From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

Abstract

An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.

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“…There are several approaches available that can be used when analyzing MD trajectories including visual inspection, calculation of energies, measuring geometric parameters such as interatomic distances, angles and similar, performing cluster analysis, generating crosscorrelation matrices, root mean square deviation and fluctuation analysis, principal component analysis (PCA), generating contact maps, checking for specific interactions like H-bonds, hydrophobic interaction, solvent accessible surface area (SASA) analysis, to name a few (Likhachev et al 2016;Baltrukevich and Podlewska 2022). All these methods represent invaluable tools that enable an interpretation of obtained data and the establishment of the connection with the experiment.…”

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“…By analysing the snapshots taken throughout the simulation time, flexibility and stability of the ligand-target complex can be predicted, location of water molecules or change in entropy of special structures can also be observed. These hidden states of the system can by no means be tested by any wet-lab technique [ 7 , 17 ]. The molecular mechanics energies combined with the Poisson-Boltzman (MM-PBSA) or generalized Born and surface area continuum solvation (MM-GBSA) are other commonly used tool for post-docking analysis to estimate the free binding energy of the ligand-target complex [ 18 ].…”

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“…Simulations can be performed in different statistical ensembles, including the extended ensembles used in multicanonical simulations or the parallel tempering method. A lot of enhanced sampling techniques [ 10 ] and different methods for the analysis of simulation data [ 11 ] have recently been proposed.…”