CPAD,Curated Protein Aggregation Database: A Repository of Manually Curated Experimental Data on Protein and Peptide Aggregation

Accurate distinction between peptide sequences that can form amyloid-fibrils or amorphous β-aggregates, identification of potential aggregation prone regions in proteins, and prediction of change in aggregation rate of a protein upon mutation(s) are critical to research on protein misfolding diseases, such as Alzheimer’s and Parkinson’s, as well as biotechnological production of protein based therapeutics. We have developed a Curated Protein Aggregation Database (CPAD), which has collected results from experimental studies performed by scientific community aimed at understanding protein/peptide aggregation. CPAD contains more than 2300 experimentally observed aggregation rates upon mutations in known amyloidogenic proteins. Each entry includes numerical values for the following parameters: change in rate of aggregation as measured by fluorescence intensity or turbidity, name and source of the protein, Uniprot and Protein Data Bank codes, single point as well as multiple mutations, and literature citation. The data in CPAD has been supplemented with five different types of additional information: (i) Amyloid fibril forming hexa-peptides, (ii) Amorphous β-aggregating hexa-peptides, (iii) Amyloid fibril forming peptides of different lengths, (iv) Amyloid fibril forming hexa-peptides whose crystal structures are available in the Protein Data Bank (PDB) and (v) Experimentally validated aggregation prone regions found in amyloidogenic proteins. Furthermore, CPAD is linked to other related databases and resources, such as Uniprot, Protein Data Bank, PUBMED, GAP, TANGO, WALTZ etc. We have set up a web interface with different search and display options so that users have the ability to get the data in multiple ways. CPAD is freely available at http://www.iitm.ac.in/bioinfo/CPAD/. The potential applications of CPAD have also been discussed.     

HSC-Explorer: A Curated Database for Hematopoietic Stem Cells

HSC-Explorer (http://mips.helmholtz-muenchen.de/HSC/) is a publicly available, integrative database containing detailed information about the early steps of hematopoiesis. The resource aims at providing fast and easy access to relevant information, in particular to the complex network of interacting cell types and molecules, from the wealth of publications in the field through visualization interfaces. It provides structured information on more than 7000 experimentally validated interactions between molecules, bioprocesses and environmental factors. Information is manually derived by critical reading of the scientific literature from expert annotators. Hematopoiesis-relevant interactions are accompanied with context information such as model organisms and experimental methods for enabling assessment of reliability and relevance of experimental results. Usage of established vocabularies facilitates downstream bioinformatics applications and to convert the results into complex networks. Several predefined datasets (Selected topics) offer insights into stem cell behavior, the stem cell niche and signaling processes supporting hematopoietic stem cell maintenance. HSC-Explorer provides a versatile web-based resource for scientists entering the field of hematopoiesis enabling users to inspect the associated biological processes through interactive graphical presentation.     

PHYMYCO-DB: A Curated Database for Analyses of Fungal Diversity and Evolution

Background

In environmental sequencing studies, fungi can be identified based on nucleic acid sequences, using either highly variable sequences as species barcodes or conserved sequences containing a high-quality phylogenetic signal. For the latter, identification relies on phylogenetic analyses and the adoption of the phylogenetic species concept.Such analysis requires that the reference sequences are well identified and deposited in public-access databases. However, many entries in the public sequence databases are problematic in terms of quality and reliability and these data require screening to ensure correct phylogenetic interpretation.

Methods and Principal Findings

To facilitate phylogenetic inferences and phylogenetic assignment, we introduce a fungal sequence database. The database PHYMYCO-DB comprises fungal sequences from GenBank that have been filtered to satisfy stringent sequence quality criteria. For the first release, two widely used molecular taxonomic markers were chosen: the nuclear SSU rRNA and EF1-α gene sequences. Following the automatic extraction and filtration, a manual curation is performed to remove problematic sequences while preserving relevant sequences useful for phylogenetic studies. As a result of curation, ∼20% of the automatically filtered sequences have been removed from the database. To demonstrate how PHYMYCO-DB can be employed, we test a set of environmental Chytridiomycota sequences obtained from deep sea samples.

Conclusion

PHYMYCO-DB offers the tools necessary to: (i) extract high quality fungal sequences for each of the 5 fungal phyla, at all taxonomic levels, (ii) extract already performed alignments, to act as ‘reference alignments’, (iii) launch alignments of personal sequences along with stored data. A total of 9120 SSU rRNA and 672 EF1-α high-quality fungal sequences are now available.The PHYMYCO-DB is accessible through the URL http://phymycodb.genouest.org/.     

