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Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides
ChemMedChem ( IF 3.6 ) Pub Date : 2022-07-26 , DOI: 10.1002/cmdc.202200291 Elena Zakharova 1 , Markus Orsi 1 , Alice Capecchi 1 , Jean-Louis Reymond 1
ChemMedChem ( IF 3.6 ) Pub Date : 2022-07-26 , DOI: 10.1002/cmdc.202200291 Elena Zakharova 1 , Markus Orsi 1 , Alice Capecchi 1 , Jean-Louis Reymond 1
Affiliation
Using machine learning models trained with bioactive peptides from DBAASP, we designed new non-hemolytic anticancer peptides (ACPs). The subsequently selected hit-compounds A1 and B1 showed IC50 activities with low micromolar range against several cancer cell lines, having adopted amphiphilic α-helical conformations. Further biological evaluations revealed membranolytic and mitochondria targeting properties of selected anticancer peptides.
中文翻译:
机器学习引导非溶血膜破坏性抗癌肽的发现
使用经过 DBAASP 生物活性肽训练的机器学习模型,我们设计了新的非溶血性抗癌肽 (ACP)。随后选择的命中化合物A1和B1对几种癌细胞系表现出低微摩尔范围的IC 50活性,并采用两亲性α-螺旋构象。进一步的生物学评估揭示了所选抗癌肽的膜溶解和线粒体靶向特性。
更新日期:2022-07-26
中文翻译:
机器学习引导非溶血膜破坏性抗癌肽的发现
使用经过 DBAASP 生物活性肽训练的机器学习模型,我们设计了新的非溶血性抗癌肽 (ACP)。随后选择的命中化合物A1和B1对几种癌细胞系表现出低微摩尔范围的IC 50活性,并采用两亲性α-螺旋构象。进一步的生物学评估揭示了所选抗癌肽的膜溶解和线粒体靶向特性。