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Computational design of novel protein binders based on structure mining and learning from data

Award Number: R01GM132117
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: 02/01/2020
PERIOD OF PERFORMANCE END DATE: 12/31/2024

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Computational design of novel protein binders based on structure mining and learning from data - Our long-term objective is to turn computational protein design into a disruptive technology platform that will enable the routine and rapid generation of reagents for detecting proteins or perturbing their functions. Currently, research and therapy rely on small molecules and/or antibodies for these tasks. These are powerful tools, but they can be slow and expensive to develop, and they do not meet all needs. Designer peptides or mini-proteins have high potential to bind extracellular or intracellular targets either as labels (e.g., for imaging) or as functional modulators (e.g., interaction inhibitors), for applications in basic and clinical research and in disease diagnosis and treatment. Existing tools for designing such custom proteins rely on experimental library screening, sometimes guided or supported by computational modeling of structure. Despite the immense value such molecules would bring to basic biomedical research and therapeutic development, there are not yet rapid and facile routes to obtaining designed proteins with desired properties. Computational methods can potentially address this need, but existing technology is not sufficiently reliable, flexible or automated for routine use. Compared to the mid-1990’s, when the modern approach to computational protein design was developed, we live in a data and technology-empowered age. The premise of this proposal is that we can increase the range of problems that can be solved using computational design, and also dramatically improve success rates, by making full use of the proven rules of sequence-structure compatibility encoded in known natural structures and their homologous sequences. The Protein Data Bank (the collection of all known protein structures) has grown 10-fold since 2000, placing us at a point where we can design novel proteins by constructing them from building blocks used in nature. We have implemented a new design framework that is based on this principle and that is different in fundamental aspects from all previously published alternatives. Tests on diverse tasks demonstrate outstanding success. To further develop our approach, we propose methodological advances that we will implement, test and then apply to protein design challenges involving detecting or inhibiting protein recognition domains. We will develop and apply methods to: automatically identify design strategies for binding to a target protein, score and rank specific design candidates, design libraries that will be screened to provide rich experimental data about successes and failures, and automatically feed experimental data back into model development in a principled way. Outcomes will include new methodology that will be shared with the community, computational predictions of high-ranked interface design sites that can inform analysis of structures and pathways, and experimentally validated designer molecules that bind to protein domains important for signaling in disease pathways.


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: 2025 ( Subtotal = $0 )
2025	2023	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Biomedical Research and Research Training	000	4	2/28/2025	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2023 ( Subtotal = $354,630 )
2023	2023	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Biomedical Research and Research Training	001	4	2/16/2023	NON-COMPETING CONTINUATION	$35,463
2023	2023	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Biomedical Research and Research Training	000	4	1/11/2023	NON-COMPETING CONTINUATION	$319,167
														Subtotal = $354,630

Issue Date FY: 2022 ( Subtotal = $354,431 )
2022	2022	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Biomedical Research and Research Training	001	3	6/27/2022	NON-COMPETING CONTINUATION	$35,442
2022	2022	TRUSTEES OF DARTMOUTH COLLEGE	6001 PARKHURST HALL STE 207	HANOVER	NH	03755	GRAFTON	USA	Biomedical Research and Research Training	000	3	12/15/2021	NON-COMPETING CONTINUATION	$318,989
														Subtotal = $354,431

Issue Date FY: 2021 ( Subtotal = $354,240 )
2021	2021	TRUSTEES OF DARTMOUTH COLLEGE	1 HINMAN DARTMOUTH COLLEGE	HANOVER	NH	03755	GRAFTON	USA	Biomedical Research and Research Training	001	2	3/8/2021	NON-COMPETING CONTINUATION	$35,423
2021	2021	TRUSTEES OF DARTMOUTH COLLEGE	1 HINMAN DARTMOUTH COLLEGE	HANOVER	NH	03755	GRAFTON	USA	Biomedical Research and Research Training	000	2	1/6/2021	NON-COMPETING CONTINUATION	$318,817
														Subtotal = $354,240

Issue Date FY: 2020 ( Subtotal = $406,408 )
2020	2020	TRUSTEES OF DARTMOUTH COLLEGE	1 HINMAN DARTMOUTH COLLEGE	HANOVER	NH	03755	GRAFTON	USA	Biomedical Research and Research Training	000	1	1/3/2020	NEW	$406,408
														Subtotal = $406,408

Grand Total All Awards = $1,469,709

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Computational design of novel protein binders based on structure mining and learning from data

Award Number: R01GM132117

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: 02/01/2020

PERIOD OF PERFORMANCE END DATE: 12/31/2024

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