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
Channels
Artificial Intelligence
view channel
Algorithm Identifies Cardiac Arrest Hotspots to Guide AED Placement
Out-of-hospital sudden cardiac arrest is common and usually fatal, and survival depends on rapid defibrillation. Many communities deploy automated external defibrillators without precise guidance, which... Read moreAI Analysis of Pericardial Fat Refines Long-Term Heart Disease Risk
Accurately identifying long-term cardiovascular disease risk in asymptomatic adults remains challenging for clinicians. Missed or underestimated risk delays preventive therapy and increases the chance... Read moreCritical Care
view channel
New Brain Stimulation Approach Targets Deep Brain Areas Without Surgery
Noninvasive modulation of deep brain structures remains a major hurdle for treating disorders tied to memory and emotion. The hippocampus is crucial to these functions, yet its depth has limited precise,... Read more
Standardized FMT Protocol May Improve Survival in Severe C. difficile Infection
Clostridioides difficile (C. difficile) infection is a leading cause of health care-acquired diarrhea, and the fulminant form carries high mortality. Many critically ill patients deteriorate despite intensive... Read moreSurgical Techniques
view channel
Single-Use System Enables Minimally Invasive Decompression for Lumbar Spinal Stenosis
Lumbar spinal stenosis is frequently driven by hypertrophic bone that narrows the canal and produces pain. Conventional decompression often relies on larger incisions and bulky retractors, adding time,... Read more
Angiography-Based Tool Matches Standard FFR for Coronary Revascularization Guidance
Cardiologists often need to determine whether coronary artery plaques are truly restricting blood flow before deciding on revascularization. The current standard, fractional flow reserve, requires vasoactive... Read more
Endoscope Enables Fallopian Tube Imaging and Cell Collection for Ovarian Cancer Surveillance
Early detection of ovarian cancer remains challenging because symptoms are nonspecific and available screening tests often fail to identify disease at a curable stage. Many high‑grade serous carcinomas... Read morePatient Care
view channel
Wearable Sleep Data Predict Adherence to Pulmonary Rehabilitation
Chronic obstructive pulmonary disease (COPD) is a long-term lung disorder that makes breathing difficult and often disturbs sleep, reducing energy for daily activities. Limited engagement in pulmonary... Read more
Revolutionary Automatic IV-Line Flushing Device to Enhance Infusion Care
More than 80% of in-hospital patients receive intravenous (IV) therapy. Every dose of IV medicine delivered in a small volume (<250 mL) infusion bag should be followed by subsequent flushing to ensure... Read moreHealth IT
view channel
Voice-Driven AI System Enables Structured GI Procedure Documentation
Documentation during gastrointestinal (GI) procedures often competes with real-time clinical decision-making and imposes a significant cognitive burden on physicians. Manual data entry and post-procedure... Read more
EMR-Based Tool Predicts Graft Failure After Kidney Transplant
Kidney transplantation offers patients with end-stage kidney disease longer survival and better quality of life than dialysis, yet graft failure remains a major challenge. Although a successful transplant... Read more
Printable Molecule-Selective Nanoparticles Enable Mass Production of Wearable Biosensors
The future of medicine is likely to focus on the personalization of healthcare—understanding exactly what an individual requires and delivering the appropriate combination of nutrients, metabolites, and... Read moreBusiness
view channel
New Partnership Expands Access to Predictive Tool for Patient Monitoring
Spacelabs Healthcare has signed an agreement with DEPTH Health, Inc. to make the Rothman Index available to hospitals and health systems through DEPTH’s Real-Time Advisor for Clinical Expert Routing (RACER)... Read more








