Improving Speed and Affordability without Compromising Accuracy: Standard Binding Free-Energy Calculations Using an Enhanced Sampling Algorithm, Multiple-Time Stepping, and Hydrogen Mass Repartitioning

by Selleckchem | Dec 21 2023

Accurate evaluation of protein-ligand binding free energies in silico is of paramount importance for understanding the mechanisms of biological regulation and providing a theoretical basis for drug design and discovery. Based on a series of atomistic molecular dynamics simulations in an explicit solvent, using well-tempered metadynamics extended adaptive biasing force (WTM-eABF) as an enhanced sampling algorithm, the so-called "geometrical route" offers a rigorous theoretical framework for binding affinity calculations that match experimental values. However, although robust, this strategy remains expensive, requiring substantial computational time to achieve convergence of the simulations. Improving the efficiency of the geometrical route, while preserving its reliability through improved ergodic sampling, is, therefore, highly desirable. In this contribution, having identified the computational bottleneck of the geometrical route, to accelerate the calculations we combine (i) a longer time step for the integration of the equations of motion with hydrogen-mass repartitioning (HMR), and (ii) multiple time-stepping (MTS) for collective-variable and biasing-force evaluation. Altogether, we performed 50 independent WTM-eABF simulations in triplicate for the "physical" separation of the Abl kinase-SH3 domain:p41 complex, following different HMR and MTS schemes, while tuning, in distinct protocols, the parameters of the enhanced-sampling algorithm. To demonstrate the consistency and reliability of the results obtained with the best-performing setups, we carried out quintuple simulations. Furthermore, we demonstrated the transferability of our method to other complexes by triplicating a 200 ns separation simulation of nine chosen protocols for the MDM2-p53:NVP-CGM097 complex. [Holzer et al. J. Med. Chem. 2015, 58, 6348-6358.] Our results, based on an aggregate simulation time of 14.4 μs, allowed an optimal set of parameters to be identified, able to accelerate convergence by a factor of three without any noticeable loss of accuracy.

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Your provided text describes a research contribution in the field of computational chemistry, particularly in the context of calculating protein-ligand binding free energies. Here's a breakdown and summary of the key points:

### Context:
- **Objective:** Accurate estimation of protein-ligand binding free energies for understanding biological regulation and aiding drug design.
- **Methodology:** Atomistic molecular dynamics simulations in explicit solvent using well-tempered metadynamics extended adaptive biasing force (WTM-eABF) as an enhanced sampling algorithm.

### Challenges:
- **Computational Bottleneck:** The "geometrical route" is a robust but computationally expensive strategy, requiring substantial time for convergence in simulations.

### Approach for Acceleration:
1. **Longer Time Step with Hydrogen-Mass Repartitioning (HMR):** Increase the integration time step for motion equations while using HMR.
2. **Multiple Time-Stepping (MTS):** Implement different time steps for collective-variable and biasing-force evaluation.

### Experimental Setup:
- **Simulations:** 50 independent WTM-eABF simulations performed in triplicate for the Abl kinase-SH3 domain:p41 complex.
- **Parameters Exploration:** Various HMR and MTS schemes tested, along with tuning parameters of the enhanced-sampling algorithm.
- **Validation:** Quintuple simulations carried out to ensure consistency and reliability.
- **Transferability:** Method tested on other complexes (e.g., MDM2-p53:NVP-CGM097) to demonstrate its applicability beyond the original system.

### Results:
- **Aggregate Simulation Time:** 14.4 μs.
- **Optimized Parameters:** Identified a set of parameters that accelerates convergence by a factor of three without sacrificing accuracy.

### Significance:
- **Contribution:** Provides an efficient strategy to improve the computational efficiency of the geometrical route for calculating binding free energies.
- **Practical Implications:** Potential acceleration in drug discovery processes by reducing the computational time required for reliable binding affinity calculations.

### Publication:
- **Reference:** Holzer et al. J. Med. Chem. 2015, 58, 6348-6358.

This research contributes to the ongoing efforts in computational biology and drug discovery, showcasing a method that balances computational efficiency with the accuracy needed for meaningful results in the study of protein-ligand interactions.