Call for an enzyme genomics initiative.
Ontology highlight
ABSTRACT:
SUBMITTER: Karp PD
PROVIDER: S-EPMC507876 | biostudies-other | 2004
REPOSITORIES: biostudies-other
Publications
I propose an Enzyme Genomics Initiative, the goal of which is to obtain at least one protein sequence for each enzyme that has previously been characterized biochemically. There are 1,437 enzyme activities for which Enzyme Commission (EC) numbers have been assigned but no sequence can be found in public protein-sequence databases. ...[more]
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Project description: Not available
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Project description:The Enzyme Function Initiative (EFI) was recently established to address the challenge of assigning reliable functions to enzymes discovered in bacterial genome projects; in this Current Topic, we review the structure and operations of the EFI. The EFI includes the Superfamily/Genome, Protein, Structure, Computation, and Data/Dissemination Cores that provide the infrastructure for reliably predicting the in vitro functions of unknown enzymes. The initial targets for functional assignment are selected from five functionally diverse superfamilies (amidohydrolase, enolase, glutathione transferase, haloalkanoic acid dehalogenase, and isoprenoid synthase), with five superfamily specific Bridging Projects experimentally testing the predicted in vitro enzymatic activities. The EFI also includes the Microbiology Core that evaluates the in vivo context of in vitro enzymatic functions and confirms the functional predictions of the EFI. The deliverables of the EFI to the scientific community include (1) development of a large-scale, multidisciplinary sequence/structure-based strategy for functional assignment of unknown enzymes discovered in genome projects (target selection, protein production, structure determination, computation, experimental enzymology, microbiology, and structure-based annotation), (2) dissemination of the strategy to the community via publications, collaborations, workshops, and symposia, (3) computational and bioinformatic tools for using the strategy, (4) provision of experimental protocols and/or reagents for enzyme production and characterization, and (5) dissemination of data via the EFI's Website, http://enzymefunction.org. The realization of multidisciplinary strategies for functional assignment will begin to define the full metabolic diversity that exists in nature and will impact basic biochemical and evolutionary understanding, as well as a wide range of applications of central importance to industrial, medicinal, and pharmaceutical efforts.
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Project description:Purpose:Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death in the Saudi Arabia (KSA). Over the last decade dyslipidemia has been the predominant risk factor in KSA. The linear relationship between low density lipoprotein cholesterol (LDL-C) levels, a marker for dyslipidemia, and progression of ASCVD is well established. The objective of this paper is to to provide an overview of the burden of disease, outline current clinical practice guidelines (CPG), examine gaps in care, and provide actionable recommendations to prevent, diagnose, and treat dyslipidemia in KSA. Results:Saudi Arabia has the highest prevalence of ASCVD in the Gulf region. Several gaps in the implementation of CPGs, including the underdiagnosis and undertreatment of dyslipidemia, inadequate primary and secondary prevention efforts, complicated by a fragmented health system have been identified. Compelling evidence indicates that target LDL-C levels are not achieved throughout the Middle East region. In addition, high-risk patients are often left unidentified with adequate treatment. Conclusion:This statement recommends specific multilevel interventions to optimize the prevention, diagnosis, and treatment of ASCVD. These recommendations focus on strengthening primary and secondary prevention through education initiatives, establishment of specialized prevention and treatment centers, and development of local and regional CPGs.
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Project description:Although most individuals recover from acute SARS-CoV-2 infection, a significant number continue to suffer from Post-Acute Sequelae of SARS-CoV-2 (PASC), including the unexplained symptoms that are frequently referred to as long COVID, which could last for weeks, months, or even years after the acute phase of illness. The National Institutes of Health is currently funding large multi-center research programs as part of its Researching COVID to Enhance Recover (RECOVER) initiative to understand why some individuals do not recover fully from COVID-19. Several ongoing pathobiology studies have provided clues to potential mechanisms contributing to this condition. These include persistence of SARS-CoV-2 antigen and/or genetic material, immune dysregulation, reactivation of other latent viral infections, microvascular dysfunction, and gut dysbiosis, among others. Although our understanding of the causes of long COVID remains incomplete, these early pathophysiologic studies suggest biological pathways that could be targeted in therapeutic trials that aim to ameliorate symptoms. Repurposed medicines and novel therapeutics deserve formal testing in clinical trial settings prior to adoption. While we endorse clinical trials, especially those that prioritize inclusion of the diverse populations most affected by COVID-19 and long COVID, we discourage off-label experimentation in uncontrolled and/or unsupervised settings. Here, we review ongoing, planned, and potential future therapeutic interventions for long COVID based on the current understanding of the pathobiological processes underlying this condition. We focus on clinical, pharmacological, and feasibility data, with the goal of informing future interventional research studies.
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Project description:The Enzyme Function Initiative, an NIH/NIGMS-supported Large-Scale Collaborative Project (EFI; U54GM093342; http://enzymefunction.org/), is focused on devising and disseminating bioinformatics and computational tools as well as experimental strategies for the prediction and assignment of functions (in vitro activities and in vivo physiological/metabolic roles) to uncharacterized enzymes discovered in genome projects. Protein sequence similarity networks (SSNs) are visually powerful tools for analyzing sequence relationships in protein families (H.J. Atkinson, J.H. Morris, T.E. Ferrin, and P.C. Babbitt, PLoS One 2009, 4, e4345). However, the members of the biological/biomedical community have not had access to the capability to generate SSNs for their "favorite" protein families. In this article we announce the EFI-EST (Enzyme Function Initiative-Enzyme Similarity Tool) web tool (http://efi.igb.illinois.edu/efi-est/) that is available without cost for the automated generation of SSNs by the community. The tool can create SSNs for the "closest neighbors" of a user-supplied protein sequence from the UniProt database (Option A) or of members of any user-supplied Pfam and/or InterPro family (Option B). We provide an introduction to SSNs, a description of EFI-EST, and a demonstration of the use of EFI-EST to explore sequence-function space in the OMP decarboxylase superfamily (PF00215). This article is designed as a tutorial that will allow members of the community to use the EFI-EST web tool for exploring sequence/function space in protein families.
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2019-05-31 | E-MTAB-7983 | biostudies-arrayexpress