Distribution and properties of the genes encoding the biosynthesis of the bacterial cofactor, pyrroloquinoline quinone.

Pyrroloquinoline quinone (PQQ) is a small, redox active molecule that serves as a cofactor for several bacterial dehydrogenases, introducing pathways for carbon utilization that confer a growth advantage. Early studies had implicated a ribosomally translated peptide as the substrate for PQQ production. This study presents a sequence- and structure-based analysis of the components of the pqq operon. We find the necessary components for PQQ production are present in 126 prokaryotes, most of which are Gram-negative and a number of which are pathogens. A total of five gene products, PqqA, PqqB, PqqC, PqqD, and PqqE, are identified as being obligatory for PQQ production. Three of the gene products in the pqq operon, PqqB, PqqC, and PqqE, are members of large protein superfamilies. By combining evolutionary conservation patterns with information from three-dimensional structures, we are able to differentiate the gene products involved in PQQ biosynthesis from those with divergent functions. The observed persistence of a conserved gene order within analyzed operons strongly suggests a role for protein-protein interactions in the course of cofactor biosynthesis. These studies propose previously unidentified roles for several of the gene products, as well as identifying possible new targets for antibiotic design and application.

Genomic analysis of the basal lineage fungus Rhizopus oryzae reveals a whole-genome duplication.

Rhizopus oryzae is the primary cause of mucormycosis, an emerging, life-threatening infection characterized by rapid angioinvasive growth with an overall mortality rate that exceeds 50%. As a representative of the paraphyletic basal group of the fungal kingdom called "zygomycetes," R. oryzae is also used as a model to study fungal evolution. Here we report the genome sequence of R. oryzae strain 99-880, isolated from a fatal case of mucormycosis. The highly repetitive 45.3 Mb genome assembly contains abundant transposable elements (TEs), comprising approximately 20% of the genome. We predicted 13,895 protein-coding genes not overlapping TEs, many of which are paralogous gene pairs. The order and genomic arrangement of the duplicated gene pairs and their common phylogenetic origin provide evidence for an ancestral whole-genome duplication (WGD) event. The WGD resulted in the duplication of nearly all subunits of the protein complexes associated with respiratory electron transport chains, the V-ATPase, and the ubiquitin-proteasome systems. The WGD, together with recent gene duplications, resulted in the expansion of multiple gene families related to cell growth and signal transduction, as well as secreted aspartic protease and subtilase protein families, which are known fungal virulence factors. The duplication of the ergosterol biosynthetic pathway, especially the major azole target, lanosterol 14alpha-demethylase (ERG11), could contribute to the variable responses of R. oryzae to different azole drugs, including voriconazole and posaconazole. Expanded families of cell-wall synthesis enzymes, essential for fungal cell integrity but absent in mammalian hosts, reveal potential targets for novel and R. oryzae-specific diagnostic and therapeutic treatments.

Diversity and dispersal of a ubiquitous protein family: acyl-CoA dehydrogenases.

Acyl-CoA dehydrogenases (ACADs), which are key enzymes in fatty acid and amino acid catabolism, form a large, pan-taxonomic protein family with at least 13 distinct subfamilies. Yet most reported ACAD members have no subfamily assigned, and little is known about the taxonomic distribution and evolution of the subfamilies. In completely sequenced genomes from approximately 210 species (eukaryotes, bacteria and archaea), we detect ACAD subfamilies by rigorous ortholog identification combining sequence similarity search with phylogeny. We then construct taxonomic subfamily-distribution profiles and build phylogenetic trees with orthologous proteins. Subfamily profiles provide unparalleled insight into the organisms' energy sources based on genome sequence alone and further predict enzyme substrate specificity, thus generating explicit working hypotheses for targeted biochemical experimentation. Eukaryotic ACAD subfamilies are traditionally considered as mitochondrial proteins, but we found evidence that in fungi one subfamily is located in peroxisomes and participates in a distinct beta-oxidation pathway. Finally, we discern horizontal transfer, duplication, loss and secondary acquisition of ACAD genes during evolution of this family. Through these unorthodox expansion strategies, the ACAD family is proficient in utilizing a large range of fatty acids and amino acids-strategies that could have shaped the evolutionary history of many other ancient protein families.

