Systematic discovery of analogous enzymes in thiamin biosynthesis

Evolving Escherichia coli to use a tRNA with a non-canonical fold as an adaptor of the genetic code

All known bacterial tRNAs adopt the canonical cloverleaf 2D and L-shaped 3D structures. We aimed to explore whether alternative tRNA structures could be introduced in bacterial translation. To this end, we crafted a vitamin-based genetic system to evolve Escherichia coli toward activity of structurally non-canonical tRNAs. The system reliably couples (escape frequency <10-12) growth with the activities of a novel orthogonal histidine suppressor tRNA (HisTUAC) and of the cognate ARS (HisS) via suppression of a GTA valine codon in the mRNA of an enzyme in thiamine biosynthesis (ThiN). Suppression results in the introduction of an essential histidine and thereby confers thiamine prototrophy. We then replaced HisTUAC in the system with non-canonical suppressor tRNAs and selected for growth. A strain evolved to utilize mini HisT, a tRNA lacking the D-arm, and we identified the responsible mutation in an RNase gene (pnp) involved in tRNA degradation. This indicated that HisS, the ribosome, and EF-Tu accept mini HisT ab initio, which we confirmed genetically and through in vitro translation experiments. Our results reveal a previously unknown flexibility of the bacterial translation machinery for the accepted fold of the adaptor of the genetic code and demonstrate the power of the vitamin-based suppression system.

Characterization of thiamine pyrophosphokinase of vitamin B1 biosynthetic pathway as a drug target of Leishmania donovani

Vitamin B1 is an essential cofactor for enzymes involved in the metabolism of carbohydrates, particularly Transketolases. These enzymes are amenable to therapeutic interventions because of their specificity. In the final step of the Vitamin B1 biosynthesis pathway, Thiamine Pyrophosphokinase (TPK) converts thiamin into its active form, Thiamin Pyrophosphate (TPP), allowing researchers to investigate the functional importance of this enzyme and the pathway's dispensability in Leishmania donovani, a protozoan parasite that causes visceral leishmaniasis. In this study, various in silico, biochemical, biophysical, and cellular assays-based experiments have been conducted to identify and characterize LdTPK, and to provide a sound platform for the discovery of potential LdTPK inhibitors. LdTPK structural modelling ensured high protein quality. Oxythiamine and pyrithiamine were found to bind well with LdTPK with considerable binding energies, and MD simulation-based experiments indicated the stability of the complexation. Additionally, LdTPK1 was found to activate ROS defense in amastigotes, and its inhibition using oxythiamine and pyrithiamine led to the growth inhibition of L. donovani promastigotes and intracellular amastigotes. These findings highlight LdTPK as a promising target for the development of new anti-leishmanial agents. An in-depth analysis of the enzymes involved in TPP biosynthesis in L. donovani has the potential to yield novel therapeutic strategies for Leishmaniasis.Communicated by Ramaswamy H. Sarma.

Inverse Potts model improves accuracy of phylogenetic profiling

MOTIVATION

Phylogenetic profiling is a powerful computational method for revealing the functions of function-unknown genes. Although conventional similarity metrics in phylogenetic profiling achieved high prediction accuracy, they have two estimation biases: an evolutionary bias and a spurious correlation bias. While previous studies reduced the evolutionary bias by considering a phylogenetic tree, few studies have analyzed the spurious correlation bias.

RESULTS

To reduce the spurious correlation bias, we developed metrics based on the inverse Potts model (IPM) for phylogenetic profiling. We also developed a metric based on both the IPM and a phylogenetic tree. In an empirical dataset analysis, we demonstrated that these IPM-based metrics improved the prediction performance of phylogenetic profiling. In addition, we found that the integration of several metrics, including the IPM-based metrics, had superior performance to a single metric.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at https://github.com/fukunagatsu/Ipm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovery of an ene-reductase for initiating flavone and flavonol catabolism in gut bacteria

Gut microbial transformations of flavonoids, an enormous class of polyphenolic compounds abundant in plant-based diets, are closely associated with human health. However, the enzymes that initiate the gut microbial metabolism of flavones and flavonols, the two most abundant groups of flavonoids, as well as their underlying molecular mechanisms of action remain unclear. Here, we discovered a flavone reductase (FLR) from the gut bacterium, Flavonifractor plautii ATCC 49531 (originally assigned as Clostridium orbiscindens DSM 6740), which specifically catalyses the hydrogenation of the C2–C3 double bond of flavones/flavonols and initiates their metabolism as a key step. Crystal structure analysis revealed the molecular basis for the distinct catalytic property of FLR. Notably, FLR and its widespread homologues represent a class of ene-reductases that has not been previously identified. Genetic and biochemical analyses further indicated the importance of FLR in gut microbial consumption of dietary and medicinal flavonoids, providing broader insight into gut microbial xenobiotic transformations and possible guidance for personalized nutrition and medicine.

Flavonoids are abundant polyphenols in plants but it is not well understood how their metabolism is initiated by microbes in the human gut. Here, the authors identify and characterise an ene-reductase from the gut bacterium, Flavonifractor plautii ATCC 49531 that catalyses the hydrogenation of the C2–C3 double bond of flavones and flavonols and present its crystal structure.

Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random-forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated "gold standard" GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.

A multi-scale coevolutionary approach to predict interactions between protein domains

Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of direct phyletic couplings, based on global statistical models of phylogenetic profiles. They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30-50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families. An in-depth discussion shows these to be biologically sensible and directly experimentally testable. Negative phyletic couplings highlight alternative solutions for the same functionality, including documented cases of convergent evolution. Thereby our work proves the strong potential of global statistical modeling approaches to genome-wide coevolutionary analysis, far beyond the established use for individual protein complexes and domain-domain interactions.

