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Computational modulator design and machine learning to target protein-protein interactions

Award Number: R35GM127040
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 05/01/2018
PERIOD OF PERFORMANCE END DATE: 04/30/2028

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Computational modulator design and machine learning to target protein-protein interactions - Abstract The overall goal of my research program is to develop and apply state-of-the-art machine learning and molecular modeling tools to facilitate the rational design of modulators of important cellular pathways for therapeutic use. Protein-protein interactions (PPIs) are central factors in cellular signaling and biological networks, and their mis-regulations lead to diseases states. Thus PPIs are biologically compelling targets for drug discovery. Despite a few notable successes, most PPIs have not been successfully targeted and remain challenging for therapeutic intervention. The fundamental challenge derives from their intrinsic structural features: the binding surfaces of many PPIs are generally large in area, flat, and dynamic. PPIs are often transient and involve multivalent contacts. One of the most promising PPI inhibitor discovery strategies is to use miniature protein domain mimetics (PDMs) to reproduce the key interface contacts utilized by nature. PDMs are advantageous as medium-sized molecules with high surface complementarity and a broader set of contact points than typical small molecules, but are still limited because—by definition—only a portion of the total PPI binding energy is captured in the interaction. The binding affinity of the synthetic domains is often lower than the cognate full-length proteins. In last five years, we have significantly advanced a pocket-guided rational design approach based on AlphaSpace to tackle this challenge. We have successfully optimized a PDM to target the KIX domain of the p300/CBP coactivator by introducing non-natural amino acids to improve pocket-fragment binding; rationally designed a novel NEMO coiled coil mimic that disrupts virus-induced NF-κB signaling and induces cell death; and successfully targeted a new binding pocket on MDM2 and MDMX with a potent dual inhibitor by elaborating hydrogen-bond stabilized alpha-helix mimetics. Meanwhile, we have developed state-of-the-art scoring functions for protein-ligand docking as well as virtual screening, advanced deep learning models to predict molecular properties and chemical reactions, and established strong and fruitful collaborations with several outstanding experimental labs in chemical biology and biophysics to discover new modulators of biomolecular interactions. These significant advances set the stage for us to further push the frontier of integrating machine learning and molecular modeling for rational drug design. Our focus in the next few years will be to establish a robust pocket-guided design platform based on AlphaSpace and machine learning for PPI orthosteric inhibitor optimization, provide physical/chemical insights and develop novel computational strategies for allosteric modulator discovery, and explore chemical space with deep sequence/graph/geometric representation learning for multi-objective molecular design. Our modulator design efforts in close collaborations with our experimental colleagues will not only rigorously test predictive power of our developed methods in real life applications, but also result in highly specific and potent modulators towards several important but challenging therapeutic targets, providing new leads for drug development.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $592,385 )
2026	2026	NEW YORK UNIVERSITY	70 WASHINGTON SQ S FL 11	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	000	9	4/21/2026	NON-COMPETING CONTINUATION	$592,385
														Subtotal = $592,385

Issue Date FY: 2025 ( Subtotal = $589,588 )
2025	2025	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	001	8	7/22/2025	NON-COMPETING CONTINUATION	$589,588
2025	2024	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	000	7	11/6/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $589,588

Issue Date FY: 2024 ( Subtotal = $590,705 )
2024	2024	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	003	7	6/19/2024	NON-COMPETING CONTINUATION	$59,071
2024	2024	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	002	7	4/18/2024	NON-COMPETING CONTINUATION	$531,634
2024	2023	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	001	6	3/18/2024	COMPETING CONTINUATION	$324,198
2024	2022	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	000	5	3/18/2024	NON-COMPETING CONTINUATION	-$324,198
														Subtotal = $590,705

Issue Date FY: 2023 ( Subtotal = $588,935 )
2023	2023	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	000	6	5/26/2023	COMPETING CONTINUATION	$588,935
														Subtotal = $588,935

Issue Date FY: 2022 ( Subtotal = $556,806 )
2022	2022	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	000	5	5/16/2022	NON-COMPETING CONTINUATION	$556,806
														Subtotal = $556,806

Issue Date FY: 2021 ( Subtotal = $556,806 )
2021	2021	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	000	4	5/5/2021	NON-COMPETING CONTINUATION	$556,806
														Subtotal = $556,806

Issue Date FY: 2020 ( Subtotal = $491,993 )
2020	2020	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	000	3	4/29/2020	NON-COMPETING CONTINUATION	$491,993
														Subtotal = $491,993

Issue Date FY: 2019 ( Subtotal = $297,553 )
2019	2019	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	000	2	5/4/2019	NON-COMPETING CONTINUATION	$297,553
														Subtotal = $297,553

Issue Date FY: 2018 ( Subtotal = $297,553 )
2018	2018	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Biomedical Research and Research Training	000	1	4/11/2018	NEW	$297,553
														Subtotal = $297,553

Grand Total All Awards = $4,562,324

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Computational modulator design and machine learning to target protein-protein interactions

Award Number: R35GM127040

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 05/01/2018

PERIOD OF PERFORMANCE END DATE: 04/30/2028

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