We discuss the diverse functions of proteases in the context of their biotechnological and medical significance, as well as analytical approaches used to determine the functional activity of these enzymes. An insight into modern approaches to studying the kinetics and specificity of proteases, based on spectral (absorption, fluorescence), mass spectrometric, immunological, calorimetric, and electrochemical methods of analysis is given. We also examine in detail electrochemical systems for determining the activity and specificity of proteases. Particular attention is given to exploring innovative electrochemical systems based on the detection of the electrochemical oxidation signal of amino acid residues, thereby eliminating the need for extra redox labels in the process of peptide synthesis. In the review, we highlight the main prospects for the further development of electrochemical systems for the study of biotechnologically and medically significant proteases, which will enable the miniaturization of the analytical process for determining the catalytic activity of these enzymes. 

The multifaceted role of proteases and modern analytical methods for investigation of their catalytic activity.

By utilizing functional genetic variation within the participants of the UK Biobank project for a largescale PheWAS study we attempted to get a better understanding of how the set of human proteases and their endogenous inhibitors are involved in common diseases. Focusing on known human proteases, their inhibitors, and known substrates, we computed their ranked-biased similarity from phenome-wide association results. Putative regulatory networks were constructed from 250 high-scoring pairs of proteases and related genes. This analysis suggested thirteen network modules, five diagnosis-based and eight biomarker-based. Through genetic associations and published literature on module members, the modules could be classified into different disease modalities including cholesterol homeostasis and high blood pressure.

Predicting protease networks through human genetics

Since there is a large variation in the symptoms shown by persons affected with ASD, analyzing genetics data using a case-control design is not straightforward. To avoid the difficult problem of defining heterogeneous groups, we used four different methods to compute a latent representation of a merged set of three psychometric tests. Computing the genetic contribution of each representation using a subset of participants with genetic data, we showed that factor analysis as well as variable autoencoders separates information contained in psychometric tests into genetically distinct phenotypic domains. Using the individual-level loadings of the domains as quantitative phenotypes in genome-wide association studies we detected statistically significant genetic associations in the domain related to insistence on routine, as well as suggestive genetic signals in other domains. We hope that these results can suggest possible domain-specific interventions in the future.

Genetic associations of dimensional autistic phenotypes

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Predicting protease networks through human genetics

Genetic associations of dimensional autistic phenotypes