Pfam Families Research Articles

IurV, Encoded by ORF VCA0231, Is Involved in the Regulation of Iron Uptake Genes in Vibrio cholerae.

The pathogen Vibrio cholerae has multiple iron acquisition systems which allow bacteria to exploit a variety of iron sources across the different environments on which it thrives. The expression of such iron uptake systems is highly regulated, mainly by the master iron homeostasis regulator Fur but also by other mechanisms. Recently, we documented that the expression of many of the iron-responsive genes is also modulated by riboflavin. Among them, the open reading frame VCA0231, repressed both by riboflavin and iron, encodes a putative transcriptional regulator of the AraC/XylS family. Nonetheless, the genes or functions affected by this factor are unknown. In the present study, a series of in silico analyses was performed in order to identify the putative functions associated with the product of VCA0231. The STRING database predicted many iron uptake genes as functional partners for the product of VCA0231. In addition, a genomic neighborhood analysis with the Enzyme Function Initiative tools detected many Pfam families involved in iron homeostasis genetically associated with VCA0231. Moreover, a phylogenetic tree showed that other AraC/XylS members known to regulate siderophore utilization in bacteria clustered together and the product of VCA0231 localized in this cluster. This suggested that the product of VCA0231, here named IurV, is involved in the regulation of iron uptake processes. RNAseq was performed to determine the transcriptional effects of a deletion in VCA0231. A total of 52 genes were overexpressed and 21 genes were downregulated in response to the iurV deletion. Among these, several iron uptake genes and other iron homeostasis-related genes were found. Six gene ontology (GO) functional terms were enriched in the upregulated genes, of which five were related to iron metabolism. The regulatory pattern observed in the transcriptomics of a subset of genes was independently confirmed by quantitative real time PCR analysis. The results indicate that IurV is a novel regulator of the AraC/XylS family involved in the repression of iron uptake genes. Whether this effect is direct or indirect remains to be determined.

Structural analysis of the sensor domain of the β-lactam antibiotic receptor VbrK from Vibrio parahaemolyticus

Bacteria express β-lactamase to counteract the bactericidal effects of β-lactam antibiotics, which are the most widely employed antibacterial drugs. In gram-negative bacteria, the expression of β-lactamase is generally regulated in response to the muropeptide that is generated from the peptidoglycan of the cell wall during β-lactam antibiotic challenge. The direct regulation of β-lactamase expression by β-lactams was recently reported in Vibrio parahaemolyticus, and this regulation is mediated by a two-component regulatory system that consists of the histidine kinase VbrK and the response regulator VbrR. VbrK directly recognizes β-lactam antibiotics using the periplasmic sensor domain (VbrKSD), a PF11884 Pfam family member, and it delivers the β-lactam signal to VbrR to induce the transcription of the β-lactamase gene. To determine the structural features of VbrKSD as the prototype of the PF11884 family and provide insights into the β-lactam antibiotic-binding mode of VbrKSD, we determined the crystal structure of VbrKSD at 1.65 Å resolution. VbrKSD folds into a unique curved rod-like structure that has not been previously reported in other families. VbrKSD consists of two domains (D1 and D2). The D1 domain contains two helix-decorated β-sheets, and the D2 domain adopts a helix-rich structure. VbrKSD features two terminal disulfide bonds, which would be the canonical property of the PF11884 family. In the VbrKSD structure, the L82 residue, which was previously shown to play a key role in β-lactam antibiotic recognition, forms a pocket along with its neighboring hydrophobic or positively charged residues.

Identification of Modules With Similar Gene Regulation and Metabolic Functions Based on Co-expression Data.

Biological systems respond to environmental perturbations and to a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. In particular, a wide variety of gene co-expression networks have been constructed in recent years thanks to the dramatic increase of experimental information obtained with techniques, such as microarrays and RNA sequencing. These networks allow the identification of groups of co-expressed genes that can function in the same process and, in turn, these networks may be related to biological functions of industrial, medical and academic interest. In this study, gene co-expression networks for 17 bacterial organisms from the COLOMBOS database were analyzed via weighted gene co-expression network analysis and clustered into modules of genes with similar expression patterns for each species. These networks were analyzed to determine relevant modules through a hypergeometric approach based on a set of transcription factors and enzymes for each genome. The richest modules were characterized using PFAM families and KEGG metabolic maps. Additionally, we conducted a Gene Ontology analysis for enrichment of biological functions. Finally, we identified modules that shared similarity through all the studied organisms by using comparative genomics.

