Because of its small size (70 kilodalton) and large content of structural disorder (>50%), the human growth hormone receptor (hGHR) falls between the cracks of conventional high-resolution structural biology methods. Here, we study the structure of the full-length hGHR in nanodiscs with small-angle x-ray scattering (SAXS) as the foundation. We develop an approach that combines SAXS, x-ray diffraction, and NMR spectroscopy data obtained on individual domains and integrate these through molecular dynamics simulations to interpret SAXS data on the full-length hGHR in nanodiscs. The hGHR domains reorient freely, resulting in a broad structural ensemble, emphasizing the need to take an ensemble view on signaling of relevance to disease states. The structure provides the first experimental model of any full-length cytokine receptor in a lipid membrane and exemplifies how integrating experimental data from several techniques computationally may access structures of membrane proteins with long, disordered regions, a widespread phenomenon in biology.

41Despite the many physiological and pathophysiological functions of the human growth 42 hormone receptor (hGHR), a detailed understanding of its modus operandi is hindered 43 by the lack of structural information of the entire receptor at the molecular level. Due 44 to its relatively small size (70 kDa) and large content of structural disorder (>50%), this 45 membrane protein falls between the cracks of conventional high-resolution structural 46 biology methods. Here, we study the structure of the full-length hGHR in nanodiscs 47 63The human growth hormone receptor (hGHR) is ubiquitously expressed 1 , and is 64 activated by human growth hormone (hGH), produced in the pituitary gland. hGHR is 65 important for regulating growth at a cellular and systemic level 1,2 , and is involved in 66 the regulation of hepatic metabolism, cardiac function, bone turnover and the immune 67 system 3 . Besides direct promotion of growth 4 , its ligand hGH can also indirectly 68 regulate growth by initiating the synthesis of insulin-like growth factor-I (IGF-I), an 69 important factor in postnatal growth 2,5,6 . Excess hGH production and mutations in the 70 hGHR gene manifest in different diseases including cancer 7 and growth deficiencies 8-71 11 , with associated cardiovascular, metabolic and respiratory difficulties 8 , and both 72 hGH-based agonists and antagonists of the receptor exist as approved drugs 12,13 . 73 74The hGHR is one of ~40 receptors belonging to the class 1 cytokine receptor family 14 . 75The family is topologically similar with a tripartite structure consisting of a folded 76 extracellular domain (ECD), a single-pass transmembrane domain (TMD), and a 77 disordered intracellular domain (ICD) [14][15][16] . A characteristic trait of these receptors is 78 the lack of intrinsic kinase activity, with the ICD instead forming a binding platform 79 for a variety of signaling kinases and regulatory proteins 15,17,18 , as well as of certain 80 specific membrane lipids 16 (Fig. 1A). Within the ECD, the receptors share a 81 characteristic cytokine receptor homology domain consisting of two fibronectin type 82 III domains (D1, N-terminal and D2, C-terminal), each with a seven stranded b-83 sandwich structure. Two hallmark disulfide bonds and a conserved WSXWS motif (X 84 is any amino acid) 19,20 located in D1 and D2, respectively, are suggested to be important 85 for cell surface localization and discrimination between signaling pathways 19,21 . In 86 hGHR, this motif is instead YGEFS 17 , but the reason for this variation has remained 87 enigmatic. Beside hGHR, group 1 of the class 1 cytokine receptor also encompasses 88 the prolactin receptor (PRLR) and the erythropoietin (EPO) receptor. This group is 89 considered to be the most structurally simple with one cytokine receptor homology 90 domain and ligand binding in a 2:1 complex 17,18 . 91 92Receptor activation is achieved by hGH binding to hGHR via two asymmetric binding 93 sites 22 , leading to structural rearrangements that are propagated through the TMD to the 94 ...

The presence of amyloid fibrils is a hallmark of several neurodegenerative diseases. Some amyloidogenic proteins, such as α-synuclein and amyloid β, can interact with lipids, and this interaction can strongly favor the formation of amyloid fibrils. In particular the primary nucleation step, i.e. the de novo formation of amyloid fibrils, has been shown to be accelerated by lipids. However, the exact mechanism of this acceleration is still mostly unclear. Here we use a range of scattering methods, such as dynamic light scattering (DLS) and small angle X-ray and neutron scattering (SAXS and SANS) to obtain structural information on the binding of α-synuclein to vesicles formed from negatively charged lipids and their co-assembly into amyloid fibrils. We find that the lipid vesicles do not simply act as a surface that catalyses the nucleation reaction, but that lipid molecules take an active role in the reaction. The binding of α-synuclein to the lipid vesicles immediately induces a major structural change in the lipid assembly, which leads to a break-up into small, cylindrical and disc-like lipid-protein particles. This transition can be largely reversed by temperature changes or proteolytic protein removal. Incubation of these small, cylindrical and disc-like lipid-α-synuclein particles for several hours, however, yields amyloid fibril formation, whereby the lipids are incorporated into the fibrils.

The combination of online size-exclusion chromatography and small-angle X-ray scattering (SEC–SAXS) is rapidly becoming a key technique for structural investigations of elaborate biophysical samples in solution. Here, a novel model-refinement strategy centred around the technique is outlined and its utility is demonstrated by analysing data series from several SEC–SAXS experiments on phospholipid bilayer nanodiscs. Using this method, a single model was globally refined against many frames from the same data series, thereby capturing the frame-to-frame tendencies of the irradiated sample. These are compared with models refined in the traditional manner, in which refinement is based on the average profile of a set of consecutive frames from the same data series without an in-depth comparison of individual frames. This is considered to be an attractive model-refinement scheme as it considerably lowers the total number of parameters refined from the data series, produces tendencies that are automatically consistent between frames, and utilizes a considerably larger portion of the recorded data than is often performed in such experiments. Additionally, a method is outlined for correcting a measured UV absorption signal by accounting for potential peak broadening by the experimental setup.

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