Research Highlights

Proposed mechanism of synergy between HCV drugs and remdesivir
Proposed mechanism of synergy between HCV drugs, that inhibit SARS-CoV2 proteases, and the antiviral drug remdesivir, which inhibits the function of the SARS-CoV2 replicase

Hepatitis C virus drugs synergize with remdesivir to suppress SARS-CoV-2 replication

Effective control of COVID-19 requires antivirals directed against SARS-CoV-2 virus. We assessed ten available HCV protease inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS- CoV-2 Mpro and HCV NS3/4A proteases, and virtual docking experiments show that all ten HCV drugs can potentially bind into the Mpro binding cleft. Seven of these HCV drugs also inhibit SARS-CoV-2 Mpro protease activity. These same seven HCV drugs inhibit SARS-CoV-2 virus replication in cell culture, Some of these drugs also synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, thereby increasing remdesivir inhibitory activity as much as 10-fold. Careful examination of these data suggests that these HCV drugs also function through a second target. Surprisingly, some of these HCV drugs were found to also bind to and inhibit the SARS-CoV-2 PLpro protease. The synergistic activity of HCV drugs with remdesivir correlates with their ability to inhibit PLpro, indicating this function is the primary basis for their synergy with remdesivir.

Image of interdomain dynamis
The complex formed between MLV integrase and the chromosomal protein BRD3 creates an interdomain linker, which has restricted conformational flexiblity

A common binding motif in the ET domain of BRD3 forms polymorphic structural interfaces with host and viral proteins

The extra-terminal (ET) domain of BRD3 is conserved among BET proteins (BRD2, BRD3, BRD4), interacting with multiple host and viral protein-protein networks. Solution NMR studies of complexes formed between BRD3-ET domain with either the 79-residue murine leukemia virus integrase (IN) C-terminal domain (IN329-408), or its 22-residue IN tail peptide (TP) (IN386-407) alone, reveal reveal a 10-residue linker region (IN379-388) tethering the SH3 domain (IN329-378) to the ET-binding motif (IN389-405)-ET complex. This linker has restricted flexibility, impacting the potential range of interdomain orientations in the IN – nucleosome complex.

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Schematic Depiction of process for simulating NOESY peak and resonance assignment data for CASP-NMR target N0968S1

Protein structure prediction assisted with sparse NMR data in CASP13

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.