Bioinformatics resources for cancer research with an emphasis on gene function and structure prediction tools

The immensely popular fields of cancer research and bioinformatics overlap in many different areas, e.g. large data repositories that allow for users to analyze data from many experiments (data handling, databases), pattern mining, microarray data analysis, and interpretation of proteomics data. There are many newly available resources in these areas that may be unfamiliar to most cancer researchers wanting to incorporate bioinformatics tools and analyses into their work, and also to bioinformaticians looking for real data to develop and test algorithms. This review reveals the interdependence of cancer research and bioinformatics, and highlight the most appropriate and useful resources available to cancer researchers. These include not only public databases, but general and specific bioinformatics tools which can be useful to the cancer researcher. The primary foci are function and structure prediction tools of protein genes. The result is a useful reference to cancer researchers and bioinformaticians studying cancer alike

Rampant exchange of the structure and function of extramembrane domains between membrane and water soluble proteins.

Of the membrane proteins of known structure, we found that a remarkable 67% of the water soluble domains are structurally similar to water soluble proteins of known structure. Moreover, 41% of known water soluble protein structures share a domain with an already known membrane protein structure. We also found that functional residues are frequently conserved between extramembrane domains of membrane and soluble proteins that share structural similarity. These results suggest membrane and soluble proteins readily exchange domains and their attendant functionalities. The exchanges between membrane and soluble proteins are particularly frequent in eukaryotes, indicating that this is an important mechanism for increasing functional complexity. The high level of structural overlap between the two classes of proteins provides an opportunity to employ the extensive information on soluble proteins to illuminate membrane protein structure and function, for which much less is known. To this end, we employed structure guided sequence alignment to elucidate the functions of membrane proteins in the human genome. Our results bridge the gap of fold space between membrane and water soluble proteins and provide a resource for the prediction of membrane protein function. A database of predicted structural and functional relationships for proteins in the human genome is provided at sbi.postech.ac.kr/emdmp

Exploring the dark matter of a mammalian proteome by protein structure and function modeling

Background: A growing body of evidence shows that gene products encoded by short open reading frames play key roles in numerous cellular processes. Yet, they are generally overlooked in genome assembly, escaping annotation because small protein-coding genes are difficult to predict computationally. Consequently, there are still a considerable number of small proteins whose functions are yet to be characterized.Results: To address this issue, we apply a collection of structural bioinformatics algorithms to infer molecular function of putative small proteins from the mouse proteome. Specifically, we construct 1,743 confident structure models of small proteins, which reveal a significant structural diversity with a noticeably high helical content. A subsequent structure-based function annotation of small protein models exposes 178,745 putative protein-protein interactions with the remaining gene products in the mouse proteome, 1,100 potential binding sites for small organic molecules and 987 metal-binding signatures.Conclusions: These results strongly indicate that many small proteins adopt three-dimensional structures and are fully functional, playing important roles in transcriptional regulation, cell signaling and metabolism. Data collected through this work is freely available to the academic community at http://www.brylinski.org/content/databases to support future studies oriented on elucidating the functions of hypothetical small proteins. © 2013 Brylinski; licensee BioMed Central Ltd

Structure-based functional inference of hypothetical proteins from Mycoplasma hyopneumoniae

Enzootic pneumonia caused by Mycoplasma hyopneumoniae is a major constraint to efficient pork production throughout the world. This pathogen has a small genome with 716 coding sequences, of which 418 are homologous to proteins with known functions. However, almost 42% of the 716 coding sequences are annotated as hypothetical proteins. Alternative methodologies such as threading and comparative modeling can be used to predict structures and functions of such hypothetical proteins. Often, these alternative methods can answer questions about the properties of a model system faster than experiments. In this study, we predicted the structures of seven proteins annotated as hypothetical in M. hyopneumoniae, using the structure-based approaches mentioned above. Three proteins were predicted to be involved in metabolic processes, two proteins in transcription and two proteins where no function could be assigned. However, the modeled structures of the last two proteins suggested experimental designs to identify their functions. Our findings are important in diminishing the gap between the lack of annotation of important metabolic pathways and the great number of hypothetical proteins in the M. hyopneumoniae genome

HCIV-1 and other tailless icosahedral internal membrane-containing viruses of the family Sphaerolipoviridae

