Computational prediction of furin cleavage sites by a hybrid method and understanding mechanism underlying diseases

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Abstract
Furin cleaves diverse types of protein precursors in the secretory pathway. The substrates for furin cleavage possess a specific 20-residue recognition sequence motif. In this report, based on the functional characterisation of the 20-residue sequence motif, we developed a furin cleavage site prediction tool, PiTou, using a hybrid method composed of a hidden Markov model and biological knowledge-based cumulative probability score functions. PiTou can accurately predict the presence and location of furin cleavage sites in protein sequences with high sensitivity (96.9%) and high specificity (97.3%). PiTou's prediction scores are biological meaningful and reflect binding strength and solvent accessibility of furin substrates. A prediction result is interpreted within cellular contexts: subcellular localisation, cellular function and interference by other dynamic protein modifications. Combining next-generation sequencing, PiTou can help with elucidating the molecular mechanism of furin cleavage-associated human diseases. PiTou has been made freely available at the associated website.