Evolutionary dynamics of the kinetochore network in eukaryotes as revealed by comparative genomics

During eukaryotic cell division, the sister chromatids of duplicated chromosomes are pulled apart by microtubules, which connect via kinetochores. The kinetochore is a multiprotein structure that links centromeres to microtubules, and that emits molecular signals in order to safeguard the equal distribution of duplicated chromosomes over daughter cells. Although microtubule‐mediated chromosome segregation is evolutionary conserved, kinetochore compositions seem to have diverged. To systematically inventory kinetochore diversity and to reconstruct its evolution, we determined orthologs of 70 kinetochore proteins in 90 phylogenetically diverse eukaryotes. The resulting ortholog sets imply that the last eukaryotic common ancestor (LECA) possessed a complex kinetochore and highlight that current‐day kinetochores differ substantially. These kinetochores diverged through gene loss, duplication, and, less frequently, invention and displacement. Various kinetochore components co‐evolved with one another, albeit in different manners. These co‐evolutionary patterns improve our understanding of kinetochore function and evolution, which we illustrated with the RZZ complex, TRIP13, the MCC, and some nuclear pore proteins. The extensive diversity of kinetochore compositions in eukaryotes poses numerous questions regarding evolutionary flexibility of essential cellular functions.

Functional Analogy in Human Metabolism: Enzymes with Different Biological Roles or Functional Redundancy?

Since enzymes catalyze almost all chemical reactions that occur in living organisms, it is crucial that genes encoding such activities are correctly identified and functionally characterized. Several studies suggest that the fraction of enzymatic activities in which multiple events of independent origin have taken place during evolution is substantial. However, this topic is still poorly explored, and a comprehensive investigation of the occurrence, distribution, and implications of these events has not been done so far. Fundamental questions, such as how analogous enzymes originate, why so many events of independent origin have apparently occurred during evolution, and what are the reasons for the coexistence in the same organism of distinct enzymatic forms catalyzing the same reaction, remain unanswered. Also, several isofunctional enzymes are still not recognized as nonhomologous, even with substantial evidence indicating different evolutionary histories. In this work, we begin to investigate the biological significance of the cooccurrence of nonhomologous isofunctional enzymes in human metabolism, characterizing functional analogous enzymes identified in metabolic pathways annotated in the human genome. Our hypothesis is that the coexistence of multiple enzymatic forms might not be interpreted as functional redundancy. Instead, these enzymatic forms may be implicated in distinct (and probably relevant) biological roles.

Unique Phylogenetic Distributions of the Ska and Dam1 Complexes Support Functional Analogy and Suggest Multiple Parallel Displacements of Ska by Dam1

Faithful chromosome segregation relies on kinetochores, the large protein complexes that connect chromatin to spindle microtubules. Although human and yeast kinetochores are largely homologous, they track microtubules with the unrelated protein complexes Ska (Ska-C, human) and Dam1 (Dam1-C, yeast). We here uncovered that Ska-C and Dam1-C are both widespread among eukaryotes, but in an exceptionally inverse manner, supporting their functional analogy. Within the complexes, all Ska-C and various Dam1-C subunits are ancient paralogs, showing that gene duplication shaped these complexes. We examined various evolutionary scenarios to explain the nearly mutually exclusive patterns of Ska-C and Dam1-C in present-day species. We propose that Ska-C was present in the last eukaryotic common ancestor, that subsequently Dam1-C displaced Ska-C in an early fungus and was horizontally transferred to diverse non-fungal lineages, displacing Ska-C in these lineages too.

ThiN as a Versatile Domain of Transcriptional Repressors and Catalytic Enzymes of Thiamine Biosynthesis

Thiamine biosynthesis is commonly regulated by a riboswitch mechanism; however, the enzymatic steps and regulation of this pathway in archaea are poorly understood. Haloferax volcanii, one of the representative archaea, uses a eukaryote-like Thi4 (thiamine thiazole synthase) for the production of the thiazole ring and condenses this ring with a pyrimidine moiety synthesized by an apparent bacterium-like ThiC (2-methyl-4-amino-5-hydroxymethylpyrimidine [HMP] phosphate synthase) branch. Here we found that archaeal Thi4 and ThiC were encoded by leaderless transcripts, ruling out a riboswitch mechanism. Instead, a novel ThiR transcription factor that harbored an N-terminal helix-turn-helix (HTH) DNA binding domain and C-terminal ThiN (TMP synthase) domain was identified. In the presence of thiamine, ThiR was found to repress the expression of thi4 and thiC by a DNA operator sequence that was conserved across archaeal phyla. Despite having a ThiN domain, ThiR was found to be catalytically inactive in compensating for the loss of ThiE (TMP synthase) function. In contrast, bifunctional ThiDN, in which the ThiN domain is fused to an N-terminal ThiD (HMP/HMP phosphate [HMP-P] kinase) domain, was found to be interchangeable for ThiE function and, thus, active in thiamine biosynthesis. A conserved Met residue of an extended α-helix near the active-site His of the ThiN domain was found to be important for ThiDN catalytic activity, whereas the corresponding Met residue was absent and the α-helix was shorter in ThiR homologs. Thus, we provide new insight into residues that distinguish catalytic from noncatalytic ThiN domains and reveal that thiamine biosynthesis in archaea is regulated by a transcriptional repressor, ThiR, and not by a riboswitch.IMPORTANCE Thiamine pyrophosphate (TPP) is a cofactor needed for the enzymatic activity of many cellular processes, including central metabolism. In archaea, thiamine biosynthesis is an apparent chimera of eukaryote- and bacterium-type pathways that is not well defined at the level of enzymatic steps or regulatory mechanisms. Here we find that ThiN is a versatile domain of transcriptional repressors and catalytic enzymes of thiamine biosynthesis in archaea. Our study provides new insight into residues that distinguish catalytic from noncatalytic ThiN domains and reveals that archaeal thiamine biosynthesis is regulated by a ThiN domain transcriptional repressor, ThiR, and not by a riboswitch.

