a conditional random field approach that combines sequence and structural information
significantly improves the prediction of protease substrate cleavage sites
Please upload a PDB file (e.g. 1AU1): (example)
Specifiy the chain name (e.g. A): (example)
Please select the protease of interest from the dropdown menu to make the prediction: (help)
select all pepsin A (A01.001) cathepsin D (A01.009) cathepsin E (A01.010) rhizopuspepsin (A01.012) aspergillopepsin I (A01.016) necepsin-1 (A01.053) HIV-1 retropepsin (A02.001) cathepsin L (C01.032) cathepsin L1 {Fasciola} sp. (C01.033) cathepsin S (C01.034) falcipain-2 (C01.046) cathepsin B (C01.060) falcipain-3 (C01.063) caspase-3 (C14.003) caspase-6 (C14.005) matrix metallopeptidase-2 (M10.003) matrix metallopeptidase-9 (M10.004) astacin (M12.001) meprin alpha subunit (M12.002) meprin beta subunit (M12.004) LAST_MAM peptidase (M12.033) chymotrypsin A (cattle-type) (S01.001) granzyme B ({Homo sapiens}-type) (S01.010) elastase-2 (S01.131) cathepsin G (S01.133) glutamyl peptidase I (S01.269) lysyl peptidase (bacteria) (S01.280)
If you find Procleave useful, please kindly cite our paper:
Li et al. "Procleave: a conditional random field approach that combines sequence and structural information significantly improves the prediction of protease substrate cleavage sites", Genomics Proteomics Bioinformatics, 2020 Feb;18(1):52-64. doi: 10.1016/j.gpb.2019.08.002.
Copyright © 2019. Biomedicine Discovery Institute and School of Biomedical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia
