Protein Structural Class Prediction via k-Separated Bigrams Using Position Specific Scoring Matrix
Harsh Saini*, Gaurav Raicar*, Alok Sharma*,**,
Sunil Lal*, Abdollah Dehzangi**, Rajeshkannan Ananthanarayanan*,
James Lyons**, Neela Biswas***, and Kuldip K. Paliwal**
*The University of the South Pacific, Fiji, Laucala Bay, Suva, Fiji
**Griffith University, Brisbane, Australia
***Royal Brisbane and Women’s Hospital, Brisbane, Australia
Protein structural class prediction (SCP) is as important task in identifying protein tertiary structure and protein functions. In this study, we propose a feature extraction technique to predict secondary structures. The technique utilizes bigram (of adjacent and k-separated amino acids) information derived from Position Specific Scoring Matrix (PSSM). The technique has shown promising results when evaluated on benchmarked Ding and Dubchak dataset.
Sunil Lal, Abdollah Dehzangi, Rajeshkannan Ananthanarayanan,
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