Designing High-Affinity Progesterone Binders: Pocket Analysis and Scaffold Selection
Pourhassan-Moghaddam, M.; Cornell, B. A.; Valenzuela, S. M.
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Molecular recognition is a central component that confers detection specificity to all biosensors. The design and use of such molecules require consideration of properties including their affinity and selectivity, plus their ease of production and engineering, for downstream commercial purposes. Progesterone (P4), is a biomarker that is extensively for various diagnostic purposes. Examples include detection of P4 as an indicator of oestrus in cattle breeding, and ovulation in human IVF programs. P4 is also thought to promote strains of breast cancer, resulting in it being an environmental pollutant of interest. The present study focusses on in-silico molecular docking trials of P4 molecules with proteins such as antibodies and receptors. We describe the geometry of novel P4-binding pockets and predict key residues that favour high affinity and selectivity for P4. The in-silico molecular docking trials were performed on various mutants of an anti-P4 antibody that had lost their P4 specificity but retained selective recognition of steroids with structures closely related to cholesterol. Reverse-docking trials permitted the identification of novel scaffolds with favourable P4 binding properties. Future reports will validate the predictions of these studies through wet lab experiments. A further opportunity for this approach is to incorporate a scaffold functionality to permit binding of the protein or receptor to other molecules or sites within a biosensor electrode. These findings, and future studies, will assist in development of enhanced biosensing platforms with custom-designed P4 binders, aiding commercialisation using in-house developed reagents to meet IP requirements and minimise scaling costs. The steroid biotechnology market, valued at over $10 billion, also benefits from novel steroid binder designs, facilitating real-time steroid biomonitoring platforms for optimising steroid bioprocesses.
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