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The magnitude of the problems of drug abuse and Neuro-AIDS warrants the development of novel approaches for testing hypotheses in diagnosis and treatment ranging from cell culture models to developing databases. In this study, cultured neurons were treated with/without HIV-TAT, ENV, or cocaine in a 2x2x2 expression study design. RNA was purified, labeled, and expression data were produced and analyzed using ANOVA. Thus, we identified 35 genes that were significantly expressed across treatment conditions. A diagram is presented showing examples of molecular relationships involving a significantly expressed gene in the current study (SOX2). Also, we use this information to discuss examples of gene expression interactions as a means to portray significance and complexity of gene expression studies in Drug Abuse and Neuro-AIDS. Furthermore, we discuss here that critical interactions remain undetected, which may be unravelled by developing robust database systems containing large datasets and gleaned information from collaborating scientists . Hence, we are developing a public domain database we named The Agora database , that will served as a shared infrastructure to query, deposit, and review information related to drug abuse and dementias including Neuro-AIDS. A workflow of this database is also outlined in this paper.

Abstract Background A number of completely sequenced eukaryotic genome data are available in the public domain. Eukaryotic genes are either 'intron containing' or 'intronless'. Eukaryotic 'intronless' genes are interesting datasets for comparative genomics and evolutionary studies. The SEGE database containing a collection of eukaryotic single exon genes is available. However, SEGE is derived using GenBank. The redundant, incomplete and heterogeneous qualities of GenBank data are a bottleneck for biological investigation in comparative genomics and evolutionary studies. Such studies often require representative gene sets from each genome and this is possible only by deriving specific datasets from completely sequenced genome data. Thus Genome SEGE, a database for 'intronless' genes in completely sequenced eukaryotic genomes, has been constructed. Availability: http://sege.ntu.edu.sg/wester/intronless Description Eukaryotic 'intronless' genes are extracted from nine completely sequenced genomes (four of which are unicellular and five of which are multi-cellular). The complete dataset is available for download. Data subsets are also available for 'intronless' pseudo-genes. The database provides information on the distribution of 'intronless' genes in different genomes together with their length distributions in each genome. Additionally, the search tool provides pre-computed PROSITE motifs for each sequence in the database with appropriate hyperlinks to InterPro. A search facility is also available through the web server. Conclusions The unique features that distinguish Genome SEGE from SEGE is the service providing representative 'intronless' datasets for completely sequenced genomes. 'Intronless' gene sets available in this database will be of use for subsequent bio-computational analysis in comparative genomics and evolutionary studies. Such analysis may help to revisit the original genome data for re-examination and re-annotation.

