Project Grant 2155095
- This Project Grant award of $300,000 from the National Science Foundation (NSF) Integrative Activities program (CFDA 47.083) supports the development and experimental validation of computational tools for simulation-guided design of DNA nanostructures and molecular systems. The project aims to integrate high-level and low-level computational models to enable more powerful and user-friendly multiscale modeling frameworks for DNA nanotechnology. Key objectives include parameterizing coarse-grained...
- The National Science Foundation (NSF) Division of Materials Research awarded a $499,999 Project Grant to Arizona State University (ASU) under the Mathematical and Physical Sciences program (CFDA 47.049). The three-year grant project aims to: 1) create 3D DNA crystal scaffolds to host and determine the structure of RNA aptamers; 2) attach and immobilize functional proteins like enzymes within the crystal lattices to develop catalytic materials; and 3) develop sub-100 nm DNA nano-crystals to...
- Federal Project Grant Award Summary Arizona State University, through its Office of Research and Sponsored Projects Administration (Orspa), was awarded $100,000 by the National Science Foundation's Office of International Science and Engineering (CFDA 47.079) effective September 1, 2025, with completion targeted for August 31, 2028. The project develops novel artificial intelligence (AI) techniques to automate the design and characterization of nucleic acid nanostructures—small, self-assembled...
- This three-year $1,198,460 Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of deep learning models for protein-DNA complex structure prediction, design, and evaluation. Professors David Baker and Frank DiMaio of the University of Washington Department of Biochemistry, along with Barry Stoddard of the Fred Hutchinson Cancer Center, will create three deep learning models: one for predicting protein-DNA...
- This $3,000,000 project grant, awarded by the National Science Foundation (NSF) under the Engineering (CFDA 47.041) program, aims to solve the long-standing challenge of developing self-amplifying ribonucleic acid (SARNA) technologies. The project, led by Trustees of Boston University, a private non-profit research university, will: Synthesize a diverse library of modified nucleoside triphosphates (ModNTPs) and use them to produce modified SARNA with extended cellular half-life. Evaluate the...
- This three-year, $275,000 National Science Foundation Division of Chemistry Project Grant will support research into deciphering RNA-based regulatory logic using interpretable machine learning approaches. The awardee, Cornell University, will collaborate with researchers to design and deploy interpretable neural networks trained on massively parallel reporter assays. This will provide insights into splicing codes that determine exon skipping and the role of 5'UTR sequences in translation...
- Alberto Perez of the University of Florida was awarded a $650,000 Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) to develop new computational methods for characterizing nucleic acid structure, recognition mechanisms, and function. Over a five-year period ending in November 2027, Dr. Perez and his research group will pursue the development of enhanced sampling methods and Bayesian inference strategies to better model the...
- This $100,000 Project Grant awarded by the National Science Foundation's Biological Sciences program (CFDA 47.074) will support the development of deep-learning models, specifically consistency models, to simulate the long-time-scale dynamics of protein structures. The project aims to address the limitations of current molecular dynamics (MD) simulations in capturing crucial long-duration protein dynamics events, such as protein folding and aggregation. By developing these advanced computational...
- This $360,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative research by professors Eriks Rozners of SUNY at Binghamton and James A. Mackay of Elizabethtown College. The project aims to develop new peptide nucleic acid (PNA) molecules capable of selectively recognizing and binding to double-stranded non-coding RNA. This research addresses an important challenge in RNA biochemistry and could enable...
- This $324,118 National Science Foundation project grant supports research to decipher RNA-based regulatory logic through interpretable machine learning models. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the three-year award to New York University will develop and apply interpretable neural networks to massive parallel reporter assay datasets. Specifically, the models aim to elucidate the splicing code that determines exon skipping and the 5'UTR code regulating...
This three-year, $338,355 Project Grant from the National Science Foundation's Division of Chemistry and Mathematical and Physical Sciences program will support the development of new machine learning methods to design DNA and RNA aptamers for molecular targets. Principal Investigator Petr Sulc of Arizona State University will use restricted Boltzmann machine architectures to analyze sequences from selection experiments and naturally occurring non-coding RNAs to extract structural and sequence motifs that determine strong binding affinity. The models will be trained and validated on experimental data, and then used to generate novel sequences for verification. This work aims to advance diagnostic, therapeutic and basic research applications by computationally designing improved molecular binders from large datasets of weakly binding sequences. Outreach activities are also planned to engage students and the public in interdisciplinary science combining modeling, simulation and biochemistry experiments.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $338.4k | 3/28/22 |