2-(N-cyclohexylamino)ethanesulfonic acid

The time course of ethyl-paraoxon hydrolysis by SisLac at 70°C was monitored following the p-nitrophenolate production at 405 nm (ε_405nm = 17 000 M⁻¹cm⁻¹) in 1-cm path length cell with a Cary WinUV spectrophotometer (Varian, Australia) and using the Cary WinUV software. Standard assays (500 µL) were performed in paraoxonase buffer CHES 50 mM pH 9, NaCl 150 mM, CoCl₂ 0.2 mM, EtOH 6% (v/v), with pH adjusted with NaOH at 70°C.
At 25°C, the phosphotriesterase, esterase and lactonase activities were analyzed monitoring absorbance variations in 200 µL reaction volumes using 96-well plates (6.2-mm path length cell) and a microplate reader (Synergy HT) using the Gen5.1 software at 25°C. For each substrate, assays were performed using organic solvent concentrations below 1%. The monitoring wavelength, the solvent used, the molar extinction coefficient and the concentration range for each substrate (Fig. 1, S1 & S2) are summarized in Table S2. Phosphotriesterase and esterase activities were performed in activity buffer. When required, DTNB at 2 mM was added to the buffer to follow hydrolysis of substrate releasing thiolate group (malathion (Fig. S1V)). Catalytic parameters for some phosphotriesters were also recorded using SDS at concentrations 0.01 and 0.1% (w/v). Lactone hydrolysis assays were performed in lactonase buffer (Bicine 2.5 mM pH 8.3, NaCl 150 mM, CoCl₂ 0.2 mM, Cresol purple 0.25 mM and 0.5% DMSO) using cresol purple (pK_a 8.3 at 25°C) as pH indicator to follow the acidification related to the lactone ring hydrolysis. Molar coefficient extinction was measured by recording absorbance of the buffer over a range of acetic acid concentrations (0–0.35 mM). The absorbance values versus acetic acid concentration were fitted to a linear regression (GraphPad Prism 5 software) with a slope corresponding to molar extinction coefficient (see Table S2). For all experiments, each point was made in triplicate and the Gen5.1 software was used to evaluate the initial velocity at each substrate concentration. Mean values were fitted to the Michaelis-Menten equation using Graph-Pad Prism 5 software to obtain the catalytic parameters. In the case of C4 AHL hydrolysis for which the substrate concentration that enable to determine the enzyme V_max could not be reached, the catalytic efficiency has been determined by fitting the linear part of the Michaelis-Menten plot to a linear regression.

High-resolution HDX data of the AppA–PpsR system from Rhodobacter sphaeroides [23 (link), 24 ] was used to benchmark Hexicon 2. Recombinant protein expression, purification and complex formation as well as continuous labeling HDX have been described previously [24 ]. Briefly, AppA (200 μM) and PpsR (400 μM) as well as the preformed AppA–PpsR₂ complex (200 μM) were diluted 20-fold in D₂O buffer containing 10 mM CHES pD 9.5, 150 mM NaCl, and 5% glycer(ol-d3). Aliquots (20 pmol AppA or AppA–PpsR₂, 40 pmol PpsR) were removed after 15, 60, 300, and 1200 s and the reaction was quenched in ice-cold aqueous 200 mM ammonium formate buffer pH 2.6, directly followed by injection into the LC-MS setup (Shimadzu Prominence HPLC, Shimadzu, Duisburg, Germany). The chromatography setup was cooled to 0.5°C in a water/ethylene glycol bath and consisted of a 2 cm guard column (Discovery Bio C18, Supelco, Bellefonte, PA) for desalting and a 10 cm analytical column (Discovery Bio Wide Pore C18 10 cm × 1 mm; 3 μm particle size, Supelco, Bellefonte, PA). The pepsin column (Applied Biosystems, Darmstadt, Germany) was operated at 10°C for on-line proteolysis during a contact time of 1 min. Separation of peptides was achieved by a 20 min acetonitrile gradient (15%-50% in H₂O with 0.6% formic acid) prior to injection into a maXis UHR-TOF (Bruker, Bremen, Germany) mass spectrometer. LC-MS spectra were acquired at a scan rate of 0.5 Hz and filtered with an intensity threshold of 100 counts. Peak detection was performed with the Apex algorithm provided by the acquisition software (Compass DataAnalysis 4.0, Bruker, Bremen, Germany), using a peak width setting of 0.02 Da.

