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Des3PI: a fragment-based approach to design cyclic peptides targeting protein–protein interactions

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Abstract

Protein–protein interactions (PPIs) play crucial roles in many cellular processes and their deregulation often leads to cellular dysfunctions. One promising way to modulate PPIs is to use peptide derivatives that bind their protein target with high affinity and high specificity. Peptide modulators are often designed using secondary structure mimics. However, fragment-based design is an alternative emergent approach in the PPI field. Most of the reported computational fragment-based libraries targeting PPIs are composed of small molecules or already approved drugs, but, according to our knowledge, no amino acid based library has been reported yet. In this context, we developed a novel fragment-based approach called Des3PI (design of peptides targeting protein–protein interactions) with a library composed of natural amino acids. All the amino acids are docked into the target surface using Autodock Vina. The resulting binding modes are geometrically clustered, and, in each cluster, the most recurrent amino acids are identified and form the hotspots that will compose the designed peptide. This approach was applied on Ras and Mcl-1 proteins, as well as on A\(\beta\) protofibril. For each target, at least five peptides generated by Des3PI were tested in silico: the peptides were first blindly docked on their target, and then, the stability of the successfully docked complexes was verified using 200 ns MD simulations. Des3PI shows very encouraging results by yielding at least 3 peptides for each protein target that succeeded in passing the two-step assessment.

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Data availability

Des3PI is composed of a set of Python scripts and of a library of natural amino acids. It requires the following packages and software to be installed on user workstation: Python 3.7.3 (https://www.python.org/downloads/), the additional Python modules pandas, biopython, matplotlib, scipy, seaborn, sklearn, and AutoDock Vina 1.1.2 (https://vina.scripps.edu/downloads/). For validation, we used ADCP 1.0 which is part of ADFRSuite 1.0 (https://ccsb.scripps.edu/adfr/downloads/) and GROMACS 2019.1 (https://manual.gromacs.org/documentation/). Des3PI code and all data reported in this publication are available in an open-source and open-access format on GitHub: https://github.com/des3pi.

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Acknowledgements

The authors thank Dr Michel Sanner for his helpful expertise in ADCP and for the fruitful discussions about peptide docking.

Funding

Maxence Delaunay’s PhD thesis is supported by a full scholarship from the French Ministry of Higher Education, Research and Innovation.

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All authors contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript.

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Correspondence to Tâp Ha-Duong.

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Delaunay, M., Ha-Duong, T. Des3PI: a fragment-based approach to design cyclic peptides targeting protein–protein interactions. J Comput Aided Mol Des 36, 605–621 (2022). https://doi.org/10.1007/s10822-022-00468-z

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