Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools.

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools.

Brief Bioinform. 2019 Jan 10;:

Authors: Su R, Hu J, Zou Q, Manavalan B, Wei L

Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.

PMID: 30649170 [PubMed – as supplied by publisher]

Source link

Related posts

UNCW Series to Explore the Role of Artificial Intelligence in Health Care – UNCW News


Showing robots how to do your chores


Visual analytics for team-based invasion sports with significant events and Markov reward process. (arXiv:1907.01221v1 [cs.AI])


This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More

Privacy & Cookies Policy