The purified SARS-CoV-2 NSP3 Mac1 protein was concentrated to 47 mg/mL, and crystallisation drops were set-up in MRC two-well crystallization microplates (Swissci, Buckinghamshire, UK) using the Mosquito Crystal robot (TTP Labtech, Cambridgeshire, UK) with protein to reservoir ratios of 1:1 and 1:2, in a 150 nl total volume equilibrated against 75 µL of reservoir solution containing 100 mM CHES pH 9.5 and 30% PEG3000. To ease crystallisation for soaking experiments, ~5 crystals were harvested and prepared as seed stock using a Seed Bead Kit (Hampton Research, Aliso Viejo, CA, US) in 100 nl of reservoir solution. An amount of 20 nl of a 1:500 dilution of the resulting seed stock was added to the crystallisation experiments. The compounds were soaked into crystals by adding 0.5 µL of dissolved compounds directly to the crystallisation drops. After incubation for 1–3 h, the crystals were harvested using reservoir solution supplemented with 20% ethylene glycol (v/v) as a cryo-protectant prior to flash freezing in liquid nitrogen. X-ray data were collected at beamline I03 at Diamond Light Source (Rutherford Appleton Laboratory, Harwell, UK) and data collection statistics are given in Supplementary Table S1.
The X-ray data were processed using the XIA2-DIALS platform [42 (link)], and phase information was obtained using the molecular replacement method with PHASER [43 (link)] using 7KQP as template. Atomic models were improved following consecutive cycles of manual building in COOT [44 (link)] and structure refinement in REFMAC5 [45 (link)]. The structures were refined to good Ramachandran statistics, and MolProbity [46 (link)] was used to validate the models prior to deposition in the PDB. The processing and refinement statistics are given in Supplementary Table S1. Structural alignments and analyses, as well as figure preparation, were carried out using PyMol (Molecular Graphics System, Version 2.3.3 Schrӧdinger, LLC., New York, NY, USA).

EPI data version 4.6 was used to code and enter the data, and STATA version 14.2 was used to analyze it. The data was summarized using descriptive statistics. A probit regression model was used to identify co-variants that affected CBHI scheme participation. A propensity score matching analysis was used to assess the impact of community-based health insurance on catastrophic healthcare expenditure.
Total household expenditure was calculated as food expenditure, which was estimated at the household level from purchased food staff and food from harvest or stock spent and loaned for the last 7 days. Then all these expenditure-related variables were converted to monthly figures plus non-food expenditure. Nonfood expenditure was estimated at the household level for the last 12 months from clothes and related expenditure, housing and related expenditure, social obligations, health expenditure, education expenditure, agricultural inputs and livestock, and death-related expenditure. Then all these expenditure-related variables were converted to monthly figures. CHE is calculated as the burden of OOP health expenditure as a nominator, from total household expenditure as a denominator at a 10% threshold, or OOP from non-food expenditure as a denominator at a 40% threshold categorized as catastrophic health expenditure. OOP payments paid by households at the point they received health services, consultation fees, drugs, hospital bills, traditional medicine, loading, board, food, shelter, and transportation for health, both inpatient and outpatient, for the last 12 months, and all these expenditures converted to monthly figures were included in the data analysis.
The comparison of households’ characteristics among insured and uninsured households, t-value with 95% CI, was computed using two-sample (independent sample) t-tests.
A chi-square (χ2) test was used to compare catastrophic health expenditure between insured and uninsured households, and cronbach’s alpha was used to assess tool reliability. The probit regression model is a method of fitting to compare the relationship of enrolling in the CBHI scheme to the co-variants. Those variables that affect participation in the scheme are selected and generated for PSM analysis. The propensity matching method constructs a statistical comparison group that is based on a model of the probability of participating in the treatment T conditional on observed characteristics X, or the propensity score: (T = 1|X) P (X) = PR (T = 1 |X). P (X) = PR (T = 1 |X) This shows that, under certain assumptions, matching on P (X) is as good as matching on X. Enrolled participants are then matched on the basis of this probability, or propensity score, to non-enrolled. The average treatment effect of the CBHI is then calculated as the mean difference in CHEs across these two groups. The necessary assumptions for the identification of the CBHI effect are conditional independence and the presence of common support. Conditional independence states, which give a set of observable covariates X that are not affected by treatment, and potential outcomes Y are independent of treatment assignment T. This assumption is also called un-confoundedness, and it implies that uptake of the CBHI is based entirely on observed characteristics. If unobserved characteristics determine CBHI participation, conditional independence will be violated, and propensity matching is not an appropriate method. Another assumption is the common support or overlap condition: 0 < P (Ti = 1|Xi) < 1. This condition reveals that treatment of the treated has a comparison to the untreated “close” in the propensity score distribution. Specifically, the effectiveness of propensity score matching also depends on having a large and roughly equal number of insured and uninsured observations so that a substantial region is common. The first matching technique is the nearest-neighbor, in which each treated unit is matched to the unit in the comparison group that presents the closest estimated propensity score. After matching, the result of the treated units is compared with the result of matched control units. The second matching method is caliper matching, which holds matching with replacement only among propensity scores within a certain range. The third matching technique is kernel matching, which assigns higher weight to observations close in terms of propensity score to a treated individual and lower weight to more distant observations.