Document Type
Peer-Reviewed Article
Publication Date
2019
Abstract
There is interest in peptide drug design, especially for targeting intracellular protein–protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design.
DOI
10.3390/molecules24244586
PubMed ID
31847417
Recommended Citation
Diller, D. J., Swanson, J., Bayden, A. S., Brown, C. J., Thean, D., Lane, D. P., ... & Audie, J. (2019). Rigorous computational and experimental investigations on MDM2/MDMX-targeted linear and macrocyclic peptides. Molecules, 24(24), 4586. Doi: 10.3390/molecules24244586
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Comments
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).