FMiR: A Curated Resource of Mitochondrial DNA Information for Fish

Mitochondrial genome sequences have been widely used for evolutionary and phylogenetic studies. Among vertebrates, fish are an important, diverse group, and their mitogenome sequences are growing rapidly in public repositories. To facilitate mitochondrial genome analysis and to explore the valuable genetic information, we developed the Fish Mitogenome Resource (FMiR) database to provide a workbench for mitogenome annotation, species identification and microsatellite marker mining. The microsatellites are also known as simple sequence repeats (SSRs) and used as molecular markers in studies on population genetics, gene duplication and marker assisted selection. Here, easy-to-use tools have been implemented for mining SSRs and for designing primers to identify species/habitat specific markers. In addition, FMiR can analyze complete or partial mitochondrial genome sequence to identify species and to deduce relational distances among sequences across species. The database presently contains curated mitochondrial genomes from 1302 fish species belonging to 297 families and 47 orders reported from saltwater and freshwater ecosystems. In addition, the database covers information on fish species such as conservation status, ecosystem, family, distribution and occurrence downloaded from the FishBase and IUCN Red List databases. Those fish information have been used to browse mitogenome information for the species belonging to a particular category. The database is scalable in terms of content and inclusion of other analytical modules. The FMiR is running under Linux operating platform on high performance server accessible at URL http://mail.nbfgr.res.in/fmir.     

NEMiD: A Web-Based Curated Microbial Diversity Database with Geo-Based Plotting

The majority of the Earth''s microbes remain unknown, and that their potential utility cannot be exploited until they are discovered and characterized. They provide wide scope for the development of new strains as well as biotechnological uses. The documentation and bioprospection of microorganisms carry enormous significance considering their relevance to human welfare. This calls for an urgent need to develop a database with emphasis on the microbial diversity of the largest untapped reservoirs in the biosphere. The data annotated in the North-East India Microbial database (NEMiD) were obtained by the isolation and characterization of microbes from different parts of the Eastern Himalayan region. The database was constructed as a relational database management system (RDBMS) for data storage in MySQL in the back-end on a Linux server and implemented in an Apache/PHP environment. This database provides a base for understanding the soil microbial diversity pattern in this megabiodiversity hotspot and indicates the distribution patterns of various organisms along with identification. The NEMiD database is freely available at www.mblabnehu.info/nemid/.     

BIND: the Biomolecular Interaction Network Database

The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD.     

PPIM: A Protein-Protein Interaction Database for Maize

Maize (Zea mays) is one of the most important crops worldwide. To understand the biological processes underlying various traits of the crop (e.g. yield and response to stress), a detailed protein-protein interaction (PPI) network is highly demanded. Unfortunately, there are very few such PPIs available in the literature. Therefore, in this work, we present the Protein-Protein Interaction Database for Maize (PPIM), which covers 2,762,560 interactions among 14,000 proteins. The PPIM contains not only accurately predicted PPIs but also those molecular interactions collected from the literature. The database is freely available at http://comp-sysbio.org/ppim with a user-friendly powerful interface. We believe that the PPIM resource can help biologists better understand the maize crop.Maize (Zea mays) is one of the most important crops in the world. Understanding the molecular mechanisms underlying various traits of maize (e.g. response to drought and salt) is important to improve the quality and yield of the crop. Although the maize genome sequence has unraveled the gene components of the crop, most traits involve complex interactions among molecules. Some protein-protein interactions (PPIs) have been experimentally determined in maize. For example, the CENTRORADIALIS8 protein was found to interact with the floral activator DLF1 protein with yeast two-hybrid assays (Danilevskaya et al., 2008), and barren stalk1 was found to interact with barren inflorescence2 with pull-down assays (Skirpan et al., 2008). Unfortunately, unlike other model organisms, there are very few molecular interactions available for maize. Therefore, a comprehensive maize interactome map is highly demanded.Recently, with more information about maize available, it has become practical to investigate the interactions between maize molecules. For example, with accumulating gene expression data, a gene coexpression network has been built to identify gene modules that play important roles in conditions of interest. With this idea, Downs et al. (2013) constructed a gene coexpression network based on gene expression data from 50 maize tissues and identified some gene modules that are important for development. By comparing the maize and rice (Oryza sativa) coexpression networks, Ficklin and Feltus (2011) identified some conserved gene modules between the two species, indicating their essential roles in crops. With protein abundance and phosphorylation data in different maize tissues across seven developmental stages, Walley et al. (2013) built a protein coexpression network to present kinase-substrate relationships. The metabolic network MaizeCyc (Monaco et al., 2013), containing enzyme catalysts, proteins, and other metabolites, has also been constructed. Focusing on maize kernel development, the expression quantitative trait loci have been investigated with RNA sequencing data (Fu et al., 2013), and the gene regulations underlying endosperm cell differentiation have been identified (Zhan et al., 2015).Despite the above efforts to identify possible interactions between molecules, no comprehensive interactome is available for maize. Most current approaches construct gene coexpression networks; however, these only describe the associations between genes and cannot tell which genes have real interactions. Under these circumstances, we present a comprehensive Protein-Protein Interaction Database for Maize (PPIM), which provides both our predicted physical and functional interactions as well as molecular interactions collected from the literature and public databases. To our knowledge, the PPIM is the most comprehensive database for maize to date. The user-friendly powerful interface accompanying the database can help biologists better explore the database.     