Several catalysis, cellular regulation, immune function, cell wall assembly, transport, signaling and inhibition occur through Protein- Protein Interactions (PPI). This is possible with the formation of specific yet stable protein-protein interfaces. Therefore, it is of interest to understand its molecular principles using structural data in relation to known function. Several interface features have been documented using known X-ray structures of protein complexes since 1975. This has improved our understanding of the interface using structural features such as interface area, binding energy, hydrophobicity, relative hydrophobicity, salt bridges and hydrogen bonds. The strength of binding between two proteins is dependent on interface size (number of residues at the interface) and thus its corresponding interface area. It is known that large interfaces have high binding energy (sum of (van der Waals) vdW, H-bonds, electrostatics). However, the selective role played by each of these energy components and more especially that of vdW is not explicitly known. Therefore, it is important to document their individual role in known protein-protein structural complexes. It is of interest to relate interface size with vdW, H-bonds and electrostatic interactions at the interfaces of protein structural complexes with known function using statistical and multiple linear regression analysis methods to identify the prominent force. We used the manually curated non-redundant dataset of 278 hetero-dimeric protein structural complexes grouped using known functions by Sowmya et al. (2015) to gain additional insight to this phenomenon using a robust inter-atomic non-covalent interaction analyzing tool PPCheck (Anshul and Sowdhamini, 2015). This dataset consists of obligatory (enzymes, regulator, biological assembly), immune and nonobligatory (enzyme and regulator inhibitors) complexes. Results show that the total binding energy is more for large interfaces. However, this is not true for its individual energy factors. Analysis shows that vdW energies contribute to about 75% ± 11% on average among all complexes and it also increases with interface size (r2 ranging from 0.67 to 0.89 with p<0.01) at 95% confidence limit irrespective of molecular function. Thus, vdW is both dominant and proportional at the interface independent of molecular function. Nevertheless, H bond energy contributes to 15% ± 6.5% on average in these complexes. It also moderately increases with interface size (r2 ranging from 0.43 to 0.61 with p<0.01) only among obligatory and immune complexes. Moreover, there is about 11.3% ± 8.7% contribution by electrostatic energy. It increases with interface size specifically among non-obligatory regulator-inhibitors (r2 = 0.44). It is implied that both H-bonds and electrostatics are neither dominant nor proportional at the interface. Nonetheless, their presence cannot be ignored in binding. Therefore, H-bonds and (or) electrostatic energy having specific role for improved stability in complexes is implied. Thus, vdW is common at the interface stabilized further with selective H-bonds and (or) electrostatic interactions at an atomic level in almost all complexes. Comparison of this observation with residue level analysis of the interface is compelling. The role by H-bonds (14.83% ± 6.5% and r2 = 0.61 with p<0.01) among obligatory and electrostatic energy (8.8% ± 4.77% and r2 = 0.63 with p <0.01) among non-obligatory complexes within interfaces (class A) having more non-polar residues than surface is influencing our inference. However, interfaces (class B) having less non-polar residues than surface show 1.5 fold more electrostatic energy on average. The interpretation of the interface using inter-atomic (vdW, H-bonds, electrostatic) interactions combined with inter-residue predominance (class A and class B) in relation to known function is the key to reveal its molecular principles with new challenges.

Protein heterodimer complexes are often involved in catalysis, regulation, assembly, immunity and inhibition. This involves the formation of stable interfaces between the interacting partners. Hence, it is of interest to describe heterodimer interfaces using known structural complexes. We use a non-redundant dataset of 192 heterodimer complex structures from the protein databank (PDB) to identify interface residues and describe their interfaces using amino-acids residue property preference. Analysis of the dataset shows that the heterodimer interfaces are often abundant in polar residues. The analysis also shows the presence of two classes of interfaces in heterodimer complexes. The first class of interfaces (class A) with more polar residues than core but less than surface is known. These interfaces are more hydrophobic than surfaces, where protein-protein binding is largely hydrophobic. The second class of interfaces (class B) with more polar residues than core and surface is shown. These interfaces are more polar than surfaces, where binding is mainly polar. Thus, these findings provide insights to the understanding of protein-protein interactions.

Unlabelled: The human immunodeficiency virus type-1 (HIV-1) gp160 (gp120-gp41 complex) trimer envelope (ENV) protein is a potential vaccine candidate for HIV/AIDS. HIV-1 vaccine development has been problematic and charge polarity as well as sequence variation across clades may relate to the difficulties. Further obstacles are caused by sequence variation between blood and brain-derived sequences, since the brain is a separate compartment for HIV-1 infection. We utilize a threedimensional residue measure of solvent exposure, accessible surface area (ASA), which shows that major segments of gp120 and gp41 known structures are solvent exposed across clades. We demonstrate a large percent sequence polarity for solvent exposed residues in gp120 and gp41. The range of sequence polarity varies across clades, blood, and brain from different geographical locations. Regression analysis shows that blood and brain gp120 and gp41 percent sequence polarity range correlate with mean Shannon entropy. These results point to the use of protein modifications to enhance HIV-1 ENV vaccines across multiple clades, blood, and brain. It should be noted that we do not address the issue of protein glycosylation here; however, this is an important issue for vaccine design and development., Abbreviations: HIV-1 - human immunodeficiency virus type 1, AIDS - acquired immunodeficiency syndrome, ENV - envelope, gp160 - 160,000d glycoprotein, gp120 - 120,000d glycoprotein, gp41 - 41,000d glycoprotein, LANL - Los Alamos National Laboratories, PDB - Protein Data Bank, HVTN - STEP HIV vaccine trial, AA - amino acids, MSA - multiple sequence alignment, ASA - accessible surface area, SNPs- single nucleotide polymorphisms, HAART - Highly Active Antiretroviral Therapy, CCR5 - C-C chemokine receptor type 5, CNS - central nervous system, HIVE - HIV encephalitis, P - polarity, NP - non-polarity, CTL - cytotoxic T lymphocyte, NIAID - National Institute of Allergy and Infectious Diseases.

