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| 001 | 634789 | ||
| 005 | 20250929151352.0 | ||
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| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Creon, Anne |0 P:(DE-H253)PIP1029188 |b 0 |u desy |
| 245 | _ | _ | |a Statistical crystallography reveals an allosteric network in SARS-CoV-2 M pro |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1755858335_963267 |2 PUB:(DE-HGF) |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 520 | _ | _ | |a To interpret and transmit biological signals, proteins use correlated motions. Experimental determination of these dynamics with atomic resolution remains a key challenge. Here, using thousands of crystals of the main protease (Mpro) from SARS-CoV-2, we were able to infer a model of the protein’s correlated motions. Mpro is regulated by concentration, becoming enzymatically active after forming a homodimer. To understand the correlated motions that enable dimerization to activate catalysis, we employed our model, predicting which regions of the dimerization domain are structurally linked to the active site. Mutations at these positions, expected to disrupt catalysis, resulted in a dramatic reduction in activity in one case, a mild effect in the second, and none in the third. Additional crystallography and biophysical experiments provide a mechanistic explanation for these results. Our work suggests that a statistical crystallography can determine protein correlated motions and rationalize their biological function. |
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| 700 | 1 | _ | |a Lane, Thomas |0 P:(DE-H253)PIP1020283 |b 11 |e Corresponding author |u desy |
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