NTDB: Thermodynamic Database for Nucleic Acids,Version 2.0

The second release of Thermodynamic Database for Nucleic Acids, NTDB 2.0, includes more than 4600 entries (250% increase over release 1.0). It contains sequence types and details of several thermodynamic parameters (enthalpy, DeltaH; entropy, DeltaS; Gibbs free energy, DeltaG; melting temperature, T(m)), experimental models and methods for extracting thermodynamic parameters, buffer conditions as well as all relevant literature information. In addition, the database statistics and references related to NTDB are included. Information on normal and modified nucleobases and nucleosides are collected in a new section 'Nucleoside' whereby data collected thus far will be release in NTDB 2.0. The NTDB is freely available at http://ntdb.chem.cuhk.edu.hk.     

A Protein Interaction Network for the Large Conductance Ca2+-activated K+ Channel in the Mouse Cochlea

The large conductance Ca²⁺-activated K⁺ or BK channel has a role in sensory/neuronal excitation, intracellular signaling, and metabolism. In the non-mammalian cochlea, the onset of BK during development correlates with increased hearing sensitivity and underlies frequency tuning in non-mammals, whereas its role is less clear in mammalian hearing. To gain insights into BK function in mammals, coimmunoprecipitation and two-dimensional PAGE, combined with mass spectrometry, were used to reveal 174 putative BKAPs from cytoplasmic and membrane/cytoskeletal fractions of mouse cochlea. Eleven BKAPs were verified using reciprocal coimmunoprecipitation, including annexin, apolipoprotein, calmodulin, hippocalcin, and myelin P0, among others. These proteins were immunocolocalized with BK in sensory and neuronal cells. A bioinformatics approach was used to mine databases to reveal binary partners and the resultant protein network, as well as to determine previous ion channel affiliations, subcellular localization, and cellular processes. The search for binary partners using the IntAct molecular interaction database produced a putative global network of 160 nodes connected with 188 edges that contained 12 major hubs. Additional mining of databases revealed that more than 50% of primary BKAPs had prior affiliations with K⁺ and Ca²⁺ channels. Although a majority of BKAPs are found in either the cytoplasm or membrane and contribute to cellular processes that primarily involve metabolism (30.5%) and trafficking/scaffolding (23.6%), at least 20% are mitochondrial-related. Among the BKAPs are chaperonins such as calreticulin, GRP78, and HSP60 that, when reduced with siRNAs, alter BKα expression in CHO cells. Studies of BKα in mitochondria revealed compartmentalization in sensory cells, whereas heterologous expression of a BK-DEC splice variant cloned from cochlea revealed a BK mitochondrial candidate. The studies described herein provide insights into BK-related functions that include not only cell excitation, but also cell signaling and apoptosis, and involve proteins concerned with Ca²⁺ regulation, structure, and hearing loss.BK¹ channels act as sensors for membrane voltage and intracellular Ca²⁺, thereby linking cell excitability, metabolism, and signaling. BK channels, also known as Slo, are large conductance channels (100–300 pS) (1) composed of four α-subunits that are regulated by four auxiliary β-subunits. The α-subunit of the BK channel has six to seven transmembrane-spanning regions (S0–S6) where the S0 domain places the N terminus extracellularly as a binding site for the beta subunit. The transmembrane domains S1-S4 are responsible for sensing voltage changes, whereas the pore forming region, between S5–S6, conducts ions. BK has a large C-terminal region that contains target sequences for channel modulation such as a Ca²⁺ bowl, two domains that regulate the conductance of K⁺ (RCK1 and RCK2), a tetramerization domain, leucine zipper motifs, a heme-binding motif, two phosphorylation sites, and a caveolin-targeting domain (2, see Ref. 3 for review). The leucine zipper motifs, contained in the C terminus, are essential for protein-protein interactions and modulating channel activity and expression.Four genes, designated as Kcnma, encode the α-subunits of the different Slo channels. These include Kcnma1 (Slo1), two similar paralogs, Kcnma2 (Slo2.1 and Slo2.2), and Kcnma3 (Slo3). The α-subunits form homotetramers that are K⁺-selective, but differ in their gating properties (2). All α-subunits have S1–S6 transmembrane