Cholera is a global disease that has persisted for millennia. The cholera toxin (CT) from Vibrio cholerae is responsible for the clinical symptoms of cholera. This toxin is a hetero-hexamer (AB(5)) complex consisting of a subunit A (CTA) with a pentamer (B(5)) of subunit B (CTB). The importance of the AB(5) complex for pathogenesis is established for the wild type O1 serogroup using known structural and functional data. However, its role is not yet documented in other known serogroups harboring sequence level residue mutations. The sequences for the toxin from different serogroups are available in GenBank (release 177). Sequence analysis reveals mutations at several sequence positions in the toxin across serogroups. Therefore, it is of interest to locate the position of these mutations in the AB(5) structure to infer complex assembly for its functional role in different serogroups. We show that mutations in the CTA are at the solvent exposed regions of the AB(5) complex, whereas those in the CTB are at the CTB/CTB interface of the homo-pentamer complex. Thus, the role of mutations at the CTB/CTB interface for B(5) complex assembly is implied. It is observed that these mutations are often non-synonymous (e.g. polar to non-polar or vice versa). The formation of the AB(5) complex involves inter-subunit residue-residue interactions at the protein-protein interfaces. Hence, these mutations, at the structurally relevant positions, are of importance for the understanding of pathogenesis by several serogroups. This is also of significance in the improvement of recombinant CT protein complex analogs for vaccine design and their use against multiple serogroups.

We analyzed RNA gene expression in neurons from 16 cases in four categories, HIV associated dementia with HIV encephalitis (HAD/HIVE), HAD alone, HIVE alone, and HIV-1-positive (HIV+)with neither HAD nor HIVE. We produced the neurons by laser capture microdissection (LCM) from cryopreserved globus pallidus. Of 55,000 gene fragments analyzed, expression of 197 genes was identified with significance (p = 0.005).We examined each gene for its position in the human genome and found a non-stochastic occurrence for only seven genes, on chromosome 22. Six of the seven genes were identified, CSNK1E (casein kinase 1 epsilon), DGCR8 (Di George syndrome critical region 8), GGA1 (Golgi associated gamma adaptin ear containing ARF binding protein 1), MAPK11 (mitogen activated protein kinase 11), SMCR7L (Smith-Magenis syndrome chromosome region candidate 7-like), andTBC1D22A (TBC1 domain family member 22A). Six genes (CSNK1E, DGCR8, GGA1, MAPK11, SMCR7L, and one unidentified gene) had similar expression profiles across HAD/HIVE, HAD, and HIVE vs. HIV+ whereas one gene (TBC1D22A) had a differing gene expression profile across these patient categories. There are several mental disease-related genes including miRNAs on chromosome 22 and two of the genes (DGCR8 and SMCR7L) identified here are mental disease-related. We speculate that dysregulation of gene expression may occur through mechanisms involving chromatin damage and remodeling. We conclude that the pathogenesis of NeuroAIDS involves dysregulation of expression of mental disease-related genes on chromosome 22 as well as additional genes on other chromosomes. The involvement of these genes as well as miRNA requires additional investigation since numerous genes appear to be involved.

HLA-DRB alleles are class II alleles that are associated with CD4+ T-cell immune response. DRB alleles are polymorphic and currently there are about 622 named in the IMGT/HLA sequence database. Each allele binds short peptides with high sensitivity and specificity. However, it has been suggested that majority of HLA alleles can be covered within few HLA supertypes, where different members of a supertype bind similar peptides showing distinct repertoires. Definition of DRB supertypes using binding data is limited to few (about 29) known alleles (< 5% of all known DRB alleles). Hence, we describe a strategy using structurally defined virtual pockets to group all known DRB alleles with regard to their overlapping peptide binding specificity.

