Divide et impera: An In Silico Screening Targeting HCMV ppUL44 Processivity Factor Homodimerization Identifies Small Molecules Inhibiting Viral Replication

Abstract

Human cytomegalovirus (HCMV) is a leading cause of severe diseases in immunocompromised individuals, including AIDS patients and transplant recipients, and in congenitally infected newborns. The utility of available drugs is limited by poor bioavailability, toxicity, and emergence of resistant strains. Therefore, it is crucial to identify new targets for therapeutic intervention. Among the latter, viral protein–protein interactions are becoming increasingly attractive. Since dimerization of HCMV DNA polymerase processivity factor ppUL44 plays an essential role in the viral life cycle, being required for oriLyt-dependent DNA replication, it can be considered a potential therapeutic target. We therefore performed an in silico screening and selected 18 small molecules (SMs) potentially interfering with ppUL44 homodimerization. Antiviral assays using recombinant HCMV TB4-UL83-YFP in the presence of the selected SMs led to the identification of four active compounds. The most active one, B3, also efficiently inhibited HCMV AD169 strain in plaque reduction assays and impaired replication of an AD169-GFP reporter virus and its ganciclovir-resistant counterpart to a similar extent. As assessed by Western blotting experiments, B3 specifically reduced viral gene expression starting from 48 h post infection, consistent with the inhibition of viral DNA synthesis measured by qPCR starting from 72 h post infection. Therefore, our data suggest that inhibition of ppUL44 dimerization could represent a new class of HCMV inhibitors, complementary to those targeting the DNA polymerase catalytic subunit or the viral terminase complex.

Toward inhibition of human cytomegalovirus replication with compounds targeting cellular proteins

“…To supersede the direct-acting antiviral drugs discussed above, novel direct-acting antiviral compounds that inhibit productive HCMV replication have been sought. In general, efforts to find such compounds have fallen into two broad areas: first, identifying compounds that target the function of the HCMV protein immediate-early 2 (IE2) using a cell-based assay to monitor transcriptional transactivation by IE2 [28][29][30][31][32][33][34][35]; and second, identifying compounds that inhibit HCMV protein-protein interactions required for HCMV replication by employing either in vitro or in silico screens to understand what compounds interact with the HCMV DNA polymerase [36][37][38]. However, none of these compounds have progressed toward clinical use for various reasons.…”

“…Moreover, such is the potential of this machine learning technology to uncover new drug targets, it would be useful to further characterize kinase targets within kinase inhibitor collections already screened for anti-HCMV compounds [73,74] and apply the machine learning approach mentioned above [78] to that data. Indeed, if possible, it would be useful to understand what data can be mined using machine learning from previous screens for anti-HCMV compounds [28,36,38,42,64] and understand how future compound screens can be designed to take advantage of developments in machine learning data analysis.…”

“…Bioinformatics may be the area that requires the most investment, as there are other technologies that could be used to identify and develop anti-HCMV compounds. For example, there are few examples where in silico screening of compounds has been used to identify inhibitors of HCMV protein function [36]. This approach could be aided by recent advances made elsewhere, including utilizing the structures of viral and cellular proteins generated by the machine learning tool AlphaFold [94][95][96][97] in in silico screening.…”