domains, whereas only Slo1 and Slo3 have an additional S0 domain and a Ca²⁺ bowl that is composed of a majority of either positively (Slo1) or negatively charged (Slo3) amino acids.BK channels are important to sensory or hair cell “tuning” in lower vertebrates. This function is reflected by the variations in channel kinetics found along the tonotopic gradient of the turtle cochlea, thereby contributing to differences in electrical resonance or tuning. In these vertebrates, BK is colocalized with L-type Ca²⁺ channels in presynaptic active zones (3) and is thus coupled to neurotransmitter release as described for the nerve muscle synapse (4, 5). Although the onset of this channel during cochlear development in both mammals and non-mammals coincides with an increase in hearing sensitivity (6, 7), its function is less clear in the former where hair cells are not frequency-tuned and studies report either the presence or the absence of hearing with the loss of BK (8, 9). The BK channel has been localized to both the outer hair cells (OHC) (10) and inner hair cells (IHC) (7, 11–13) in mammals. However, unlike non-mammals, the BK channel appears in both synaptic and extrasynaptic sites near the apical end or neck of the IHC (9).More than 100,000 expressed sequence tags have been identified in the vertebrate cochlea (14), thus, the use of yeast two-hybrid screening to determine BKAPs is a difficult task. However, recent developments in proteomics in combination with immunoprecipitation and LC-MS/MS analysis, allow for the efficient identification of interacting partners. Thus far, more than forty different expressed sequence tags have been identified in other tissues; most of these proteins interact with the C terminus of the channel to modulate expression as well as function (15).In the present study, we determined putative BKAPs in mouse cochlea by coIP and mass spectrometry followed by further validation using reciprocal coIP, colocalization, and siRNA. We identified 174 BKAPs in 30-day-old mouse cochlea, which were further analyzed using bioinformatics. A BK interactome revealed several insights into BK function and common cellular pathways and processes. This approach identified novel BKα complexes with important roles in development, calcium binding, and chaperone activity as well as hearing loss.     

Predicting the Binding Patterns of Hub Proteins: A Study Using Yeast Protein Interaction Networks

Background

Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH) with one or two binding sites, or multiple-interface hubs (MIH) with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations) or party hubs (i.e., simultaneously interact with multiple partners).

Methodology

Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB) protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.

Conclusions

Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.

Availability

We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.     

PPICurator: A Tool for Extracting Comprehensive Protein–Protein Interaction Information

Protein–protein interaction extraction through biological literature curation is widely employed for proteome analysis. There is a strong need for a tool that can assist researchers in extracting comprehensive PPI information through literature curation, which is critical in research on protein, for example, construction of protein interaction network, identification of protein signaling pathway, and discovery of meaningful protein interaction. However, most of current tools can only extract PPI relations. None of them are capable of extracting other important PPI information, such as interaction directions, effects, and functional annotations. To address these issues, this paper proposes PPICurator, a novel tool for extracting comprehensive PPI information with a variety of logic and syntax features based on a new support vector machine classifier. PPICurator provides a friendly web‐based user interface. It is a platform that automates the extraction of comprehensive PPI information through literature, including PPI relations, as well as their confidential scores, interaction directions, effects, and functional annotations. Thus, PPICurator is more comprehensive than state‐of‐the‐art tools. Moreover, it outperforms state‐of‐the‐art tools in the accuracy of PPI relation extraction measured by F‐score and recall on the widely used open datasets. PPICurator is available at https://ppicurator.hupo.org.cn .     