Molecular replacement using structure predictions from databases.

Molecular replacement (MR) is the predominant route to solution of the phase problem in macromolecular crystallography. Where the lack of a suitable homologue precludes conventional MR, one option is to predict the target structure using bioinformatics. Such modelling, in the absence of homologous templates, is called ab initio or de novo modelling. Recently, the accuracy of such models has improved significantly as a result of the availability, in many cases, of residue-contact predictions derived from evolutionary covariance analysis. Covariance-assisted ab initio models representing structurally uncharacterized Pfam families are now available on a large scale in databases, potentially representing a valuable and easily accessible supplement to the PDB as a source of search models. Here, the unconventional MR pipeline AMPLE is employed to explore the value of structure predictions in the GREMLIN and PconsFam databases. It was tested whether these deposited predictions, processed in various ways, could solve the structures of PDB entries that were subsequently deposited. The results were encouraging: nine of 27 GREMLIN cases were solved, covering target lengths of 109-355 residues and a resolution range of 1.4-2.9 Å, and with target-model shared sequence identity as low as 20%. The cluster-and-truncate approach in AMPLE proved to be essential for most successes. For the overall lower quality structure predictions in the PconsFam database, remodelling with Rosetta within the AMPLE pipeline proved to be the best approach, generating ensemble search models from single-structure deposits. Finally, it is shown that the AMPLE-obtained search models deriving from GREMLIN deposits are of sufficiently high quality to be selected by the sequence-independent MR pipeline SIMBAD. Overall, the results help to point the way towards the optimal use of the expanding databases of ab initio structure predictions.

Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families

IntroductionThe ocean microbiome represents one of the largest microbiomes and produces nearly half of the primary energy on the planet through photosynthesis or chemosynthesis. Using recent advances in marine genomics, we explore new applications of oceanic metagenomes for protein structure and function prediction.ResultsBy processing 1.3 TB of high-quality reads from the Tara Oceans data, we obtain 97 million non-redundant genes. Of the 5721 Pfam families that lack experimental structures, 2801 have at least one member associated with the oceanic metagenomics dataset. We apply C-QUARK, a deep-learning contact-guided ab initio structure prediction pipeline, to model 27 families, where 20 are predicted to have a reliable fold with estimated template modeling score (TM-score) at least 0.5. Detailed analyses reveal that the abundance of microbial genera in the ocean is highly correlated to the frequency of occurrence in the modeled Pfam families, suggesting the significant role of the Tara Oceans genomes in the contact-map prediction and subsequent ab initio folding simulations. Of interesting note, PF15461, which has a majority of members coming from ocean-related bacteria, is identified as an important photosynthetic protein by structure-based function annotations. The pipeline is extended to a set of 417 Pfam families, built on the combination of Tara with other metagenomics datasets, which results in 235 families with an estimated TM-score over 0.5.ConclusionsThese results demonstrate a new avenue to improve the capacity of protein structure and function modeling through marine metagenomics, especially for difficult proteins with few homologous sequences.

Exploration of Aldol Reactions Catalyzed by Stereoselective Pyruvate Aldolases with 2‐Oxobutyric Acid as Nucleophile

AbstractPyruvate aldolases from Pfam family PF03328, recently described to be able to use hydroxypyruvic acid as nucleophile substrate, were shown to also catalyse aldol adducts formation from 2‐oxobutyric acid. A 1H NMR‐based assay was used to screen 21 aldolases for their activity towards 2‐oxobutyric acid and to predict their stereoselectivity at position 3 of the aldol adducts. The best biocatalysts were then proved to be strictly (3S)‐selective. Contrary to known enzymes from this family that are seldom stereoselective at position 4, three aldolases were highlighted as allowing a selective access to (3S,4R) or (3S,4S) aldols. Seven 3‐deoxy‐3‐methylulosonic acids never described before were finally prepared and isolated with good yields and stereoselectivities.magnified image

