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Alternate recognition by dengue protease: Proteolytic and binding assays provide functional evidence beyond an induced-fit.
Behnam, Mira A M; Klein, Christian D.
Affiliation
  • Behnam MAM; Medicinal Chemistry, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Im Neuenheimer Feld 364, 69120, Heidelberg, Germany.
  • Klein CD; Medicinal Chemistry, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Im Neuenheimer Feld 364, 69120, Heidelberg, Germany. Electronic address: c.klein@uni-heidelberg.de.
Biochimie ; 2024 Jun 11.
Article in En | MEDLINE | ID: mdl-38871044
ABSTRACT
Proteases are key enzymes in viral replication, and interfering with these targets is the basis for therapeutic interventions. We previously introduced a hypothesis about conformational selection in the protease of dengue virus and related flaviviruses, based on conformational plasticity noted in X-ray structures. The present work presents the first functional evidence for alternate recognition by the dengue protease, in a mechanism based primarily on conformational selection rather than induced-fit. Recognition of distinct substrates and inhibitors in proteolytic and binding assays varies to a different extent, depending on factors reported to influence the protease structure. The pH, salinity, buffer type, and temperature cause a change in binding, proteolysis, or inhibition behavior. Using representative inhibitors with distinct structural scaffolds, we identify two contrasting binding profiles to dengue protease. Noticeable effects are observed in the binding assay upon inclusion of a non-ionic detergent in comparison to the proteolytic assay. The findings highlight the impact of the selection of testing conditions on the observed ligand affinity or inhibitory potency. From a broader scope, the dengue protease presents an example, where the induced-fit paradigm appears insufficient to explain binding events with the biological target. Furthermore, this protein reveals the complexity of comparing or combining biochemical assay data obtained under different conditions. This can be particularly critical for artificial intelligence (AI) approaches in drug discovery that rely on large datasets of compounds activity, compiled from different sources using non-identical testing procedures. In such cases, mismatched results will compromise the model quality and its predictive power.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Biochimie Year: 2024 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Biochimie Year: 2024 Document type: Article Affiliation country:
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