Theoretical and Computational Chemistry

Populating Chemical Space with Peptides using a Genetic Algorithm

Alice Capecchi University of Bern
,

Abstract

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 chemical space marked by a molecule of interest. Herein we report the peptide design genetic algorithm (PDGA, code available at https://github.com/reymondgroup/PeptideDesignGA), a computational tool capable of producing peptide sequences of various chain topologies (linear, cyclic/polycyclic or dendritic) in proximity of any molecule of interest in a chemical space defined by MXFP, an atom-pair fingerprint describing molecular shape and pharmacophores. We show that PDGA generates high similarity analogs of bioactive peptides, including in selected cases known active analogs, as well as of non-peptide targets. We illustrate the chemical space accessible by PDGA with an interactive 3D-map of the MXFP property space available at http://faerun.gdb.tools/. PDGA should be generally useful to generate peptides at any location in chemical space.

Version notes

In the second version on the manuscript paragraphs were extended and few points clarified.

Content

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Supplementary material

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Peptidespace SI
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Peptidespacev2