Ten Critical Pitfalls in Molecular Dynamics Simulations and Strategies for Mitigation

Molecular dynamics (MD) serves as a powerful computational microscope, offering atomistic insights often unattainable through experimental methods alone. With the increasing accessibility of user-friendly software and comprehensive tutorials, the barrier to entry for new researchers has lowered significantly. However, MD is not merely a procedural task; it is a rigorous physics simulation where minor methodological errors can lead to scientifically inaccurate results.

For early-stage researchers, the challenge often lies in subtle errors—mistakes that do not cause the simulation to crash but instead yield misleading trajectories. These errors often remain undetected until the peer-review stage, potentially invalidating months of computational effort.

This article outlines ten of the most frequent methodological errors in molecular dynamics and provides best practices to ensure simulation reliability and reproducibility.

1. Inadequate Preparation of Initial Structures

The validity of a simulation is strictly limited by the quality of the starting model. A common oversight is assuming that a structure downloaded from the Protein Data Bank (PDB) is immediately ready for simulation. Raw PDB files often contain experimental artifacts, such as missing atoms, unresolved residues, or crystal packing contacts that are not relevant in solution.

The Solution: Rigorous structure preparation is mandatory. This includes:

• Modeling missing loops.
• Resolving steric clashes.
• Crucially, assigning correct protonation states (e.g., Histidine tautomers) based on the specific pH of the environment rather than default settings.

Tools such as pdbfixer, H++, or PropKa are essential for this standardization.

2. Incompatible or Inappropriate Force Field Selection

A force field represents the mathematical parameter set governing atomic interactions. A significant error involves selecting a force field based on popularity rather than its suitability for the specific molecular system (e.g., using a protein-centric force field for a complex membrane system without validation). Furthermore, combining parameters from different force fields—such as mixing CHARMM protein parameters with OPLS ligand parameters—can introduce severe imbalances in potential energy functions.

The Solution: Select a force field explicitly validated for your molecule class. If a complex system requires multiple parameter sets, ensure they share compatible functional forms and Lennard-Jones combination rules.

3. Mismatching Water Models

Force fields are generally parameterized in conjunction with specific water models. Using a force field designed for TIP3P water with a more modern 4-point model (like OPC), or vice versa, is a frequent technical error.

The Solution: Verify the primary literature for your chosen force field to identify the water model used during its parameterization. Using a mismatched water model can fundamentally alter solution density, protein-solvent interactions, and diffusion rates, rendering thermodynamic properties inaccurate.

4. Uncritical Acceptance of Automated Ligand Topologies

When simulating non-standard residues or ligands, researchers often rely on automated servers (e.g., CGenFF, PRODRG, or MCPB) to generate topology files. Treating these servers as “black boxes” is dangerous, as automated algorithms often approximate partial charges and bond parameters incorrectly for complex chemical groups.

The Solution: Automated output should be treated as a draft.

• Always inspect the log files for high “penalty scores” or confidence warnings.
• For rigorous work, validate partial charges using Quantum Mechanics (QM) calculations and verify bonded parameters against experimental data or higher-level theory.

5. Insufficient Minimization and Equilibration

Initiating the production run (data collection) before the system has thermodynamically relaxed is a primary cause of instability. If high-energy contacts are not resolved, the system may exhibit unrealistic structural distortions or numerical instability.

The Solution:

• Minimization: Continue until the maximum force on any atom falls below a strict threshold (e.g., 1000 kJ/mol/nm).
• Equilibration: Monitor temperature (NVT) and pressure (NPT) phases carefully. Production should only commence once system density and potential energy have converged to a stable plateau.

6. Incorrect Time Step Selection

The integration time step dictates the frequency at which the equations of motion are solved. Selecting a time step that is too large causes numerical instability (integration errors), while a time step that is too small results in computational inefficiency.

The Solution: For standard biological simulations, a 2 femtosecond (fs) time step is appropriate, provided that bonds involving hydrogen atoms are constrained using algorithms such as LINCS or SHAKE. Larger time steps (e.g., 4 fs) should only be used if Hydrogen Mass Repartitioning (HMR) is applied.

7. Neglecting Periodic Boundary Condition (PBC) Artifacts

Periodic Boundary Conditions are essential for simulating bulk systems, but they introduce visual and analytical artifacts, such as molecules appearing to split across simulation box boundaries.

The Solution: Raw trajectories must be post-processed prior to analysis. Metrics such as Radius of Gyration ($R_g$) or RMSD will yield erroneous results if calculated on “broken” molecules. Use trajectory processing tools (e.g., gmx trjconv in GROMACS or cpptraj in AMBER) to re-center the protein and “unwrap” molecules across the periodic boundaries.

8. Insufficient Sampling and Lack of Replicates

Molecular dynamics is inherently stochastic; a single trajectory represents only one probabilistic pathway of the system. Relying on a single simulation run can lead to the “Anecdotal Evidence” fallacy, where a rare event is mistaken for a general property of the system.

The Solution: Conduct replicate simulations. Ideally, run the same system 3 to 5 times with different initial velocity distributions (random seeds). Conclusions should be drawn from statistical trends observed across multiple independent replicates, not a single isolated run.

9. Over-reliance on RMSD as a Stability Metric

Root Mean Square Deviation (RMSD) is the most common metric for assessing structural stability, but it is insufficient on its own. A plateauing RMSD indicates only that the global structure is not deviating further from the reference; it does not rule out local distortions, broken hydrogen bond networks, or incorrect energetic behavior.

The Solution: Employ a multidimensional analysis strategy. Combine RMSD with:

• Root Mean Square Fluctuation (RMSF)
• Radius of Gyration ($R_g$)
• Solvent Accessible Surface Area (SASA)
• Clustering analysis

This provides a comprehensive view of system stability and dynamics.

10. Lack of Experimental Validation

A simulation that runs without errors is not necessarily physically accurate. A major pitfall is failing to benchmark simulation results against known experimental observables.

The Solution: Always attempt to correlate simulation data with experimental results. Comparing calculated B-factors with X-ray crystallography data, or comparing NMR observables (such as chemical shifts or NOEs) with simulated values, provides the necessary validation to ensure the simulation reflects physical reality.