Hetero dimer (different monomers) interfaces are involved in catalysis and regulation through the formation of interface active sites. This is critical in cell and molecular biology events. The physical and chemical factors determining the formation of the interface active sites is often large in numbers. The combined role of interacting features is frequently combinatorial and additive in nature. Therefore, it is important to determine the physical and chemical features of such interactions. A number of such features have been documented in literature since 1975. However, the use of such interaction features in the prediction of interaction partners and sites given their sequences is still a challenge. In a non-redundant dataset of 156 hetero-dimer structures determined by X-ray crystallography, the interacting partners are often varying in size and thus, size variation between subunits is an important factor in determining the mode of interface formation. The size of protein subunits interacting are either small-small, largelarge, medium-medium, large-small, large-medium and small-medium. It should also be noted that the interface formed between subunits have physical interactions at N terminal (N), C terminal (C) and middle (M) region of the protein with reference to their sequences in one dimension. These features are believed to have application in the prediction of interaction partners and sites from sequences. However, the use of such features for interaction prediction from sequence is not currently clear.

The formation of protein homodimer complexes for molecular catalysis and regulation is fascinating. The homodimer formation through 2S (2 state), 3SMI (3 state with monomer intermediate) and 3SDI (3 state with dimer intermediate) folding mechanism is known for 47 homodimer structures. Our dataset of forty-seven homodimers consists of twenty-eight 2S, twelve 3SMI and seven 3SDI. The dataset is characterized using monomer length, interface area and interface/total (I/T) residue ratio. It is found that 2S are often small in size with large I/T ratio and 3SDI are frequently large in size with small I/T ratio. Nonetheless, 3SMI have a mixture of these features. Hence, we used these parameters to develop a decision tree model. The decision tree model produced positive predictive values (PPV) of 72% for 2S, 58% for 3SMI and 57% for 3SDI in cross validation. Thus, the method finds application in assigning homodimers with folding mechanism.

Homodimers have a role in catalysis and regulation through the formation of stable interfaces. These interfaces are formed through different folding mechanisms such as 2-state without stable intermediate (2S), 3-state with monomer intermediate (3SMI) and 3-state with dimer intermediate (3SDI). Therefore, it is of interest to understand folding mechanism using structural features at the interfaces. Several studies have documented the significance of structural features for the understanding of homodimer folding mechanisms. However, the known features provide limited information for understanding homodimer folding mechanisms. Hence, we created an extended dataset of 47 homodimers (twenty eight 2S, twelve 3SMI and seven 3SDI) to examine the types of interfaces in protein homodimers. 2S are usually small sized, 3SMI are often medium sized and 3SDI often exist as large sized proteins. The ratio of interface to total (I/T) residue is large in 2S and small in 3SMI and 3SDI. Hence, we used I/T measure to group 2S, 3SMI and 3SDI into categories with large I/T (>> 50%), moderate I/T (50 - 25%) and small I/T (<< 25%) interfaces. The grouping is further sub-grouped based on the type of physical interaction visualized at the interface using representations in two dimensions (2D). 2D representation of the interface shows eight different forms of interactions in these homodimers. 2S homodimers frequently have large I/T and thus, utilize the entire protein structure in the formation of the interface where the individual subunits are heavily inter communicated with each other. This is not true in the case of 3SMI and 3SDI. 3SMI subunits usually interact with each other at the interface with a gentle touch-like contact and hence, they have low I/T ratio. 3SDI are often quite different in interaction compared to 3SMI and their subunits do deeply interact at the interface with only one part of the surface and hence also having low I/T ratio.

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.

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.

