Dimerization of a flocculent protein from Moringa oleifera: experimental evidence and in silico interpretation.

Many proteins exist in dimeric and other oligomeric forms to gain stability and functional advantages. In this study, the dimerization property of a coagulant protein (MO2.1) from Moringa oleifera seeds was addressed through laboratory experiments, protein-protein docking studies and binding free energy calculations. The structure of MO2.1 was predicted by homology modelling, while binding free energy and residues-distance profile analyses provided insight into the energetics and structural factors for dimer formation. Since the coagulation activities of the monomeric and dimeric forms of MO2.1 were comparable, it was concluded that oligomerization does not affect the biological activity of the protein.

Structural features for homodimer folding mechanism.

The homodimers have essential role in catalysis and regulation. The homodimer folding mechanism through 2-state without stable intermediate (2S), 3-state with monomer intermediate (3SMI) and 3-state with dimer intermediate (3SDI) is fascinating. 23MI and 3SDI constitute 3-state (3S). Hence, it is important to differentiate 2S, 3SMI and 3SDI homodimers using structural features. We used the dataset of Li et al. [L. Li, K. Gunasekaran, J.G. Gan, C. Zhanhua, P. Shapshak, M.K. Sakharkar, P. Kangueane, Structural features differentiate the mechanisms between 2S and 3S folding of homodimers, Bioinformation 1 (2005) 42-49] consisting of twenty-five 2S, ten 3SMI and six 3SDI homodimer structures for the study. Interface to total (I/T) residues ratio is large for 2S than 3SMI and 3SDI. Interface to total residues ratio is similar for 3SMI (mean monomer length (ML)=208) and 3SDI (mean monomer length (ML)=404) despite difference in mean monomer size. Interface residues correlate with monomer size in 2S (Pearson's correlation coefficient (r); r(2)=0.41) and 3SMI (r(2)=0.52). This is not true for 3SDI with interface residues and monomer length (r(2)=0.17). Interface area (B/2) does not correlate with interface residues (r(2)<0.001) and monomer size (r(2)=0.023) in 2S. This is despite a relationship with interface residues and monomer size (r(2)=0.41) in 2S. However, this is not true for 3SMI (r(2)=0.61 with interface residues and r(2)=0.25 with monomer size). In 3SDI, a different relationship is seen (r(2)=0.28 with interface residues and r(2)=0.09 with size). The mean hydrophobicity factor (H(f)) is 3-fold less in 3S than 2S. H(f) does not correlate with interface area in 2S (r(2)=0.03) and 3SDI (r(2)=0.0). However, a weak causal relation is seen in 3SMI (r(2)=0.23). Hydrophilic amino acid residues (E, R, K, S and Q) are prominent in 2S than 3S. Charged negative amino acid residues (D, E) are more than positive amino acid residues (R, K, H) in 2S and charged positive amino acid residues (R, K, H) are more than negative amino acid residues (D, E) in 3S. These features help to distinguish 2S, 3SMI and 3SDI providing insights to homodimer folding and binding.

Class II HLA-peptide binding prediction using structural principles.

The precise prediction of class II human leukocyte antigen (HLA) peptide binding finds application in epitope design for the development of vaccines and diagnostics of diseases associated with CD4+ T-cellular immunity. HLA II binding peptides have an extended conformation at the binding groove unlike class I. This increases peptide binding combinations of varying length at the groove, having an eventual effect in the host immune response to infectious agents. Here we describe the development of a prediction model using information gleaned from HLA II-peptide (HLA II-p) structural data. We created a manually curated dataset of 15 HLA II-p structural complexes from Protein databank (PDB). The dataset was used to develop virtual binding pockets for accommodating HLA-II-specific short peptides. The binding of peptides to the virtual pockets is estimated using the Q matrix (a quantitative matrix based on amino acid residue properties). Internal cross-validation of the model using the 15 HLA II-p structural complexes produced an accuracy of 53% with a sensitivity of 53%. The model was further evaluated using a dataset of 3676 class II-specific peptides consisting of 1188 binders and 2488 nonbinders derived from MHCBN (a database of HLA binders and nonbinders). The model produced an accuracy of 53% with 70.8% specificity and 27.6% sensitivity. The positive predictive value (PPV) was 62% and the negative predictive value (NPV) 58%. A 62% PPV suggests that the model fairly predicts a good number of binders among predicted binders and thus that the success rate among predicted binder for further verification is good. The described model is simple and rapid, with large HLA allele coverage representing the sampled global population, despite weak prediction accuracy. The ability of the model to predict a wide array of defined class II alleles is found to be applicable for proteome-wide scanning of parasitic genomes.

Alzheimer's disease and HIV associated dementia related genes: I. location and function.

Alzheimer's disease (AD), the most common cause of dementia, has few clinical similarities to HIV-1-associated dementia (HAD). However, genes were identified related among these dementias. Discovering correlations between gene function, expression, and structure in the human genome continues to aid in understanding the similarities between pathogenesis of these two dementing disorders. The current work attempts to identify relationships between these dementias in spite of their clinical differences, based on genomic structure, function, and expression. In this comparative study, the NCBI Entrez Genome Database is used to detect these relationships. This approach serves as a model for future diagnosis and treatment in the clinical arena as well as suggesting parallel pathways of disease mechanisms. Identifying a correlation among expression, structure, and function of genes involved in pathogenesis of these dementing disorders, may assist to understand better their interaction with each other and the human genome.