Metalloproteomics: high-throughput structural and functional annotation of proteins in structural genomics

A high-throughput method for measuring transition metal content based on quantitation of X-ray fluorescence signals was used to analyze 654 proteins selected as targets by the New York Structural GenomiX Research Consortium. Over 10% showed the presence of transition metal atoms in stoichiometric amounts; these totals as well as the abundance distribution are similar to those of the Protein Data Bank. Bioinformatics analysis of the identified metalloproteins in most cases supported the metalloprotein annotation; identification of the conserved metal binding motif was also shown to be useful in verifying structural models of the proteins. Metalloproteomics provides a rapid structural and functional annotation for these sequences and is shown to be approximately 95% accurate in predicting the presence or absence of stoichiometric metal content. The project's goal is to assay at least 1 member from each Pfam family; approximately 500 Pfam families have been characterized with respect to transition metal content so far.     

Functional annotation of hypothetical proteins - A review

The complete human genome sequences in the public database provide ways to understand the blue print of life. As of June 29, 2006, 27 archaeal, 326 bacterial and 21 eukaryotes is complete genomes are available and the sequencing for 316 bacterial, 24 archaeal, 126 eukaryotic genomes are in progress. The traditional biochemical/molecular experiments can assign accurate functions for genes in these genomes. However, the process is time-consuming and costly. Despite several efforts, only 50-60 % of genes have been annotated in most completely sequenced genomes. Automated genome sequence analysis and annotation may provide ways to understand genomes. Thus, determination of protein function is one of the challenging problems of the post-genome era. This demands bioinformatics to predict functions of un-annotated protein sequences by developing efficient tools. Here, we discuss some of the recent and popular approaches developed in Bioinformatics to predict functions for hypothetical proteins.     

Functional identification of novel activities: activity-based selection of proteins from complete proteomes

The identification of proteins with desired activities, especially from complex samples such as plasma and whole blood, is a continual challenge. We have developed a technology platform called Functional Identification of Novel Activities (FIoNA) to discover desired protein activities from complex biological samples. FIoNA uses immobilized libraries of combinatorial peptide ligands to purify and concentrate essentially all of the components of a complex mixture on ligands synthesized on individual beads. No depletion or prefractionation of the starting material is performed before it is incubated with the library, and no a priori knowledge of the active protein or of the ligand to which it binds is required. Instead, the protein-loaded beads are individually evaluated en masse in disease- relevant assays to identify proteins possessing a desired function. Beads associated with the activity are selected, and the ligand is sequenced and resynthesized in bulk on the original backbone for purification and characterization of the active component. Here we illustrate the use of FIoNA in a cell proliferation assay to detect a growth factor present in conditioned cell medium at nanogram/milliliter concentrations. We also have selected beads associated with hydrolysis of nerve agent analogs in assays performed in 100,000-well microtiter plates.     

FACT: Functional annotation transfer between proteins with similar feature architectures

Background  

The increasing number of sequenced genomes provides the basis for exploring the genetic and functional diversity within the tree of life. Only a tiny fraction of the encoded proteins undergoes a thorough experimental characterization. For the remainder, bioinformatics annotation tools are the only means to infer their function. Exploiting significant sequence similarities to already characterized proteins, commonly taken as evidence for homology, is the prevalent method to deduce functional equivalence. Such methods fail when homologs are too diverged, or when they have assumed a different function. Finally, due to convergent evolution, functional equivalence is not necessarily linked to common ancestry. Therefore complementary approaches are required to identify functional equivalents.     

Heterologous expression of plasmodial proteins for structural studies and functional annotation

Malaria remains the world's most devastating tropical infectious disease with as many as 40% of the world population living in risk areas. The widespread resistance of Plasmodium parasites to the cost-effective chloroquine and antifolates has forced the introduction of more costly drug combinations, such as Coartem^®. In the absence of a vaccine in the foreseeable future, one strategy to address the growing malaria problem is to identify and characterize new and durable antimalarial drug targets, the majority of which are parasite proteins. Biochemical and structure-activity analysis of these proteins is ultimately essential in the characterization of such targets but requires large amounts of functional protein. Even though heterologous protein production has now become a relatively routine endeavour for most proteins of diverse origins, the functional expression of soluble plasmodial proteins is highly problematic and slows the progress of antimalarial drug target discovery. Here the status quo of heterologous production of plasmodial proteins is presented, constraints are highlighted and alternative strategies and hosts for functional expression and annotation of plasmodial proteins are reviewed.     

