Proteins belonging to PD-(D/E)XK phosphodiesterases constitute a functionally diverse superfamily with representatives involved in replication, restriction, DNA repair and tRNA–intron splicing. Their malfunction in humans triggers severe diseases, such as Fanconi anemia and Xeroderma pigmentosum. To date there have been several attempts to identify and classify new PD-(D/E)KK phosphodiesterases using remote homology detection methods. Such efforts are complicated, because the superfamily exhibits extreme sequence and structural divergence. Using advanced homology detection methods supported with superfamily-wide domain architecture and horizontal gene transfer analyses, we provide a comprehensive reclassification of proteins containing a PD-(D/E)XK domain. The PD-(D/E)XK phosphodiesterases span over 21 900 proteins, which can be classified into 121 groups of various families. Eleven of them, including DUF4420, DUF3883, DUF4263, COG5482, COG1395, Tsp45I, HaeII, Eco47II, ScaI, HpaII and Replic_Relax, are newly assigned to the PD-(D/E)XK superfamily. Some groups of PD-(D/E)XK proteins are present in all domains of life, whereas others occur within small numbers of organisms. We observed multiple horizontal gene transfers even between human pathogenic bacteria or from Prokaryota to Eukaryota. Uncommon domain arrangements greatly elaborate the PD-(D/E)XK world. These include domain architectures suggesting regulatory roles in Eukaryotes, like stress sensing and cell-cycle regulation. Our results may inspire further experimental studies aimed at identification of exact biological functions, specific substrates and molecular mechanisms of reactions performed by these highly diverse proteins.

Ribonuclease H-like (RNHL) superfamily, also called the retroviral integrase superfamily, groups together numerous enzymes involved in nucleic acid metabolism and implicated in many biological processes, including replication, homologous recombination, DNA repair, transposition and RNA interference. The RNHL superfamily proteins show extensive divergence of sequences and structures. We conducted database searches to identify members of the RNHL superfamily (including those previously unknown), yielding >60 000 unique domain sequences. Our analysis led to the identification of new RNHL superfamily members, such as RRXRR (PF14239), DUF460 (PF04312, COG2433), DUF3010 (PF11215), DUF429 (PF04250 and COG2410, COG4328, COG4923), DUF1092 (PF06485), COG5558, OrfB_IS605 (PF01385, COG0675) and Peptidase_A17 (PF05380). Based on the clustering analysis we grouped all identified RNHL domain sequences into 152 families. Phylogenetic studies revealed relationships between these families, and suggested a possible history of the evolution of RNHL fold and its active site. Our results revealed clear division of the RNHL superfamily into exonucleases and endonucleases. Structural analyses of features characteristic for particular groups revealed a correlation between the orientation of the C-terminal helix with the exonuclease/endonuclease function and the architecture of the active site. Our analysis provides a comprehensive picture of sequence-structure-function relationships in the RNHL superfamily that may guide functional studies of the previously uncharacterized protein families.

Sequencing microRNA, reduced representation sequencing, Hi-C technology and any method requiring the use of in-house barcodes result in sequencing libraries with low initial sequence diversity. Sequencing such data on the Illumina platform typically produces low quality data due to the limitations of the Illumina cluster calling algorithm. Moreover, even in the case of diverse samples, these limitations are causing substantial inaccuracies in multiplexed sample assignment (sample bleeding). Such inaccuracies are unacceptable in clinical applications, and in some other fields (e.g. detection of rare variants). Here, we discuss how both problems with quality of low-diversity samples and sample bleeding are caused by incorrect detection of clusters on the flowcell during initial sequencing cycles. We propose simple software modifications (Long Template Protocol) that overcome this problem. We present experimental results showing that our Long Template Protocol remarkably increases data quality for low diversity samples, as compared with the standard analysis protocol; it also substantially reduces sample bleeding for all samples. For comprehensiveness, we also discuss and compare experimental results from alternative approaches to sequencing low diversity samples. First, we discuss how the low diversity problem, if caused by barcodes, can be avoided altogether at the barcode design stage. Second and third, we present modified guidelines, which are more stringent than the manufacturer’s, for mixing low diversity samples with diverse samples and lowering cluster density, which in our experience consistently produces high quality data from low diversity samples. Fourth and fifth, we present rescue strategies that can be applied when sequencing results in low quality data and when there is no more biological material available. In such cases, we propose that the flowcell be re-hybridized and sequenced again using our Long Template Protocol. Alternatively, we discuss how analysis can be repeated from saved sequencing images using the Long Template Protocol to increase accuracy.

