Activity based Deubiquitinase profiling reveals OTUB1 from oryza sativa Hydrolyzes Linear Ubiquitin Chains by Bi-modal Activation

Met1 type ubiquitination and deubiquitination are involved in the regulation of many fundamental processes such as inflammation and innate immunity, and their interference by pathogens can suppress immune responses in human cells. However, no plant-derived deubiquitinases (DUBs) against Met1 ubiquitin chains have been reported. Using a dehydroalanine (DHA)-bearing Met1 diubiquitin (Met1-diUb) suicide probe, synthesized in one-pot, we identified OTUB1 from Oryza sativa (OsOTUB1) and uncovered its preference for Met1 ubiquitin chains. Also, by resolving the apo structure of OsOTUB1 and its complex with Ub or Met1-diUb, we demonstrated that OsOTUB1 hydrolyses Met1 ubiquitin chains by activation of both the distal and proximal ubiquitin, which is different from OTULIN and expands our mechanistic understanding of the DUB-mediated hydrolysis of Met1 ubiquitin chains. Through large-scale sequence alignment and hydrolysis experiments, two sites in the S1' pocket of the OTUB subfamily (OTUBs) were found to determine the hydrolytic ability of OTUBs against Met1 ubiquitin chains, regardless of species. Furthermore, by analyzing the species distribution of OTUBs capable of hydrolyzing Met1 ubiquitin chains, we found that whereas this activity does not exist in metazoans, it is conserved in green plants (Viridiplantae). This discovery may inform studies of the differentiation between primitive plants and animals.

Petabase-scale sequence alignment catalyses viral discovery

Public sequence data represents a major opportunity for viral discovery, but its exploration has been inhibited by a lack of efficient methods for searching this corpus, which is currently at the petabase scale and growing exponentially. To address the ongoing pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 and expand the known sequence diversity of viruses, we aligned pangenomes for coronaviruses (CoV) and other viral families to 5.6 petabases of public sequencing data from 3.8 million biologically diverse samples. To implement this strategy, we developed a cloud computing architecture, Serratus, tailored for ultra-high throughput sequence alignment at the petabase scale. From this search, we identified and assembled thousands of CoV and CoV-like genomes and genome fragments ranging from known strains to putatively novel genera. We generalise this strategy to other viral families, identifying several novel deltaviruses and huge bacteriophages. To catalyse a new era of viral discovery we made millions of viral alignments and family identifications freely available to the research community. Expanding the known diversity and zoonotic reservoirs of CoV and other emerging pathogens can accelerate vaccine and therapeutic developments for the current pandemic, and help us anticipate and mitigate future ones.

Seqminer2: an efficient tool to query and retrieve genotypes for statistical genetics analyses from biobank scale sequence dataset

Summary Here, we present a highly efficient R-package seqminer2 for querying and retrieving sequence variants from biobank scale datasets of millions of individuals and hundreds of millions of genetic variants. Seqminer2 implements a novel variant-based index for querying VCF/BCF files. It improves the speed of query and retrieval by several magnitudes compared to the state-of-the-art tools based upon tabix. It also reimplements support for BGEN and PLINK format, which improves speed over alternative implementations. The improved efficiency and comprehensive support for popular file formats will facilitate method development, software prototyping and data analysis of biobank scale sequence datasets in R. Availability and implementation The seqminer2 R package is available from https://github.com/zhanxw/seqminer. Scripts used for the benchmarks are available in https://github.com/yang-lina/seqminer/blob/master/seqminer2%20benchmark%20script.txt. Supplementary information Supplementary data are available at Bioinformatics online.

Large-scale sequence similarity analysis reveals the scope of sequence and function divergence in PilZ domain proteins

PilZ domain-containing proteins constitute a superfamily of widely distributed bacterial signalling proteins. Although studies have established the canonical PilZ domain as an adaptor protein domain evolved to specifically bind the second messenger c-di-GMP, mounting evidence suggest that the PilZ domain has undergone enormous divergent evolution to generate a superfamily of proteins that are characterized by a wide range of c-di-GMP-binding affinity, binding partners and cellular functions. The divergent evolution has even generated families of non-canonical PilZ domains that completely lack c-di-GMP binding ability. In this study, we performed a large-scale sequence analysis on more than 28,000 single- and di-domain PilZ proteins using the sequence similarity networking tool created originally to analyse functionally diverse enzyme superfamilies. The sequence similarity networks (SSN) generated by the analysis feature a large number of putative isofunctional protein clusters, and thus, provide an unprecedented panoramic view of the sequence-function relationship and function diversification in PilZ proteins. Some of the protein clusters in the networks are considered as unexplored clusters that contain proteins with completely unknown biological function; whereas others contain one, two or a few functionally known proteins, and therefore, enabling us to infer the cellular function of uncharacterized homologs or orthologs. With the ultimate goal of elucidating the diverse roles played by PilZ proteins in bacterial signal transduction, the work described here will facilitate the annotation of the vast number of PilZ proteins encoded by bacterial genome and help to prioritize functionally unknown PilZ proteins for future studies.ImportanceAlthough PilZ domain is best known as the protein domain evolved specifically for the binding of the second messenger c-di-GMP, divergent evolution has generated a superfamily of PilZ proteins with a diversity of ligand or protein-binding properties and cellular functions. We analysed the sequences of more than 28,000 PilZ proteins using the sequence similarity networking (SSN) tool to yield a global view of the sequence-function relationship and function diversification in PilZ proteins. The results will facilitate the annotation of the vast number of PilZ proteins encoded by bacterial genomes and help us prioritize PilZ proteins for future studies.