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      Discovery of an essential nucleotidylating activity associated with a newly delineated conserved domain in the RNA polymerase-containing protein of all nidoviruses

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          Abstract

          RNA viruses encode an RNA-dependent RNA polymerase (RdRp) that catalyzes the synthesis of their RNA(s). In the case of positive-stranded RNA viruses belonging to the order Nidovirales, the RdRp resides in a replicase subunit that is unusually large. Bioinformatics analysis of this non-structural protein has now revealed a nidoviral signature domain (genetic marker) that is N-terminally adjacent to the RdRp and has no apparent homologs elsewhere. Based on its conservation profile, this domain is proposed to have nucleotidylation activity. We used recombinant non-structural protein 9 of the arterivirus equine arteritis virus (EAV) and different biochemical assays, including irreversible labeling with a GTP analog followed by a proteomics analysis, to demonstrate the manganese-dependent covalent binding of guanosine and uridine phosphates to a lysine/histidine residue. Most likely this was the invariant lysine of the newly identified domain, named nidovirus RdRp-associated nucleotidyltransferase (NiRAN), whose substitution with alanine severely diminished the described binding. Furthermore, this mutation crippled EAV and prevented the replication of severe acute respiratory syndrome coronavirus (SARS-CoV) in cell culture, indicating that NiRAN is essential for nidoviruses. Potential functions supported by NiRAN may include nucleic acid ligation, mRNA capping and protein-primed RNA synthesis, possibilities that remain to be explored in future studies.

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          Most cited references 58

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          Amino acid substitution matrices from protein blocks.

          Methods for alignment of protein sequences typically measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.
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            Protein homology detection by HMM-HMM comparison.

            Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction and evolution. We have generalized the alignment of protein sequences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile HMMs. We present a method for detecting distant homologous relationships between proteins based on this approach. The method (HHsearch) is benchmarked together with BLAST, PSI-BLAST, HMMER and the profile-profile comparison tools PROF_SIM and COMPASS, in an all-against-all comparison of a database of 3691 protein domains from SCOP 1.63 with pairwise sequence identities below 20%.Sensitivity: When the predicted secondary structure is included in the HMMs, HHsearch is able to detect between 2.7 and 4.2 times more homologs than PSI-BLAST or HMMER and between 1.44 and 1.9 times more than COMPASS or PROF_SIM for a rate of false positives of 10%. Approximately half of the improvement over the profile-profile comparison methods is attributable to the use of profile HMMs in place of simple profiles. Alignment quality: Higher sensitivity is mirrored by an increased alignment quality. HHsearch produced 1.2, 1.7 and 3.3 times more good alignments ('balanced' score >0.3) than the next best method (COMPASS), and 1.6, 2.9 and 9.4 times more than PSI-BLAST, at the family, superfamily and fold level, respectively.Speed: HHsearch scans a query of 200 residues against 3691 domains in 33 s on an AMD64 2GHz PC. This is 10 times faster than PROF_SIM and 17 times faster than COMPASS.
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              R: A Language and environmental for statistical computing

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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                30 September 2015
                24 August 2015
                24 August 2015
                : 43
                : 17
                : 8416-8434
                Affiliations
                [1 ]Department of Medical Microbiology, Leiden University Medical Center, Leiden, 2300 RC, Leiden, The Netherlands
                [2 ]Department of Immunohematology and Blood transfusion, Leiden University Medical Center, Leiden, 2300 RC, Leiden, The Netherlands
                [3 ]Leiden Institute of Chemistry, Leiden University, 2300 CC, Leiden, The Netherlands
                [4 ]Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119899 Moscow, Russia
                [5 ]Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119899 Moscow, Russia
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +31 71 5261652; Fax: +31 71 5266761; Email: a.e.gorbalenya@ 123456lumc.nl
                []Deceased; of blessed memory.
                Article
                10.1093/nar/gkv838
                4787807
                26304538
                © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                Page count
                Pages: 19
                Product
                Categories
                Nucleic Acid Enzymes
                Custom metadata
                30 September 2015

                Genetics

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