Structure network-based landscape of rhodopsin misfolding by mutations and algorithmic prediction of small chaperone action

PSNtools for standalone and web-based structure network analyses of conformational ensembles

“…PSN analysis allowed to gain insights into the structural determinants of the Nougaret Congenital Night Blindness linked to a missense mutation in the G protein transducin [68] . Last but not least, PSNtools served to study a conformational disease, the autosomal dominant Retinitis Pigmentosa (adRP) linked to mutations in the GPCR rod opsin [13] , [24] , [69] . Thermal or mechanical unfolding simulations coupled to the PSN analysis were, indeed, combined with in vitro subcellular localization analyses to infer the effects of 33 adRP rod opsin mutations on stability and transport of the protein in the absence and presence of the natural ligand 11- cis -retinal [24] .…”

“…Thermal or mechanical unfolding simulations coupled to the PSN analysis were, indeed, combined with in vitro subcellular localization analyses to infer the effects of 33 adRP rod opsin mutations on stability and transport of the protein in the absence and presence of the natural ligand 11- cis -retinal [24] . The definition of an index of structure network perturbation relying on hubs and links was instrumental in clustering the adRP rod opsin mutants and in building a computational model for algorithmic prediction of the structural/functional effects of novel adRP mutations and for aiding the design of small chaperones with therapeutic potential [24] , [69] . The model allowed also to infer a structure network-based landscape of rod opsin misfolding by mutation [69] .…”

“…The definition of an index of structure network perturbation relying on hubs and links was instrumental in clustering the adRP rod opsin mutants and in building a computational model for algorithmic prediction of the structural/functional effects of novel adRP mutations and for aiding the design of small chaperones with therapeutic potential [24] , [69] . The model allowed also to infer a structure network-based landscape of rod opsin misfolding by mutation [69] .…”

“…For non-equilibrium simulations such as, for example, mechanical unfolding, lower frequency cutoffs (e.g. 20–25%) are worth using [13] , [69] .…”

Delineating the activation mechanism and conformational landscape of a class B G protein-coupled receptor glucagon receptor

“…Molecular dynamics (MD) simulations that stand on the static crystal structure can predict atomic-level motion and capture the dynamic information of conformational transitions [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] . As a result of computation and algorithmic promotion, MD simulations have become an important source of complementary information in crystallography and a primary tool for mechanism research [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] , [65] .…”