Phosphorylation
of intrinsically disordered proteins/regions
(IDPs/IDRs)
has a profound effect in biological functions such as cell signaling,
protein folding or unfolding, and long-range allosteric effects. However,
here we focus on two IDPs, namely 83-residue IDR transcription factor
Ash1 and 92-residue long N-terminal region of CDK inhibitor Sic1 protein,
found in Saccharomyces cerevisiae,
for which experimental measurements of average conformational properties,
namely, radius of gyration and structure factor, indicate negligible
changes upon phosphorylation. Here, we show that a judicious dissection
of conformational ensemble via combination of unsupervised machine
learning and extensive molecular dynamics (MD) trajectories can highlight
key differences and similarities among the phosphorylated and wild-type
IDP. In particular, we develop Markov state model (MSM) using the
latent-space dimensions of an autoencoder, trained using multi-microsecond
long MD simulation trajectories. Examination of structural changes
among the states, prior to and upon phosphorylation, captured several
similarities and differences in their backbone contact maps, secondary
structure, and torsion angles. Hydrogen bonding analysis revealed
that phosphorylation not only increases the number of hydrogen bonds
but also switches the pattern of hydrogen bonding between the backbone
and side chain atoms with the phosphorylated residues. We also observe
that although phosphorylation introduces salt bridges, there is a
loss of the cation–π interaction. Phosphorylation also
improved the probability for long-range hydrophobic contacts and also
enhanced interaction with water molecules and improved the local structure
of water as evident from the geometric order parameters. The observations
on these machine-learnt states gave important insights, as it would
otherwise be difficult to determine experimentally which is important,
if we were to understand the role of phosphorylation of IDPs in their
biological functions.