Development, characterization, and operational details of an enzymatic, air-segmented continuous-flow analytical method for colorimetric determination of nitrate + nitrite in natural-water samples is described. This method is similar to U.S. Environmental Protection Agency method 353.2 and U.S. Geological Survey method 1-2545-90 except that nitrate is reduced to nitrite by soluble nitrate reductase (NaR, EC 1.6.6.1) purified from corn leaves rather than a packed-bed cadmium reactor. A three-channel, air-segmented continuous-flow analyzer-configured for simultaneous determination of nitrite (0.020-1.000 mg-N/L) and nitrate + nitrite (0.05-5.00 mg-N/L) by the nitrate reductase and cadmium reduction methods-was used to characterize analytical performance of the enzymatic reduction method. At a sampling rate of 90 h(-1), sample interaction was less than 1% for all three methods. Method detection limits were 0.001 mg of NO2- -N/L for nitrite, 0.003 mg of NO3-+ NO2- -N/L for nitrate + nitrite by the cadmium-reduction method, and 0.006 mg of NO3- + NO2- -N/L for nitrate + nitrite bythe enzymatic-reduction method. Reduction of nitrate to nitrite by both methods was greater than 95% complete overthe entire calibration range. The difference between the means of nitrate + nitrite concentrations in 124 natural-water samples determined simultaneously bythe two methods was not significantly different from zero at the p = 0.05 level.

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Advances in structural biology and the exponential increase in the amount of high-quality experimental structural data available in the Protein Data Bank has motivated numerous studies to tackle the grand challenge of predicting protein structures. In 2020 AlphaFold2 revolutionized the field using a combination of artificial intelligence and the evolutionary information contained in multiple sequence alignments. Antibodies are one of the most important classes of biotherapeutic proteins. Accurate structure models are a prerequisite to advance biophysical property predictions and consequently antibody design. Specialized tools used to predict antibody structures based on different principles have profited from current advances in protein structure prediction based on artificial intelligence. Here, we emphasize the importance of reliable protein structure models and highlight the enormous advances in the field, but we also aim to increase awareness that protein structure models, and in particular antibody models, may suffer from structural inaccuracies, namely incorrect cis-amide bonds, wrong stereochemistry or clashes. We show that these inaccuracies affect biophysical property predictions such as surface hydrophobicity. Thus, we stress the importance of carefully reviewing protein structure models before investing further computing power and setting up experiments. To facilitate the assessment of model quality, we provide a tool “TopModel” to validate structure models.