Spatial Profiling and Conformational Analysis of Sildenafil in PDE5 Drug Binding Using Gemini AI

Authors

  • Rosan Bhoi 8th Semester B. Pharm Student, BKCP, Nuapada, Odisha, INDIA
  • Om Prakash Pradhan 8th Semester B. Pharm Student, BKCP, Nuapada, Odisha, INDIA
  • Saroj Meher 8th Semester B. Pharm Student, BKCP, Nuapada, Odisha, INDIA
  • Satyajit Dhal 8th Semester B. Pharm Student, BKCP, Nuapada, Odisha, INDIA
  • Akshya Kumar Mishra Associate Professor, Department of Microbiology, BKCP, Nuapada, Odisha, INDIA

DOI:

https://doi.org/10.55544/jrasb.5.1.15

Keywords:

Phosphodiesterase-5 (PDE5), Sildenafil, Conformational Analysis, Molecular Dynamics, Spatial Profiling, Generative AI (Gemini), H-loop dynamics, Structure-Based Drug Design

Abstract

Background: Phosphodiesterase-5 (PDE5) is a critical enzyme regulating intracellular cGMP levels, and its selective inhibition by Sildenafil is a cornerstone therapy for vascular disorders. While the static crystallographic binding mode of Sildenafil is well-documented, the dynamic conformational landscape of the enzyme-inhibitor interaction is complex. Deciphering how Sildenafil influences the intrinsic flexibility of PDE5 in solution is essential for a complete mechanistic understanding of its high potency. Traditional computational methods for analyzing high-dimensional simulation data are often labor-intensive, presenting an opportunity for acceleration via advanced AI interpretation.

Objective: The primary objective of this study is to leverage Generative AI (Gemini) to conduct a comprehensive 3D spatial profiling and dynamic conformational analysis of the Sildenafil-PDE5 complex. The study aims to utilize AI to automate the interpretation of molecular interactions, map critical residues within the catalytic pocket, and quantify ligand-induced structural shifts in regulatory loops.

Methods: An integrated computational framework was established, combining high-throughput molecular docking and 100 ns molecular dynamics (MD) simulations. Generative AI (Gemini) was utilized as the primary analytical engine to parse raw spatial coordinate data and numerical trajectory arrays (RMSD and RMSF). The AI was prompted to identify stable hydrogen bonding networks, interpret hydrophobic packing interactions, and statistically evaluate changes in protein backbone flexibility between apo- and holo-states.

Results: AI-driven spatial profiling identified a highly durable bidentate hydrogen bond between Sildenafil’s pyrazolopyrimidinone core and the conserved Gln817 residue as the essential spatial anchor (maintaining >88% occupancy during simulation). Thermodynamic interpretation revealed that binding affinity is overwhelmingly driven by extensive hydrophobic Van der Waals contacts within the Q-pocket. Crucially, AI-parsed conformational analysis demonstrated that Sildenafil binding significantly rigidifies the overall enzyme structure. The data revealed a specific "conformational lock" on the highly flexible H-loop (residues 660–683), drastically reducing its root mean square fluctuation (RMSF) from approximately 4.10 Å in the apo-state to 1.85 Å in the bound state.

Conclusion: Sildenafil acts not merely as a passive steric blocker, but as a dynamic conformational clamp that actively restricts the mobility of the H-loop gate, rendering the enzyme catalytically inert. Furthermore, this study demonstrates the transformative utility of integrating Generative AI into structural biology, successfully accelerating the translation of complex raw simulation data into actionable mechanistic insights for rational drug design.

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References

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Published

2026-02-28

How to Cite

Bhoi, R., Pradhan, O. P., Meher, S., Dhal, S., & Mishra, A. K. (2026). Spatial Profiling and Conformational Analysis of Sildenafil in PDE5 Drug Binding Using Gemini AI. Journal for Research in Applied Sciences and Biotechnology, 5(1), 135–140. https://doi.org/10.55544/jrasb.5.1.15

Issue

Vol. 5 No. 1 (2026): February Issue

Section

Articles

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Copyright (c) 2026 Rosan Bhoi, Om Prakash Pradhan, Saroj Meher, Satyajit Dhal, Akshya Kumar Mishra

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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