Gabelli, Sandra B. (ORCID:0000000312055204)

DOE OSTI2024

The structural basis for 2′−5′/3′−5′-cGAMP synthesis by cGAS

Abstract cGAS activates innate immune responses against cytosolic double-stranded DNA. Here, by determining crystal structures of cGAS at various reaction stages, we report a unifying catalytic mechanism. apo-cGAS assumes an array of inactive conformations and binds NTPs nonproductively. Dimerization-coupled double-stranded DNA-binding then affixes the active site into a rigid lock for productive metal•substrate binding. A web-like network of protein•NTP, intra-NTP, and inter-NTP interactions ensures the stepwise synthesis of 2′−5′/3′−5′-linked cGAMP while discriminating against noncognate NTPs and off-pathway intermediates. One divalent metal is sufficient for productive substrate binding, and capturing the second divalent metal is tightly coupled to nucleotide and linkage specificities, a process which manganese is preferred over magnesium by 100-fold. Additionally, we elucidate how mouse cGAS achieves more stringent NTP and linkage specificities than human cGAS. Together, our results reveal that an adaptable, yet precise lock-and-key-like mechanism underpins cGAS catalysis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A simple technique to classify diffraction data from dynamic proteins according to individual polymorphs

One often observes small but measurable differences in the diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and may reveal the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step that could extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). To the extent that different states adopt different molecular structures, one expects to observe differences in the crystals; each of the polystates will create a polymorph of the crystals. After mixed-state diffraction data have been measured, deliberately or inadvertently, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here, this problem is addressed using a simple multi-factor clustering approach that classifies each data set using independent observables, thereby assigning each data set to the correct location in conformational space. This procedure is illustrated using two independent observables, unit-cell parameters and intensities, to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. It is observed that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.

36 MATERIALS SCIENCE↗