dc.description.abstract |
Membrane proteins are the basic constituent of a cell that manage intra and
extracellular processes of a cell. About 20-30% of genes of eukaryotic organisms are
encoded from membrane proteins. In addition, almost 50% of drugs are directly targeted
against membrane proteins. Owing to the significant role of membrane proteins in living
organisms, the identification of membrane proteins with substantial accuracy is essential.
However, the annotation of membrane proteins through conventional methods is difficult,
sometimes even impossible. Therefore, membrane proteins are predicted from topogenic
sequences using computational intelligence techniques. In this study, we conducted our
research in two phases regarding the prediction of membrane protein types and structures.
In Phase-I, regarding the prediction of membrane protein types, four different ways are
explored in order to enhance true prediction.
In the first part of phase-I, membrane protein types are predicted using Composite
protein sequence representation followed by the application of principal component
analysis in conjunction with individual classifiers. In the second part, the notion of
ensemble classification is utilized. In part three, an error correction code is incorporated
with Support Vector Machine using evolutionary profiles (Position Specific Scoring
Matrix) and SAAC based features. Finally, in part four, a two-layer web predictor Mem-
PHybrid is developed. Mem-PHybrid accomplishes the prediction in two steps. First, a
protein query is identified as a membrane or a non-membrane protein. In case of
membrane protein, then its type is predicted.
In the second phase of this research, the structure of membrane protein is
recognized as alpha-helix transmembrane or outer membrane proteins. In case of alpha-
helix transmembrane proteins, features are explored from protein sequences by two
feature extraction schemes of distinct natures; including physicochemical properties and
compositional index of amino acids. Singular value decomposition is employed to extract
high variation features. A hybrid feature vector is formed by combining the different
types of features. Weighted Random Forest is then used as a classification algorithm. On
the other hand, in case of outer membrane proteins, protein sequences are represented by
Amino acid composition, PseAA composition, and SAAC along with their hybrid
models. Genetic programming, K-nearest neighbor, and fuzzy K-nearest neighbor are
adopted as classification algorithms.
Through the simulation study, we observed that the prediction performance of our
proposed approaches in case of both types and structures prediction is better compared to
existing state of the arts/approaches. Finally, we conclude that our proposed approach for
membrane proteins might play a significant role in Computational Biology, Molecular
Biology, Bioinformatics, and thus might help in applications related to drug discovery. In
addition, the related web predictors provide sufficient information to researchers and
academicians in future research. |
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