A Novel Transcriptional Regulator Related to Thiamine Phosphate Synthase Controls Thiamine Metabolism Genes in Archaea

Thiamine (vitamin B₁) is a precursor of thiamine pyrophosphate (TPP), an essential coenzyme in the central metabolism of all living organisms. Bacterial thiamine biosynthesis and salvage genes are controlled at the RNA level by TPP-responsive riboswitches. In Archaea, TPP riboswitches are restricted to the Thermoplasmatales order. Mechanisms of transcriptional control of thiamine genes in other archaeal lineages remain unknown. Using the comparative genomics approach, we identified a novel family of transcriptional regulators (named ThiR) controlling thiamine biosynthesis and transport genes in diverse lineages in the Crenarchaeota phylum as well as in the Halobacteria and Thermococci classes of the Euryarchaeota ThiR regulators are composed of an N-terminal DNA-binding domain and a C-terminal ligand-binding domain, which is similar to the archaeal thiamine phosphate synthase ThiN. By using comparative genomics, we predicted ThiR-binding DNA motifs and reconstructed ThiR regulons in 67 genomes representing all above-mentioned lineages. The predicted ThiR-binding motifs are characterized by palindromic symmetry with several distinct lineage-specific consensus sequences. In addition to thiamine biosynthesis genes, the reconstructed ThiR regulons include various transporters for thiamine and its precursors. Bioinformatics predictions were experimentally validated by in vitro DNA-binding assays with the recombinant ThiR protein from the hyperthermophilic archaeon Metallosphaera yellowstonensis MK1. Thiamine phosphate and, to some extent, TPP and hydroxyethylthiazole phosphate were required for the binding of ThiR to its DNA targets, suggesting that ThiR is derepressed by limitation of thiamine phosphates. The thiamine phosphate-binding residues previously identified in ThiN are highly conserved in ThiR regulators, suggesting a conserved mechanism for effector recognition.

IMPORTANCE

Thiamine pyrophosphate is a cofactor for many essential enzymes for glucose and energy metabolism. Thiamine or vitamin B₁ biosynthesis and its transcriptional regulation in Archaea are poorly understood. We applied the comparative genomics approach to identify a novel family of regulators for the transcriptional control of thiamine metabolism genes in Archaea and reconstructed the respective regulons. The predicted ThiR regulons in archaeal genomes control the majority of thiamine biosynthesis genes. The reconstructed regulon content suggests that numerous uptake transporters for thiamine and/or its precursors are encoded in archaeal genomes. The ThiR regulon was experimentally validated by DNA-binding assays with Metallosphaera spp. These discoveries contribute to our understanding of metabolic and regulatory networks involved in vitamin homeostasis in diverse lineages of Archaea.

Characterization of Thiamin Phosphate Kinase in the Hyperthermophilic Archaeon Pyrobaculum calidifontis

Thiamin pyrophosphate is an essential cofactor in all living systems. In its biosynthesis, the thiamin structure is initially formed as thiamin phosphate from a thiazole and a pyrimidine moiety, and then thiamin pyrophosphate is synthesized from thiamin phosphate. Many eubacterial cells directly synthesize thiamin pyrophosphate by the phosphorylation of thiamin phosphate by thiamin phosphate kinase (ThiL), whereas this final step occurs in two stages in eukaryotic cells and some eubacterial cells: hydrolysis of thiamin phosphate to free thiamin and its pyrophosphorylation by thiamin pyrophosphokinase. In addition, some eubacteria have thiamin kinase, a salvage enzyme that converts the incorporated thiamin from the environment to thiamin phosphate. This final step in thiamin biosynthesis has never been experimentally investigated in archaea, although the putative thiL genes are found in their genome database. In this study, we observed thiamin phosphate kinase activity in the soluble fraction of the hyperthermophilic archaeon Pyrobaculum calidifontis. On the other hand, neither thiamin pyrophosphokinase nor thiamin kinase activity was detected, suggesting that in this archaeon the phosphorylation of thiamin phosphate is only way to synthesize thiamin pyrophosphate and it cannot use exogenous thiamin for the salvage synthesis of thiamin pyrophosphate. We also investigated the kinetic properties of thiamin phosphate kinase activity using the recombinant ThiL protein from P. calidifontis. Furthermore, the results obtained by site-directed mutagenesis suggest that the Ser196 of ThiL protein plays a pivotal role in the catalytic process.

The identification of an integral membrane, cytochrome c urate oxidase completes the catalytic repertoire of a therapeutic enzyme

In living organisms, the conversion of urate into allantoin requires three consecutive enzymes. The pathway was lost in hominid, predisposing humans to hyperuricemia and gout. Among other species, the genomic distribution of the two last enzymes of the pathway is wider than that of urate oxidase (Uox), suggesting the presence of unknown genes encoding Uox. Here we combine gene network analysis with association rule learning to identify the missing urate oxidase. In contrast with the known soluble Uox, the identified gene (puuD) encodes a membrane protein with a C-terminal cytochrome c. The 8-helix transmembrane domain corresponds to DUF989, a family without similarity to known proteins. Gene deletion in a PuuD-encoding organism (Agrobacterium fabrum) abolished urate degradation capacity; the phenotype was fully restored by complementation with a cytosolic Uox from zebrafish. Consistent with H₂O₂ production by zfUox, urate oxidation in the complemented strain caused a four-fold increase of catalase. No increase was observed in the wild-type, suggesting that urate oxidation by PuuD proceeds through cytochrome c-mediated electron transfer. These findings identify a missing link in purine catabolism, assign a biochemical activity to a domain of unknown function (DUF989), and complete the catalytic repertoire of an enzyme useful for human therapy.

Extensive Identification of Bacterial Riboflavin Transporters and Their Distribution across Bacterial Species

Riboflavin, the precursor for the cofactors flavin mononucleotide (FMN) and flavin adenine dinucleotide, is an essential metabolite in all organisms. While the functions for de novo riboflavin biosynthesis and riboflavin import may coexist in bacteria, the extent of this co-occurrence is undetermined. The RibM, RibN, RfuABCD and the energy-coupling factor-RibU bacterial riboflavin transporters have been experimentally characterized. In addition, ImpX, RfnT and RibXY are proposed as riboflavin transporters based on positional clustering with riboflavin biosynthetic pathway (RBP) genes or conservation of the FMN riboswitch regulatory element. Here, we searched for the FMN riboswitch in bacterial genomes to identify genes encoding riboflavin transporters and assessed their distribution among bacteria. Two new putative riboflavin transporters were identified: RibZ in Clostridium and RibV in Mesoplasma florum. Trans-complementation of an Escherichia coli riboflavin auxotroph strain confirmed the riboflavin transport activity of RibZ from Clostridium difficile, RibXY from Chloroflexus aurantiacus, ImpX from Fusobacterium nucleatum and RfnT from Ochrobactrum anthropi. The analysis of the genomic distribution of all known bacterial riboflavin transporters revealed that most occur in species possessing the RBP and that some bacteria may even encode functional riboflavin transporters from two different families. Our results indicate that some species possess ancestral riboflavin transporters, while others possess transporters that appear to have evolved recently. Moreover, our data suggest that unidentified riboflavin transporters also exist. The present study doubles the number of experimentally characterized riboflavin transporters and suggests a specific, non-accessory role for these proteins in riboflavin-prototrophic bacteria.