Functional, structural, and immunological compartmentalisation of malaria invasive proteins

Conserved Plasmodium falciparum merozoite high activity binding peptides (HABPs) involved in red blood cell (RBC) invasion which are present in merozoite surface proteins (MSPs) involved in attachment, rolling over RBC, those derived from soluble proteins loosely bound to the membrane, and those present in microneme and rhoptry organelles have an alpha-helical structure and bind with high affinity to HLA-DR52 molecules. On the contrary, conserved HABPs belonging to molecules anchored to the membrane by a GPI tail, or a transmembranal region, or those molecules presenting PEXEL motifs have a strand, turn or unordered configuration and bind with high affinity to HLA-DR53 molecules. Such functional, cellular, structural, and immunological compartmentalisation has tremendous implications in subunit-based, multi-epitope, synthetic, anti-malarial vaccine development.     

Representative proteomes: a stable, scalable and unbiased proteome set for sequence analysis and functional annotation

The accelerating growth in the number of protein sequences taxes both the computational and manual resources needed to analyze them. One approach to dealing with this problem is to minimize the number of proteins subjected to such analysis in a way that minimizes loss of information. To this end we have developed a set of Representative Proteomes (RPs), each selected from a Representative Proteome Group (RPG) containing similar proteomes calculated based on co-membership in UniRef50 clusters. A Representative Proteome is the proteome that can best represent all the proteomes in its group in terms of the majority of the sequence space and information. RPs at 75%, 55%, 35% and 15% co-membership threshold (CMT) are provided to allow users to decrease or increase the granularity of the sequence space based on their requirements. We find that a CMT of 55% (RP55) most closely follows standard taxonomic classifications. Further analysis of this set reveals that sequence space is reduced by more than 80% relative to UniProtKB, while retaining both sequence diversity (over 95% of InterPro domains) and annotation information (93% of experimentally characterized proteins). All sets can be browsed and are available for sequence similarity searches and download at http://www.proteininformationresource.org/rps, while the set of 637 RPs determined using a 55% CMT are also available for text searches. Potential applications include sequence similarity searches, protein classification and targeted protein annotation and characterization.     

Functional annotation of proteomic sequences based on consensus of sequence and structural analysis

To maximise the assignment of function of the proteins encoded by a genome and to aid the search for novel drug targets, there is an emerging need for sensitive methods of predicting protein function on a genome-wide basis. GeneAtlas is an automated, high-throughput pipeline for the prediction of protein structure and function using sequence similarity detection, homology modelling and fold recognition methods. GeneAtlas is described in detail here. To test GeneAtlas, a 'virtual' genome was used, a subset of PDB structures from the SCOP database, in which the functional relationships are known. GeneAtlas detects additional relationships by building 3D models in comparison with the sequence searching method PSI-BLAST. Functionally related proteins with sequence identity below the twilight zone can be recognised correctly.     

Functional differentiation of proteins: implications for structural genomics

Structural genomics is a broad initiative of various centers aiming to provide complete coverage of protein structure space. Because it is not feasible to experimentally determine the structures of all proteins, it is generally agreed that the only viable strategy to achieve such coverage is to carefully select specific proteins (targets), determine their structure experimentally, and then use comparative modeling techniques to model the rest. Here we suggest that structural genomics centers refine the structure-driven approach in target selection by adopting function-based criteria. We suggest targeting functionally divergent superfamilies within a given structural fold so that each function receives a structural characterization. We have developed a method to do so, and an itemized survey of several functionally rich folds shows that they are only partially functionally characterized. We call upon structural genomics centers to consider this approach and upon computational biologists to further develop function-based targeting methods.     

Phosphoprotein analysis: from proteins to proteomes

Characterization of protein modification by phosphorylation is one of the major tasks that have to be accomplished in the post-genomic era. Phosphorylation is a key reversible modification occurring mainly on serine, threonine and tyrosine residues that can regulate enzymatic activity, subcellular localization, complex formation and degradation of proteins. The understanding of the regulatory role played by phosphorylation begins with the discovery and identification of phosphoproteins and then by determining how, where and when these phosphorylation events take place. Because phosphorylation is a dynamic process difficult to quantify, we must at first acquire an inventory of phosphoproteins and characterize their phosphorylation sites. Several experimental strategies can be used to explore the phosphorylation status of proteins from individual moieties to phosphoproteomes. In this review, we will examine and catalogue how proteomics techniques can be used to answer specific questions related to protein phosphorylation. Hence, we will discuss the different methods for enrichment of phospho-proteins and -peptides, and then the various technologies for their identification, quantitation and validation.     

