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Biophysics in the Understanding, Diagnosis, and Treatment of Infectious Diseases Poster Abstracts
75
35-POS
Board 35
Prediction of Cleavage Specificity in Proteases by Biased Sequence Search Threading
Gonca Ozdemir Isik,
Asuman Nevra Ozer
.
Marmara University, Istanbul, Turkey.
The assessment of substrate specificity in infectious disease-related 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 Human Immunodeficiency Virus 1 (HIV-1)
protease, the Hepatitis C Virus (HCV) NS3/4A serine protease and the Adenovirus 2 (AdV2)
cysteine protease were investigated by the computational biased sequence search threading
(BSST) methodology. Available crystal structures and template structures for the substrate-
bound proteases, which were created in silico by performing various peptide building and
docking procedures followed by energy minimization and molecular dynamics simulations, were
utilized. 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. Overall,
supported by the successful outcomes with the case studies of HIV-1 protease, 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.