Click the funnel to place intermediate states, observe how mutations alter energy barriers and folding kinetics
Protein folding is guided by a funnel-shaped free energy landscape. The unfolded state occupies a vast conformational space at high free energy (wide top of the funnel), while the native state is a unique, low-energy conformation (narrow bottom). As the polypeptide chain explores conformations, it tends to move downhill on the landscape, driven by hydrophobic collapse, hydrogen bonding, and van der Waals interactions. The funnel metaphor captures both the thermodynamic bias toward the native state and the progressive restriction of conformational freedom. Local minima along the landscape represent metastable intermediate states or kinetic traps that can slow folding. The roughness of the landscape determines whether folding is a smooth two-state process or involves populated intermediates.
The energy profile is modeled as G(Q) = ΔG·(1−Q)² + roughness·Σ sin(k·Q·π) − Σ intermediates, where Q is the fraction of native contacts (reaction coordinate), ΔG is the funnel depth, and intermediates are Gaussian wells placed by the user. The funnel width represents conformational entropy: w(Q) ∝ (1−Q)^0.7, wider at low Q (many unfolded conformations) and narrower at high Q (few native-like conformations). Folding dynamics use overdamped Langevin equation: dQ/dt = −(1/γ)·dG/dQ + √(2kT/γ)·η(t), where γ is friction and η(t) is Gaussian white noise. Folding rate follows transition state theory: k_f = (k_BT/h)·exp(−ΔG‡/RT), where ΔG‡ is the highest energy barrier along the pathway.
Mutations reshape the folding energy landscape in predictable ways. Stabilizing mutations (e.g., introducing disulfide bonds, optimizing hydrophobic core packing) deepen the native well, increasing thermodynamic stability and accelerating folding. Destabilizing mutations (e.g., replacing buried hydrophobic residues with polar ones) raise the native state energy, potentially causing misfolding or aggregation. Mutations can also introduce kinetic traps — local minima that were not present in the wild-type landscape — which dramatically slow folding by trapping the protein in non-productive conformations. Frustrated landscapes arise when mutations create competing low-energy states, preventing efficient funneling to the native state. Conversely, evolutionary optimization produces smooth funnels that minimize frustration, enabling fast and reliable folding.
Start with the Wild Type preset to see a smooth energy funnel. Click directly on the funnel canvas to place intermediate states — each click adds a local minimum at that position. Switch to Remove mode to delete intermediates by clicking near them. Press Animate to watch a folding trajectory: a ball rolls down the energy landscape, potentially getting trapped at intermediates. Use the Mutation Presets to see how different mutations reshape the landscape. Stabilizing makes the funnel deeper and folding faster. Destabilizing raises the native state. Kinetic Trap introduces a deep intermediate that slows folding. Frustrated creates a rugged landscape with many competing minima. Fast Folder shows an optimized smooth funnel.