MemHyb

MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM. About 50% of available drugs are targeted against membrane proteins. Knowledge of membrane protein’s structure and function has great importance in biological and pharmacological research. Therefore, an automated method is exceedingly advantageous, which can help in identifying the new membrane protein types based on their primary sequence. In this paper, we tackle the interesting problem of classifying membrane protein types using their sequence information. We consider both evolutionary and physicochemical features and provide them to our classification system based on support vector machine (SVM) with error correction code. We employ a powerful sequence encoding scheme by fusing position specific scoring matrix and split amino acid composition to effectively discriminate membrane protein types. Linear, polynomial, and RBF based-SVM with Bose, Chaudhuri, Hocquenghem coding are trained and tested. The highest success rate of 91.1% and 93.4% on two datasets is obtained by RBF-SVM using leave-one-out cross-validation. Thus, our proposed approach is an effective tool for the discrimination of membrane protein types and might be helpful to researchers/academicians working in the field of drug discovery, cell biology, and bioinformatics. The web server for the proposed MemHyb-SVM is accessible at url{http://111.68.99.218/MemHyb-SVM}.


References in zbMATH (referenced in 15 articles , 1 standard article )

Showing results 1 to 15 of 15.
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  1. Ahmad, Jamal; Hayat, Maqsood: MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components (2019)
  2. 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)
  3. 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)
  4. Jia, Cangzhi; Yang, Qing; Zou, Quan: NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC (2018)
  5. Sankari, E. Siva; Manimegalai, D.: Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC (2018)
  6. Khan, Muslim; Hayat, Maqsood; Khan, Sher Afzal; Ahmad, Saeed; Iqbal, Nadeem: Bi-PSSM: position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins (2017)
  7. Ali, Farman; Hayat, Maqsood: Classification of membrane protein types using voting feature interval in combination with Chou’s pseudo amino acid composition (2015)
  8. Han, Guo-Sheng; Yu, Zu-Guo; Anh, Vo: A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou’s PseAAC (2014)
  9. Hayat, Maqsood; Tahir, Muhammad; Khan, Sher Afzal: Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces (2014)
  10. Tahir, Muhammad; Khan, Asifullah; Kaya, Hüseyin: Protein subcellular localization in human and hamster cell lines: employing local ternary patterns of fluorescence microscopy images (2014)
  11. Feng, Peng-Mian; Ding, Hui; Chen, Wei; Lin, Hao: Naïve Bayes classifier with feature selection to identify phage virion proteins (2013)
  12. Huang, Chao; Yuan, Jing-Qi: Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions (2013)
  13. Sharma, Alok; Lyons, James; Dehzangi, Abdollah; Paliwal, Kuldip K.: A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition (2013)
  14. Hayat, Maqsood; Khan, Asifullah: MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM (2012)
  15. Li, Tao; Li, Qian-Zhong: Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure (2012)