Salvage of the thiamin pyrimidine moiety by plant TenA proteins lacking an active-site cysteine

The TenA protein family occurs in prokaryotes, plants and fungi; it has two subfamilies, one (TenA_C) having an active-site cysteine, the other (TenA_E) not. TenA_C proteins participate in thiamin salvage by hydrolysing the thiamin breakdown product amino-HMP (4-amino-5-aminomethyl-2-methylpyrimidine) to HMP (4-amino-5-hydroxymethyl-2-methylpyrimidine); the function of TenA_E proteins is unknown. Comparative analysis of prokaryote and plant genomes predicted that (i) TenA_E has a salvage role similar to, but not identical with, that of TenA_C and (ii) that TenA_E and TenA_C also have non-salvage roles since they occur in organisms that cannot make thiamin. Recombinant Arabidopsis and maize TenA_E proteins (At3g16990, GRMZM2G080501) hydrolysed amino-HMP to HMP and, far more actively, hydrolysed the N-formyl derivative of amino-HMP to amino-HMP. Ablating the At3g16990 gene in a line with a null mutation in the HMP biosynthesis gene ThiC prevented its rescue by amino-HMP. Ablating At3g16990 in the wild-type increased sensitivity to paraquat-induced oxidative stress; HMP overcame this increased sensitivity. Furthermore, the expression of TenA_E and ThiC genes in Arabidopsis and maize was inversely correlated. These results indicate that TenA_E proteins mediate amidohydrolase and aminohydrolase steps in the salvage of thiamin breakdown products. As such products can be toxic, TenA_E proteins may also pre-empt toxicity.

Novel TPP-riboswitch activators bypass metabolic enzyme dependency

Riboswitches are conserved regions within mRNA molecules that bind specific metabolites and regulate gene expression. TPP-riboswitches, which respond to thiamine pyrophosphate (TPP), are involved in the regulation of thiamine metabolism in numerous bacteria. As these regulatory RNAs are often modulating essential biosynthesis pathways they have become increasingly interesting as promising antibacterial targets. Here, we describe thiamine analogs containing a central 1,2,3-triazole group to induce repression of thiM-riboswitch dependent gene expression in different E. coli strains. Additionally, we show that compound activation is dependent on proteins involved in the metabolic pathways of thiamine uptake and synthesis. The most promising molecule, triazolethiamine (TT), shows concentration dependent reporter gene repression that is dependent on the presence of thiamine kinase ThiK, whereas the effect of pyrithiamine (PT), a known TPP-riboswitch modulator, is ThiK independent. We further show that this dependence can be bypassed by triazolethiamine-derivatives that bear phosphate-mimicking moieties. As triazolethiamine reveals superior activity compared to pyrithiamine, it represents a very promising starting point for developing novel antibacterial compounds that target TPP-riboswitches. Riboswitch-targeting compounds engage diverse endogenous mechanisms to attain in vivo activity. These findings are of importance for the understanding of compounds that require metabolic activation to achieve effective riboswitch modulation and they enable the design of novel compound generations that are independent of endogenous activation mechanisms.

Practical aspects of protein co-evolution

Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these.

Targeting the vitamin biosynthesis pathways for the treatment of malaria

The most severe form of malaria is Malaria tropica, caused by Plasmodium falciparum. There are more than 1 billion people that are exposed to malaria parasites leading to more than 500,000 deaths annually. Vaccines are not available and the increasing drug resistance of the parasite prioritizes the need for novel drug targets and chemotherapeutics, which should be ideally designed to target selectively the parasite. In this sense, parasite-specific pathways, such as the vitamin biosyntheses, represent perfect drug-target characteristics because they are absent in humans. In the past, the vitamin B9 (folate) metabolism has been exploited by antifolates to treat infections caused by malaria parasites. Recently, two further vitamin biosynthesis pathways - for the vitamins B6 (pyridoxine) and B1 (thiamine) - have been identified in Plasmodium and analyzed for their suitability to discover new drugs. In this review, the current status of the druggability of plasmodial vitamin biosynthesis pathways is summarized.

Shared protein complex subunits contribute to explaining disrupted co-occurrence

The gene composition of present-day genomes has been shaped by a complicated evolutionary history, resulting in diverse distributions of genes across genomes. The pattern of presence and absence of a gene in different genomes is called its phylogenetic profile. It has been shown that proteins whose encoding genes have highly similar profiles tend to be functionally related: As these genes were gained and lost together, their encoded proteins can probably only perform their full function if both are present. However, a large proportion of genes encoding interacting proteins do not have matching profiles. In this study, we analysed one possible reason for this, namely that phylogenetic profiles can be affected by multi-functional proteins such as shared subunits of two or more protein complexes. We found that by considering triplets of proteins, of which one protein is multi-functional, a large fraction of disturbed co-occurrence patterns can be explained.

Every genome of current day species contains a very unique selection of genes. Why a specific genome is composed of exactly those genes is determined by many factors, but often not resolvable. It seems plausible that interacting genes would either occur together or be absent together, because if one of them is alone, it might not be able to perform its function properly, just as a bolt can only perform its function together with a nut and vice versa. However, it turns out that interacting genes very often do not nicely co-occur across a wide range of species, and frequently one gene can be found but the other not. In this study, we investigated the co-occurrences of multi-functional proteins and found that they are often maintained in a genome, even if one of their interaction partners is lost. This is because they can still perform some functions with other interaction partners that are still present. We can show that this has a noticeable effect on genome compositions and can explain otherwise surprisingly mismatching co-occurrence patterns of interacting genes.

Experimental evidence validating the computational inference of functional associations from gene fusion events: a critical survey

More than a decade ago, a number of methods were proposed for the inference of protein interactions, using whole-genome information from gene clusters, gene fusions and phylogenetic profiles. This structural and evolutionary view of entire genomes has provided a valuable approach for the functional characterization of proteins, especially those without sequence similarity to proteins of known function. Furthermore, this view has raised the real possibility to detect functional associations of genes and their corresponding proteins for any entire genome sequence. Yet, despite these exciting developments, there have been relatively few cases of real use of these methods outside the computational biology field, as reflected from citation analysis. These methods have the potential to be used in high-throughput experimental settings in functional genomics and proteomics to validate results with very high accuracy and good coverage. In this critical survey, we provide a comprehensive overview of 30 most prominent examples of single pairwise protein interaction cases in small-scale studies, where protein interactions have either been detected by gene fusion or yielded additional, corroborating evidence from biochemical observations. Our conclusion is that with the derivation of a validated gold-standard corpus and better data integration with big experiments, gene fusion detection can truly become a valuable tool for large-scale experimental biology.

