# A data-driven approach towards generation of permeable peptide therapeutics

> **NIH NIH DP2** · UNIVERSITY OF OREGON · 2021 · $1,301,850

## Abstract

Project Summary
 A staggering number of potential targets for combating diseases fall into a category called undruggable,
targets that are not accessible to two commonly used drug modalites: antibodies and small molecules. These
undruggable targets often reside inside the cells and cannot be accessed by antibodies, which are too large to
cross the cell membrane. Their flat surfaces deviate from the common deep pockets that are often hit by small
molecules. To untap the potential of these targets, we need a therapeutic modality that can target shallow
pockets and cross the cell membrane. Peptides are at the size range and composition that can be an ideal
choice as this alternative modality.
 Despite efforts in developing high affinity peptide binders, generating permeable binders has been a
long-standing challenge. The few examples of cell permeable functional peptides are often developed through
error and trial or via many rounds of modification and testing. The challenge in obtaining permeable binders is
mainly due to lack of high throughput methods to screen for permeability, which has not only limited the power
of library-based screening for obtaining permeable binders, but has also resulted in a limited amount of
experimental data from which one can deduce rules that govern a peptide permeable. Thus, the use of
peptides as therapeutics to drug the undruggable has remained largely underdeveloped.
 This proposal uses an interdisciplinary approach to address this challenge. We generate a network to
represent the peptide space in a meaningful manner. We then cluster this network and select for experimental
testing a set that is truly representative of the peptide space. The results of our tests, which include both
artificial membrane analysis and cellular uptake in bacterial and mammalian cells, can thus be generalized to
the entire space. This unprecedented dataset will then be used as a starting point to gain better understanding
of peptide permeability and to develop experimental and computational methods for rapid identification of
permeable peptide binders that can be used as leads for therapeutic development. We will use this dataset to
develop an automated generative algorithm that computationally designs permeable peptides that can target a
target interface of interest.
 This proposal is a leap in the field of peptide therapeutic development. The dataset generated during this
work and methods we develop will be the stepping stone for many researchers interested in drug discovery,
physics-based models of permeability, and peptide therapeutics.

## Key facts

- **NIH application ID:** 10241206
- **Project number:** 1DP2GM146249-01
- **Recipient organization:** UNIVERSITY OF OREGON
- **Principal Investigator:** Parisa Hosseinzadeh
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,301,850
- **Award type:** 1
- **Project period:** 2021-09-22 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241206

## Citation

> US National Institutes of Health, RePORTER application 10241206, A data-driven approach towards generation of permeable peptide therapeutics (1DP2GM146249-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10241206. Licensed CC0.

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