# Computational design of novel protein binders based on structure mining and learning from data

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2021 · $354,240

## Abstract

Our long-term objective is to turn computational protein design into a disruptive technology platform that will
enable the routine and rapid generation of reagents for detecting proteins or perturbing their functions. Currently,
research and therapy rely on small molecules and/or antibodies for these tasks. These are powerful tools, but
they can be slow and expensive to develop, and they do not meet all needs. Designer peptides or mini-proteins
have high potential to bind extracellular or intracellular targets either as labels (e.g., for imaging) or as functional
modulators (e.g., interaction inhibitors), for applications in basic and clinical research and in disease diagnosis
and treatment. Existing tools for designing such custom proteins rely on experimental library screening,
sometimes guided or supported by computational modeling of structure. Despite the immense value such
molecules would bring to basic biomedical research and therapeutic development, there are not yet rapid and
facile routes to obtaining designed proteins with desired properties. Computational methods can potentially
address this need, but existing technology is not sufficiently reliable, flexible or automated for routine use.
Compared to the mid-1990’s, when the modern approach to computational protein design was developed, we
live in a data and technology-empowered age. The premise of this proposal is that we can increase the range of
problems that can be solved using computational design, and also dramatically improve success rates, by
making full use of the proven rules of sequence-structure compatibility encoded in known natural structures and
their homologous sequences. The Protein Data Bank (the collection of all known protein structures) has grown
10-fold since 2000, placing us at a point where we can design novel proteins by constructing them from building
blocks used in nature. We have implemented a new design framework that is based on this principle and that is
different in fundamental aspects from all previously published alternatives. Tests on diverse tasks demonstrate
outstanding success. To further develop our approach, we propose methodological advances that we will
implement, test and then apply to protein design challenges involving detecting or inhibiting protein recognition
domains. We will develop and apply methods to: automatically identify design strategies for binding to a target
protein, score and rank specific design candidates, design libraries that will be screened to provide rich
experimental data about successes and failures, and automatically feed experimental data back into model
development in a principled way. Outcomes will include new methodology that will be shared with the community,
computational predictions of high-ranked interface design sites that can inform analysis of structures and
pathways, and experimentally validated designer molecules that bind to protein domains important for signaling
in disease pathways.

## Key facts

- **NIH application ID:** 10079500
- **Project number:** 5R01GM132117-02
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Gevorg Grigoryan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $354,240
- **Award type:** 5
- **Project period:** 2020-02-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10079500

## Citation

> US National Institutes of Health, RePORTER application 10079500, Computational design of novel protein binders based on structure mining and learning from data (5R01GM132117-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10079500. Licensed CC0.

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