Functional annotation of putative hypothetical proteins from Candida dubliniensis

An extensive analysis of C. dubliniensis proteomics data showed that ~ 22% protein are conserved hypothetical proteins (HPs) whose function is still not determined precisely. Analysis of gene sequence of HPs provides a platform to establish sequence–function relationships to a more profound understanding of the molecular machinery of organisms at systems level. Here we have combined the latest versions of bioinformatics tools including, protein family, motifs, intrinsic features from the amino acid sequence, sequence–function relationship, pathway analysis, etc. to assign a precise function to HPs for which no any experimental information is available. Our results show that 27 HPs have well defined functions and we categorized them as enzyme, nucleic acid binding, transport protein, etc. Five HPs showed adhesin character that is likely to be essential for the survival of yeast and pathogenesis. We also addressed issues related to the sub-cellular localization and signal peptide identification which provides an idea about its colocalization and function. The outcome of the present study may facilitate better understanding of mechanism of virulence, drug resistance, pathogenesis, adaptability to host, tolerance for host immune response, and drug discovery for treatment of C. dubliniensis infections.     

Functional annotation by identification of local surface similarities: a novel tool for structural genomics

Background  

Protein function is often dependent on subsets of solvent-exposed residues that may exist in a similar three-dimensional configuration in non homologous proteins thus having different order and/or spacing in the sequence. Hence, functional annotation by means of sequence or fold similarity is not adequate for such cases.     

Functional annotation of hierarchical modularity

In biological networks of molecular interactions in a cell, network motifs that are biologically relevant are also functionally coherent, or form functional modules. These functionally coherent modules combine in a hierarchical manner into larger, less cohesive subsystems, thus revealing one of the essential design principles of system-level cellular organization and function-hierarchical modularity. Arguably, hierarchical modularity has not been explicitly taken into consideration by most, if not all, functional annotation systems. As a result, the existing methods would often fail to assign a statistically significant functional coherence score to biologically relevant molecular machines. We developed a methodology for hierarchical functional annotation. Given the hierarchical taxonomy of functional concepts (e.g., Gene Ontology) and the association of individual genes or proteins with these concepts (e.g., GO terms), our method will assign a Hierarchical Modularity Score (HMS) to each node in the hierarchy of functional modules; the HMS score and its p-value measure functional coherence of each module in the hierarchy. While existing methods annotate each module with a set of "enriched" functional terms in a bag of genes, our complementary method provides the hierarchical functional annotation of the modules and their hierarchically organized components. A hierarchical organization of functional modules often comes as a bi-product of cluster analysis of gene expression data or protein interaction data. Otherwise, our method will automatically build such a hierarchy by directly incorporating the functional taxonomy information into the hierarchy search process and by allowing multi-functional genes to be part of more than one component in the hierarchy. In addition, its underlying HMS scoring metric ensures that functional specificity of the terms across different levels of the hierarchical taxonomy is properly treated. We have evaluated our method using Saccharomyces cerevisiae data from KEGG and MIPS databases and several other computationally derived and curated datasets. The code and additional supplemental files can be obtained from http://code.google.com/p/functional-annotation-of-hierarchical-modularity/ (Accessed 2012 March 13).     

Prion-like proteins and their computational identification in proteomes

Introduction: The aberrant or misfolded forms of the prion protein have been described as the causative agents of rare transmissible spongiform encephalopathies. In addition, proteins associated with frequently occurring neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, are shown to share prion-like properties and to spread the disease in the brain.

Areas covered: Interest in the prion phenomenon has crystallized in a series of computational methods aimed at uncovering prion-like proteins at the proteome level. These programs rely on the identification of sequence signatures similar to those of yeast prions, whose structural conversion is driven by specific domains enriched in glutamine/asparagine residues. A myriad of prion-like candidates, similar to those in yeast, are predicted to exist in organisms across all kingdoms of life. We review here the role of prions, prionoids and prion-like proteins in health and disease, with a special focus on the algorithms and databases developed for their prediction and classification.

Expert commentary: Computational approaches provide novel insights into prion-like protein functions, their regulation and their role in disease.     

Sequence-based feature prediction and annotation of proteins

A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome.