# Computational and Experimental Investigation and Design of Protein Interaction Specificity

> **NIH NIH R35** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2024 · $541,786

## Abstract

Protein-protein interactions transmit information, shape cell structure, assemble complexes, and enable
chemical transformations that support life. Mapping and decoding the human interactome to establish which
interactions occur, what functions they support, and how interactions are altered in disease are critical goals
for biology. There is also a biomedical imperative to learn to inhibit or modulate protein interactions for
discovery research and the development of new therapies. This proposal presents an integrated program of
computational and experimental studies of protein-protein interactions that involve short linear motifs (SLiMs)
binding to modular, structurally conserved interaction domains. SLiM are abundant, with estimates of more
than 105 binding motifs in the human proteome, and they play critical roles in signal transduction and the
assembly of structural and regulatory complexes that are implicated in disease. The domains that bind to
SLiMs, such as EVH1, TRAF, SH3, WW, etc., occur in many copies in the proteome due to the expansion of
paralogous families by domain duplication and divergence. This research program will address two key
questions. (1) The paralog specificity question: How do the interactions made by paralogous protein domains
overlap vs. differ, and how are distinct binding profiles encoded in similar sequences and structures?
Answering this will provide currently missing links in the interactome and support the prediction and design of
paralog-specific interactions, which will improve our knowledge of disease pathways and how to target them.
(2) The SLiM specificity question: What sequence/structure features determine SLiM binding and how is this
regulated? Learning the features that distinguish real interactors from myriad motif-matching false positives
in the proteome will uncover mechanisms of SLiM recognition and support the prediction of new interactions.
This proposal focuses on developing new methods and models that will be applied to study biomedically
important SLiM-binding EVH1 and Atg8-like domains. EVH1 domains are found in proteins that bind to proline-
rich motifs, including members of the Ena/VASP family that regulate cancer cell invasion and metastasis.
Atg8-like proteins are critical for autophagy and participate in forming the autophagosome and recruiting cargo
for degradation by binding to selective autophagy receptors. Increased or decreased autophagy contributes
to many diseases via poorly understood mechanisms. The proposed studies will combine high-throughput
interaction mapping using experimental cell-surface display screening with data-driven modeling using deep
learning to support the detection, prediction, and design of new interactions. The screening-plus-modeling
approach will reveal new interaction partners for each family that broaden our understanding of cell biology,
elucidate mechanisms of specificity, and provide new techniques for designing selective inhibitors of these
and other...

## Key facts

- **NIH application ID:** 10851769
- **Project number:** 5R35GM149227-02
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** AMY E KEATING
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $541,786
- **Award type:** 5
- **Project period:** 2023-06-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10851769

## Citation

> US National Institutes of Health, RePORTER application 10851769, Computational and Experimental Investigation and Design of Protein Interaction Specificity (5R35GM149227-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10851769. Licensed CC0.

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