# Computational design of new protein structures and interactions

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $348,525

## Abstract

PROJECT SUMMARY/ABSTRACT
Computational design has immense potential to create new protein functions with applications in biotechnology,
biology, and medicine. However, despite exciting progress in designing proteins with de novo structures, our
ability to design proteins with new functions lags behind. A key reason for this discrepancy is that function
typically requires protein geometries that deviate from the “idealized” folds of de novo designed structures and
that are hence more difficult to design. The long-term objective of our work is to advance computational design
to make predictive design of more complex functions possible. The specific objective of this proposal is to
address the generally unsolved problem of designing proteins that bind new small molecule ligands. A particular
application is the design of new sensor/actuators: proteins that can detect a user-defined small molecule signal
and trigger a biological response (such as protein signaling or gene expression). Significant applications of
such sensor/actuators include maximizing production of industrially valuable chemicals in metabolic engineering,
creating precise tools for dissecting biological processes in cell signaling, and achieving tight regulation in
emerging cancer therapies. Our work in the prior project period has advanced methods for binding site design
and applied them to engineer the first computationally designed chemically-induced protein dimerization system,
which senses and responds to a new ligand in living cells; a crystal structure confirmed the accuracy of the de
novo designed binding site. Despite this key progress, there are significant barriers to generalize the approach.
The first step in engineering new ligand binding sites is generally to identify desired binding site geometries
(constellations of amino acid side chains coordinating the ligand). The second step is then to place those
geometries into a suitable protein termed “scaffold”. This approach is critically limited by available geometries,
both for binding sites and scaffolds to accommodate them. To address these problems, we propose two key
methodological innovations: Aim 1 will establish and experimentally test a new computational method to
generate millions of possible binding site geometries de novo that can be built into proteins. Aim 2 will develop
and test a new computational approach to build “de novo fold families” (sets of custom-shaped de novo designed
proteins) by systematically varying the geometries of structural elements within a given fold topology, to be used
as scaffolds. Feasibility is supported by preliminary results for both aims; we have designed new binding sites
(prior period), and have solved structures of 3 de novo designed proteins with the same fold but distinct
geometries. The proposed studies innovate in creating both new methods and new molecules that expand
designable structures and functions and overcome problems with current approaches limited by available
ge...

## Key facts

- **NIH application ID:** 9997704
- **Project number:** 2R01GM110089-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Tanja Kortemme
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $348,525
- **Award type:** 2
- **Project period:** 2015-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9997704

## Citation

> US National Institutes of Health, RePORTER application 9997704, Computational design of new protein structures and interactions (2R01GM110089-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9997704. Licensed CC0.

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