# Computational Tools to Characterize the Effects of Protein and RNA Variability in Function and Interactions

> **NIH NIH R35** · UNIVERSITY OF TEXAS DALLAS · 2021 · $61,830

## Abstract

Project Summary
 The scientific goals of the funded parent project of this proposal (R35 MIRA for ESI) include the
development of global probabilistic and computational models of biomolecules that characterize and quantify
the landscape of protein variability and their interactions. The ultimate goal is to elucidate the landscape of
functional mutations, which is hidden within the much larger non-functional space. We are using this
functional landscape to engineer hybrid transcriptional regulators as well as to predict specificity networks in
two-component systems. Another important goal is to devise models to characterize the sequence dependance
on protein-protein and protein-RNA interactions. These models will allow us to encode and predict
recognition from inferred landscapes and to integrate our results with experimental technologies. Devising the
spectrum of functional biomolecular variability sculpted by evolutionary processes will be used to estimate the
effects of mutations in disease, antibiotic resistance, biomolecular sensor design and the impact of sequence
composition on interaction networks. As the research program of the parent grant benefits from generative
modeling and atomistic molecular simulations, we have turned our attention to build a framework that will
produce more accurate estimation of the sequence statistics of biomolecules and perform molecular dynamics
in an efficient way. For this reason, we request additional funds to build a new Graphical Processing Unit
(GPU) system based on NVIDIA A100 technology and large memory CPU nodes. This technology will
accelerate the goals of the parent research program in problems related to machine learning, biomolecular
simulation and linear algebra calculations.

## Key facts

- **NIH application ID:** 10387914
- **Project number:** 3R35GM133631-03S1
- **Recipient organization:** UNIVERSITY OF TEXAS DALLAS
- **Principal Investigator:** Alonso Faruck Morcos
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $61,830
- **Award type:** 3
- **Project period:** 2019-09-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10387914

## Citation

> US National Institutes of Health, RePORTER application 10387914, Computational Tools to Characterize the Effects of Protein and RNA Variability in Function and Interactions (3R35GM133631-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10387914. Licensed CC0.

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