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

delete2022-02-22
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OA
AI
J
Javier L. Baylon *
O
Oleg Ursu
A
Anja Muždalo
A
Anne Mai Wassermann
G
Gregory L. Adams
M
Martin Spale
P
Petr Mejzlík
A
Anna Gromek
V
Viktor Pisarenko
D
Dzianis Hancharyk
E
Esteban Jenkins
D
David Bednář
K
Kamila Clarová
M
Meir Glick
D
Danny A. Bitton *
DOI:10.1021/acs.jcim.1c01360delete
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Abstract

Abstract

En 中文
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 (Peptide Sequence Alignment 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.
Keywords:
CYCLIC-PEPTIDES
DRUG DISCOVERY
N-METHYLATION
IN-VITRO
PROTEIN
MAFFT
THERAPY
THERAPEUTICS
PARALLELIZATION
PERMEABILITY

Journal

Journal of Chemical Information and Modeling cover
Journal of Chemical Information and Modeling
IF:
5.3
Papers:
9.1K
Citations:
4.0W

Organization

U
university of chemistry & technology, prague
Scholars:
5.6K
Papers: 4.4K
Citations: 3
M
merck & company usa
Scholars:
6.3K
Papers: 3.5K
Citations: 5
M
merck & company
Scholars:
1.8W
Papers: 8.7K
Citations: 11
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