Members of the virus family Sphaerolipoviridae include both archaeal viruses and bacteriophages that possess a tailless icosahedral capsid with an internal membrane. The genera Alpha-and Betasphaerolipovirus comprise viruses that infect halophilic euryarchaea, whereas viruses of thermophilic Thermus bacteria belong to the genus Gammasphaerolipovirus. Both sequence-based and structural clustering of the major capsid proteins and ATPases of sphaerolipoviruses yield three distinct clades corresponding to these three genera. Conserved virion architectural principles observed in sphaerolipoviruses suggest that these viruses belong to the PRD1-adenovirus structural lineage. Here we focus on archaeal alphasphaerolipoviruses and their related putative proviruses. The highest sequence similarities among alphasphaerolipoviruses are observed in the core structural elements of their virions: the two major capsid proteins, the major membrane protein, and a putative packaging ATPase. A recently described tailless icosahedral haloarchaeal virus, Haloarcula californiae icosahedral virus 1 (HCIV-1), has a double-stranded DNA genome and an internal membrane lining the capsid. HCIV-1 shares significant similarities with the other tailless icosahedral internal membrane-containing haloarchaeal viruses of the family Sphaerolipoviridae. The proposal to include a new virus species, Haloarcula virus HCIV1, into the genus Alphasphaerolipovirus was submitted to the International Committee on Taxonomy of Viruses (ICTV) in 2016.Peer reviewe

FINDSITE-metal: Integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level

The rapid accumulation of gene sequences, many of which are hypothetical proteins with unknown function, has stimulated the development of accurate computational tools for protein function prediction with evolution/structure-based approaches showing considerable promise. In this article, we present FINDSITE-metal, a new threading-based method designed specifically to detect metal-binding sites in modeled protein structures. Comprehensive benchmarks using different quality protein structures show that weakly homologous protein models provide sufficient structural information for quite accurate annotation by FINDSITE-metal. Combining structure/evolutionary information with machine learning results in highly accurate metal-binding annotations; for protein models constructed by TASSER, whose average Cα RMSD from the native structure is 8.9 Å, 59.5% (71.9%) of the best of top five predicted metal locations are within 4 Å (8 Å) from a bound metal in the crystal structure. For most of the targets, multiple metal-binding sites are detected with the best predicted binding site at rank 1 and within the top two ranks in 65.6% and 83.1% of the cases, respectively. Furthermore, for iron, copper, zinc, calcium, and magnesium ions, the binding metal can be predicted with high, typically 70% to 90%, accuracy. FINDSITE-metal also provides a set of confidence indexes that help assess the reliability of predictions. Finally, we describe the proteome-wide application of FINDSITE-metal that quantifies the metal-binding complement of the human proteome. FINDSITE-metal is freely available to the academic community at http://cssb.biology.gatech.edu/findsite-metal/. © 2010 Wiley-Liss, Inc

GWIDD: Genome-wide protein docking database

Structural information on interacting proteins is important for understanding life processes at the molecular level. Genome-wide docking database is an integrated resource for structural studies of protein–protein interactions on the genome scale, which combines the available experimental data with models obtained by docking techniques. Current database version (August 2009) contains 25 559 experimental and modeled 3D structures for 771 organisms spanned over the entire universe of life from viruses to humans. Data are organized in a relational database with user-friendly search interface allowing exploration of the database content by a number of parameters. Search results can be interactively previewed and downloaded as PDB-formatted files, along with the information relevant to the specified interactions. The resource is freely available at http://gwidd.bioinformatics.ku.edu

Ectodomain Architecture Affects Sequence and Functional Evolution of Vertebrate Toll-like Receptors

Toll-like receptors (TLRs) are crucial components of innate immunity that specifically recognize diverse pathogen-associated molecular patterns from pathogens. The continuous hydrogen-bond network (asparagine ladder) formed among the asparagine residues on the concave surfaces of neighboring leucine-rich repeat modules assists in stabilizing the overall shape of TLR ectodomains responsible for ligand recognition. Analysis of 28 types of vertebrate TLRs showed that their ectodomains possessed three types of architectures: a single-domain architecture with an intact asparagine ladder, a three-domain architecture with the ladder interrupted in the middle, and a trans-three-domain architecture with the ladder broken in both termini. Based on a phylogenetic analysis, the three vertebrate TLR architectures arose during early evolution. The 1428 vertebrate TLRs can be divided into eight families based on sequence and structural differences. TLRs ligand specificities are affected by their ectodomain architectures. Three-domain TLRs bind hydrophobic ligands, whereas single-domain and trans-three-domain TLRs mainly recognize hydrophilic ligands. Analysis of 39 vertebrate genomes suggested that the number of single-domain TLR genes in terrestrial vertebrate genomes decreased by half compared to aquatic vertebrate genomes. Single-domain TLR genes underwent stronger purifying selective pressures than three-domain TLR genes in mammals. Overall, ectodomain architecture influences the sequence and functional evolution of vertebrate TLRs