Download MendeleyTriplEP-CPP: Algorithm for Predicting the Properties of Peptide Sequences.
Serebrennikova, M., Grafskaia, E., Maltsev, D., Ivanova, K., Bashkirov, P., Kornilov, F., Volynsky, P., Efremov, R., Bocharov, E., Lazarev, V.(2024) Int J Mol Sci 25
- PubMed: 38999985 Search on PubMedSearch on PubMed Central
- DOI: https://doi.org/10.3390/ijms25136869
- Primary Citation Related Structures:
8R89 - PubMed Abstract:
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 Rhopilema esculentum venom proteome through this study.
- Laboratory of Genetic Engineering, Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia.
Organizational Affiliation:
