# Characterizing the Effects of Protein and RNA Variability in Molecular Function and Interactions

> **NIH NIH R35** · UNIVERSITY OF TEXAS DALLAS · 2020 · $374,916

## Abstract

Proposal Summary
This MIRA for ESI project proposes to investigate and characterize functional mutational variability in
biomolecules, its connections to molecular evolution and the ability to use these landscapes in scientific and
biomedical applications. It is composed of two scientific objectives that delineate the vision of our laboratory.
Our first goal is to develop global probabilistic and computational models that will help us answer the
hypothesis that the landscape of protein variability and their interactions can be characterized and quantified.
We will build our framework by creating probabilistic models based on large quantities of data obtained from
sequencing and we will make detailed predictions and experimental confirmation on the effect of specific
mutations predicted by our methodology. We are interested in the landscape of functional mutations, which is
much harder to characterize than the disruptive mutational space. For our second goal, we will expand our
hypothesis to molecular interactions that include nucleic acids, particularly protein-RNA interactions. We will
integrate sequencing technology and computational approaches to infer mutational landscapes of protein-
RNA recognition. We will test the hypothesis that not only native nucleic acid motifs can be selectively
recognized but also variants derived from our quantitative models. We will develop a framework to encode
and predict recognition from inferred landscapes and plan to integrate our results with experimental
technologies on RNA binding proteins that could help confirm our hypothesis.
In the past few years our lab has been able to infer global models of families of protein sequences and quantify
coevolutionary signals from these models successfully. These global models have had an impact in the study
of protein folding, protein dynamics and the prediction of protein complexes as well as their applications in
druggable interface discovery and drug-gene interactions. Functional variants of biomolecules are hard to
elucidate, as the disruptive mutational space is dominant. The PI was able to show that signals of amino acid
or nucleic acid coevolution can also be used as predictive tools to explore and encode the functional
mutational space of biomolecules. This idea represents a paradigm shift where quantification of evolutionary
signals can be used as a discovery mechanism. The overall vision of this project aims to quantify and uncover
the spectrum of functional biomolecular variability sculpted by evolutionary processes. This will help us work on
developments related to biomedicine, such as inferring the effects of mutations in disease, antibiotic resistance,
biomolecular sensor design and how sequence composition has an impact on interaction networks.

## Key facts

- **NIH application ID:** 9996730
- **Project number:** 5R35GM133631-02
- **Recipient organization:** UNIVERSITY OF TEXAS DALLAS
- **Principal Investigator:** Alonso Faruck Morcos
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $374,916
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9996730

## Citation

> US National Institutes of Health, RePORTER application 9996730, Characterizing the Effects of Protein and RNA Variability in Molecular Function and Interactions (5R35GM133631-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9996730. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*