PyFuncover: full proteome search for a specific function using BLAST and PFAM

Python Function uncover (PyFuncover) is a new bioinformatic tool able to search proteins with a specific function in a full proteome. The pipeline coded in python uses BLAST alignment and the sequences from a PFAM family as the search seed. We tested PyFuncover using the fatty acid-binding family (FABP) Lipocalin_7 from PFAM (version 32, 2019) against the Homo sapiens NCBI proteome. After applying the scoring function in all the BLAST results, the data were classified and submitted to a GO-TERM analysis using bioDBnet. Analyses showed that all families of FABPs were ranked within the top scores. Included within this category were also families able to bind to hydrophobic molecules similar to fatty acids such as the retinol acid transporter and the cellular retinoic acid-binding protein.

Developing structural profile matrices for protein secondary structure and solvent accessibility prediction.

Predicting secondary structure and solvent accessibility of proteins are among the essential steps that preclude more elaborate 3D structure prediction tasks. Incorporating class label information contained in templates with known structures has the potential to improve the accuracy of prediction methods. Building a structural profile matrix is one such technique that provides a distribution for class labels at each amino acid position of the target. In this paper, a new structural profiling technique is proposed that is based on deriving PFAM families and is combined with an existing approach. Cross-validation experiments on two benchmark datasets and at various similarity intervals demonstrate that the proposed profiling strategy performs significantly better than Homolpro, a state-of-the-art method for incorporating template information, as assessed by statistical hypothesis tests. The DSPRED method can be accessed by visiting the PSP server at http://psp.agu.edu.tr. Source code and binaries are freely available at https://github.com/yusufzaferaydin/dspred. Supplementary data are available at Bioinformatics online.

A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) – structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated. Our approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer associated genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.

SCOPe: classification of large macromolecular structures in the structural classification of proteins-extended database.

The SCOPe (Structural Classification of Proteins—extended, https://scop.berkeley.edu) database hierarchically classifies domains from the majority of proteins of known structure according to their structural and evolutionary relationships. SCOPe also incorporates and updates the ASTRAL compendium, which provides multiple databases and tools to aid in the analysis of the sequences and structures of proteins classified in SCOPe. Protein structures are classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.07, we have focused our manual curation efforts on larger protein structures, including the spliceosome, proteasome and RNA polymerase I, as well as many other Pfam families that had not previously been classified. Domains from these large protein complexes are distinctive in several ways: novel non-globular folds are more common, and domains from previously observed protein families often have N- or C-terminal extensions that were disordered or not present in previous structures. The current monthly release update, SCOPe 2.07–2018-10–18, classifies 90 992 PDB entries (about two thirds of PDB entries).

RNA-Seq Analysis for Assessing the Early Response to DSP Toxins in Mytilus galloprovincialis Digestive Gland and Gill.

The harmful effects of diarrhetic shellfish poisoning (DSP) toxins on mammalian cell lines have been widely assessed. Studies in bivalves suggest that mussels display a resistance to the cytogenotoxic effects of DSP toxins. Further, it seems that the bigger the exposure, the more resistant mussels become. To elucidate the early genetic response of mussels against these toxins, the digestive gland and the gill transcriptomes of Mytilus galloprovincialis after Prorocentrum lima exposure (100,000 cells/L, 48 h) were de novo assembled based on the sequencing of 8 cDNA libraries obtained using an Illumina HiSeq 2000 platform. The assembly provided 95,702 contigs. A total of 2286 and 4523 differentially expressed transcripts were obtained in the digestive gland and the gill, respectively, indicating tissue-specific transcriptome responses. These transcripts were annotated and functionally enriched, showing 44 and 60 significant Pfam families in the digestive gland and the gill, respectively. Quantitative PCR (qPCR) was performed to validate the differential expression patterns of several genes related to lipid and carbohydrate metabolism, energy production, genome integrity and defense, suggesting their participation in the protective mechanism. This work provides knowledge of the early response against DSP toxins in the mussel M. galloprovincialis and useful information for further research on the molecular mechanisms of the bivalve resistance to these toxins.