PharmDB-K: Integrated Bio-Pharmacological Network Database for Traditional Korean Medicine

Despite the growing attention given to Traditional Medicine (TM) worldwide, there is no well-known, publicly available, integrated bio-pharmacological Traditional Korean Medicine (TKM) database for researchers in drug discovery. In this study, we have constructed PharmDB-K, which offers comprehensive information relating to TKM-associated drugs (compound), disease indication, and protein relationships. To explore the underlying molecular interaction of TKM, we integrated fourteen different databases, six Pharmacopoeias, and literature, and established a massive bio-pharmacological network for TKM and experimentally validated some cases predicted from the PharmDB-K analyses. Currently, PharmDB-K contains information about 262 TKMs, 7,815 drugs, 3,721 diseases, 32,373 proteins, and 1,887 side effects. One of the unique sets of information in PharmDB-K includes 400 indicator compounds used for standardization of herbal medicine. Furthermore, we are operating PharmDB-K via phExplorer (a network visualization software) and BioMart (a data federation framework) for convenient search and analysis of the TKM network. Database URL: http://pharmdb-k.org, http://biomart.i-pharm.org.     

Altered Network Topologies and Hub Organization in Adults with Autism: A Resting-State fMRI Study

Recent functional magnetic resonance imaging (fMRI) studies on autism spectrum condition (ASC) have identified dysfunctions in specific brain networks involved in social and non-social cognition that persist into adulthood. Although increasing numbers of fMRI studies have revealed atypical functional connectivity in the adult ASC brain, such functional alterations at the network level have not yet been fully characterized within the recently developed graph-theoretical framework. Here, we applied a graph-theoretical analysis to resting-state fMRI data acquired from 46 adults with ASC and 46 age- and gender-matched controls, to investigate the topological properties and organization of autistic brain network. Analyses of global metrics revealed that, relative to the controls, participants with ASC exhibited significant decreases in clustering coefficient and characteristic path length, indicating a shift towards randomized organization. Furthermore, analyses of local metrics revealed a significantly altered organization of the hub nodes in ASC, as shown by analyses of hub disruption indices using multiple local metrics and by a loss of “hubness” in several nodes (e.g., the bilateral superior temporal sulcus, right dorsolateral prefrontal cortex, and precuneus) that are critical for social and non-social cognitive functions. In particular, local metrics of the anterior cingulate cortex consistently showed significant negative correlations with the Autism-Spectrum Quotient score. Our results demonstrate altered patterns of global and local topological properties that may underlie impaired social and non-social cognition in ASC.     

CyanoPhyChe: A Database for Physico-Chemical Properties,Structure and Biochemical Pathway Information of Cyanobacterial Proteins

CyanoPhyChe is a user friendly database that one can browse through for physico-chemical properties, structure and biochemical pathway information of cyanobacterial proteins. We downloaded all the protein sequences from the cyanobacterial genome database for calculating the physico-chemical properties, such as molecular weight, net charge of protein, isoelectric point, molar extinction coefficient, canonical variable for solubility, grand average hydropathy, aliphatic index, and number of charged residues. Based on the physico-chemical properties, we provide the polarity, structural stability and probability of a protein entering in to an inclusion body (PEPIB). We used the data generated on physico-chemical properties, structure and biochemical pathway information of all cyanobacterial proteins to construct CyanoPhyChe. The data can be used for optimizing methods of expression and characterization of cyanobacterial proteins. Moreover, the ‘Search’ and data export options provided will be useful for proteome analysis. Secondary structure was predicted for all the cyanobacterial proteins using PSIPRED tool and the data generated is made accessible to researchers working on cyanobacteria. In addition, external links are provided to biological databases such as PDB and KEGG for molecular structure and biochemical pathway information, respectively. External links are also provided to different cyanobacterial databases. CyanoPhyChe can be accessed from the following URL: http://bif.uohyd.ac.in/cpc.     

MPID: MHC-Peptide Interaction Database for sequence-structure-function information on peptides binding to MHC molecules

SUMMARY: Binding of short antigenic peptides to Major histocompatibility complex (MHC) proteins is the first step in T-cell mediated immune response. To understand the structural principles governing MHC-specific peptide recognition and binding, we have developed the MHC-Peptide Interaction Database (MPID), containing sequence-structure-function information. MPID (version 1.2) contains curated x-ray crystallographic data on 86 MHC peptide complexes, with precomputed interaction parameters (solvent accessibility, hydrogen bonds, gap volume and gap index). A user-friendly web interface and query tools will facilitate the development of predictive algorithms for MHC-peptide binding from a structural viewpoint. AVAILABILITY: Freely accessible from http://surya.bic.nus.edu.sg/mpid.