The spatio-temporal reduction and oxidation of protein thiols is an essential mechanism in signal transduction in all kingdoms of life. Thioredoxin (Trx) family proteins efficiently catalyze thiol-disulfide exchange reactions and the proteins are widely recognized for their importance in the operation of thiol switches. Trx family proteins have a broad and at the same time very distinct substrate specificity – a prerequisite for redox switching. Despite of multiple efforts, the true nature for this specificity is still under debate. Here, we comprehensively compare the classification/clustering of various redoxins from all domains of life based on their similarity in amino acid sequence, tertiary structure, and their electrostatic properties. We correlate these similarities to the existence of common interaction partners, identified in various previous studies and suggested by proteomic screenings. These analyses confirm that primary and tertiary structure similarity, and thereby all common classification systems, do not correlate to the target specificity of the proteins as thiol-disulfide oxidoreductases. Instead, a number of examples clearly demonstrate the importance of electrostatic similarity for their target specificity, independent of their belonging to the Trx or glutaredoxin subfamilies.

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Measuring the similarity between point clouds is required in many areas. In autonomous driving, point clouds for 3D perception are estimated from camera images but these estimations are error-prone. Furthermore, there is a lack of measures for quality quantification using ground truth. In this paper, we derive conditions point cloud comparisons need to fulfill and accordingly evaluate the Chamfer distance, a lower bound of the Gromov Wasserstein metric, and the ratio measure. We show that the ratio measure is not affected by erroneous points and therefore introduce the new measure “average ratio”. All measures are evaluated and compared using exemplary point clouds. We discuss characteristics, advantages and drawbacks with respect to interpretability, noise resistance, environmental representation, and computation.