Transcriptional response after exposure to domoic acid-producing Pseudo-nitzschia in the digestive gland of the mussel Mytilus galloprovincialis

Bivalve molluscs are filter feeding species that can accumulate biotoxins in their body tissues during harmful algal blooms. Amnesic Shellfish Poisoning (ASP) is caused by species of the diatom genus Pseudo-nitzschia, which produces the toxin domoic acid. The Mytilus galloprovincialis digestive gland transcriptome was de novo assembled based on the sequencing of 12 cDNA libraries, six obtained from control mussels and six from mussels naturally exposed to domoic acid-producing diatom Pseudo-nitzschia australis. After de novo assembly 94,727 transcripts were obtained, with an average length of 1015 bp and a N50 length of 761 bp. The assembled transcripts were clustered (homology > 90%) into 69,294 unigenes. Differential gene expression analysis was performed (DESeq2 algorithm) in the digestive gland following exposure to the toxic algae. A total of 1158 differentially expressed unigenes (absolute fold change > 1.5 and p-value < 0.05) were detected: 686 up-regulated and 472 down-regulated. Several membrane transporters belonging to the family of the SLC (solute carriers) were over-expressed in exposed mussels. Functional enrichment was performed using Pfam annotations obtained from the genes differentially expressed, 37 Pfam families were found to be significantly (FDR adjusted p-value < 0.1) enriched. Some of these families (sulfotransferases, aldo/keto reductases, carboxylesterases, C1q domain and fibrinogen C-terminal globular domain) could be putatively involved in detoxification processes, in the response against of the oxidative stress and in immunological processes. Protein network analysis with STRING algorithm found alteration of the Notch signaling pathway under the action of domoic acid-producing Pseudo-nitzschia. In conclusion, this study provides a high quality reference transcriptome of M. galloprovincialis digestive gland and identifies potential genes involved in the response to domoic acid.

Large-scale structure prediction by improved contact predictions and model quality assessment.

MotivationAccurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known.ResultsWe present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these, 415 have not been reported before.Availability and ImplementationDatasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/. All programs used here are freely available.

Identification of Proteases and Protease Inhibitors in Allergenic and Non-Allergenic Pollen.

Pollen is one of the most common causes of allergy worldwide, making the study of their molecular composition crucial for the advancement of allergy research. Despite substantial efforts in this field, it is not yet clear why some plant pollens strongly provoke allergies while others do not. However, proteases and protease inhibitors from allergen sources are known to play an important role in the development of pollen allergies. In this study, we aim to uncover differences in the transcriptional pattern of proteases and protease inhibitors in Betula verrucosa and Pinus sylvestris pollen as models for high and low allergenic potential, respectively. We applied RNA sequencing to Betula verrucosa and Pinus sylvestris pollen. After de-novo assembly we derived general functional profiles of the protein coding transcripts. By utilization of domain based functional annotation we identified potential proteases and protease inhibitors and compared their expression in the two types of pollen. Functional profiles are highly similar between Betula verrucosa and Pinus sylvestris pollen. Both pollen contain proteases and inhibitors from 53 and 7 Pfam families, respectively. Some of the members comprised within those families are implicated in facilitating allergen entry, while others are known allergens themselves. Our work revealed several candidate proteins which, with further investigation, represent exciting new leads in elucidating the process behind allergic sensitization.

Predicting accurate contacts in thousands of Pfam domain families using PconsC3.

A few years ago it was shown that by using a maximum entropy approach to describe couplings between columns in a multiple sequence alignment it is possible to significantly increase the accuracy of residue contact predictions. For very large protein families with more than 1000 effective sequences the accuracy is sufficient to produce accurate models of proteins as well as complexes. Today, for about half of all Pfam domain families no structure is known, but unfortunately most of these families have at most a few hundred members, i.e. are too small for such contact prediction methods. To extend accurate contact predictions to the thousands of smaller protein families we present PconsC3, a fast and improved method for protein contact predictions that can be used for families with even 100 effective sequence members. PconsC3 outperforms direct coupling analysis (DCA) methods significantly independent on family size, secondary structure content, contact range, or the number of selected contacts. PconsC3 is available as a web server and downloadable version at http://c3.pcons.net . The downloadable version is free for all to use and licensed under the GNU General Public License, version 2. At this site contact predictions for most Pfam families are also available. We do estimate that more than 4000 contact maps for Pfam families of unknown structure have more than 50% of the top-ranked contacts predicted correctly. arne@bioinfo.se. Supplementary data are available at Bioinformatics online.

Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis

Proteins have evolved to perform diverse cellular functions, from serving as reaction catalysts to coordinating cellular propagation and development. Frequently, proteins do not exert their full potential as monomers but rather undergo concerted interactions as either homo-oligomers or with other proteins as hetero-oligomers. The experimental study of such protein complexes and interactions has been arduous. Theoretical structure prediction methods are an attractive alternative. Here, we investigate homo-oligomeric interfaces by tracing residue coevolution via the global statistical direct coupling analysis (DCA). DCA can accurately infer spatial adjacencies between residues. These adjacencies can be included as constraints in structure prediction techniques to predict high-resolution models. By taking advantage of the ongoing exponential growth of sequence databases, we go significantly beyond anecdotal cases of a few protein families and apply DCA to a systematic large-scale study of nearly 2,000 Pfam protein families with sufficient sequence information and structurally resolved homo-oligomeric interfaces. We find that large interfaces are commonly identified by DCA. We further demonstrate that DCA can differentiate between subfamilies with different binding modes within one large Pfam family. Sequence-derived contact information for the subfamilies proves sufficient to assemble accurate structural models of the diverse protein-oligomers. Thus, we provide an approach to investigate oligomerization for arbitrary protein families leading to structural models complementary to often-difficult experimental methods. Combined with ever more abundant sequential data, we anticipate that this study will be instrumental to allow the structural description of many heteroprotein complexes in the future.

The conundrum of UDP-Glc entrance into the yeast ER lumen.

UDP-Glc entrance into the endoplasmic reticulum (ER) of eukaryotic cells is a key step in the quality control of glycoprotein folding, a mechanism requiring transfer of a Glc residue from the nucleotide sugar (NS) to glycoprotein folding intermediates by the UDP-Glc:glycoprotein glucosyltransferase (UGGT). According to a bioinformatics search there are only eight genes in the Schizosaccharomyces pombe genome belonging to the three Pfam families to which all known nucleotide-sugar transporters (NSTs) of the secretory pathway belong. The protein products of two of them (hut1+ and yea4+) localize to the ER, those of genes gms1+, vrg4+, pet1+, pet2+ and pet3+ to the Golgi, whereas that of gms2+ has an unknown location. Here we demonstrate that (1) Δhut1 and Δgpt1 (UGGT null) mutants share several phenotypic features; (2) Δhut1 mutants show a 50% reduction in UDP-Glc transport into ER-derived membranes; (3) in vivo UDP-Glc ER entrance occurred in Δhut1Δyea4Δgms2 mutants and in cells in which Δhut1 disruption was combined with that of each of four of the genes encoding Golgi-located proteins. Therefore, disruption of all genes whose products localize to the ER or have an unknown location did not obliterate UDP-Glc ER entrance. We conclude that the hut1+ gene product is involved in UDP-Glc entrance into the ER, but that at least another as yet unknown NST displaying an unconventional sequence operates in the yeast secretory pathway. This conclusion agrees with our previous results showing that UDP-Glc entrance into the yeast ER does not follow the classical NST antiport mechanism.

