Computational intelligence in cell-penetrating peptide discovery: emerging paradigms and predictive frameworks
DOI:
10.29303/sjp.v7i1.677Downloads
Abstract
Researchers are increasingly fascinated by cell penetrating peptides (CPPs) owing to their ability to deliver biomolecules within cells. This unique capability makes CPPs an incredibly valuable asset in fields such as drug delivery, gene therapy and imaging where precision is paramount. However, the design and prediction of CPPs with optimal attributes and augmented cellular uptake still pose a challenge. With this literature review piece, we hope to shed light on significant progress that has been made within the field of in silico prediction techniques for CPPs. In particular our focus lies on discussing how these methodologies can assist with discovering and optimizing promising CPP candidates more efficiently. To accomplish this goal respectfully and comprehensively, we will examine computational methods like machine learning algorithms, sequence-based techniques, and structure-based modeling. These techniques employ large-scale databases, extensive peptide libraries, and advanced algorithms to scrutinize peptide properties, predict CPP activity, and optimize CPP sequences. Within this analysis, we find a significant emphasis placed on integrating physicochemical features, sequence motifs, and structural information into predictive models relating to CPP development. These factors are essential to predicting successful peptide uptake within cells while presenting various challenges during this process. Furthermore, the review highlights considerable strides in silico prediction techniques for CPPs, demonstrating great promise in accelerating their identification for further development. Incorporating computational tools and experimental validation holds tremendous promise in facilitating the design of CPPs with enhanced properties, thus advancing the field of intracellular delivery and therapeutics.
Keywords:
cell-penetrating peptides, cellular uptake, in silico prediction techniques, machine learning, toxicity predictionReferences
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