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Polymers and Self Assembly: From Biology to Nanomaterials Poster Session II
31-POS
Board 31
Prediction of Cleavage Specificity in Proteases by Biased Sequence Search Threading
Gonca Ozdemir Isik,
Nevra Ozer
.
Marmara University, Istanbul, Turkey.
The assessment of substrate specificity in proteases is crucial in drug development studies, where
interpreting the adaptability of residue positions can be useful in understanding how inhibitors
might best fit within the substrate binding sites. In this work, the substrate variability and
substrate specificity of the Hepatitis C virus (HCV) NS3/4A serine protease and the Adenovirus
2 (AdV2) cysteine protease was investigated by the computational biased sequence search
threading (BSST) methodology. Using available crystal structures, the template structures for the
substrate-bound proteases were created in silico by performing various peptide building and
docking procedures followed by energy minimization and molecular dynamics simulations.
BSST was performed starting with known binding, nonbinding and random peptide sequences
that were threaded onto the template complex structures, and low energy sequences were
searched using low-resolution knowledge-based potentials. Then, target sequences of yet
unidentified potential substrates were predicted by statistical probability approaches applied on
the low energy sequences. The results show that the majority of the predicted substrate positions
correspond to the natural substrate sequences with conserved amino acid preferences. For
NS3/4A serine protease cleavage, significant selection for Pro at P2 and Cys at P1 positions is
observed, where these positions are correlated with increased cleavage efficiency hence are
probably less tolerant to change. For AdV2 cysteine protease, BSST produces similar significant
results for both type 1 (XGX-G) and type 2 (XGG-X) consensus cleavage sites, where P2 and
P1’ positions have Gly with highest percentage in type 1 (XGX-G) while P2 and P1 positions
have Gly in type 2 (XGG-X). Overall, supported by the successful outcomes with the case
studies of HCV NS3/4A serine protease and AdV2 cysteine protease here, BSST seems to be a
powerful methodology for prediction of substrate specificity in protease systems.