A potassium ion channel simulated with a universal neural network potential

Abstract: Potassium ion channels are critical components of biology. They conduct potassium ions across the cell membrane with remarkable speed and selectivity. Understanding how they do this is crucially important for applications in neuroscience, medicine, and materials science. However, many fundamental questions about the mechanism they use remain unresolved, partly because it is extremely difficult to computationally model due to the scale and complexity of the necessary simulations. Here, the selectivity filter (SF) of the KcsA potassium ion channel is simulated using Orb-D3, a recently released universal neural network potential. A previously unreported hydrogen bond between water in the SF and the T75 hydroxyl side group at the entrance to the SF is observed. This hydrogen bond appears to stabilize water in the SF, enabling a soft knock-on transport mechanism where water is co-transported through the SF with a reasonable conductivity (80 $\pm$ 20 pS). Carbonyl backbone flipping is also observed at new sites in the SF. This work demonstrates the potential of universal neural network potentials to provide insights into previously intractable questions about complex systems far outside their training data distribution.

• The paper presents a soft knock-on mechanism for K+ ions with an observed conductance rate of 80 ±20 pS.
• The paper identifies a key hydrogen bond at the selectivity filter that stabilizes water molecules during ion transport.
• The paper observes unprecedented carbonyl backbone flipping, offering fresh insights into ion conduction modulation.

Insights into Potassium Ion Channel Simulation Using Universal Neural Network Potentials

The paper presents a study on the simulation of the selectivity filter (SF) of the KcsA potassium ion channel using Orb-D3, a universal neural network potential. Potassium ion channels are essential membrane proteins known for their high selectivity and rapid conduction of K$^+$ ions, playing critical roles in numerous biological processes such as nerve impulse propagation and muscle contraction. Despite their importance, several fundamental aspects of the ion-conduction mechanism remain unclear, particularly whether these ions transit through the channel as dehydrated chains (hard knock-on mechanism) or with water (soft knock-on mechanism).

The research leverages Orb-D3, a neural network potential trained on a diverse set of density functional theory (DFT) calculations, to simulate the mechanisms by which K$^+$ ions permeate the SF. Notably, the study observes a soft knock-on mechanism facilitated by the formation of a hydrogen bond between the water molecule within the SF and the T75 hydroxyl side group. This stabilizing hydrogen bond appears crucial for the transient presence of water molecules inside the SF, contradicting conventional classical molecular dynamics (CMD) simulations that favor a hard knock-on mechanism.

Key Observations and Numerical Results

The simulations reveal several pivotal findings, including:

1. Soft Knock-On Mechanism: The study provides evidence for a soft knock-on transport mechanism. Simulations initialized with a water molecule in the S4 position, supported by a T75 hydrogen bond, demonstrated this mechanism facilitating the movement of water along with K$^+$ ions. The observed ion conductance rate of 80 $\pm$ 20 pS aligns well with experimental conductance measurements, which range between 40-250 pS under certain conditions.
2. Hydrogen Bonding: The work identifies a previously unreported hydrogen bond at the SF entrance, which is pivotal for the stabilization of water molecules co-transported with ions. This observation challenges existing models where CMD simulations typically underestimate ion conductance by predicting minimal water presence in the SF.
3. Carbonyl Backbone Flipping: An unprecedented flipping of the G77 and V76 carbonyls is observed, which might reveal new insights into how the SF modulates to accommodate ions and water.
4. Mutation Analysis: The study hypothesizes that mutating the T75 residue, which plays a role in forming hydrogen bonds, should significantly reduce ion conductance. This is supported by existing experimental findings where T75C mutations impair K$^+$ transport.

Implications and Future Prospects

The implications of utilizing universal neural network potentials are substantial in extending our understanding of protein dynamics far beyond traditional CMD simulations. The ability to accurately simulate these complex systems enables exploration into phenomena like c-type inactivation, ion selectivity, and gating mechanisms under physiological conditions.

Future research should focus on improving model accuracy by integrating higher-level DFT data, extending simulation timescales with enhanced sampling techniques, and potentially coupling CMD and neural network potentials to simulate larger portions of the protein environment. Such advancements could enable a deeper exploration of the dynamic behaviors of ion channels, further elucidating their biophysical properties and widening the scope for novel biomimetic applications in materials science and medicine.

In conclusion, the research highlights the transformative potential of universal neural network potentials in biophysical simulations, offering new perspectives on the nuanced mechanisms of ion transport in potassium channels and paving the way for a wealth of insights across biological and material sciences.