Assignment of function to a domain of unknown function: DUF1537 is a new kinase family in catabolic pathways for acid sugars

Using a large-scale "genomic enzymology" approach, we (i) assigned novel ATP-dependent four-carbon acid sugar kinase functions to members of the DUF1537 protein family (domain of unknown function; Pfam families PF07005 and PF17042) and (ii) discovered novel catabolic pathways for d-threonate, l-threonate, and d-erythronate. The experimentally determined ligand specificities of several solute binding proteins (SBPs) for TRAP (tripartite ATP-independent permease) transporters for four-carbon acids, including d-erythronate and l-erythronate, were used to constrain the substrates for the catabolic pathways that degrade the SBP ligands to intermediates in central carbon metabolism. Sequence similarity networks and genome neighborhood networks were used to identify the enzyme components of the pathways. Conserved genome neighborhoods encoded SBPs as well as permease components of the TRAP transporters, members of the DUF1537 family, and a member of the 4-hydroxy-l-threonine 4-phosphate dehydrogenase (PdxA) oxidative decarboxylase, class II aldolase, or ribulose 1,5-bisphosphate carboxylase/oxygenase, large subunit (RuBisCO) superfamily. Because the characterized substrates of members of the PdxA, class II aldolase, and RuBisCO superfamilies are phosphorylated, we postulated that the members of the DUF1537 family are novel ATP-dependent kinases that participate in catabolic pathways for four-carbon acid sugars. We determined that (i) the DUF1537/PdxA pair participates in a pathway for the conversion of d-threonate to dihydroxyacetone phosphate and CO2 and (ii) the DUF1537/class II aldolase pair participates in pathways for the conversion of d-erythronate and l-threonate (epimers at carbon-3) to dihydroxyacetone phosphate and CO2 The physiological importance of these pathways was demonstrated in vivo by phenotypic and genetic analyses.

Genomics of high molecular weight plasmids isolated from an on-farm biopurification system.

The use of biopurification systems (BPS) constitutes an efficient strategy to eliminate pesticides from polluted wastewaters from farm activities. BPS environments contain a high microbial density and diversity facilitating the exchange of information among bacteria, mediated by mobile genetic elements (MGEs), which play a key role in bacterial adaptation and evolution in such environments. Here we sequenced and characterized high-molecular-weight plasmids from a bacterial collection of an on-farm BPS. The high-throughput-sequencing of the plasmid pool yielded a total of several Mb sequence information. Assembly of the sequence data resulted in six complete replicons. Using in silico analyses we identified plasmid replication genes whose encoding proteins represent 13 different Pfam families, as well as proteins involved in plasmid conjugation, indicating a large diversity of plasmid replicons and suggesting the occurrence of horizontal gene transfer (HGT) events within the habitat analyzed. In addition, genes conferring resistance to 10 classes of antimicrobial compounds and those encoding enzymes potentially involved in pesticide and aromatic hydrocarbon degradation were found. Global analysis of the plasmid pool suggest that the analyzed BPS represents a key environment for further studies addressing the dissemination of MGEs carrying catabolic genes and pathway assembly regarding degradation capabilities.

Assembly of Lipoic Acid on Its Cognate Enzymes: an Extraordinary and Essential Biosynthetic Pathway.

Although the structure of lipoic acid and its role in bacterial metabolism were clear over 50 years ago, it is only in the past decade that the pathways of biosynthesis of this universally conserved cofactor have become understood. Unlike most cofactors, lipoic acid must be covalently bound to its cognate enzyme proteins (the 2-oxoacid dehydrogenases and the glycine cleavage system) in order to function in central metabolism. Indeed, the cofactor is assembled on its cognate proteins rather than being assembled and subsequently attached as in the typical pathway, like that of biotin attachment. The first lipoate biosynthetic pathway determined was that of Escherichia coli, which utilizes two enzymes to form the active lipoylated protein from a fatty acid biosynthetic intermediate. Recently, a more complex pathway requiring four proteins was discovered in Bacillus subtilis, which is probably an evolutionary relic. This pathway requires the H protein of the glycine cleavage system of single-carbon metabolism to form active (lipoyl) 2-oxoacid dehydrogenases. The bacterial pathways inform the lipoate pathways of eukaryotic organisms. Plants use the E. coli pathway, whereas mammals and fungi probably use the B. subtilis pathway. The lipoate metabolism enzymes (except those of sulfur insertion) are members of PFAM family PF03099 (the cofactor transferase family). Although these enzymes share some sequence similarity, they catalyze three markedly distinct enzyme reactions, making the usual assignment of function based on alignments prone to frequent mistaken annotations. This state of affairs has possibly clouded the interpretation of one of the disorders of human lipoate metabolism.