Populating Chemical Space with Peptides Using a Genetic Algorithm

Capecchi, Alice; Zhang, Alain; Reymond, Jean-Louis (2020). Populating Chemical Space with Peptides Using a Genetic Algorithm. Journal of chemical information and modeling, 60(1), pp. 121-132. American Chemical Society 10.1021/acs.jcim.9b01014

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In drug discovery, one uses chemical space as a concept to organize molecules according to their structures and properties. One often would like to generate new possible molecules at a specific location in the chemical space marked by a molecule of interest. Herein, we report the peptide design genetic algorithm (PDGA, code available at https://github.com/reymond-group/PeptideDesignGA), a computational tool capable of producing peptide sequences of various topologies (linear, cyclic/polycyclic, or dendritic) in proximity of any molecule of interest in a chemical space defined by macromolecule extended atom-pair fingerprint (MXFP), an atom-pair fingerprint describing molecular shape and pharmacophores. We show that the PDGA generates high-similarity analogues of bioactive peptides with diverse peptide chain topologies and of nonpeptide target molecules. We illustrate the chemical space accessible by the PDGA with an interactive 3D map of the MXFP property space available at http://faerun.gdb.tools/. The PDGA should be generally useful to generate peptides at any location in the chemical space.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Departement of Chemistry and Biochemistry

UniBE Contributor:

Capecchi, Alice and Reymond, Jean-Louis

Subjects:

500 Science > 570 Life sciences; biology
500 Science > 540 Chemistry

ISSN:

1549-9596

Publisher:

American Chemical Society

Language:

English

Submitter:

Sandra Tanja Zbinden Di Biase

Date Deposited:

24 Jan 2020 15:09

Last Modified:

29 Jan 2020 01:34

Publisher DOI:

10.1021/acs.jcim.9b01014

PubMed ID:

31868369

BORIS DOI:

10.7892/boris.138521

URI:

https://boris.unibe.ch/id/eprint/138521

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