Enzymes suffer from high cost, complex purification, and low stability. Development of low‐cost artificial enzymes of comparative or higher effectiveness is desired. Given its complexity, it is desired to presume their activities prior to experiments. While computational approaches demonstrate success in modeling nanozyme activities, they require assumptions about the system to be made. Machine learning (ML) is an alternative approach towards data‐driven material property prediction achieving high performance even on multicomponent complex systems. Despite the growing demand for customized nanozymes, there is no open access nanozyme database. Here, a user‐friendly expandable database of >300 existing inorganic nanozymes is developed by data collection from >100 articles. Data analysis is performed to reveal the features responsible for catalytic activities of nanozymes, and new descriptors are proposed for its ML‐assisted prediction. A random forest regression (RFR) model for evaluation of nanozyme peroxidase activity is developed and optimized by correlation‐based feature selection and hyperparameter tuning, achieving performance up to R2 = 0.796 for Kcat and R2 = 0.627 for Km. Experiment‐confirmed unknown nanozyme activity prediction is also demonstrated. Moreover, the DiZyme expandable, open‐access resource containing the database, predictive algorithm, and visualization tool is developed to boost novel nanozyme discovery worldwide (https://dizyme.net).
Magnetically controlled enzymatic
composites have received much
attention for both therapeutic and industrial applications. Until
now, such materials have been composed of at least four components:
the enzyme, magnetic nanoparticles, their stabilizing components,
and an organic or inorganic (or hybrid) matrix as a carrier. However,
such compositions affect the magnetic response and the enzymatic activity,
and also pose obstacles for intravenous administration, because of
regulatory restrictions. Here, we present a methodology for the creation
of magnetic bioactive nanocomposites composed of only two biocompatible
components: an enzyme and magnetite nanoparticles. A series of magnetic
biocomposites with a full set of therapeutical and industrial proteins
(carbonic anhydrase, ovalbumin, horseradish peroxidase, acid phosphatase,
proteinase, and xylanase) were successfully created by the direct
entrapment of the proteins within a sol–gel magnetite matrix
specially developed for these aims. The activity of the entrapped
enzymes was studied at different temperatures and concentrations,
and it was found that they showed remarkable thermal stabilization
induced by the ferria matrix. For instance, entrapped carbonic anhydrase
catalyzed the decomposition of p-nitrophenylacetate
at a temperature of 90 °C, while free enzyme completely loses
activity and denaturates already at 70 °C. Magnetic characterization
of the obtained biomaterials is provided.
The paper classifies and generalizes innovative technologies for assessing the competencies and skills of the 21st century students in the education system. Taking into account the requirements of the international academic community, criteria for assessing students' competencies in the context of the formation of functional literacy, the ability to study and evaluate independence, global competence and meta-subject skills (soft skills) are considered. The necessity of ensuring continuity of assessment of students' competencies at the levels of general and higher education, as well as the results of formative assessment as an integrative innovative educational technology for the formation, control and evaluation of educational achievements, is emphasized. The possibilities of formative assessment for the development of meta-subject competencies of students are considered. It contains information on the innovative Evidence-Centered Design competency assessment methodology, which contributes to the further development of control and evaluation activities to ensure reliable and valid assessment of students’ achievements and quality assurance in a competency-based approach.
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