Evolution, homology conservation, and identification of unique sequence signatures in GH19 family chitinases.

The discovery of GH (Glycoside Hydrolase) 19 chitinases in Streptomyces sp. raises the possibility of the presence of these proteins in other bacterial species, since they were initially thought to be confined to higher plants. The present study mainly concentrates on the phylogenetic distribution and homology conservation in GH19 family chitinases. Extensive database searches are performed to identify the presence of GH19 family chitinases in the three major super kingdoms of life. Multiple sequence alignment of all the identified GH19 chitinase family members resulted in the identification of globally conserved residues. We further identified conserved sequence motifs across the major sub groups within the family. Estimation of evolutionary distance between the various bacterial and plant chitinases are carried out to better understand the pattern of evolution. Our study also supports the horizontal gene transfer theory, which states that GH19 chitinase genes are transferred from higher plants to bacteria. Further, the present study sheds light on the phylogenetic distribution and identifies unique sequence signatures that define GH19 chitinase family of proteins. The identified motifs could be used as markers to delineate uncharacterized GH19 family chitinases. The estimation of evolutionary distance between chitinase identified in plants and bacteria shows that the flowering plants are more related to chitinase in actinobacteria than that of identified in purple bacteria. We propose a model to elucidate the natural history of GH19 family chitinases.

Towards the MHC-peptide combinatorics.

The exponentially increased sequence information on major histocompatibility complex (MHC) alleles points to the existence of a high degree of polymorphism within them. To understand the functional consequences of MHC alleles, 36 nonredundant MHC-peptide complexes in the protein data bank (PDB) were examined. Induced fit molecular recognition patterns such as those in MHC-peptide complexes are governed by numerous rules. The 36 complexes were clustered into 19 subgroups based on allele specificity and peptide length. The subgroups were further analyzed for identifying common features in MHC-peptide binding pattern. The four major observations made during the investigation were: (1) the positional preference of peptide residues defined by percentage burial upon complex formation is shown for all the 19 subgroups and the burial profiles within entries in a given subgroup are found to be similar; (2) in class I specific 8- and 9-mer peptides, the fourth residue is consistently solvent exposed, however this observation is not consistent in class I specific 10-mer peptides; (3) an anchor-shift in positional preference is observed towards the C terminal as the peptide length increases in class II specific peptides; and (4) peptide backbone atoms are proportionately dominant at the MHC-peptide interface.

Knowledge-based grouping of modeled HLA peptide complexes.

Human leukocyte antigens are the most polymorphic of human genes and multiple sequence alignment shows that such polymorphisms are clustered in the functional peptide binding domains. Because of such polymorphism among the peptide binding residues, the prediction of peptides that bind to specific HLA molecules is very difficult. In recent years two different types of computer based prediction methods have been developed and both the methods have their own advantages and disadvantages. The nonavailability of allele specific binding data restricts the use of knowledge-based prediction methods for a wide range of HLA alleles. Alternatively, the modeling scheme appears to be a promising predictive tool for the selection of peptides that bind to specific HLA molecules. The scoring of the modeled HLA-peptide complexes is a major concern. The use of knowledge based rules (van der Waals clashes and solvent exposed hydrophobic residues) to distinguish binders from nonbinders is applied in the present study. The rules based on (1) number of observed atomic clashes between the modeled peptide and the HLA structure, and (2) number of solvent exposed hydrophobic residues on the modeled peptide effectively discriminate experimentally known binders from poor/nonbinders. Solved crystal complexes show no vdW Clash (vdWC) in 95% cases and no solvent exposed hydrophobic peptide residues (SEHPR) were seen in 86% cases. In our attempt to compare experimental binding data with the predicted scores by this scoring scheme, 77% of the peptides are correctly grouped as good binders with a sensitivity of 71%.

Molecular modeling of the minor histocompatibility antigen HA-1 peptides binding to HLA-A alleles.

Mismatch of the minor histocompatibility antigen HA-1 has been shown to correlate with graft-versus-host disease in HLA-matched sibling marrow transplants. The HA-1H peptide (VLHDDLLEA) that generates this response is known to be presented by HLA-A*0201. In order to understand the interaction of HA-1 peptides with other HLA-A alleles, we have used the LOOK interface to construct molecular models of both HA-1H peptide (VLHDDLLEA) and HA-1R peptide (VLRDDLLEA) binding with 103 HLA-A alleles. The results show that in addition to A*0201, 21/103 other HLA-A alleles should be able to bind HA-1H peptide but not HA-1R peptide. Based on the modeled predictions, HLA alleles can be categorised into 4 groups with respect to their interaction with HA-1 peptides: Group 1 - bind HA-1H peptide but not HA-1R peptide; Group 2 - bind HA-1R peptide but not HA-1H peptide; Group 3 - bind both HA-1H and HA-1R peptides; Group 4 - bind neither peptide. These predicted binding patterns of HA-1 peptides will be useful as an aid for defining a wider pool of HLA-A alleles in which HA-1 disparities among donor-recipient pairs can be investigated.

IE-Kb: intron exon knowledge base.

SUMMARY: IE-Kb (Intron Exon-Knowledge base) illustrates the intron-exon dynamics in eukaryotic genes. We have developed three different knowledge sets, namely 'Non-redundant ExInt', 'Non-redundant Pfam-ExInt complement' and 'Non-redundant GenBank eukaryotic subdivisional sets' to understand this phenomenon. Statistical analysis is performed on each knowledge set and the results are made available online. The entries in knowledge sets are ranked based on their intron length, exon length and protein length with relational hyper-links to the corresponding intron phase, intron position, intron sequence, gene definition and parent GenBank entry.