Evolution of a new function in an esterase: simple amino acid substitutions enable the activity present in the larger paralog, BioH

Gene duplication and divergence are essential processes for the evolution of new activities. Divergence may be gradual, involving simple amino acid residue substitutions, or drastic, such that larger structural elements are inserted, deleted or rearranged. Vast protein sequence comparisons, supported by some experimental evidence, argue that large structural modifications have been necessary for certain catalytic activities to evolve. However, it is not clear whether these activities could not have been attained by gradual changes. Interestingly, catalytic promiscuity could play a fundamental evolutionary role: a preexistent secondary activity could be increased by simple amino acid residue substitutions that do not affect the enzyme's primary activity. The promiscuous profile of the enzyme may be modified gradually by genetic drift, making a pool of potentially useful activities that can be selected before duplication. In this work, we used random mutagenesis and in vivo selection to evolve the Pseudomonas aeruginosa PAO1 carboxylesterase PA3859, a small protein, to attain the function of BioH, a much larger paralog involved in biotin biosynthesis. BioH was chosen as a target activity because it provides a highly sensitive selection for evolved enzymatic activities by auxotrophy complementation. After only two cycles of directed evolution, mutants with the ability to efficiently complement biotin auxotrophy were selected. The in vivo and in vitro characterization showed that the activity of one of our mutant proteins was similar to that of the wild-type BioH enzyme. Our results demonstrate that it is possible to evolve enzymatic activities present in larger proteins by discrete amino acid substitutions.

Applications of system-level models of metabolism for analysis of bacterial physiology and identification of new drug targets

For nearly all of the 20th century, biologists gained considerable insights into the fundamental principles of cellular dynamics by examining select modules of biochemical processes. This form of analysis provides detailed information about the workings of the examined pathways. However, any attempt to alter the normal function of bacteria (perhaps for industrial or medicinal goals) requires a detailed global understanding of cellular mechanisms. The reductionist mode of analysis cannot provide the required information for developing the needed perspective on the complex interactions of biochemical pathways. Thankfully, the increasing availability of microbial genomic, transcriptomic, proteomic and other high-throughput data permits system-level analyses of microbiology. During the past two decades, systems biologists have developed constraint-based genome-scale models (GSM) of metabolism for a variety of pathogens. These models are important tools for assessing the metabolic capabilities of various genotypes. Simultaneously, new computational methods have been developed that use these network reconstructions to answer an array of important immunological questions. The objective of this article is to briefly review some of the uses of GSMs for studying bacterial metabolism under different conditions and to discuss how the calculated solutions can be used for rational design of drugs.

FUNCTION PREDICTION OF UNCHARACTERIZED PROTEINS

Function prediction of uncharacterized protein sequences generated by genome projects has emerged as an important focus for computational biology. We have categorized several approaches beyond traditional sequence similarity that utilize the overwhelmingly large amounts of available data for computational function prediction, including structure-, association (genomic context)-, interaction (cellular context)-, process (metabolic context)-, and proteomics-experiment-based methods. Because they incorporate structural and experimental data that is not used in sequence-based methods, they can provide additional accuracy and reliability to protein function prediction. Here, first we review the definition of protein function. Then the recent developments of these methods are introduced with special focus on the type of predictions that can be made. The need for further development of comprehensive systems biology techniques that can utilize the ever-increasing data presented by the genomics and proteomics communities is emphasized. For the readers' convenience, tables of useful online resources in each category are included. The role of computational scientists in the near future of biological research and the interplay between computational and experimental biology are also addressed.

Systematic computational prediction of protein interaction networks

Determining the network of physical protein associations is an important first step in developing mechanistic evidence for elucidating biological pathways. Despite rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. Here we describe computational methods for significantly expanding protein association networks. We describe methods for integrating multiple independent sources of evidence to obtain higher quality predictions and we compare the major publicly available resources available for experimentalists to use.

Inferring functional modules of protein families with probabilistic topic models

BACKGROUND

Genome and metagenome studies have identified thousands of protein families whose functions are poorly understood and for which techniques for functional characterization provide only partial information. For such proteins, the genome context can give further information about their functional context.

RESULTS

We describe a Bayesian method, based on a probabilistic topic model, which directly identifies functional modules of protein families. The method explores the co-occurrence patterns of protein families across a collection of sequence samples to infer a probabilistic model of arbitrarily-sized functional modules.

CONCLUSIONS

We show that our method identifies protein modules - some of which correspond to well-known biological processes - that are tightly interconnected with known functional interactions and are different from the interactions identified by pairwise co-occurrence. The modules are not specific to any given organism and may combine different realizations of a protein complex or pathway within different taxa.

Evolution of bacterial phosphoglycerate mutases: non-homologous isofunctional enzymes undergoing gene losses, gains and lateral transfers

Background

The glycolytic phosphoglycerate mutases exist as non-homologous isofunctional enzymes (NISE) having independent evolutionary origins and no similarity in primary sequence, 3D structure, or catalytic mechanism. Cofactor-dependent PGM (dPGM) requires 2,3-bisphosphoglycerate for activity; cofactor-independent PGM (iPGM) does not. The PGM profile of any given bacterium is unpredictable and some organisms such as Escherichia coli encode both forms.

Methods/Principal Findings

To examine the distribution of PGM NISE throughout the Bacteria, and gain insight into the evolutionary processes that shape their phyletic profiles, we searched bacterial genome sequences for the presence of dPGM and iPGM. Both forms exhibited patchy distributions throughout the bacterial domain. Species within the same genus, or even strains of the same species, frequently differ in their PGM repertoire. The distribution is further complicated by the common occurrence of dPGM paralogs, while iPGM paralogs are rare. Larger genomes are more likely to accommodate PGM paralogs or both NISE forms. Lateral gene transfers have shaped the PGM profiles with intradomain and interdomain transfers apparent. Archaeal-type iPGM was identified in many bacteria, often as the sole PGM. To address the function of PGM NISE in an organism encoding both forms, we analyzed recombinant enzymes from E. coli. Both NISE were active mutases, but the specific activity of dPGM greatly exceeded that of iPGM, which showed highest activity in the presence of manganese. We created PGM null mutants in E. coli and discovered the ΔdPGM mutant grew slowly due to a delay in exiting stationary phase. Overexpression of dPGM or iPGM overcame this defect.

