EcDHFR-NADPH-ligand systems
Representative trimethoprim analogues are modelled in the cofactor-bound EcDHFR pocket for comparable trajectory analysis.
Molecular Dynamics IN PROTO-NOOS: Comparative Analysis of EcDHFR-NADPH-Ligand Complex Dynamics for Selected Trimethoprim Analogues
A PROTO-NOOS biocomplex-analysis protocol for moving beyond geometric docking scores by integrating molecular dynamics, enhanced sampling, binding energetics, and local electronic-structure descriptors for EcDHFR-NADPH-ligand systems.
Abstract
Escherichia coli dihydrofolate reductase (EcDHFR) is a validated antibacterial target whose ligand recognition cannot be adequately described by docking scores alone, a limitation shared by any protein-ligand system of comparable complexity.
We present a multi-scale computational protocol designed to characterize EcDHFR-ligand complexes with physicochemical and electronic detail, while generating structured data suitable for downstream machine-learning parameterization. Classical molecular dynamics (MD) simulations assess binding-mode stability, residue-level flexibility, and dominant conformational states of the binding pocket, including its response to structurally diverse ligands. PLUMED-driven metadynamics further probes pocket conformational transitions. MM-GBSA provides comparative binding energetics across selected systems. For representative trajectory frames, CP2K is used to examine the local electronic structure of the binding region, including atomic charge distribution, charge transfer, and polarization effects, phenomena systematically neglected by classical force fields.
Together, these analyses build a domain-informed dataset that links computed descriptors to experimentally known values, enabling iterative refinement of earlier-stage approximations in the pipeline. The methodology is developed for EcDHFR but is designed to generalize to homologous targets.
We argue that integrating structural, energetic, and electronic descriptors is a prerequisite for meaningful in silico candidate selection beyond geometric scoring.
MDbinding-mode stability, pocket flexibility, and ligand-response dynamics
MetaDPLUMED-driven sampling of pocket conformational transitions
MM-GBSAcomparative binding energetics across selected complexes
CP2Klocal electronic structure, charge transfer, and polarization descriptors
Protocol Design
This page records the current poster-stage protocol. The manuscript is not attached yet, so the abstract is used as the scientific description and the poster carries the visual summary.
MD stability checks
Trajectory descriptors capture binding-mode persistence, residue-level flexibility, and recurring pocket states.
PLUMED metadynamics
Biased sampling probes conformational transitions that short unbiased simulations may under-sample.
MM-GBSA and CP2K
Energetic and electronic descriptors expose effects omitted by docking scores and classical force fields.
Poster Figures
The available graphics summarise the biocomplex-analysis branch: system preparation, conformational sampling, energetic comparison, and electronic descriptors for data products that can feed later machine-learning refinement.
Multi-scale biocomplex analysis
Connects structural modelling, MD, enhanced sampling, binding energetics, and electronic-structure analysis.
Pocket response and stability
Tracks how ligand identity changes binding-pocket geometry, local flexibility, and conformational state occupancy.
Complex-level descriptors
Uses representative conformations to organize contact, energetic, and electronic descriptors into a reusable dataset.
Charge and polarization effects
CP2K-derived local electronic descriptors capture charge redistribution absent from classical docking scores.
Poster
The poster is the current citable visual artifact for this branch. A manuscript link can be added here once the text exists.