Graphical Representations of Protein Sequences for Alignment-Free Comparative and Predictive Studies. Recognition of Protease Inhibition Pattern from H-Depleted Molecular Graph Representation of Protease Sequences
Biomacromolecular information is hinged by sequence and structure representations. Because structure is often more conserved than sequence, achieving function inference from structural similarity is easier than from sequence analysis. However, structural information is sparse and only available for a small part of the protein space. Detecting subtle similarities between proteins from sequence depends strongly on the representations used. Continuous-space representations yield promising results in comparative evolution analysis, structural classification and sequence-function/property relationship studies. These simple methods provide a pre-classification and/or feature generation stages to sophisticated classification methods. We review the state-of-the-art in protein sequence graphical representations along with some derived metrics for statistical pattern recognition analysis. In addition, the binding stability pattern of protease-inhibitor complexes is modelled from H-depleted molecular graph representation of protease sequences and ligands using support vector machines with about 80% prediction accuracy.
Keywords: Chaos game representation, protein graph, QSAR analysis, pseudo-folding representation, molecular biology, homologous sequences, protein stability, nucleic acids, DNA sequences, molecular pseudo-graphs, amino acid, Autorrelation, C-atom adjacency matrix, enzyme inhibition, polygon, Hydrophobic-Polar, bacteria, polygalacturonases, Leishmania parasites, codon, biomolecules, lattice models, hydrophobic polar, dodecahedron, vector, pseudo-folded, electro-physiological, cells, protease's, enzyme-ligand interactions, Ligands, peptide, nonpeptide ligands, Proteochemometrics, Modeling, cross-correlation, crossvalidation
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