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Updated: Jan 1, 2026

Construction of Cyclic Cell-Penetrating Peptides for Enhanced Penetration of Biological Barriers

Construction of Cyclic Cell-Penetrating Peptides for Enhanced Penetration of Biological Barriers

Published on: September 19, 2022

Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides.

David J Diller^1,2, Jon Swanson^1,3, Alexander S Bayden^1,4

¹CMDBioscience, 5 Park Avenue, New Haven, CT 06511, USA.

Molecules (Basel, Switzerland)

|December 19, 2019

Summary

This summary is machine-generated.

This study validates CMDInventus, a computational platform for peptide drug design. It accurately models and predicts peptide interactions with cancer targets like MDM2/MDMX, aiding future drug discovery.

Keywords:

alanine scan d-amino acid scan free energy calculation peptide design

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Area of Science:

Computational chemistry
Drug design
Oncology

Background:

Peptide drug design is crucial for targeting intracellular protein-protein interactions.
Computational platforms require experimental validation for drug design applications.

Purpose of the Study:

To validate the CMDInventus computational platform for peptide drug design.
To assess its accuracy in modeling and predicting peptide interactions with MDM2/MDMX oncology targets.

Main Methods:

Utilized CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore, CMDyscore) for retrospective analysis.
Applied CMDescore, CMDyscore, and CMDboltzmann for prospective prediction of binding affinities.
Performed Ala-scan and D-amino acid scan of ATSP-7041 peptide.

Main Results:

CMDInventus modules accurately reproduced structural and binding data in retrospective MDM2/MDMX studies.
Prospective predictions of binding affinities for ATSP-7041 Ala-scan were accurate.
CMDboltzmann successfully predicted outcomes of a novel D-amino acid scan for ATSP-7041.

Conclusions:

CMDInventus is rigorously validated as a computational tool for peptide drug design.
The platform demonstrates utility in modeling and predicting peptide structural and binding properties.
Supports the use of CMDInventus for advancing peptide-based therapeutic strategies.