Metabolic profiling of Parkinson's disease: evidence of biomarker from gene expression analysis and rapid neural network detection

<p>Abstract</p> <p>Background</p> <p>Parkinson's disease (PD) is a neurodegenerative disorder. The diagnosis of Parkinsonism is challenging because currently none of the clinical tests have been proven to help in diagnosis. PD may produce characteristic perturbations in the metabolome and such variations can be used as the marker for detection of disease. To test this hypothesis, we used proton NMR and multivariate analysis followed by neural network pattern detection.</p> <p>Methods & Results</p> <p>¹H nuclear magnetic resonance spectroscopy analysis was carried out on plasma samples of 37 healthy controls and 43 drug-naive patients with PD. Focus on 22 targeted metabolites, 17 were decreased and 5 were elevated in PD patients (p < 0.05). Partial least squares discriminant analysis (PLS-DA) showed that pyruvate is the key metabolite, which contributes to the separation of PD from control samples. Furthermore, gene expression analysis shows significant (p < 0.05) change in expression of <it>PDHB </it>and <it>NPFF </it>genes leading to increased pyruvate concentration in blood plasma. Moreover, the implementation of ¹H- NMR spectral pattern in neural network algorithm shows 97.14% accuracy in the detection of disease progression.</p> <p>Conclusion</p> <p>The results increase the prospect of a robust molecular definition in detection of PD through the early symptomatic phase of the disease. This is an ultimate opening for therapeutic intervention. If validated in a genuinely prospective fashion in larger samples, the biomarker trajectories described here will go a long way to facilitate the development of useful therapies. Moreover, implementation of neural network will be a breakthrough in clinical screening and rapid detection of PD.</p

Glycosylation site prediction using ensembles of Support Vector Machine classifiers

Article discussing the performance of different computational methods for prediction of glycosylation sites from amino acid sequences

Bioinformatics and molecular modeling in glycobiology

The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein–carbohydrate interaction are reviewed

Database analysis of O-glycosylation sites in proteins.

Statistical analysis was carried out to study the sequential aspects of amino acids around the O-glycosylated Ser/Thr. 992 sequences containing O-glycosylated Ser/Thr were selected from the O-GLYCBASE database of O-glycosylated proteins. The frequency of occurrence of amino acid residues around the glycosylated Ser/Thr revealed that there is an increased number of proline residues around the O-glycosylation sites in comparison with the nonglycosylated serine and threonine residues. The deviation parameter calculated as a measure of preferential and nonpreferential occurrence of amino acid residues around the glycosylation site shows that Pro has the maximum preference around the O-glycosylation site. Pro at +3 and/or -1 positions strongly favors glycosylation irrespective of single and multiple glycosylation sites. In addition, serine and threonine are preferred around the multiple glycosylation sites due to the effect of clusters of closely spaced glycosylated Ser/Thr. The preference of amino acids around the sites of mucin-type glycosylation is found likely to be similar to that of the O-glycosylation sites when taken together, but the acidic amino acids are more preferred around Ser/Thr in mucin-type glycosylation when compared totally. Aromatic amino acids hinder O-glycosylation in contrast to N-glycosylation. Cysteine and amino acids with bulky side chains inhibit O-glycosylation. The preference of certain potential sequence motifs of glycosylation has been discussed

A database analysis of potential glycosylating Asn-X-Ser/Thr consensus sequences

An analysis of the frequency of occurrence of various residues at position X was carried out on the consensus glycosylating sequence Asn-X-Ser/Thr using the PDB three-dimensional database. 488 non-homologous proteins bearing 696 Asn-X-Ser/Thr (X not equal Pro) sequences were analysed. More than 65% of Asn residues, when they occur as part of the consensus sequence, lie on the surface of the protein, implying a potentiality for glycosylation. A deviation parameter (DP) was calculated as a measure of preferential (positive) or non-preferential (negative) selection. At the X position in the consensus-sequence segment, the amino acids Gly, Asn and Phe have statistically significant positive DP values. The high value of DP for Asn is a consequence of the preferential occurrence of homodoublets, while for Phe it may be a consequence of the stacking interaction of the aromatic ring with the glycan. Gly at the X position in the consensus glycosylating sequence may be functionally significant owing to its preference and its high percentage of occurrence in proteins. The Ramachandran (Phi,Psi) angles around Gly in the consensus sequence show clustering in the region which is disallowed for non-glycyl residues. In this region, a hydrogen bond between the side chain of Asn and the peptide backbone/side chain of Ser/Thr is possible, reflecting a positional as well as a conformational role in the consensus glycosylating sequence. For the 44 confirmed N-glycosylating sequences, an in-depth analysis of the (Psi(N), Phi(X), Phi(X), Phi(S/T)) dihedral angles, which position the side chains of Asn and Ser/Thr, shows that these can be grouped into nine conformational states. In most cases, a direct or water-mediated hydrogen bond between OD1 of Asn and OG of Ser/Thr is possible, reflecting the possible importance of this hydrogen bonding in the glycosylation process