Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

Sarfraz Ahmad; Muhammad Usman Mirza; John F. Trant

doi:10.1016/j.jpha.2023.04.008

Volume 13 Issue 5

May 2023

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Sarfraz Ahmad, Muhammad Usman Mirza, John F. Trant. Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study[J]. Journal of Pharmaceutical Analysis, 2023, 13(5): 523-534. doi: 10.1016/j.jpha.2023.04.008

Citation:

Sarfraz Ahmad, Muhammad Usman Mirza, John F. Trant. Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study[J]. Journal of Pharmaceutical Analysis, 2023, 13(5): 523-534. doi: 10.1016/j.jpha.2023.04.008

Citation:

Sarfraz Ahmad, Muhammad Usman Mirza, John F. Trant. Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study[J]. Journal of Pharmaceutical Analysis, 2023, 13(5): 523-534. doi: 10.1016/j.jpha.2023.04.008

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Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

doi: 10.1016/j.jpha.2023.04.008

a. Department of Chemistry and Biochemistry, University of Windsor, Windsor N9B 3P4, Ontario, Canada;
b. Binary Star Research Services, LaSalle N9J 3X8, Ontario, Canada

Funds:

The authors highly acknowledge the Natural Sciences and Engineering Research Council of Canada (Grant No.: 2018-06338) for providing the resources to conduct this work. Sarfraz Ahmad, Muhammad Usman Mirza and John F. Trant wish to recognize that this work was made possible by the facilities of the Shared Hierarchical Academic Research Computing Network (SHARCNET: www.sharcnet.ca) and Compute/Calculation Canada, now the Digital Research Alliance of Canada (https://alliancecan.ca/en). The authors are also affiliated with Binary Star Research Services (https://binarystarchem.ca/). The content of this article is exclusively the responsibility of the authors and does not necessarily reflect the official views of Binary Star Research Services. Binary Star Research Services had no input into the conclusions of this article.

Received Date: Jan. 05, 2023
Accepted Date: Apr. 13, 2023
Rev Recd Date: Mar. 07, 2023
Publish Date: Apr. 15, 2023

Abstract

Abstract

Peptide-based therapeutics are increasingly pushing to the forefront of biomedicine with their promise of high specificity and low toxicity. Although noncanonical residues can always be used, employing only the natural 20 residues restricts the chemical space to a finite dimension allowing for comprehensive in silico screening. Towards this goal, the dataset comprising all possible di-, tri-, and tetra-peptide combinations of the canonical residues has been previously reported. However, with increasing computational power, the comprehensive set of pentapeptides is now also feasible for screening as the comprehensive set of cyclic peptides comprising four or five residues. Here, we provide both the complete and prefiltered libraries of all di-, tri-, tetra-, and penta-peptide sequences from 20 canonical amino acids and their homodetic (N-to-C-terminal) cyclic homologues. The FASTA, simplified molecular-input line-entry system (SMILES), and structure-data file (SDF)-three dimension (3D) libraries can be readily used for screening against protein targets. We also provide a simple method and tool for conducting identity-based filtering. Access to this dataset will accelerate small peptide screening workflows and encourage their use in drug discovery campaigns. As a case study, the developed library was screened against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease to identify potential small peptide inhibitors.
- Dipeptides,
- Tripeptides,
- Tetrapeptides,
- Pentapeptides,
- N-to-C-terminal cyclic peptides,
- Peptide library

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References(71)

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