# Developing a computational platform for induced-fit and chemogenetic drug design

> **NIH NIH DP1** · VANDERBILT UNIVERSITY · 2024 · $475,500

## Abstract

PROJECT SUMMARY
Prescription opioid therapy plays a critical role in the clinical management of pain in multiple acute and chronic
settings. The challenges of effective pain management have led to over 2 million adults in the US, and over 12
million globally, with an opioid use disorder (OUD). OUD accounts for over 120,000 deaths annually worldwide.
The dominant target of therapeutic opioids is the µ-opioid receptor (MOR). The analgesic effects of MOR
agonists are due to Gα,i/o/z-protein signaling, and it has been proposed that undesirable side-effects of MOR
agonists, such as respiratory depression and tolerance, can be mitigated through partial recruitment of Gi/o/z-
protein subtypes. Thus, it is of clinical interest to determine the relationship between MOR signaling and
analgesia versus side-effects to guide the design of therapeutic agonists that selectively activate the desired
signaling pathway. G-protein coupled receptors (GPCRs), including MOR, are known to adopt a range of
different functionally distinct configurations upon engaging orthosteric modulators and/or intracellular effector
proteins. These induced-fit structural rearrangements cannot be modeled with existing computer-aided drug
discovery algorithms during docking or design due to the time and resources required.
It is the objective of this proposal to develop a customizable, multi-purpose computer-aided drug
design (CADD) platform that can efficiently model largescale induced-fit conformational changes
during small molecule and/or receptor sequence design. Completion of the proposal will enable structure-
based design of biased agonists and DREADDs (Designer Receptors Exclusively Activated by Designer
Drugs). This proposal will include innovative algorithms that leverage deep learning protein structure prediction
methods and ultra-large make-on-demand chemical libraries to rapidly screen synthetically accessible
molecules for those that can induce conformational changes required to activate G¬i¬-protein signaling in
MOR. In collaboration, I will synthesize (Dr. Craig Lindsley), functionally validate (Drs. Craig Lindsley, Heidi
Hamm, and Vsevolod Gurevich), and structurally characterize (Drs. Beili Wu and Matthias Elgeti) designed
molecules and DREADDs. Experimentally validated partial and biased agonists and DREADDs will be fed back
into the computational platform to be used as starting points for subsequent rounds of optimization. In this way,
we will establish a computational-experimental iterative feedback loop.

## Key facts

- **NIH application ID:** 10833213
- **Project number:** 5DP1DA058349-02
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Benjamin Patrick Brown
- **Activity code:** DP1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $475,500
- **Award type:** 5
- **Project period:** 2023-05-01 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10833213

## Citation

> US National Institutes of Health, RePORTER application 10833213, Developing a computational platform for induced-fit and chemogenetic drug design (5DP1DA058349-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10833213. Licensed CC0.

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