You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:

- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.
- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.
- Prevention: The content covers measures to prevent the spread of COVID-19.
- Mechanism: The paragraph explains the biological mechanisms of the virus.
- Case Report: The content is a report of a COVID-19 case.
- Transmission: The paragraph discusses how COVID-19 is transmitted.
- Diagnosis: The content is about diagnosing COVID-19 in individuals.

Your classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: ["Topic1", "Topic2", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.

Article:

By repurposing DNICs designed for other medicinal purposes, the possibility of protease inhibition was investigated in silico using AutoDock 4.2.6 (AD4) and in vitro via a FRET protease assay. AD4 was validated as a predictive computational tool for coordinatively unsaturated DNIC binding using the only known crystal structure of a protein-bound DNIC, PDB-1ZGN (calculation RMSD = 1.77). From the in silico data the dimeric DNICs TGTA-RRE, [(mu-S-TGTA)Fe(NO)2]2 (TGTA = 1-thio-beta-d-glucose tetraacetate) and TG-RRE, [(mu-S-TG)Fe(NO)2]2 (TG = 1-thio-beta-d-glucose) were identified as promising leads for inhibition via coordinative inhibition at Cys-145 of the SARS-CoV-2 Main Protease (SC2Mpro). In vitro studies indicate inhibition of protease activity upon DNIC treatment, with an IC50 of 38 +/- 2 muM for TGTA-RRE and 33 +/- 2 muM for TG-RRE. This study presents a simple computational method for predicting DNIC-protein interactions; the in vitro study is consistent with in silico leads.
["Mechanism", "Treatment"]