TESTLoc: protein subcellular localization prediction from EST data.

BACKGROUND: The eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes. RESULTS: We developed a new predictor, TESTLoc, suited for subcellular localization prediction of proteins based on their partial sequence conceptually translated from ESTs (EST-peptides). Support Vector Machine (SVM) is used as computational method and EST-peptides are represented by different features such as amino acid composition and physicochemical properties. When TESTLoc was applied to the most challenging test case (plant data), it yielded high accuracy (~85%). CONCLUSIONS: TESTLoc is a localization prediction tool tailored for EST data. It provides a variety of models for the users to choose from, and is available for download at http://megasun.bch.umontreal.ca/~shenyq/TESTLoc/TESTLoc.html.

Plasticity of a key metabolic pathway in fungi.

Beta oxidation is the principal metabolic pathway for fatty acid degradation. The pathway is virtually universally present throughout eukaryotes yet displays different forms in enzyme architecture, substrate specificity, and subcellular location. In this review, we examine beta oxidation across the fungal kingdom by conducting a large-scale in silico screen and localization prediction for all relevant enzymes in >50 species. The survey reveals that fungi exhibit an astounding diversity of beta oxidation pathways and shows that the combined presence of distinct mitochondrial and peroxisomal pathways is the prevailing and likely ancestral type of beta oxidation in fungi. In addition, the available information indicates that the mitochondrial pathway was lost in the common ancestor of Saccharomycetes. Finally, we infer the existence of a hybrid peroxisomal pathway in several Sordariomycetes, including Neurospora crassa. In these cases, a typically mitochondrion-located enzyme compensates for the lack of a peroxisomal one.

EST databases and web tools for EST projects.

This chapter outlines key considerations for constructing and implementing an EST database. Instead of showing the technological details step by step, emphasis is put on the design of an EST database suited to the specific needs of EST projects and how to choose the most suitable tools. Using TBestDB as an example, we illustrate the essential factors to be considered for database construction and the steps for data population and annotation. This process employs technologies such as PostgreSQL, Perl, and PHP to build the database and interface, and tools such as AutoFACT for data processing and annotation. We discuss these in comparison to other available technologies and tools, and explain the reasons for our choices.

'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools.

BACKGROUND: Knowing the subcellular location of proteins provides clues to their function as well as the interconnectivity of biological processes. Dozens of tools are available for predicting protein location in the eukaryotic cell. Each tool performs well on certain data sets, but their predictions often disagree for a given protein. Since the individual tools each have particular strengths, we set out to integrate them in a way that optimally exploits their potential. The method we present here is applicable to various subcellular locations, but tailored for predicting whether or not a protein is localized in mitochondria. Knowledge of the mitochondrial proteome is relevant to understanding the role of this organelle in global cellular processes. RESULTS: In order to develop a method for enhanced prediction of subcellular localization, we integrated the outputs of available localization prediction tools by several strategies, and tested the performance of each strategy with known mitochondrial proteins. The accuracy obtained (up to 92%) surpasses by far the individual tools. The method of integration proved crucial to the performance. For the prediction of mitochondrion-located proteins, integration via a two-layer decision tree clearly outperforms simpler methods, as it allows emphasis of biologically relevant features such as the mitochondrial targeting peptide and transmembrane domains. CONCLUSION: We developed an approach that enhances the prediction accuracy of mitochondrial proteins by uniting the strength of specialized tools. The combination of machine-learning based integration with biological expert knowledge leads to improved performance. This approach also alleviates the conundrum of how to choose between conflicting predictions. Our approach is easy to implement, and applicable to predicting subcellular locations other than mitochondria, as well as other biological features. For a trial of our approach, we provide a webservice for mitochondrial protein prediction (named YimLOC), which can be accessed through the AnaBench suite at http://anabench.bcm.umontreal.ca/anabench/. The source code is provided in the Additional File 2.