HIVcleave

HIVcleave: a web-server for predicting human immunodeficiency virus protease cleavage sites in proteins. According to the ”distorted key theory” [K.C. Chou, Analytical Biochemistry, 233 (1996) 1-14], the information of cleavage sites of proteins by HIV (human immunodeficiency virus) protease is very useful for finding effective inhibitors against HIV, the culprit of AIDS (acquired immunodeficiency syndrome). To meet the increasing need in this regard, a web-server called HIVcleave was established at http://chou.med.harvard.edu/bioinf/HIV/. In this note we provide a step-to-step guide for how to use HIVcleave to identify the cleavage sites of a query protein sequence by HIV-1 and HIV-2 proteases, respectively.


References in zbMATH (referenced in 11 articles )

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  1. Tian, Baoguang; Wu, Xue; Chen, Cheng; Qiu, Wenying; Ma, Qin; Yu, Bin: Predicting protein-protein interactions by fusing various Chou’s pseudo components and using wavelet denoising approach (2019)
  2. Fatemi, Mohammad H.; Heidari, Afsane; Gharaghani, Sajjad: QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors (2015)
  3. Aygün, E.; Oommen, B. J.; Cataltepe, Z.: Peptide classification using optimal and information theoretic syntactic modeling (2010) ioport
  4. Heider, Dominik; Verheyen, Jens; Hoffmann, Daniel: Predicting bevirimat resistance of HIV-1 from genotype (2010) ioport
  5. Huang, Wei; Zhang, Jianmin; Wang, Yurong; Huang, Dan: A simple method to analyze the similarity of biological sequences based on the fuzzy theory (2010)
  6. Yang, Jie; Li, Jia-Huang; Wang, Jin; Zhang, Chen-Yu: Molecular modeling of BAD complex resided in a mitochondrion integrating glycolysis and apoptosis (2010)
  7. Anand, Ashish; Suganthan, P. N.: Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates (2009)
  8. 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)
  9. Rögnvaldsson, Thorsteinn S.; Etchells, Terence A.; You, Liwen; Garwicz, Daniel; Jarman, Ian H.; Lisboa, Paulo J. G.: How to find simple and accurate rules for viral protease cleavage specificities (2009) ioport
  10. Shao, Xiaojian; Tian, Yingjie; Wu, Lingyun; Wang, Yong; Jing, Ling; Deng, Naiyang: Predicting DNA- and RNA-binding proteins from sequences with kernel methods (2009)
  11. 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)