iPTM-mLys: identifying multiple lysine PTM sites and their different types. MOTIVATION: Post-translational modification, abbreviated as PTM, refers to the change of the amino acid side chains of a protein after its biosynthesis. Owing to its significance for in-depth understanding various biological processes and developing effective drugs, prediction of PTM sites in proteins have currently become a hot topic in bioinformatics. Although many computational methods were established to identify various single-label PTM types and their occurrence sites in proteins, no method has ever been developed for multi-label PTM types. As one of the most frequently observed PTMs, the K-PTM, namely, the modification occurring at lysine (K), can be usually accommodated with many different types, such as ’acetylation’, ’crotonylation’, ’methylation’ and ’succinylation’. Now we are facing an interesting challenge: given an uncharacterized protein sequence containing many K residues, which ones can accommodate two or more types of PTM, which ones only one, and which ones none? RESULTS: To address this problem, a multi-label predictor called IPTM-MLYS: has been developed. It represents the first multi-label PTM predictor ever established. The novel predictor is featured by incorporating the sequence-coupled effects into the general PseAAC, and by fusing an array of basic random forest classifiers into an ensemble system. Rigorous cross-validations via a set of multi-label metrics indicate that the first multi-label PTM predictor is very promising and encouraging. AVAILABILITY AND IMPLEMENTATION: For the convenience of most experimental scientists, a user-friendly web-server for iPTM-mLys has been established at http://www.jci-bioinfo.cn/iPTM-mLys, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.

References in zbMATH (referenced in 26 articles )

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  1. Adilina, Sheikh; Farid, Dewan Md; Shatabda, Swakkhar: Effective DNA binding protein prediction by using key features via Chou’s general PseAAC (2019)
  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. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  4. Khan, Yaser Daanial; Jamil, Mehreen; Hussain, Waqar; Rasool, Nouman; Khan, Sher Afzal; Chou, Kuo-Chen: pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments (2019)
  5. Ning, Qiao; Ma, Zhiqiang; Zhao, Xiaowei: Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components (2019)
  6. Pan, Yi; Wang, Shiyuan; Zhang, Qi; Lu, Qianzi; Su, Dongqing; Zuo, Yongchun; Yang, Lei: Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions (2019)
  7. Tahir, Muhammad; Tayara, Hilal; Chong, Kil To: iRNA-PseKNC(2methyl): identify RNA 2’-O-methylation sites by convolution neural network and Chou’s pseudo components (2019)
  8. Wang, Lidong; Zhang, Ruijun; Mu, Yashuang: Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou’s general PseAAC (2019)
  9. Zhao, Wei; Li, Guang-Ping; Wang, Jun; Zhou, Yuan-Ke; Gao, Yang; Du, Pu-Feng: Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions (2019)
  10. Zhao, Xiaowei; Zhang, Ye; Ning, Qiao; Zhang, Hongrui; Ji, Jinchao; Yin, Minghao: Identifying N(^6)-methyladenosine sites using extreme gradient boosting system optimized by particle swarm optimizer (2019)
  11. Akbar, Shahid; Hayat, Maqsood: iMethyl-STTNC: identification of N(^6)-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences (2018)
  12. Arif, Muhammad; Hayat, Maqsood; Jan, Zahoor: IMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition (2018)
  13. Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)
  14. Ju, Zhe; Wang, Shi-Yun: Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm (2018)
  15. Liang, Yunyun; Zhang, Shengli: Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence (2018)
  16. Mei, Juan; Fu, Yi; Zhao, Ji: Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition (2018)
  17. Qiu, Wenying; Li, Shan; Cui, Xiaowen; Yu, Zhaomin; Wang, Minghui; Du, Junwei; Peng, Yanjun; Yu, Bin: Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou’s pseudo-amino acid composition (2018)
  18. Sabooh, M. Fazli; Iqbal, Nadeem; Khan, Mukhtaj; Khan, Muslim; Maqbool, H. F.: Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC (2018)
  19. Sankari, E. Siva; Manimegalai, D.: Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC (2018)
  20. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)

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