Version 1
: Received: 8 September 2024 / Approved: 8 September 2024 / Online: 11 September 2024 (10:58:35 CEST)
How to cite:
Li, W. Scalable Ligand-Receptor Binding Affinity Landscape: A Case Study with Ziconotide and CaV2.2. Preprints2024, 2024090877. https://doi.org/10.20944/preprints202409.0877.v1
Li, W. Scalable Ligand-Receptor Binding Affinity Landscape: A Case Study with Ziconotide and CaV2.2. Preprints 2024, 2024090877. https://doi.org/10.20944/preprints202409.0877.v1
Li, W. Scalable Ligand-Receptor Binding Affinity Landscape: A Case Study with Ziconotide and CaV2.2. Preprints2024, 2024090877. https://doi.org/10.20944/preprints202409.0877.v1
APA Style
Li, W. (2024). Scalable Ligand-Receptor Binding Affinity Landscape: A Case Study with Ziconotide and Ca<sub>V</sub>2.2. Preprints. https://doi.org/10.20944/preprints202409.0877.v1
Chicago/Turabian Style
Li, W. 2024 "Scalable Ligand-Receptor Binding Affinity Landscape: A Case Study with Ziconotide and Ca<sub>V</sub>2.2" Preprints. https://doi.org/10.20944/preprints202409.0877.v1
Abstract
The optimization of ligand-receptor binding affinities is essential for enhancing therapeutic efficacy and specificity, particularly in the design of peptide-based drugs. ziconotide, a potent blocker of the N-type calcium channel CaV2.2, has demonstrated therapeutic potential in the treatment of moderate to severe chronic pain. However, there is limited structural and binding affinity data available for scaling the design of ziconotide analogues with improved efficacy and specificity. In this study, I present a structural biophysics-based approach to develop a scalable in silico framework for generating and analyzing ligand-receptor binding affinity landscapes, focusing on ziconotide and its receptor, CaV2.2. This framework integrates advanced computational structural tools and binding affinity calculations to produce high-accuracy structural and intermolecular binding data. The insights gained from this scalable approach will guide the design of ziconotide analogues with enhanced efficacy, offering a powerful computational workflow for high-throughput ligand optimization. Additionally, when combined with artificial intelligence (AI) algorithms, this computational workflow generates high-accuracy structural and biophysical data, towards an AI- and structural biophysics-driven paradigm shift in peptide discovery and design.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
