Computationally Designed Peptide Illuminates Coronavirus Protease Activity

Computational peptide improves coronavirus protease detection.
Two COVID-19 test tubes with red caps on a blue surface.
Current coronavirus tests detect viral material, but may not show whether the virus is still active.Maksim Goncharenok/ Pexels
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Current coronavirus diagnostics mainly rely on nucleic acid amplification, antibody detection, or viral antigen detection. Although real-time quantitative polymerase chain reaction (RT-qPCR) is highly sensitive, viral RNA can remain detectable long after infectivity has declined, making it difficult to distinguish active infection from residual genetic material. Antibody tests may respond too late to diagnose current infection, while antigen tests generally detect the presence of structural proteins rather than viral activity. By contrast, 3CLPro cleaves viral polyproteins at multiple sites and is indispensable for coronavirus replication. Its highly conserved substrate-binding pocket therefore offers a promising basis for developing activity-sensitive analytical tools that can complement existing detection methods.

A study published in Engineering in Life Sciences on 22 June 2026 by Tao Wang's & Cheng Zhu's team, Tianjin University, reports an optimized synthetic substrate that substantially improves the responsiveness of luciferase- and green fluorescent protein-based 3CLPro sensors1.

The researchers began with the crystal structure of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 3CLPro bound to its native eight-amino-acid substrate, SAVLQSGF. Because the enzyme shows more than 96% sequence identity among global coronavirus strains and retains a particularly conserved substrate-recognition pocket, they used Rosetta FastDesign and the deep-learning model ProteinMPNN to generate alternative peptide sequences while preserving the cleavage site. AlphaFold3 predicted that the candidates occupied the catalytic pocket in conformations resembling the native substrate. Of four selected designs, the peptide PVILQYTT, designated P1, showed a Rosetta energy score of −75.8 Rosetta energy units, compared with −56.1 for the native sequence. A sequence-similarity search also indicated that P1 was distinct from eukaryotic proteins, supporting its orthogonality. The team then expressed 3CLPro at different levels in human embryonic kidney 293T cells to test three reporter architectures. In the first, the peptide was inserted into Gaussia luciferase (Gluc), so protease cleavage reduced luminescence. The native substrate produced a 23% reduction, whereas P1 produced a 67% decrease. Across increasing 3CLPro levels, P1-IN-Gluc displayed a dynamic response range of 52%–80%, compared with 18%–30% for the native sequence. In a second design, Gluc was anchored inside the cell by a transmembrane helix. Cleavage released the enzyme for secretion, creating a gain-of-signal response. A flexible linker generated a 47% signal increase and a dynamic range of 42%–55%, outperforming a rigid linker. For fluorescence-based detection, P1 was incorporated into FlipGFP, which switches from a dark to a fluorescent state after protease cleavage. 3CLPro increased fluorescence 3.9-fold with P1 but only 1.2-fold with the native substrate. As 3CLPro expression rose sixfold, the P1 sensor produced a 2.0- to 5.0-fold fluorescence increase. Finally, P1-IN-Gluc responded dose-dependently to the 3CLPro inhibitor Paxlovid: luminescence rose from a baseline of 7.5×104 to 1.5×105 and 3.0×105 after treatment with 50 and 100 nanomolar, respectively.

White beads spelling “COVID-19” among spiky coronavirus models on a red background.
A computationally designed peptide shows promise for tracking coronavirus protease activity in cells.Edward Jenner/ Pexels

Overall, the study demonstrates how computational protein design can transform a conserved viral enzyme substrate into several interchangeable activity-reporting modules. The results establish a cell-based proof of concept rather than a clinical diagnostic test, and further work must evaluate the sensors in virus-infected samples and determine their specificity, sensitivity, and practical detection limits. Nevertheless, the approach provides a flexible foundation for measuring coronavirus protease activity, comparing candidate inhibitors, and developing future assays that report functional viral activity alongside conventional measurements of viral presence.

Reference:

1) https://www.maxapress.com/article/doi/10.48130/els-0026-0002

(Newswise/HG)

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