1 research outputs found
Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge
As
the length of molecular dynamics (MD) trajectories grows with
increasing computational power, so does the importance of clustering
methods for partitioning trajectories into conformational bins. Of
the methods available, the vast majority require users to either have
some <i>a priori</i> knowledge about the system to be clustered
or to tune clustering parameters through trial and error. Here we
present non-parametric uses of two modern clustering techniques suitable
for first-pass investigation of an MD trajectory. Being non-parametric,
these methods require neither prior knowledge nor parameter tuning.
The first method, HDBSCAN, is fastrelative to other popular
clustering methodsand is able to group unstructured or intrinsically
disordered systems (such as intrinsically disordered proteins, or
IDPs) into bins that represent global conformational shifts. HDBSCAN
is also useful for determining the overall stability of a systemas
it tends to group stable systems into one or two binsand identifying
transition events between metastable states. The second method, iMWK-Means,
with explicit rescaling followed by K-Means, while slower than HDBSCAN,
performs well with stable, structured systems such as folded proteins
and is able to identify higher resolution details such as changes
in relative position of secondary structural elements. Used in conjunction,
these clustering methods allow a user to discern quickly and without
prior knowledge the stability of a simulated system and identify both
local and global conformational changes