Human leukocyte antigen (HLA) molecules involved in immune function by binding to short peptides (8-20 residues) have different sequences in different individuals belonging to distinct ethnic population. Hence, the peptide-binding function of HLA alleles is specific. Class I HLA alleles (alternative forms of a gene) are associated with CD8+ T cells, and their allele-specific sequence information is available at the IMGT/HLA database. The available sequences are one-dimensional (ID), and the peptide-binding functional inference often requires 3-dimensional (3D) structural models of respective alleles. Hence, 3D structures were constructed for 1,000 class I HLA alleles (310 A, 570 B, and 120 C) using MODELLER (a comparative protein modeling program for modeling protein structures). The electrostatic distribution maps were generated for each modeled structure using Deep View (Swiss PDB Viewer Version 3.7). The 1,000 models were then grouped into different categories by visual inspection of their electrostatic distribution maps in the peptide binding grooves. The distribution of the models based on electrostatic distribution was 30% negative (300), 1% positive (12), 8% neutral (84), and 60% (604) mixed (random mixture of negative, positive, and neutral). This grouping provides insight toward the inference for functional overlap among HLA alleles.

Short peptides binding to specific human leukocyte antigen (HLA) alleles elicit immune response. These candidate peptides have potential utility in peptide vaccine design and development. The binding of peptides to allele-specific HLA molecule is estimated using competitive binding assay and biochemical binding constants. Application of this method for proteome-wide screening in parasites, viruses, and virulent bacterial strains is laborious and expensive. However, short listing of candidate peptides using prediction approaches have been realized lately. Prediction of peptide binding to HLA alleles using structural and modeling principles has gained momentum in recent years. Here, we discuss the current status of such prediction.

The human leukocyte antigen (HLA) alleles are extremely polymorphic among ethnic population, and the peptide-binding specificity varies for different alleles in a combinatorial manner. However, it has been suggested that majority of alleles can be covered within few HLA supertypes, where different members of a supertype bind similar peptides, yet exhibiting distinct repertoires. Nonetheless, the structural basis for HLA supertype-like function is not clearly known. Here, we use structural data to explain the molecular basis for HLA-A2 supertypes.

It is known that binding free energy of protein-protein interaction is mainly contributed by hot spot (high energy) interface residues. Here, we investigate the characteristics of hot spots by examining inter-atomic sidechain-sidechain interactions using a dataset of 296 alanine-mutated interface residues. Results show that hot spots participate in strong and energetically favorable sidechain-sidechain interactions. Subsequently, we describe a novel, yet simple 'hot spot' prediction model with an accuracy that is similar to many available approaches. The model is also shown to efficiently distinguish specific protein-protein interactions from non-specific interactions.

Gene fusion produces proteins with novel structural architectures during evolution. Recent comparative genome analysis shows several cases of fusion/fission across distant phylogeny. However, the selection forces driving gene fusion are not fully understood due to the lack of structural, dynamics and kinetics data. Available structural data at PDB (protein databank) contains limited cases of structural pairs describing fused and un-fused structures. Nonetheless, we identified a pair of IGPS (imidazole glycerol phosphate synthetase) structures (comprising of HisF - glutaminase unit and HisH - cyclase unit) from S. cerevisiae (SC) and T. thermophilus (TT). The HisF-HisH structural units are domains in SC and subunits in TT. Hence, they are fused in SC and un-fused in TT. Subsequently, a domain-domain interface is formed in SC and a subunit-subunit interface in TT between HisF and HisH. Our interest is to document the structure and dynamics differences between fused and un-fused IGPS. Therefore, we probed into the structures of fused IGPS in SC and un-fused IGPS in TT using molecular dynamics simulation for 5ns. Simulation shows that fused IGPS in SC has larger interface area between HisF-HisH and greater radius of gyration compared to un-fused IGPS in TT. These structural features for the first time demonstrate the evolutionary advantage in generating proteins with novel structural architecture through gene fusion.

Protein lengths vary considerably from a few to thousands of amino acids and length variations are documented to have multiple effects. A computational approach to investigate the functional impact of protein length variation in genetic disorders is presented. The genes for huge proteins are found to have more introns. Our analysis also shows greater involvement of huge proteins in hereditary diseases.

