Rapid response to emerging biomedical challenges and threats

Area of Science:

• Biomedical research
• Structural biology
• Infectious disease epidemiology

Background:

• The COVID-19 pandemic spurred a massive publication output, including nearly 100,000 papers and 1,000 SARS-CoV-2 macromolecular models in the Protein Data Bank.
• The rapid influx of structural data necessitates robust methods for quality assessment.

Purpose of the Study:

• To describe a novel approach for evaluating the quality and correctness of large-scale structural data related to COVID-19.
• To present a template resource (https://covid19.bioreproducibility.org) for assessing biomedical data quality during global health emergencies.

Main Methods:

• Utilized the https://covid19.bioreproducibility.org resource for data evaluation.
• Developed a methodology applicable to massive datasets of structural information.

Main Results:

• The described approach provides a framework for assessing the quality of numerous COVID-19 structural models.
• The resource facilitates the evaluation of data correctness and model quality.

Conclusions:

• Implementing this methodology can significantly reduce information noise in biomedical research.
• Widespread adoption of this approach can substantially improve the reproducibility of scientific studies, particularly during large-scale health crises.