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An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.
The HCV NS5A protein plays multiple roles during viral replication, including viral genome replication and virus particle assembly. The crystal structures of the NS5A N-terminal domain indicated the potential existence of the NS5A dimers formed via at least two or more distinct dimeric interfaces. However, it is unknown whether these different forms of NS5A dimers are involved in its numerous functions. To address this question, we mutated the residues lining the two different NS5A dimer interfaces and determined their effects on NS5A self-interaction, NS5A-cyclophilin A (CypA) interaction, HCV RNA replication and infectious virus production. We found that the mutations targeting either of two dimeric interfaces disrupted the NS5A self-interaction in cells. The NS5A dimer-interrupting mutations also inhibited both viral RNA replication and infectious virus production with some genotypic differences. We also determined that reduced NS5A self-interaction was associated with altered NS5A-CypA interaction, NS5A hyperphosphorylation and NS5A subcellular localization, providing the mechanistic bases for the role of NS5A self-interaction in multiple steps of HCV replication. The NS5A oligomers formed via different interfaces are likely its functional form, since the residues at two different dimeric interfaces played similar roles in different aspects of NS5A functions and, consequently, HCV replication. In conclusion, this study provides novel insight into the functional significance of NS5A self-interaction in different steps of the HCV replication, potentially, in the form of oligomers formed via multiple dimeric interfaces.