A method for identification of selenoprotein genes in archaeal genomes

Bioinformatics of Metalloproteins and Metalloproteomes

Trace metals are inorganic elements that are required for all organisms in very low quantities. They serve as cofactors and activators of metalloproteins involved in a variety of key cellular processes. While substantial effort has been made in experimental characterization of metalloproteins and their functions, the application of bioinformatics in the research of metalloproteins and metalloproteomes is still limited. In the last few years, computational prediction and comparative genomics of metalloprotein genes have arisen, which provide significant insights into their distribution, function, and evolution in nature. This review aims to offer an overview of recent advances in bioinformatic analysis of metalloproteins, mainly focusing on metalloprotein prediction and the use of different metals across the tree of life. We describe current computational approaches for the identification of metalloprotein genes and metal-binding sites/patterns in proteins, and then introduce a set of related databases. Furthermore, we discuss the latest research progress in comparative genomics of several important metals in both prokaryotes and eukaryotes, which demonstrates divergent and dynamic evolutionary patterns of different metalloprotein families and metalloproteomes. Overall, bioinformatic studies of metalloproteins provide a foundation for systematic understanding of trace metal utilization in all three domains of life.

Comparative genomics and metagenomics of the metallomes

Recent achievements and advances in comparative genomic and metagenomic analyses of trace metals were reviewed and discussed.

Multiple amino acid sequence alignment nitrogenase component 1: insights into phylogenetics and structure-function relationships

Amino acid residues critical for a protein's structure-function are retained by natural selection and these residues are identified by the level of variance in co-aligned homologous protein sequences. The relevant residues in the nitrogen fixation Component 1 α- and β-subunits were identified by the alignment of 95 protein sequences. Proteins were included from species encompassing multiple microbial phyla and diverse ecological niches as well as the nitrogen fixation genotypes, anf, nif, and vnf, which encode proteins associated with cofactors differing at one metal site. After adjusting for differences in sequence length, insertions, and deletions, the remaining >85% of the sequence co-aligned the subunits from the three genotypes. Six Groups, designated Anf, Vnf , and Nif I-IV, were assigned based upon genetic origin, sequence adjustments, and conserved residues. Both subunits subdivided into the same groups. Invariant and single variant residues were identified and were defined as “core” for nitrogenase function. Three species in Group Nif-III, Candidatus Desulforudis audaxviator, Desulfotomaculum kuznetsovii, and Thermodesulfatator indicus, were found to have a seleno-cysteine that replaces one cysteinyl ligand of the 8Fe:7S, P-cluster. Subsets of invariant residues, limited to individual groups, were identified; these unique residues help identify the gene of origin (anf, nif, or vnf) yet should not be considered diagnostic of the metal content of associated cofactors. Fourteen of the 19 residues that compose the cofactor pocket are invariant or single variant; the other five residues are highly variable but do not correlate with the putative metal content of the cofactor. The variable residues are clustered on one side of the cofactor, away from other functional centers in the three dimensional structure. Many of the invariant and single variant residues were not previously recognized as potentially critical and their identification provides the bases for new analyses of the three-dimensional structure and for mutagenesis studies.

SECISearch3 and Seblastian: new tools for prediction of SECIS elements and selenoproteins

Selenoproteins are proteins containing an uncommon amino acid selenocysteine (Sec). Sec is inserted by a specific translational machinery that recognizes a stem-loop structure, the SECIS element, at the 3′ UTR of selenoprotein genes and recodes a UGA codon within the coding sequence. As UGA is normally a translational stop signal, selenoproteins are generally misannotated and designated tools have to be developed for this class of proteins. Here, we present two new computational methods for selenoprotein identification and analysis, which we provide publicly through the web servers at http://gladyshevlab.org/SelenoproteinPredictionServer or http://seblastian.crg.es. SECISearch3 replaces its predecessor SECISearch as a tool for prediction of eukaryotic SECIS elements. Seblastian is a new method for selenoprotein gene detection that uses SECISearch3 and then predicts selenoprotein sequences encoded upstream of SECIS elements. Seblastian is able to both identify known selenoproteins and predict new selenoproteins. By applying these tools to diverse eukaryotic genomes, we provide a ranked list of newly predicted selenoproteins together with their annotated cysteine-containing homologues. An analysis of a representative candidate belonging to the AhpC family shows how the use of Sec in this protein evolved in bacterial and eukaryotic lineages.

Selenoprofiles: profile-based scanning of eukaryotic genome sequences for selenoprotein genes

Motivation: Selenoproteins are a group of proteins that contain selenocysteine (Sec), a rare amino acid inserted co-translationally into the protein chain. The Sec codon is UGA, which is normally a stop codon. In selenoproteins, UGA is recoded to Sec in presence of specific features on selenoprotein gene transcripts. Due to the dual role of the UGA codon, selenoprotein prediction and annotation are difficult tasks, and even known selenoproteins are often misannotated in genome databases.

Results: We present an homology-based in silico method to scan genomes for members of the known eukaryotic selenoprotein families: selenoprofiles. The core of the method is a set of manually curated highly reliable multiple sequence alignments of selenoprotein families, which are used as queries to scan genomic sequences. Results of the scan are processed through a number of steps, to produce highly accurate predictions of selenoprotein genes with little or no human intervention. Selenoprofiles is a valuable tool for bioinformatic characterization of eukaryotic selenoproteomes, and can complement genome annotation pipelines.

Availability and Implementation: Selenoprofiles is a python-built pipeline that internally runs psitblastn, exonerate, genewise, SECISearch and a number of custom-made scripts and programs. The program is available at http://big.crg.cat/services/selenoprofiles. The predictions presented in this article are available through DAS at http://genome.crg.cat:9000/das/Selenoprofiles_ensembl.

Contact:marco.mariotti@crg.es

Supplementary information:Supplementary data are available at Bioinformatics online.

Selenocysteine, pyrrolysine, and the unique energy metabolism of methanogenic archaea

Methanogenic archaea are a group of strictly anaerobic microorganisms characterized by their strict dependence on the process of methanogenesis for energy conservation. Among the archaea, they are also the only known group synthesizing proteins containing selenocysteine or pyrrolysine. All but one of the known archaeal pyrrolysine-containing and all but two of the confirmed archaeal selenocysteine-containing protein are involved in methanogenesis. Synthesis of these proteins proceeds through suppression of translational stop codons but otherwise the two systems are fundamentally different. This paper highlights these differences and summarizes the recent developments in selenocysteine- and pyrrolysine-related research on archaea and aims to put this knowledge into the context of their unique energy metabolism.