to occur only between side chains. The model reduced the dimensionality of the conformational space and led to smoother side chain representations. They used effective time-averaged potential functions to account for the fine details and rapidly changing variables. The simplified model was tested on a bovine pancreatic trypsin inhibitor (PTI) and successfully simulated the stable conformation of a folded protein. The authors also proposed a hierarchical approach, starting with simplified models and gradually incorporating more atoms, to understand and simulate com­plex biological assembly processes [17, 18]. Their work laid the foundation for MD simulations in protein folding and biomolecular studies.

for MD simulation. However, it is not strictly necessary, for example, with the Desmond engine [46], which applies a uniform background charge distribution to neutralise the system in the Ewald summation [47]. Moreover, systems can be set up in a salt solution rather than a pure solvent or even include other small molecules as buffers in a mixed-solvent model [48].

The following terms commonly describe the intramolecular potential energy: harmonic bond stretching (Eq. 8.2), angle bending (Eq. 8.3), torsional angle defini­tion as Fourier series (Eq. 8.4), Coulomb electrostatics and Lennard-Jones potential (Eq. 8.5)[51].

characteristics of a given system, which can encompass an atomistic protein model or experimental data. The properties one intends to replicate in the model determine the classification of the coarse-grained force fields. For instance, free energy con­servation is the focus of the MARTINI [40] force field and the simplex method [73]. Another example is inverse Monte Carlo with structure-based coarse-grained modelling, emphasising the radial distribution [75].

times. Given the principle of energy conservation within the ensemble required for the MD calculations and the almost derisory effects arising from changes in the floating-point precision or hardware, multiple runs of these syst ems would lead to very similar thermodynamical and, consequently, conformational properties. In this sense, the difference between replicas is in the initial velocities generated randomly according to the Maxwell distribution [81]. As a result, the velocities are unique for each replica, which leads to the production of different simulation trajectories. However, even replicas with identical velocities can produce distinct trajectories for various reasons, including machi ne-specific settings and the specification of the compiling [81]. In theory, the ergodic principle claims that the velocities have no impact on infinite dynamic simulations [82 ]. While molecular dynamics are not run eternally in real life, random velocities are strictly important, especially for short­scale MDs. The random initial velocity assignment ensures that the results of every simulation are slightly different, even if the other settings are identical. In other words, the random velocities provide the opportunity to observe real-world phe­nomena happening with the same system at different time points. Moreover, multiple replicas ensure a specific movement or interaction observed not in a single simula­tion but in a statist ically significant number of replicas is not biased or artificially generated but related to the real-world evidence. The choice between the multiple simulation replicas over a single but long-scale one is frequently a question that should be answered before the initiation of the MD according to the aims and resources of the project.