A Genetic Interaction between Hepatitis C Virus NS4B and NS3 Is Important for RNA Replication is a research paper published in Journal of Virology (2008). On theSindex it has a DataRank of 2.5. It has been cited 45 times, with 44 citing works in its 1-hop citation network.
ABSTRACT Hepatitis C virus (HCV) nonstructural protein 4B (NS4B), a poorly characterized integral membrane protein, is thought to function as a scaffold for replication complex assembly; however, functional interactions with the other HCV nonstructural proteins within this complex have not been defined. We report that a Con1 chimeric subgenomic replicon containing the NS4B gene from the closely related H77 isolate is defective for RNA replication in a transient assay, suggesting that H77 NS4B is unable to productively interact with the Con1 replication machinery. The H77 NS4B sequences that proved detrimental for Con1 RNA replication resided in the predicted N- and C-terminal cytoplasmic domains as well as the central transmembrane region. Selection for Con1 derivatives that could utilize the entire H77 NS4B or hybrid Con1-H77 NS4B proteins yielded mutants containing single amino acid substitutions in NS3 and NS4A. The second-site mutations in NS3 partially restored the replication of Con1 chimeras containing the N-terminal or transmembrane domains of H77 NS4B. In contrast, the deleterious H77-specific sequences in the C terminus of NS4B, which mapped to a cluster of four amino acids, were completely suppressed by second-site substitutions in NS3. Collectively, these results provide the first evidence for a genetic interaction between NS4B and NS3 important for productive HCV RNA replication.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.574
From this paper's citation signal
Citation Network Contribution
1.9
From 41 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 23% comes from its base citations and 77% from the citation network (41 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
Click a node to highlight its connections. Use scroll to zoom. Drag to pan.