Conclusions/Significance

Our biochemical and genetic analyses in E. coli firmly establish dPGM and iPGM as NISE. Metabolic redundancy is indicated since only larger genomes encode both forms. Non-orthologous gene displacement can fully account for the non-uniform PGM distribution we report across the bacterial domain.

Building and analysing genome-scale metabolic models

Reconstructing a model of the metabolic network of an organism from its annotated genome sequence would seem, at first sight, to be one of the most straightforward tasks in functional genomics, even if the various data sources required were never designed with this application in mind. The number of genome-scale metabolic models is, however, lagging far behind the number of sequenced genomes and is likely to continue to do so unless the model-building process can be accelerated. Two aspects that could usefully be improved are the ability to find the sources of error in a nascent model rapidly, and the generation of tenable hypotheses concerning solutions that would improve a model. We will illustrate these issues with approaches we have developed in the course of building metabolic models of Streptococcus agalactiae and Arabidopsis thaliana.

Loss and gain of GroEL in the Mollicutes

GroEL is a chaperone thought of as essential for bacterial life. However, some species of Mollicutes are missing GroEL. We use phylogenetic analysis to show that the presence of GroEL is polyphyletic among the Mollicutes, and that there is evidence for lateral gene transfer of GroEL to Mycoplasma penetrans from the Proteobacteria. Furthermore, we propose that the presence of GroEL in Mycoplasma may be required for invasion of host tissue, suggesting that GroEL may act as an adhesin-invasin.

Databases of protein-protein interactions and complexes

In the current understanding, translation of genomic sequences into proteins is the most important path for realization of genome information. In exercising their intended function, proteins work together through various forms of direct (physical) or indirect interaction mechanisms. For a variety of basic functions, many proteins form a large complex representing a molecular machine or a macromolecular super-structural building block. After several high-throughput techniques for detection of protein-protein interactions had matured, protein interaction data became available in a large scale and curated databases for protein-protein interactions (PPIs) are a new necessity for efficient research. Here, their scope, annotation quality, and retrieval tools are reviewed. In addition, attention is paid to portals that provide unified access to a variety of such databases with added annotation value.

Non-homologous isofunctional enzymes: a systematic analysis of alternative solutions in enzyme evolution

BACKGROUND

Evolutionarily unrelated proteins that catalyze the same biochemical reactions are often referred to as analogous - as opposed to homologous - enzymes. The existence of numerous alternative, non-homologous enzyme isoforms presents an interesting evolutionary problem; it also complicates genome-based reconstruction of the metabolic pathways in a variety of organisms. In 1998, a systematic search for analogous enzymes resulted in the identification of 105 Enzyme Commission (EC) numbers that included two or more proteins without detectable sequence similarity to each other, including 34 EC nodes where proteins were known (or predicted) to have distinct structural folds, indicating independent evolutionary origins. In the past 12 years, many putative non-homologous isofunctional enzymes were identified in newly sequenced genomes. In addition, efforts in structural genomics resulted in a vastly improved structural coverage of proteomes, providing for definitive assessment of (non)homologous relationships between proteins.

RESULTS

We report the results of a comprehensive search for non-homologous isofunctional enzymes (NISE) that yielded 185 EC nodes with two or more experimentally characterized - or predicted - structurally unrelated proteins. Of these NISE sets, only 74 were from the original 1998 list. Structural assignments of the NISE show over-representation of proteins with the TIM barrel fold and the nucleotide-binding Rossmann fold. From the functional perspective, the set of NISE is enriched in hydrolases, particularly carbohydrate hydrolases, and in enzymes involved in defense against oxidative stress.

CONCLUSIONS

These results indicate that at least some of the non-homologous isofunctional enzymes were recruited relatively recently from enzyme families that are active against related substrates and are sufficiently flexible to accommodate changes in substrate specificity.

Inference of functional relations in predicted protein networks with a machine learning approach

Background

Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome.

Methodology/Principal Findings

We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at http://ecid.bioinfo.cnio.es/.

Conclusions

We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs. We selected the AODE classifier as the appropriate tool to perform this task. AODE is particularly useful to extract valuable information from large unbalanced and heterogeneous data sets. The combination of the information provided by five prediction interaction prediction methods with some simple sequence features in APPIA is useful in establishing reliability values and helpful to prioritize functional interactions that can be further experimentally characterized.

Transcript analysis of parasitic females of the sedentary semi-endoparasitic nematode Rotylenchulus reniformis

Rotylenchulus reniformis, the reniform nematode, is a sedentary semi-endoparasitic nematode capable of infecting >300 plant species, including a large number of crops such as cotton, soybean, and pineapple. In contrast to other economically important plant-parasitic nematodes, molecular genetic data regarding the R. reniformis transcriptome is virtually nonexistant. Herein, we present a survey of R. reniformis ESTs that were sequenced from a sedentary parasitic female cDNA library. Cluster analysis of 2004 high quality ESTs produced 123 contigs and 508 singletons for a total of 631 R. reniformis unigenes. BLASTX analyses revealed that 39% of all unigenes showed similarity to known proteins (E<or=1.0e-04). R. reniformis genes homologous to known parasitism genes were identified and included beta-1,4-endoglucanase, fatty acid- and retinol-binding proteins, and an esophageal gland cell-specific gene from Heterodera glycines. Furthermore, a putative ortholog of an enzyme involved in thiamin biosynthesis, thought to exist solely in prokaryotes, fungi, and plants, was identified. Lastly, 114 R. reniformis unigenes orthologous to RNAi-lethal Caenorhabditis elegans genes were discovered. The work described here offers a glimpse into the transcriptome of a sedentary semi-endoparasitic nematode which (i) provides the transcript sequence data necessary for investigating engineered resistance against R. reniformis and (ii) hints at the existance of a thiamin biosynthesis pathway in an animal.

Construction and analysis of protein-protein interaction networks

Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein–protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations.

