequence, activity, stability, and...
This Project Grant award from the National Institute of General Medical Sciences (NIGMS) under the Biomedical Research and Research Training (CFDA 93.859) program provides $350,000 to Electronic Biosciences, Inc., a small biotechnology company in San Diego, California, to develop and demonstrate a novel enzyme-based, single-molecule protein sequencing system. The proposed technology aims to enable scalable, multiplexed protein sequencing with high accuracy and throughput, surpassing the...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering program (CFDA 47.070) provides $101,368 to North Carolina Agricultural and Technical State University (NC A&T) to develop new computational methods for de novo protein sequencing and filling gaps in protein scaffolds. The key products and services to be delivered include: Through a two-phase approach, the researchers will first analyze top-down and bottom-up mass...
This Project Grant award of $150,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of deep learning-based consistency models to enable more efficient and long-duration molecular dynamics simulations of protein dynamics. The key products of this 2-year project, which runs from August 2025 to July 2027, include: Developing prototype consistency models trained on molecular dynamics trajectories...

DEEP LEARNING AUGMENTED PROTEIN MAPPING SOFTWARE TO SCREEN LARGE COMPOUND LIBRARIES - 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.

Posted 1/20/22, 12:00 AM