Understanding How Coronavirus Disguises Itself to Hide Inside Host Cells and Replicate May Help Develop COVID-19 Treatment
By HospiMedica International staff writers Posted on 28 Jul 2020 |
Illustration
Researchers have discovered that the SARS-CoV-2 virus molecules make themselves unrecognizable to host cells by tricking the immune system with camouflage, thus paving the way for drug development for the treatment of COVID-19.
Researchers at The University of Texas Health Science Center (San Antonio, TX, USA) resolved the structure of an enzyme called nsp16, which the coronavirus produces and then uses to modify its messenger RNA cap. These modifications fool the cell, as a result of which the viral messenger RNA becomes considered as part of the cell’s own code and not foreign. Deciphering the 3D structure of nsp16 paves the way for rational design of antiviral drugs for COVID-19 and other emerging coronavirus infections, according to Dr. Yogesh Gupta, PhD, the study lead author from the Joe R. and Teresa Lozano Long School of Medicine at UT Health San Antonio. The drugs, new small molecules, would inhibit nsp16 from making the modifications. The immune system would then pounce on the invading virus, recognizing it as foreign.
“Yogesh’s work discovered the 3D structure of a key enzyme of the COVID-19 virus required for its replication and found a pocket in it that can be targeted to inhibit that enzyme. This is a fundamental advance in our understanding of the virus,” said study coauthor Robert Hromas, MD, professor and dean of the Long School of Medicine.
Related Links:
The University of Texas Health Science Center
Researchers at The University of Texas Health Science Center (San Antonio, TX, USA) resolved the structure of an enzyme called nsp16, which the coronavirus produces and then uses to modify its messenger RNA cap. These modifications fool the cell, as a result of which the viral messenger RNA becomes considered as part of the cell’s own code and not foreign. Deciphering the 3D structure of nsp16 paves the way for rational design of antiviral drugs for COVID-19 and other emerging coronavirus infections, according to Dr. Yogesh Gupta, PhD, the study lead author from the Joe R. and Teresa Lozano Long School of Medicine at UT Health San Antonio. The drugs, new small molecules, would inhibit nsp16 from making the modifications. The immune system would then pounce on the invading virus, recognizing it as foreign.
“Yogesh’s work discovered the 3D structure of a key enzyme of the COVID-19 virus required for its replication and found a pocket in it that can be targeted to inhibit that enzyme. This is a fundamental advance in our understanding of the virus,” said study coauthor Robert Hromas, MD, professor and dean of the Long School of Medicine.
Related Links:
The University of Texas Health Science Center
Latest COVID-19 News
- Low-Cost System Detects SARS-CoV-2 Virus in Hospital Air Using High-Tech Bubbles
- World's First Inhalable COVID-19 Vaccine Approved in China
- COVID-19 Vaccine Patch Fights SARS-CoV-2 Variants Better than Needles
- Blood Viscosity Testing Can Predict Risk of Death in Hospitalized COVID-19 Patients
- ‘Covid Computer’ Uses AI to Detect COVID-19 from Chest CT Scans
- MRI Lung-Imaging Technique Shows Cause of Long-COVID Symptoms
- Chest CT Scans of COVID-19 Patients Could Help Distinguish Between SARS-CoV-2 Variants
- Specialized MRI Detects Lung Abnormalities in Non-Hospitalized Long COVID Patients
- AI Algorithm Identifies Hospitalized Patients at Highest Risk of Dying From COVID-19
- Sweat Sensor Detects Key Biomarkers That Provide Early Warning of COVID-19 and Flu
- Study Assesses Impact of COVID-19 on Ventilation/Perfusion Scintigraphy
- CT Imaging Study Finds Vaccination Reduces Risk of COVID-19 Associated Pulmonary Embolism
- Third Day in Hospital a ‘Tipping Point’ in Severity of COVID-19 Pneumonia
- Longer Interval Between COVID-19 Vaccines Generates Up to Nine Times as Many Antibodies
- AI Model for Monitoring COVID-19 Predicts Mortality Within First 30 Days of Admission
- AI Predicts COVID Prognosis at Near-Expert Level Based Off CT Scans