Thursday, February 19, 2026 2/19/2026

Novel deep learning frameworks for predicting nucleosome-binding proteins

Award Number: R21GM152740
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: 03/01/2024
PERIOD OF PERFORMANCE END DATE: 02/28/2027

Group Awards By:

View Award Description

Novel deep learning frameworks for predicting nucleosome-binding proteins - Project Summary: Novel deep learning frameworks for predicting nucleosome-binding proteins Nucleosome-binding proteins (NBPs) such as pioneer transcription factors play an important role in cell fate changes during organogenesis, cell differentiation, and reprogramming. The ability of nucleosome binding is encoded within their DNA-binding domains. However, experimental identification of the NBPs is expensive and time-consuming with low sensitivity. The proposed research aims to develop novel deep learning frameworks to predict the NBPs. In Aim 1, we will develop a novel two-stage transfer learning framework. In the first stage, the knowledge of the protein language model ProtT5 will be extracted as feature embeddings to represent the amino acid sequences of the proteins. The information from the 1-D sequences will be combined with that from their 3- D structures, which will be processed by a state-of-the-art Graph Neural Network (GNN) for 3-D molecules. In the second stage, the GNN model will be first initialized by annotated DNA-binding proteins (DBPs) and then fine-tuned on a NBP dataset. In Aim 2, we will design and implement a novel transfer learning-continual learning framework to predict the NBPs in a species-specific manner. The transfer learning module initializes a novel Bayesian online inference model with the knowledge acquired from the DBP prediction model of Aim 1 through a prior for generative probabilistic neural networks. The continual learning module based on probabilistic inferences will be able to constantly update learned neural network parameters with the DBPs of a given species without forgetting the previously acquired knowledge. Lastly, the trained DBP prediction model will be fine-tuned to make predictions of species-specific NBPs. Overall, this proof-of-concept study aims to gain technical knowledge on the proposed frameworks and evaluate their feasibility for predicting the nucleosome binders. These frameworks, if successful, will have a potential to broadly advance biomedical research by being generalized to other types of intermolecular interactions, e.g., protein-RNA interactions and protein-protein interactions.


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 = $0 )
2026	2025	ROCHESTER INSTITUTE OF TECHNOLOGY	1 LOMB MEMORIAL DR	ROCHESTER	NY	14623	MONROE	USA	Biomedical Research and Research Training	000	2	1/6/2026	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $230,330 )
2025	2025	ROCHESTER INSTITUTE OF TECHNOLOGY	1 LOMB MEMORIAL DR	ROCHESTER	NY	14623	MONROE	USA	Biomedical Research and Research Training	000	2	6/6/2025	NON-COMPETING CONTINUATION	$207,297
2025	2025	ROCHESTER INSTITUTE OF TECHNOLOGY	1 LOMB MEMORIAL DR	ROCHESTER	NY	14623	MONROE	USA	Biomedical Research and Research Training	001	2	6/11/2025	NON-COMPETING CONTINUATION	$23,033
														Subtotal = $230,330

Issue Date FY: 2024 ( Subtotal = $148,000 )
2024	2024	ROCHESTER INSTITUTE OF TECHNOLOGY	1 LOMB MEMORIAL DR	ROCHESTER	NY	14623	MONROE	USA	Biomedical Research and Research Training	000	1	2/1/2024	NEW	$148,000
														Subtotal = $148,000

Grand Total All Awards = $378,330

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Novel deep learning frameworks for predicting nucleosome-binding proteins

Award Number: R21GM152740

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: 03/01/2024

PERIOD OF PERFORMANCE END DATE: 02/28/2027

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer