化学信息学
可视化
计算机科学
化学
计算生物学
序列(生物学)
虚拟筛选
药物发现
生物信息学
肽
数据挖掘
组合化学
生物信息学
化学
生物
生物化学
基因
作者
Javier L. Baylon,Oleg Ursu,Anja Muždalo,Anne Mai Wassermann,Gregory L. Adams,Martin Spale,Petr Mejzlík,Anna Gromek,Viktor Pisarenko,Dzianis Hancharyk,Esteban Jenkins,David Bednář,Charlie Chang,Kamila Clarová,Meir Glick,Danny A. Bitton
标识
DOI:10.1021/acs.jcim.1c01360
摘要
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.
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