bioRxivBiochemistry
Molecular Basis for Variations in the Sensitivity of Pathogenic Rhodopsin Variants to 9-cis-Retinal
F. J. Roushar, A. G. McKee, C. P. Kuntz, J. T. Ortega, W. D. Penn, H. Woods, L. M. Chamness, V. Most, J. Meiler, B. Jastrzebska, J. P. Schlebach
Abstract
Over 100 mutations in the rhodopsin gene have been linked to a spectrum of retinopathies that include retinitis pigmentosa and congenital stationary night blindness. Though most of these variants exhibit a loss of function, the molecular defects caused by these underlying mutations vary considerably. In this work, we utilize deep mutational scanning to quantitatively compare the plasma membrane expression of 123 known pathogenic rhodopsin variants in the presence and absence of the stabilizing cofactor 9-cis-retinal. We identify 69 retinopathy variants, including 20 previously uncharacterized variants, that exhibit diminished plasma membrane expression in HEK293T cells. 67 of these apparent class II variants exhibit a measurable increase in expression in the presence of 9-cis-retinal. However, the magnitude of the response to this molecule varies considerably across this spectrum of mutations. Evaluation of the observed shifts in relation to thermodynamic estimates for the coupling between binding and folding suggests underlying differences in stability constrains the magnitude of their response to retinal. Nevertheless, estimates from computational modeling suggest many of the least sensitive variants also directly compromise binding. Finally, we evaluate the functional properties of three previous uncharacterized, retinal-sensitive variants ({Delta}N73, S131P, and R135G) and show that two retain residual function in vitro. Together, our results provide a comprehensive experimental characterization of the proteostatic properties of retinopathy variants and their response to retinal.
- organism: human
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: protein folding & dynamics
- research focus: drug response / drug discovery
- approach: deep learning
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