Transcription analysis of central metabolism genes in Escherichia coli. Possible roles of sigma38 in their expression, as a response to carbon limitation

The phosphoenolpyruvate: carbohydrate transferase system (PTS) transports glucose in Escherichia coli. Previous work demonstrated that strains lacking PTS, such as PB11, grow slow on glucose. PB11 has a reduced expression of glycolytic, and upregulates poxB and acs genes as compared to the parental strain JM101, when growing on glucose. The products of the latter genes are involved in the production of AcetylCoA. Inactivation of rpoS that codes for the RNA polymerase σ³⁸ subunit, reduces further (50%) growth of PB11, indicating that σ³⁸ plays a central role in the expression of central metabolism genes in slowly growing cells. In fact, transcription levels of glycolytic genes is reduced in strain PB11rpoS⁻ as compared to PB11. In this report we studied the role of σ⁷⁰ and σ³⁸ in the expression of the complete glycolytic pathway and poxB and acs genes in certain PTS⁻ strains and their rpoS⁻ derivatives. We determined the transcription start sites (TSSs) and the corresponding promoters, in strains JM101, PB11, its derivative PB12 that recovered its growth capacity, and in their rpoS⁻ derivatives, by 5′RACE and pyrosequencing. In all these genes the presence of sequences resembling σ³⁸ recognition sites allowed the proposition that they could be transcribed by both sigma factors, from overlapping putative promoters that initiate transcription at the same site. Fourteen new TSSs were identified in seventeen genes. Besides, more than 30 putative promoters were proposed and we confirmed ten previously reported. In vitro transcription experiments support the functionality of putative dual promoters. Alternatives that could also explain lower transcription levels of the rpoS⁻ derivatives are discussed. We propose that the presence if real, of both σ⁷⁰ and σ³⁸ dependent promoters in all glycolytic genes and operons could allow a differential transcription of these central metabolism genes by both sigma subunits as an adaptation response to carbon limitation.

Predicting protein-protein interactions in the context of protein evolution

Here we review the methods for the prediction of protein interactions and the ideas in protein evolution that relate to them. The evolutionary assumptions implicit in many of the protein interaction prediction methods are elucidated. We draw attention to the caution needed in deploying certain evolutionary assumptions, in particular cross-organism transfer of interactions by sequence homology, and discuss the known issues in deriving interaction predictions from evidence of co-evolution. We also conject that there is evolutionary knowledge yet to be exploited in the prediction of interactions, in particular the heterogeneity of interactions, the increasing availability of interaction data from multiple species, and the models of protein interaction network growth.

Asymmetric relationships between proteins shape genome evolution

An investigation of metabolic networks in E. coli and S. cerevisiae reveals that asymmetric protein interactions affect gene expression, the relative effect of gene-knockouts and genome evolution.

Background

The relationships between proteins are often asymmetric: one protein (A) depends for its function on another protein (B), but the second protein does not depend on the first. In metabolic networks there are multiple pathways that converge into one central pathway. The enzymes in the converging pathways depend on the enzymes in the central pathway, but the enzymes in the latter do not depend on any specific enzyme in the converging pathways. Asymmetric relations are analogous to the “if->then” logical relation where A implies B, but B does not imply A (A->B).

Results

We show that the majority of relationships between enzymes in metabolic flux models of metabolism in Escherichia coli and Saccharomyces cerevisiae are asymmetric. We show furthermore that these asymmetric relationships are reflected in the expression of the genes encoding those enzymes, the effect of gene knockouts and the evolution of genomes. From the asymmetric relative dependency, one would expect that the gene that is relatively independent (B) can occur without the other dependent gene (A), but not the reverse. Indeed, when only one gene of an A->B pair is expressed, is essential, is present in a genome after an evolutionary gain or loss, it tends to be the independent gene (B). This bias is strongest for genes encoding proteins whose asymmetric relationship is evolutionarily conserved.

Conclusions

The asymmetric relations between proteins that arise from the system properties of metabolic networks affect gene expression, the relative effect of gene knockouts and genome evolution in a predictable manner.

Genomic reconstruction of Shewanella oneidensis MR-1 metabolism reveals a previously uncharacterized machinery for lactate utilization

The ability to use lactate as a sole source of carbon and energy is one of the key metabolic signatures of Shewanellae, a diverse group of dissimilatory metal-reducing bacteria commonly found in aquatic and sedimentary environments. Nonetheless, homology searches failed to recognize orthologs of previously described bacterial d- or l-lactate oxidizing enzymes (Escherichia coli genes dld and lldD) in any of the 13 analyzed genomes of Shewanella spp. By using comparative genomic techniques, we identified a conserved chromosomal gene cluster in Shewanella oneidensis MR-1 (locus tag: SO_1522-SO_1518) containing lactate permease and candidate genes for both d- and l-lactate dehydrogenase enzymes. The predicted d-LDH gene (dld-II, SO_1521) is a distant homolog of FAD-dependent lactate dehydrogenase from yeast, whereas the predicted l-LDH is encoded by 3 genes with previously unknown functions (lldEGF, SO_1520-SO_1518). Through a combination of genetic and biochemical techniques, we experimentally confirmed the predicted physiological role of these novel genes in S. oneidensis MR-1 and carried out successful functional validation studies in Escherichia coli and Bacillus subtilis. We conclusively showed that dld-II and lldEFG encode fully functional d-and l-LDH enzymes, which catalyze the oxidation of the respective lactate stereoisomers to pyruvate. Notably, the S. oneidensis MR-1 LldEFG enzyme is a previously uncharacterized example of a multisubunit lactate oxidase. Comparative analysis of >400 bacterial species revealed the presence of LldEFG and Dld-II in a broad range of diverse species accentuating the potential importance of these previously unknown proteins in microbial metabolism.

