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PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization.

Javier L BaylonOleg UrsuAnja MuzdaloAnne Mai WassermannGregory L AdamsMartin SpalePetr MejzlikAnna GromekViktor PisarenkoDzianis HancharykEsteban JenkinsDavid BednarCharlie ChangKamila ClarovaMeir GlickDanny A Bitton
Published in: Journal of chemical information and modeling (2022)
Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, current molecular design capabilities that inform medicinal chemistry decisions on peptide programs are limited. More specifically, there are unmet needs for structure-activity relationship (SAR) analysis and visualization of linear, cyclic, and cross-linked peptides containing non-natural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA ( Pep tide Se quence A lignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multiple sequence alignment of non-natural amino acids and enhanced visualization with the hierarchical editing language for macromolecules (HELM). Via stepwise SAR analysis of a ChEMBL peptide data set, we demonstrate the utility of PepSeA to accelerate decision making in lead optimization campaigns in pharmaceutical setting. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.
Keyphrases
  • amino acid
  • drug discovery
  • structure activity relationship
  • crispr cas
  • electron microscopy
  • public health
  • autism spectrum disorder
  • big data
  • risk assessment
  • machine learning
  • capillary electrophoresis