Computer-Designed Antiviral Proteins Protect Lab-Grown Human Cells from SARS-CoV-2
By HospiMedica International staff writers Posted on 14 Sep 2020 |
Image: Computer designed antivirals against the pandemic coronavirus (Photo courtesy of University of Washington)
Researchers from the University of Washington (UW) Medicine (Seattle, Wash., USA) have shown that computer-designed, synthetic miniproteins act as potent, stable antivirals that block coronavirus infection of cultured human cells.
In the experiments, the lead antiviral candidate, named LCB1, rivaled the best-known SARS-CoV-2 neutralizing antibodies in its protective actions. LCB1 is currently being evaluated in rodents. Coronaviruses are studded with spike proteins that latch onto human cells to enable the virus to break in and infect them. Drugs that interfere with this entry mechanism could treat or even prevent infection.
Researchers from the Institute for Protein Design at the UW Medicine used computer design to originate new proteins that bind tightly to SARS-CoV-2 spike protein and obstruct it from infecting cells. Beginning in January, more than two million candidate spike-binding proteins were designed on the computer. Over 118,000 were then produced and tested in the lab.
The researchers created antiviral proteins through two approaches. First, a segment of the ACE2 receptor, which SARS-CoV-2 naturally binds to on the surface of human cells, was incorporated into a series of small protein scaffolds. Second, completely synthetic proteins were designed from scratch. The latter method produced the most potent antivirals, including LCB1, which is roughly six times more potent on a per mass basis than the most effective monoclonal antibodies reported thus far. To confirm that the new antiviral proteins attached to the coronavirus spike protein as intended, the team collected snapshots of the two molecules interacting by using cryo-electron microscopy.
"The hyperstable minibinders provide promising starting points for new SARS-CoV-2 therapeutics," the antiviral research team wrote in their study pre-print, "and illustrate the power of computational protein design for rapidly generating potential therapeutic candidates against pandemic threats."
"Although extensive clinical testing is still needed, we believe the best of these computer-generated antivirals are quite promising," said lead author Longxing Cao, a postdoctoral scholar at the Institute for Protein Design. "They appear to block SARS-CoV-2 infection at least as well as monoclonal antibodies, but are much easier to produce and far more stable, potentially eliminating the need for refrigeration."
"Our success in designing high-affinity antiviral proteins from scratch is further proof that computational protein design can be used to create promising drug candidates," said senior author and Howard Hughes Medical Institute Investigator David Baker, professor of biochemistry at the UW School of Medicine and head of the Institute for Protein Design.
Related Links:
University of Washington
In the experiments, the lead antiviral candidate, named LCB1, rivaled the best-known SARS-CoV-2 neutralizing antibodies in its protective actions. LCB1 is currently being evaluated in rodents. Coronaviruses are studded with spike proteins that latch onto human cells to enable the virus to break in and infect them. Drugs that interfere with this entry mechanism could treat or even prevent infection.
Researchers from the Institute for Protein Design at the UW Medicine used computer design to originate new proteins that bind tightly to SARS-CoV-2 spike protein and obstruct it from infecting cells. Beginning in January, more than two million candidate spike-binding proteins were designed on the computer. Over 118,000 were then produced and tested in the lab.
The researchers created antiviral proteins through two approaches. First, a segment of the ACE2 receptor, which SARS-CoV-2 naturally binds to on the surface of human cells, was incorporated into a series of small protein scaffolds. Second, completely synthetic proteins were designed from scratch. The latter method produced the most potent antivirals, including LCB1, which is roughly six times more potent on a per mass basis than the most effective monoclonal antibodies reported thus far. To confirm that the new antiviral proteins attached to the coronavirus spike protein as intended, the team collected snapshots of the two molecules interacting by using cryo-electron microscopy.
"The hyperstable minibinders provide promising starting points for new SARS-CoV-2 therapeutics," the antiviral research team wrote in their study pre-print, "and illustrate the power of computational protein design for rapidly generating potential therapeutic candidates against pandemic threats."
"Although extensive clinical testing is still needed, we believe the best of these computer-generated antivirals are quite promising," said lead author Longxing Cao, a postdoctoral scholar at the Institute for Protein Design. "They appear to block SARS-CoV-2 infection at least as well as monoclonal antibodies, but are much easier to produce and far more stable, potentially eliminating the need for refrigeration."
"Our success in designing high-affinity antiviral proteins from scratch is further proof that computational protein design can be used to create promising drug candidates," said senior author and Howard Hughes Medical Institute Investigator David Baker, professor of biochemistry at the UW School of Medicine and head of the Institute for Protein Design.
Related Links:
University of Washington
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