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 or, 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.

References in zbMATH (referenced in 16 articles )

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  1. Kuwabara, Tomohiko; Igarashi, Kensuke: \textitThermotogalesorigin scenario of eukaryogenesis (2020)
  2. Hussain, Waqar; Khan, Yaser Daanial; Rasool, Nouman; Khan, Sher Afzal; Chou, Kuo-Chen: SPrenylC-PseAAC: a sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins (2019)
  3. Massahi, Aslan; Çalık, Pınar: In-silico determination of \textitPichiapastoris signal peptides for extracellular recombinant protein production (2015)
  4. Yang, Lianping; Zhang, Xiangde; Zhu, Hegui: Alignment free comparison: similarity distribution between the DNA primary sequences based on the shortest absent word (2012)
  5. Esmaeili, Maryam; Mohabatkar, Hassan; Mohsenzadeh, Sasan: Using the concept of Chou’s pseudo amino acid composition for risk type prediction of human papillomaviruses (2010)
  6. Georgiou, D. N.; Karakasidis, T. E.; Nieto, Juan J.; Torres, A.: A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets (2010)
  7. Yu, Lezheng; Guo, Yanzhi; Li, Yizhou; Li, Gongbing; Li, Menglong; Luo, Jiesi; Xiong, Wenjia; Qin, Wenli: SecretP: identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition (2010)
  8. Anand, Ashish; Suganthan, P. N.: Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates (2009)
  9. Choo, Khar Heng; Tan, Tin Wee; Ranganathan, Shoba: A comprehensive assessment of N-terminal signal peptides prediction methods (2009) ioport
  10. Frenkel, Zakharia M.; Frenkel, Zeev M.; Trifonov, Edward N.; Snir, Sagi: Structural relatedness via flow networks in protein sequence space (2009)
  11. Georgiou, D. N.; Karakasidis, T. E.; Nieto, J. J.; Torres, A.: Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou’s pseudo amino acid composition (2009)
  12. Jahandideh, Samad; Hoseini, Somayyeh; Jahandideh, Mina; Hoseini, Afsaneh; Miri Disfani, Fatemeh: (\gamma)-turn types prediction in proteins using the two-stage hybrid neural discriminant model (2009)
  13. Liu, Xin; Zhao, Ya-Pu: Donut-shaped fingerprint in homologous polypeptide relationships -- a topological feature related to pathogenic structural changes in conformational disease (2009)
  14. Shao, Xiaojian; Tian, Yingjie; Wu, Lingyun; Wang, Yong; Jing, Ling; Deng, Naiyang: Predicting DNA- and RNA-binding proteins from sequences with kernel methods (2009)
  15. Yang, Jian-Yi; Peng, Zhen-Ling; Yu, Zu-Guo; Zhang, Rui-Jie; Anh, Vo; Wang, Desheng: Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation (2009)
  16. Zeng, Yu-hong; Guo, Yan-zhi; Xiao, Rong-quan; Yang, Li; Yu, Le-zheng; Li, Meng-long: Using the augmented Chou’s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach (2009)