ABSTRACT
The genome of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) coronavirus has a capping modification at the 5'-untranslated region (UTR) to prevent its degradation by host nucleases. These modifications are performed by the Nsp10/14 and Nsp10/16 heterodimers using S-adenosylmethionine as the methyl donor. Nsp10/16 heterodimer is responsible for the methylation at the ribose 2'-O position of the first nucleotide. To investigate the conformational changes of the complex during 2'-O methyltransferase activity, we used a fixed-target serial synchrotron crystallography method at room temperature. We determined crystal structures of Nsp10/16 with substrates and products that revealed the states before and after methylation, occurring within the crystals during the experiments. Here we report the crystal structure of Nsp10/16 in complex with Cap-1 analog (m7GpppAm2'-O). Inhibition of Nsp16 activity may reduce viral proliferation, making this protein an attractive drug target.
Subject(s)
RNA Caps/metabolism , RNA, Messenger/metabolism , RNA, Viral/metabolism , SARS-CoV-2/chemistry , Crystallography , Methylation , Methyltransferases/chemistry , Methyltransferases/metabolism , Multiprotein Complexes/chemistry , Multiprotein Complexes/metabolism , RNA Cap Analogs/chemistry , RNA Cap Analogs/metabolism , RNA Caps/chemistry , RNA, Messenger/chemistry , RNA, Viral/chemistry , S-Adenosylhomocysteine/chemistry , S-Adenosylhomocysteine/metabolism , S-Adenosylmethionine/chemistry , S-Adenosylmethionine/metabolism , SARS-CoV-2/genetics , SARS-CoV-2/metabolism , Synchrotrons , Viral Nonstructural Proteins/chemistry , Viral Nonstructural Proteins/metabolism , Viral Regulatory and Accessory Proteins/chemistry , Viral Regulatory and Accessory Proteins/metabolismABSTRACT
Powerful detectors at modern experimental facilities routinely collect data at multiple GB/s. Online analysis methods are needed to enable the collection of only interesting subsets of such massive data streams, such as by explicitly discarding some data elements or by directing instruments to relevant areas of experimental space. Thus, methods are required for configuring and running distributed computing pipelines-what we call flows-that link instruments, computers (e.g., for analysis, simulation, artificial intelligence [AI] model training), edge computing (e.g., for analysis), data stores, metadata catalogs, and high-speed networks. We review common patterns associated with such flows and describe methods for instantiating these patterns. We present experiences with the application of these methods to the processing of data from five different scientific instruments, each of which engages powerful computers for data inversion,model training, or other purposes. We also discuss implications of such methods for operators and users of scientific facilities.