Publications by authors named "Aaron L Feller"
Language modeling applied to biological data has significantly advanced the prediction of membrane penetration for small molecule drugs and natural peptides. However, accurately predicting membrane diffusion for peptides with pharmacologically relevant modifications remains a substantial challenge. Here, we introduce PeptideCLM, a peptide-focused chemical language model capable of encoding peptides with chemical modifications, unnatural or non-canonical amino acids, and cyclizations.
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- Chemical similarity searches traditionally focus on structure-based comparisons to find potential pharmaceutical leads.
- This research introduces a chemical language model that uses prompt engineering and two separate string representation algorithms to improve the search process for chemical compounds.
- The new method reveals molecules with similar functions, even if they have different structures than the initial query, potentially paving the way for discovering new molecular classes that meet target functions.
Microcins are a class of antimicrobial peptides produced by certain Gram-negative bacterial species to kill or inhibit the growth of competing bacteria. Only 10 unique, experimentally validated class II microcins have been identified, and the majority of these come from Escherichia coli. Although the current representation of microcins is sparse, they exhibit a diverse array of molecular functionalities, uptake mechanisms, and target specificities.
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