Both thiamine uptake and biosynthesis of thiamine precursors are required for intracellular replication of Listeria monocytogenes

Thiamine pyrophosphate is an essential cofactor involved in central metabolism and amino acid biosynthesis and is derived from thiamine (vitamin B(1)). The extent to which this metabolite is available to bacterial pathogens replicating within host cells is still little understood. Growth studies using modified minimal Welshimer's broth (mMWB) supplemented with thiamine or the thiamine precursor hydroxymethylpyrimidine (HMP) showed that Listeria monocytogenes, in agreement with bioinformatic prediction, is able to synthesize thiamine only in the presence of HMP. This appears to be due to a lack of ThiC, which is involved in HMP synthesis. The knockout of thiD (lmo0317), which probably catalyzes the phosphorylation of HMP, inhibited growth in mMWB supplemented with HMP and reduced the replication rate of L. monocytogenes in epithelial cells. Mutation of a predicted thiamine transporter gene, lmo1429, led to reduced proliferation of L. monocytogenes in mMWB containing thiamine or thiamine phosphates and also within epithelial cells but had no influence on the expression of the virulence factors Hly and ActA. The toxic thiamine analogue pyrithiamine inhibited growth of wild-type strain EGD but not of the transporter mutant EGDDeltathiT. We also demonstrated that ThiT binds thiamine, a finding compatible with ThiT acting as the substrate-binding component of a multimeric thiamine transporter complex. These data provide experimental evidence that Lmo1429 homologs including Bacillus YuaJ are necessary for thiamine transport in gram-positive bacteria and are therefore proposed to be annotated "ThiT." Taken together, these data indicate that concurrent thiamine uptake and biosynthesis of thiamine precursors is a strategy of L. monocytogenes and possibly other facultative intracellular pathogens to enable proliferation within the cytoplasm.

AnEnPi: identification and annotation of analogous enzymes

Background

Enzymes are responsible for the catalysis of the biochemical reactions in metabolic pathways. Analogous enzymes are able to catalyze the same reactions, but they present no significant sequence similarity at the primary level, and possibly different tertiary structures as well. They are thought to have arisen as the result of independent evolutionary events. A detailed study of analogous enzymes may reveal new catalytic mechanisms, add information about the origin and evolution of biochemical pathways and disclose potential targets for drug development.

Results

In this work, we have constructed and implemented a new approach, AnEnPi (the Analogous Enzyme Pipeline), using a combination of bioinformatics tools like BLAST, HMMer, and in-house scripts, to assist in the identification, annotation, comparison and study of analogous and homologous enzymes. The algorithm for the detection of analogy is based i) on the construction of groups of homologous enzymes and ii) on the identification of cases where a given enzymatic activity is performed by two or more proteins without significant similarity between their primary structures. We applied this approach to a dataset obtained from KEGG Comprising all annotated enzymes, which resulted in the identification of 986 EC classes where putative analogy was detected (40.5% of all EC classes). AnEnPi is of considerable value in the construction of initial datasets that can be further curated, particularly in gene and genome annotation, in studies involving molecular evolution and metabolism and in the identification of new potential drug targets.

Conclusion

AnEnPi is an efficient tool for detection and annotation of analogous enzymes and other enzymes in whole genomes. It is available for academic use at:

Protein co-evolution, co-adaptation and interactions

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

Inferring modules of functionally interacting proteins using the Bond Energy Algorithm

BACKGROUND

Non-homology based methods such as phylogenetic profiles are effective for predicting functional relationships between proteins with no considerable sequence or structure similarity. Those methods rely heavily on traditional similarity metrics defined on pairs of phylogenetic patterns. Proteins do not exclusively interact in pairs as the final biological function of a protein in the cellular context is often hold by a group of proteins. In order to accurately infer modules of functionally interacting proteins, the consideration of not only direct but also indirect relationships is required. In this paper, we used the Bond Energy Algorithm (BEA) to predict functionally related groups of proteins. With BEA we create clusters of phylogenetic profiles based on the associations of the surrounding elements of the analyzed data using a metric that considers linked relationships among elements in the data set.

RESULTS

Using phylogenetic profiles obtained from the Cluster of Orthologous Groups of Proteins (COG) database, we conducted a series of clustering experiments using BEA to predict (upper level) relationships between profiles. We evaluated our results by comparing with COG's functional categories, And even more, with the experimentally determined functional relationships between proteins provided by the DIP and ECOCYC databases. Our results demonstrate that BEA is capable of predicting meaningful modules of functionally related proteins. BEA outperforms traditionally used clustering methods, such as k-means and hierarchical clustering by predicting functional relationships between proteins with higher accuracy.

CONCLUSION

This study shows that the linked relationships of phylogenetic profiles obtained by BEA is useful for detecting functional associations between profiles and extending functional modules not found by traditional methods. BEA is capable of detecting relationship among phylogenetic patterns by linking them through a common element shared in a group. Additionally, we discuss how the proposed method may become more powerful if other criteria to classify different levels of protein functional interactions, as gene neighborhood or protein fusion information, is provided.

Predicting biological networks from genomic data

Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate to high-throughput experimental methods.

Metabolism of halophilic archaea

In spite of their common hypersaline environment, halophilic archaea are surprisingly different in their nutritional demands and metabolic pathways. The metabolic diversity of halophilic archaea was investigated at the genomic level through systematic metabolic reconstruction and comparative analysis of four completely sequenced species: Halobacterium salinarum, Haloarcula marismortui, Haloquadratum walsbyi, and the haloalkaliphile Natronomonas pharaonis. The comparative study reveals different sets of enzyme genes amongst halophilic archaea, e.g. in glycerol degradation, pentose metabolism, and folate synthesis. The carefully assessed metabolic data represent a reliable resource for future system biology approaches as it also links to current experimental data on (halo)archaea from the literature.

Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution

The gap between the amount of genome information released by genome sequencing projects and our knowledge about the proteins' functions is rapidly increasing. To fill this gap, various 'genomic-context' methods have been proposed that exploit sequenced genomes to predict the functions of the encoded proteins. One class of methods, phylogenetic profiling, predicts protein function by correlating the phylogenetic distribution of genes with that of other genes or phenotypic characteristics. The functions of a number of proteins, including ones of medical relevance, have thus been predicted and subsequently confirmed experimentally. Additionally, various approaches to measure the similarity of phylogenetic profiles and to account for the phylogenetic bias in the data have been proposed. We review the successful applications of phylogenetic profiling and analyse the performance of various profile similarity measures with a set of one microsporidial and 25 fungal genomes. In the fungi, phylogenetic profiling yields high-confidence predictions for the highest and only the highest scoring gene pairs illustrating both the power and the limitations of the approach. Both practical examples and theoretical considerations suggest that in order to get a reliable and specific picture of a protein's function, results from phylogenetic profiling have to be combined with other sources of evidence.