PASS2 database for the structure-based sequence alignment of distantly related SCOP domain superfamilies: update to version 5 and added features.

Structure-based sequence alignment is an essential step in assessing and analysing the relationship of distantly related proteins. PASS2 is a database that records such alignments for protein domain superfamilies and has been constantly updated periodically. This update of the PASS2 version, named as PASS2.5, directly corresponds to the SCOPe 2.04 release. All SCOPe structural domains that share less than 40% sequence identity, as defined by the ASTRAL compendium of protein structures, are included. The current version includes 1977 superfamilies and has been assembled utilizing the structure-based sequence alignment protocol. Such an alignment is obtained initially through MATT, followed by a refinement through the COMPARER program. The JOY program has been used for structural annotations of such alignments. In this update, we have automated the protocol and focused on inclusion of new features such as mapping of GO terms, absolutely conserved residues among the domains in a superfamily and inclusion of PDBs, that are absent in SCOPe 2.04, using the HMM profiles from the alignments of the superfamily members and are provided as a separate list. We have also implemented a more user-friendly manner of data presentation and options for downloading more features. PASS2.5 version is available at http://caps.ncbs.res.in/pass2/.

SInCRe-structural interactome computational resource for Mycobacterium tuberculosis.

We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein-protein and protein-small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein-protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host-pathogen protein-protein interactions. Together they provide prerequisites for identification of off-target binding.

Rebelling for a reason: protein structural "outliers".

Analysis of structural variation in domain superfamilies can reveal constraints in protein evolution which aids protein structure prediction and classification. Structure-based sequence alignment of distantly related proteins, organized in PASS2 database, provides clues about structurally conserved regions among different functional families. Some superfamily members show large structural differences which are functionally relevant. This paper analyses the impact of structural divergence on function for multi-member superfamilies, selected from the PASS2 superfamily alignment database. Functional annotations within superfamilies, with structural outliers or 'rebels', are discussed in the context of structural variations. Overall, these data reinforce the idea that functional similarities cannot be extrapolated from mere structural conservation. The implication for fold-function prediction is that the functional annotations can only be inherited with very careful consideration, especially at low sequence identities.

Defining putative T cell epitopes from PE and PPE families of proteins of Mycobacterium tuberculosis with vaccine potential.

The identification of T cell epitopes from immune relevant antigens of Mycobacterium tuberculosis is a critical step in the development of a vaccine covering diverse populations. Two multigene families, PE-PGRS and PPE make up about 10% of the M. tuberculosis genome. However, the functions of the proteins coded by these large numbers of genes are unknown. All possible nonameric peptide sequences from PE and PPE proteins were analysed in silico for their ability to bind to 33 alleles of class I HLA. These results reveal that of all PE and PPE proteins, a significant number of these peptides are predicted to be high-affinity HLA binders, irrespective of the length of the protein. The pathogen peptides that could behave as self or partially self-peptides in the host were eliminated using a comparative study with human proteome, thus reducing the number of peptides for analysis. The structural basis for recognition of the nonamers by the respective HLA molecules thus predicted was analyzed by molecular modeling. The structural analysis showed good correlation with the binding prediction. The analysis also led to an understanding of the binding profile of the peptides with respect to different alleles of class I HLA. The predicted epitopes can be tested experimentally for their inclusion in a potential vaccine against tuberculosis that is HLA haplotype-specific.

Molecular mechanism of dimerization of Bowman-Birk inhibitors. Pivotal role of ASP76 in the dimerzation.

Horsegram (Dolichos biflorus), a protein-rich leguminous pulse, is a crop native to Southeast Asia and tropical Africa. The seeds contain multiple forms of Bowman-Birk type inhibitors. The major inhibitor HGI-III, from the native seed with 76 amino acid residues exists as a dimer. The amino acid sequence of three isoforms of Bowman-Birk inhibitor from germinated horsegram, designated as HGGI-I, HGGI-II, and HGGI-III, have been obtained by sequential Edman analyses of the pyridylethylated inhibitors and peptides derived therefrom by enzymatic and chemical cleavage. The HGGIs are monomers, comprising of 66, 65, and 60 amino acid residues, respectively. HGGI-III from the germinated seed differs from the native seed inhibitor in the physiological deletion of a dodecapeptide at the amino terminus and a tetrapeptide, -SHDD, at the carboxyl terminus. The study of the state of association of HGI-III, by size-exclusion chromatography and SDS-PAGE in the presence of 1 mM ZnCl2, has revealed the role of charged interactions in the monomer <--> dimer equilibria. Chemical modification studies of Lys and Arg have confirmed the role of charge interactions in the above equilibria. These results support the premise that a unique interaction, which stabilizes the dimer, is the cause of self-association in the inhibitors. This interaction in HGI-III involves the epsilon-amino group of the Lys24 (P1 residue) at the first reactive site of one monomer and the carboxyl of an Asp86 at the carboxyl terminus of the second monomer. Identification of the role of these individual amino acids in the structure and stability of the dimer was accomplished by chemical modifications, multiple sequence alignment of legume Bowman-Birk inhibitors, and homology modeling. The state of association may also influence the physiological and functional role of these inhibitors.

Extracting hydrogen-bond signature patterns from protein structure data.

Classification of protein sequences and structures into families is a fundamental task in biology, and it is often used as a basis for designing experiments for gaining further knowledge. Some relationships between proteins are detected by the similarities in their sequences, and many more by the similarities in their structures. Despite this, there are a number of examples of functionally similar molecules without any recognisable sequence or structure similarities, and there are also a number of protein molecules that share common structural scaffolds but exhibit different functions. Newer methods of comparing molecules are required in order to detect similarities and dissimilarities in protein molecules. In this article, it is proposed that the precise 3-dimensional disposition of key residues in a protein molecule is what matters for its function, or what conveys the "meaning" for a biological system, but not what means it uses to achieve this. The concept of comparing two molecules through their intramolecular interaction networks is explored, since these networks dictate the disposition of amino acids in a protein structure. First, signature patterns, or fingerprints, of interaction networks in pre-classified protein structural families are computed using an approach to find structural equivalences and consensus hydrogen bonds. Five examples from different structural classes are illustrated. These patterns are then used to search the entire Protein Data Bank, an approach through which new, unexpected similarities have been found. The potential for finding relationships through this approach is highlighted. The use of hydrogen-bond fingerprints as a new metric for measuring similarities in protein structures is also described.

The Database of Quantitative Cellular Signaling: management and analysis of chemical kinetic models of signaling networks.

MOTIVATION: Analysis of cellular signaling interactions is expected to pose an enormous informatics challenge, perhaps even larger than analyzing the genome. The complex networks arising from signaling processes are traditionally represented as block diagrams. A key step in the evolution toward a more quantitative understanding of signaling is to explicitly specify the kinetics of all chemical reaction steps in a pathway. Technical advances in proteomics and high-throughput protein interaction assays promise a flood of such quantitative data. While annotations, molecular information and pathway connectivity have been compiled in several databases, and there are several proposals for general cell model description languages, there is currently little experience with databases of chemical kinetics and reaction level models of signaling networks. RESULTS: The Database of Quantitative Cellular Signaling is a repository of models of signaling pathways. It is intended both to serve the growing field of chemical-reaction level simulation of signaling networks, and to anticipate issues in large-scale data management for signaling chemistry. AVAILABILITY: The Database of Quantitative Cellular Signaling is available at http://doqcs.ncbs.res.in. Links to the signaling model simulator, GENESIS/Kinetikit are at http://www.ncbs.res.in/~bhalla/kkit/index.html and are also provided from within the database. The database source code is available under the GNU Public License.