Abstract
In recent years the analysis of molecular dynamics trajectories using dimensionality reduction algorithms has become commonplace. These algorithms seek to find a low-dimensional representation of a trajectory that is, according to a well-defined criterion, optimal. A number of different strategies for generating projections of trajectories have been proposed but little has been done to systematically compare how these various approaches fare when it comes to analysing trajectories for biomolecules in explicit solvent. In the following paper, we have thus analyzed a molecular dynamics trajectory of the C-terminal fragment of the immunoglobulin binding domain B1 of protein G of Streptococcus modeled in explicit solvent using a range of different dimensionality reduction algorithms. We have then tried to systematically compare the projections generated using each of these algorithms by using a clustering algorithm to find the positions and extents of the basins in the high-dimensional energy landscape. We find that no algorithm outshines all the other in terms of the quality of the projection it generates. Instead, all the algorithms do a reasonable job when it comes to building a projection that separates some of the configurations that lie in different basins. Having said that, however, all the algorithms struggle to project the basins because they all have a large intrinsic dimensionality.
Highlights
For many years researchers have sought to determine whether it is possible to predict the tertiary structure of a protein from the amino acid sequence alone
We have analyzed a molecular dynamics trajectory for a short protein molecule using a number of different dimensionality reduction algorithms
The results we have are in some senses reassuring as all the algorithms do a reasonable job when it comes to giving a representation of the trajectory that gives a sense of the structural diversity that one observes in the trajectory
Summary
For many years researchers have sought to determine whether it is possible to predict the tertiary structure of a protein from the amino acid sequence alone. Molecular dynamics (MD) simulations with force fields that model the interactions between the atoms in the biomolecule have emerged as a useful tool for investigating the dynamical structure of proteins. This technique is, important for IDPs as the experiments alone often. Oftentimes decisions as to how to plot the relationships between the trajectory frames are made using chemical or physical intuition about the problem under study In these cases, some function/s of the atomic positions - usually referred to as collective variables or CVs - is computed for each of the trajectory frames. The positions of each of the trajectory frames in the lowdimensional CV space can be plotted so that conclusions can be drawn about the parts of space that were sampled in the trajectory
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