Engineering topics

Fujimoto, Grant M.

Publications and source records attributed to Fujimoto, Grant M..

3 records

DOE OSTI2019

Regulation of phosphoribosyl ubiquitination by a calmodulin-dependent glutamylase

The bacterial pathogen Legionella pneumophila creates an intracellular niche permissive for its replication by extensively modulating the function of host cells using hundreds of effector proteins delivered via its Dot/Icm secretion system1. Among these, members of the SidE family (SidEs) regulate multiple cellular processes by a unique phosphoribosyl ubiquitination mechanism that bypasses the requirement of ATP and the E1, E2 enzymes of the canonical ubiquitination machinery2-4. The activity of SidEs is regulated by SidJ, another Dot/Icm effector5, but the mechanism of such regulation is not completely understood6,7. Here we demonstrate that SidJ functions to inhibit the activity of SidEs by inducing covalent attachment of a glutamate moiety to Glu860 of SdeA, which is one of the catalytic residues for the mono-ADP-ribosyltransferase activity involved in ubiquitin activation2. Furthermore, we found that the inhibition of SidEs activity by SidJ is restricted in host cells and such spatial regulation is achieved by the requirement of calmodulin (CaM), a eukaryote-specific protein, for its activity. Our results reveal a novel mechanism of regulation in bacterial virulence that features a posttranslational modification and its dependence upon a co-factor specific for host cells.

Gan, Ninghai↗

DOE OSTI2018

Author Correction: Informed-Proteomics: open-source software package for top-down proteomics

Correction to: Nature Methods 14, 909–914, https://doi.org/10.1038/nmeth.4388 (2017), published online 7 August 2017. In the version of this article initially published, the authors erroneously reported the search mode that was used for ProSightPC 3.0 in the Online Methods and in Supplementary Table 3. The results presented in Fig. 5 were obtained with ‘absolute mass’ search mode, not ‘biomarker discovery’ search mode. The ‘biomarker discovery’ search mode of ProSightPC 3.0 looks for subsequences of those contained in the annotated proteoform database (e.g., truncated forms from degradation and/or cleavage). This search mode is expected to generate similar numbers of identifications as Informed-Proteomics, but is also expected to take dramatically longer (~480 CPU hours). Unfortunately, because of these heavy computational requirements, the authors were unable to complete an analysis using this search mode. They chose to use ‘absolute mass’ mode to illustrate the effect of search mode and database choice on the results. ‘Absolute mass’ mode is the most restrictive of the search modes illustrated in Fig. 5, as it searches only for proteoforms explicitly listed in the proteoform database within a user-defined mass tolerance. In addition, in the supplementary information originally published online, Supplementary Table 3 incorrectly stated that ProSightPC v3.0 was used in ‘biomarker discovery’ mode. ‘Absolute mass’ mode was the mode actually used in this comparison. These errors have been corrected in the HTML and PDF versions of this article and in the associated supplementary information.

Park, Jungkap↗

DOE OSTI2018

Blazing Signature Filter: a library for fast pairwise similarity comparisons

Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. Whether the task is to identify co-expressed genes in large-scale expression surveys or to predict combinations of gene knockouts which would elicit a similar phenotype, the underlying computational task is often a multi-dimensional similarity test. As datasets continue to grow, improvements to the efficiency, sensitivity or specificity of such computation will have broad impacts as it allows scientists to more completely explore the wealth of scientific data. A significant practical drawback of large-scale data mining is the vast majority of pairwise comparisons are unlikely to be relevant, meaning that they do not share a signature of interest. It is therefore essential to efficiently identify these unproductive comparisons as rapidly as possible and exclude them from more time-intensive similarity calculations. The Blazing Signature Filter (BSF) is a highly efficient pairwise similarity algorithm which enables extensive data mining within a reasonable amount of time. The algorithm transforms datasets into binary metrics, allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity. As a result, the BSF can scale to high dimensionality and rapidly filter unproductive pairwise comparison. Furthermore, two bioinformatics applications of the tool are presented to demonstrate the ability to scale to billions of pairwise comparisons and the usefulness of this approach.

59 BASIC BIOLOGICAL SCIENCES↗