한빛사논문
- 조회368
Xin Zhang 1, Lesong Wei 2, Xiucai Ye 2, Kai Zhang 1, Saisai Teng 1, Zhongshen Li 1, Junru Jin 1, Min Jae Kim 3, Tetsuya Sakurai 2, Lizhen Cui 1, Balachandran Manavalan 3, Leyi Wei 1
1Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.
2Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.
3Department of integrative Biotechnology, College of Biotechnology & Bioengineering, Sungkyunkwan University, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of Korea.
Xin Zhang and Lesong Wei authors contributed equally to this work and should be considered co-first authors.
Corresponding authors: Leyi Wei, Balachandran Manavalan
Abstract
Background: Cell-penetrating peptides (CPPs) have received considerable attention as a means of transporting pharmacologically active molecules into living cells without damaging the cell membrane, and thus hold great promise as future therapeutics. Recently, several machine learning-based algorithms have been proposed for predicting CPPs. However, most existing predictive methods do not consider the agreement (disagreement) between similar (dissimilar) CPPs and depend heavily on expert knowledge-based handcrafted features.
Results: In this study, we present SiameseCPP, a novel deep learning framework for automated CPPs prediction. SiameseCPP learns discriminative representations of CPPs based on a well-pretrained model and a Siamese neural network consisting of a transformer and gated recurrent units. Contrastive learning is used for the first time to build a CPP predictive model. Comprehensive experiments demonstrate that our proposed SiameseCPP is superior to existing baseline models for predicting CPPs. Moreover, SiameseCPP also achieves good performance on other functional peptide datasets, exhibiting satisfactory generalization ability.
논문정보
- Research article
- 2022년 12월 (BRIC 등록일 2022-12-26)
- 국내(교신)+국외 연구진
- 생명과학 > 응용생물화학
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