iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition

In this paper, a two-tier intelligent automated predictor has been developed called iMem-2LSAAC, which classifies protein sequence as membrane or non-membrane in first-tier (phase1) and in case of membrane the second-tier (phase2) identifies functional types of membrane protein. Quantitative attributes were extracted from protein sequences by applying three discrete features extraction schemes namely amino acid composition, pseudo amino acid composition and split amino acid composition (SAAC). Various learning algorithms were investigated by using jackknife test to select the best one for predictor. Experimental results exhibited that the highest predictive outcomes were yielded by SVM in conjunction with SAAC feature space on all examined datasets. The true classification rate of iMem-2LSAAC predictor is significantly higher than that of other state-of- the- art methods so far in the literature. Finally, it is expected that the proposed predictor will provide a solid framework for the development of pharmaceutical drug discovery and might be useful for researchers and academia.