The formation of homodimer complexes for interface stability, catalysis and regulation is intriguing. The mechanisms of homodimer complexations are even more interesting. Some homodimers form without intermediates (two-state (2S)) and others through the formation of stable intermediates (three-state (3S)). Here, we analyze 41 homodimer (25 2S and 16 3S) structures determined by X-ray crystallography to estimate structural differences between them. The analysis suggests that a combination of structural properties such as monomer length, subunit interface area, ratio of interface to interior hydrophobicity can predominately distinguish 2S and 3S homodimers. These findings are useful in the prediction of homodimer folding and binding mechanisms using structural data.

A profile of exon-intron lengths in genes shows a normal distribution. This observation suggests that different genes may have portions of their total exon and/or intron lengths in common. In order to explore the common exon-intron structural patterns that may arise due to common lengths across genes, we compared the exon-intron length patterns of annotated human genes. We discovered 1762278 conserved arrangements of exon-intron length across the otherwise unrelated and diverse genomic landscape. The existence of common exon-intron length patterns across unrelated genes suggests for their role of in gene assemblage and human genome design and architecture.

Protein dimers are either homodimers (complexation of identical monomers) or heterodimers (complexation of non-identical monomers). These dimers are common in catalysis and regulation. However, the molecular principles of protein dimer interactions are difficult to understand mainly due to the geometrical and chemical characteristics of proteins. Nonetheless, the principles of protein dimer interactions are often studied using a dataset of 3D structural complexes determined by X-ray crystallography. A number of physical and chemical properties govern protein dimer interactions. Yet, a handful of such properties are known to dominate protein dimer interfaces. Here, we discuss the differences between homodimer and heterodimer interfaces using a selected set of interface properties.

Unlabelled: The current challenge in synthetic vaccine design is the development of a methodology to identify and test short antigen peptides as potential T-cell epitopes. Recently, we described a HLA-peptide binding model (using structural properties) capable of predicting peptides binding to any HLA allele. Consequently, we have developed a web server named T-EPITOPE DESIGNER to facilitate HLA-peptide binding prediction. The prediction server is based on a model that defines peptide binding pockets using information gleaned from X-ray crystal structures of HLA-peptide complexes, followed by the estimation of peptide binding to binding pockets. Thus, the prediction server enables the calculation of peptide binding to HLA alleles. This model is superior to many existing methods because of its potential application to any given HLA allele whose sequence is clearly defined. The web server finds potential application in T cell epitope vaccine design., Availability: http://www.bioinformation.net/ted/

Protein dimer interfaces (homodimer - same polypeptide and heterodimer - different polypeptide) display geometric and chemical properties that give the non-covalent assembly its stability and specificity. Therefore, it is important to understand the molecular principles of dimer interaction. Several studies on homodimer interaction are available. However, a study on the effect of ligands (i.e. non-peptide compounds) on subunit interactions is not available. Hence, we generated a dataset of 62 identical homodimer pairs (one structure determined with an interface ligand and the other without an interface ligand) and analyzed the effect of interface ligands on dimer interface. The analysis suggests that homodimer interfaces having ligands are less hydrophobic with small interface area compared to those without ligands. We also found that ligands occupying = 7% interface area have negligible effect on dimer interaction.

Human fusion proteins consisting of two or more fusion partners of prokaryotic origin exhibit accreted function. Recent studies have elucidated the importance of fusion proteins in complex regulatory networks. The significance of fusion proteins in cellular networks and their evolutionary mechanism is largely unknown. Here, we discuss the association of six fusion proteins with the citric acid cycle. We define possible gene fusion scenarios and show that they produce metabolites with high connectivity for complex networking. Complex networking of metabolites requires proteins with incremental structural architectures and functional capabilities. Such higher order functionality is frequently provided by fusion proteins. Therefore, evolution of fusion proteins capable of producing metabolites with greater connectivity for enhanced cross-talk between pathways is critical for the selection of multiple trajectories in maintaining a stoichiometric balance during regulation. The association of six fusion proteins with the citric acid cycle and their capability to produce metabolites with high connectivity index is intriguing. This suggests that fusion gene products and their evolution have had a key role in the selection of complex multifaceted networks. In addition, we propose that fusion proteins have gained additive biochemical function for a balanced regulation of metabolic networks.