MitProt-Pred: Predicting mitochondrial proteins of Plasmodium falciparum parasite using diverse physiochemical properties and ensemble classification.

Mitochondrial protein of Plasmodium falciparum is an important target for anti-malarial drugs. Experimental approaches for detecting mitochondrial proteins are costly and time consuming. Therefore, MitProt-Pred is developed that utilizes Bi-profile Bayes, Pseudo Average Chemical Shift, Split Amino Acid Composition, and Pseudo Amino Acid Composition based features of the protein sequences. Hybrid feature space is also developed by combining different individual feature spaces. These feature spaces are learned and exploited through SVM based ensemble. MitProt-Pred achieved significantly improved prediction performance for two standard datasets. We also developed the score level ensemble, which outperforms the feature level ensemble.

Predicting G-protein-coupled receptors families using different physiochemical properties and pseudo amino acid composition.

G-protein-coupled receptors (GPCRs) initiate signaling pathways via trimetric guanine nucleotide-binding proteins. GPCRs are classified based on their ligand-binding properties and molecular phylogenetic analyses. Nonetheless, these later analyses are in most case dependent on multiple sequence alignments, themselves dependent on human intervention and expertise. Alignment-free classifications of GPCR sequences, in addition to being unbiased, present many applications uncovering hidden physicochemical parameters shared among specific groups of receptors, to being used in automated workflows for large-scale molecular modeling applications. Current alignment-free classification methods, however, do not reach a full accuracy. This chapter discusses how GPCRs amino acid sequences can be classified using pseudo amino acid composition and multiscale energy representation of different physiochemical properties of amino acids. A hybrid feature extraction strategy is shown to be suitable to represent GPCRs and to be able to exploit GPCR amino acid sequence discrimination capability in spatial as well as transform domain. Classification strategies such as support vector machine and probabilistic neural network are then discussed in regards to GPCRs classification. The work of GPCR-Hybrid web predictor is also discussed.