Laboratory of Genetic Engineering, Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia.
Moscow Center for Advanced Studies 20, Kulakova Str., Moscow 123592, Russia.
Int J Mol Sci. 2024 Jun 22;25(13):6869. doi: 10.3390/ijms25136869.
Advancements in medicine and pharmacology have led to the development of systems that deliver biologically active molecules inside cells, increasing drug concentrations at target sites. This improves effectiveness and duration of action and reduces side effects on healthy tissues. Cell-penetrating peptides (CPPs) show promise in this area. While traditional medicinal chemistry methods have been used to develop CPPs, machine learning techniques can speed up and reduce costs in the search for new peptides. A predictive algorithm based on machine learning models was created to identify novel CPP sequences using molecular descriptors using a combination of algorithms like k-nearest neighbors, gradient boosting, and random forest. Some potential CPPs were found and tested for cytotoxicity and penetrating ability. A new low-toxicity CPP was discovered from the venom proteome through this study.
医学和药理学的进步促使能够将生物活性分子递送到细胞内的系统得以发展，从而提高靶部位的药物浓度。这提高了药物的效果和作用持续时间，并减少了对健康组织的副作用。细胞穿透肽（CPP）在这方面显示出了前景。虽然传统的药物化学方法已被用于开发 CPP，但机器学习技术可以加快速度并降低寻找新肽的成本。创建了一个基于机器学习模型的预测算法，使用分子描述符结合 K 最近邻、梯度提升和随机森林等算法来识别使用新型 CPP 序列。发现并测试了一些潜在的 CPP 的细胞毒性和穿透能力。通过这项研究，从毒液蛋白质组中发现了一种新的低毒性 CPP。
