β-bridge, β-turn, bend, and loop or others [89]. As the physical process of protein folding is tightened up with the various biological events and processes [90], deriving seconda ry structure information from MD trajectories can provide insights into the structural behaviour of the protein (Fig. 8.5c). Although to date, the possibility of computational prediction is limited to helix, strand, or loop (coil), the results can highlight valuable changes in the investigated structure correlated with the impact of the ligand, mutation or normal protein function. For instance, a notable shift in the secondary structure composition of a specific helix could be observed when the protein interacts with an agonist (Fig. 8.5c, left chart; the helix maintains a helical structure for around 50% of the simulation time across 5 replicas) compared to antagonist (Fig. 8.5c, right chart; the helix keeps a helical structure for about 95% of the simulation time across 5 replicas). This observation can provide a hint for further in-depth system analysis. Computational results of the secondary structure prediction can be further supported with circular dichroism (CD), which provides an experimental evaluation of proteins’ secondary structure. Up to this point, secondary structure calculations along MD trajectories are somewhat rare and frequently require custom coding for a system.

PCA is a prominent approach for evaluating large datasets with several dimensions or variables per observation. It enhances the data’s readabi lity, while maintaining the most information, besides facilitating visualisation. In other words, PCA retains the

Distance analysis is a powerful tool that enables the exploration of changes in relevant distances within a system across the course of MD simulation. This tech­nique involves measuring the distances between specific pairs of atoms, residues, or structural elements within a biomolecular system. For instance, these selections could involve comparing the distances between two atoms (such as Cα atoms),

Distance analysis can be employed to understand the dynamic behaviour of multimeric assemblies and the interdependence of their constituent monomeric units across different systems (Fig. 8.5a)[106]. Selecting several intervals for calculating distance can provide comprehensive data to track the co-dependent movements of monomeric substructures (Fig. 8.6a,d1–d4). Another useful applica- tion of distance analysis is in understanding the impact of mutations. For instance, d5 on Fig. 8.6a describes the change of N-lobe composition upon mutation.

It is important to note that distance analysis generates lots of data, which can rapidly accumulate with longer simulation times. To extract meaningful trends from this data and avoid delving into the noise, statistical met hods are often employed to explore and visualise the information (Fig. 8.6b). Common approaches include calculating averages and standard deviations to provide a basic numerical assess­ment of dynamic processes (Fig. 8.6b, upper panel). However, for a more detailed examination, other strategies like box plots can be utilised to demonstrate the distribution of motions across various systems (Fig. 8.6b, box plots highlighted in yellow, and blue showcase the difference between wild type and mutant protein).

There are various types of moving averages, depending on various factors such as data structure—for our purpose we employ simple moving average (SMA), which uses a sliding window to take the average over a set number of time periods. Once applied and plotted, one can observe the main trend in the distance change. As demonstrated in Fig. 8.6b, the SMA applied along the timescale shows the gradual opening of specific protein substructures (indicated by the yellow line), suggesting studying the origin of this diverging movement.