Signal-3L swMATH ID: 26857 Software Authors: Shen, H.B.; Chou, K.C. Description: Signal-3L: a 3-layer approach for predicting signal peptide. Functioning as an ”address tag” that directs nascent proteins to their proper cellular and extracellular locations, signal peptides have become a crucial tool in finding new drugs or reprogramming cells for gene therapy. To effectively and timely use such a tool, however, the first important thing is to develop an automated method for rapidly and accurately identifying the signal peptide for a given nascent protein. With the avalanche of new protein sequences generated in the post-genomic era, the challenge has become even more urgent and critical. In this paper, we have developed a novel method for predicting signal peptide sequences and their cleavage sites in human, plant, animal, eukaryotic, Gram-positive, and Gram-negative protein sequences, respectively. The new predictor is called Signal-3L that consists of three prediction engines working, respectively, for the following three progressively deepening layers: (1) identifying a query protein as secretory or non-secretory by an ensemble classifier formed by fusing many individual OET-KNN (optimized evidence-theoretic K nearest neighbor) classifiers operated in various dimensions of PseAA (pseudo amino acid) composition spaces; (2) selecting a set of candidates for the possible signal peptide cleavage sites of a query secretory protein by a subsite-coupled discrimination algorithm; (3) determining the final cleavage site by fusing the global sequence alignment outcome for each of the aforementioned candidates through a voting system. Signal-3L is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-3L is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-3L/ or http://202.120.37.186/bioinf/Signal-3L, where, to further support the demand of the related areas, the signal peptides identified by Signal-3L for all the protein entries in Swiss-Prot databank that do not have signal peptide annotations or are annotated with uncertain terms but are classified by Signal-3L as secretory proteins are provided in a downloadable file. The large-scale file is prepared with Microsoft Excel and named ”Tab-Signal-3L.xls”, and will be updated once a year to include new protein entries and reflect the continuous development of Signal-3L. Homepage: https://www.ncbi.nlm.nih.gov/pubmed/17880924 Related Software: Signal-CF; Cell-PLoc; Memtype-2L; EzyPred; ProtIdent; Euk-mPLoc; GPCR-CA; Euk-PLoc; HIVcleave; Phobius; Hum-PLoc; Hum-mPLoc; AAindex; GPCR-GIA; BLAST; PSI-BLAST; Pajek; LIBSVM; SPEPlip; iLoc-Plant Cited in: 15 Publications all top 5 Cited by 55 Authors 2 Georgiou, Dimitrios N. 2 Guo, Yanzhi 2 Karakasidis, Theodoros E. 2 Li, Menglong 2 Nieto Roig, Juan Jose 2 Torres, Angela 2 Yu, Lezheng 1 Anand, Ashish 1 Anh, Vo V. 1 Çalık, Pınar 1 Chou, Kuochen 1 Deng, Naiyang 1 Esmaeili, Maryam 1 Frenkel, Zakharia M. 1 Frenkel, Zeev M. 1 Hoseini, Afsaneh 1 Hoseini, Somayyeh 1 Hussain, Waqar 1 Igarashi, Kensuke 1 Jahandideh, Mina 1 Jahandideh, Samad 1 Jing, Ling 1 Khan, Sher Afzal 1 Khan, Yaser Daanial 1 Kuwabara, Tomohiko 1 Li, Gongbing 1 Li, Yizhou 1 Liu, Xin 1 Luo, Jiesi 1 Massahi, Aslan 1 Miri Disfani, Fatemeh 1 Mohabatkar, Hassan 1 Mohsenzadeh, Sasan 1 Peng, Zhen-Ling 1 Qin, Wenli 1 Rasool, Nouman 1 Shao, Xiaojian 1 Snir, Sagi 1 Suganthan, Ponnuthurai Nagaratnam 1 Tian, Yingjie 1 Trifonov, Edward N. 1 Wang, Desheng 1 Wang, Yong 1 Wu, Lingyun 1 Xiao, Rong-quan 1 Xiong, Wenjia 1 Yang, Jianyi 1 Yang, Li 1 Yang, Lianping 1 Yu, Zuguo 1 Zeng, Yuhong 1 Zhang, Ruijie 1 Zhang, Xiangde 1 Zhao, Yapu 1 Zhu, Hegui Cited in 1 Serial 15 Journal of Theoretical Biology all top 5 Cited in 6 Fields 15 Biology and other natural sciences (92-XX) 7 Statistics (62-XX) 6 Computer science (68-XX) 1 Mathematical logic and foundations (03-XX) 1 Combinatorics (05-XX) 1 Game theory, economics, finance, and other social and behavioral sciences (91-XX) Citations by Year