Fig 1.
Illustration of PD1-PDL1 interaction (PDB id: 4ZQK) and mechanism of Action of PD-1 Inhibitors.
Table 1.
Consensus molecular docking results for 31 phytochemicals against PD-1 (PDB ID: 6J14). Binding energies (kcal/mol) and PLANTS SCORE obtained from seven different docking algorithms: AutoDock Tools (ADT), iDock, LeDock, Qvina2, Smina, Vina, and PLANTS.
Table 2.
Rank-by-rank consensus scoring results for molecular docking analysis. Individual rankings from seven docking algorithms were combined using radar plot area calculations, where smaller areas indicate better overall performance. Final ranking represents consensus performance across all methods, with rank 1 being the best. Top 6 compounds (highlighted) were selected for further analysis.
Fig 2.
Radar plot visualization of consensus molecular docking results using Rank-by-Rank (RbR) methodology.
Each axis represents a ranking from different docking algorithms. Smaller radar plot areas indicate better consensus performance across multiple algorithms. The top six compounds (IMPHY014498, IMPHY003388, IMPHY012536, IMPHY004834, IMPHY014076, IMPHY003213) showing the smallest areas were selected for subsequent DFT and MD analysis.
Fig 3.
Density Functional Theory (DFT) analysis results for top-ranked compounds.
For each molecule: (A) optimized molecular structure obtained using ORCA software with B3LYP/6-31G(d,p) basis set, (B) Highest Occupied Molecular Orbital (HOMO) distribution shown in blue/purple indicating electron-rich regions available for bonding, and (C) Lowest Unoccupied Molecular Orbital (LUMO) shown in green representing electron-deficient areas capable of accepting electrons. Electronic properties calculated include HOMO-LUMO gap, ionization potential, electron affinity, and dipole moment for drug-target interaction prediction.
Table 3.
Comparative DFT Analysis of Candidate Molecules.
Fig 4.
Comparative RMSD trajectories of five ligand-bound protein complexes across a 300 ns simulation period.
Fig 5.
RMSF for Multiple Ligand-Bound Protein Complexes, highlighting local flexibility variations.
Fig 6.
Comparative analysis of RMSF by residue number for distinct ligand-protein complexes, indicating differential local flexibility.
Fig 7.
Comparative Rg trajectories of five ligand-bound protein complexes across a 300 ns simulation period.
Fig 8.
SASA Profiles of protein-ligand complexes over 300 ns Molecular Dynamics Simulations.
Fig 9.
Distribution of protein conformational states mapped onto the first two principal components (PC1 and PC2) for a selection of ligand-bound complexes over a 300 ns simulation.
Fig 10.
Contour and 3D Visualization of the Free Energy Landscape for all five ligand-bound proteins, Demonstrating Stable Conformational Minima and Transition States.
Fig 11.
Temporal analysis of hydrogen bond counts in multiple ligand-protein complexes over 300 ns molecular dynamics simulations.
Table 4.
MM-PBSA binding free energy analysis.
Table 5.
Ranking for In Vitro Validation.
Fig 12.
Comprehensive Visualization of IMPHY004834-PD-1 Interaction: (a) 3D Ribbon Model (b) Surface Electrostatic Potential Map, and (c) Detailed Interaction Schematic Highlighting Specific Residue Contacts and Binding Modes.
Fig 13.
Comprehensive Visualization of IMPHY012536-PD-1 Interaction: (a) 3D Ribbon Model, (b) Surface Electrostatic Potential Map, and (c) Detailed Interaction Schematic Highlighting Specific Residue Contacts and Binding Modes.