PIN-like domains constitute a widespread superfamily of nucleases, diverse in terms of the reaction mechanism, substrate specificity, biological function and taxonomic distribution. Proteins with PIN-like domains are involved in central cellular processes, such as DNA replication and repair, mRNA degradation, transcription regulation and ncRNA maturation. In this work, we identify and classify the most complete set of PIN-like domains to provide the first comprehensive analysis of sequence–structure–function relationships within the whole PIN domain-like superfamily. Transitive sequence searches using highly sensitive methods for remote homology detection led to the identification of several new families, including representatives of Pfam (DUF1308, DUF4935) and CDD (COG2454), and 23 other families not classified in the public domain databases. Further sequence clustering revealed relationships between individual sequence clusters and showed heterogeneity within some families, suggesting a possible functional divergence. With five structural groups, 70 defined clusters, over 100,000 proteins, and broad biological functions, the PIN domain-like superfamily constitutes one of the largest and most diverse nuclease superfamilies. Detailed analyses of sequences and structures, domain architectures, and genomic contexts allowed us to predict biological function of several new families, including new toxin-antitoxin components, proteins involved in tRNA/rRNA maturation and transcription/translation regulation.

FAM46 proteins, encoded in all known animal genomes, belong to the nucleotidyltransferase (NTase) fold superfamily. All four human FAM46 paralogs (FAM46A, FAM46B, FAM46C, FAM46D) are thought to be involved in several diseases, with FAM46C reported as a causal driver of multiple myeloma; however, their exact functions remain unknown. By using a combination of various bioinformatics analyses (e.g. domain architecture, cellular localization) and exhaustive literature and database searches (e.g. expression profiles, protein interactors), we classified FAM46 proteins as active non-canonical poly(A) polymerases, which modify cytosolic and/or nuclear RNA 3′ ends. These proteins may thus regulate gene expression and probably play a critical role during cell differentiation. A detailed analysis of sequence and structure diversity of known NTases possessing PAP/OAS1 SBD domain, combined with state-of-the-art comparative modelling, allowed us to identify potential active site residues responsible for catalysis and substrate binding. We also explored the role of single point mutations found in human cancers and propose that FAM46 genes may be involved in the development of other major malignancies including lung, colorectal, hepatocellular, head and neck, urothelial, endometrial and renal papillary carcinomas and melanoma. Identification of these novel enzymes taking part in RNA metabolism in eukaryotes may guide their further functional studies.

The His-Me finger endonucleases, also known as HNH or ββα-metal endonucleases, form a large and diverse protein superfamily. The His-Me finger domain can be found in proteins that play an essential role in cells, including genome maintenance, intron homing, host defense and target offense. Its overall structural compactness and non-specificity make it a perfectly-tailored pathogenic module that participates on both sides of inter- and intra-organismal competition. An extremely low sequence similarity across the superfamily makes it difficult to identify and classify new His-Me fingers. Using state-of-the-art distant homology detection methods, we provide an updated and systematic classification of His-Me finger proteins. In this work, we identified over 100 000 proteins and clustered them into 38 groups, of which three groups are new and cannot be found in any existing public domain database of protein families. Based on an analysis of sequences, structures, domain architectures, and genomic contexts, we provide a careful functional annotation of the poorly characterized members of this superfamily. Our results may inspire further experimental investigations that should address the predicted activity and clarify the potential substrates, to provide more detailed insights into the fundamental biological roles of these proteins.