# Understanding and Designing Cyclic Peptides

> **NIH NIH R01** · TUFTS UNIVERSITY MEDFORD · 2021 · $270,098

## Abstract

Protein–protein interactions (PPIs) have many important cellular roles, including in transcription, protein
degradation, protein translocation, signal transduction, and molecule and vesicular transport. The ability to
selectively modulate PPIs thus provides a valuable means to control specific biological processes for
therapeutic intervention. Unfortunately, owing to their flat and large interfaces, PPIs are challenging to target
using traditional small molecule drugs. Cyclic peptides (CPs) represent a promising solution to target PPIs –
they can directly mimic the binding partners of the target protein interface and have enhanced biostability and
bioavailability compared to their linear counterparts. Despite several examples of CPs successfully used as
therapeutics, including as immune system suppressants, antibiotics, and antifungals, most of these examples
are actually natural products or their derivatives, rather than the products of successful de novo CP
development. One of the key reasons that novel, functional CPs are difficult to design is our current inability to
efficiently and reliably predict CP three-dimensional structures. Our long-term objective is the rational design of
functional CPs to target specific PPIs of interest. In this proposal, our specific aims are to (1) develop a
computational method to address current challenges in CP structure prediction; (2) fill the substantial
knowledge gap regarding CP sequence–structure relationships; and (3) validate a platform to rationally design
CPs with desired structures.
 In our first aim, we will develop an enhanced sampling method for CP structure prediction by taking
advantage of the constrained nature of CPs. We hypothesize that CPs have only a limited set of motions they
can use to switch conformations and that these essential transitional motions of CPs can be leveraged to
greatly accelerate conformational sampling, allowing efficient CP structure prediction in explicit solvent. In the
second aim, we will systematically vary the sequences of CPs and apply our enhanced sampling methods to
simulate their structures. From this study, we will extract general principles of how primary amino acid
sequences affect CP structures. Moreover, we will integrate these principles into algorithms to predict CP
structures and guide CP design. In our third aim, we will integrate our capability to simulate CP structures and
our knowledge of CP sequence–structure relationships to design and experimentally validate CPs that mimic
hot loops at PPIs. The proposed work will greatly enable the continued development of CPs as modulators of
PPIs, advance our understanding of such important molecular interactions in normal and disease biology, and
provide possible means to target specific PPIs for therapeutic interventions.

## Key facts

- **NIH application ID:** 10240640
- **Project number:** 5R01GM124160-05
- **Recipient organization:** TUFTS UNIVERSITY MEDFORD
- **Principal Investigator:** Yu-Shan Lin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $270,098
- **Award type:** 5
- **Project period:** 2017-09-11 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10240640

## Citation

> US National Institutes of Health, RePORTER application 10240640, Understanding and Designing Cyclic Peptides (5R01GM124160-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10240640. Licensed CC0.

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