Balachandran Manavalan (아주대학교) | 한빛사논문 > 한빛사

상위피인용논문

조회524

아주대학교

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Brief. Bioinform., Volume 21, Issue 2, March 2020, Pages 408–420, | https://doi.org/10.1093/bib/bby124 Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

Su R.^a, Hu J.^a, Zou Q.^b, Manavalan B.^c,*, Wei L.^a,*

^aCollege of Intelligence and Computing, Tianjin University, Tianjin, China
^bInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
^cDepartment of Physiology, Ajou University School of Medicine, Suwon, South Korea

^*Corresponding author.

Abstract

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.

논문정보

형식Review Paper
게재일2020년 03월 (BRIC 등록일 2021-08-24)
연구진국내(교신)+국외 연구진
분야 생명과학 > 응용생물화학
피인용횟수60회 이상 인용된 논문

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