A Markov State Model of Solvent Features Reveals Water Dynamics in Protein-Peptide Binding

Abstract

In this work, we investigate the role of solvent in the binding reaction of p53 transactivation domain (TAD) peptide to its receptor MDM2. Previously, our group generated 831 μs of explicit-solvent aggregate molecular simulation trajectory data for the MDM2-p53 peptide binding reaction using large-scale distributed computing, and subsequently built a Markov State Model (MSM) of the binding reaction (Zhou et al. 2017). Here, we perform tICA anlaysis and construct an MSM using only solvent-based structural features, and find a remarkably similar landscape and implied timescales for the slowest motions. The solvent shells contributing most to the first tICA eigenvector are those centered on Lys24 and Thr18 of p53 TAD peptide in the range of 3–6 Å. Important solvent shells were visualized to reveal wetting and dewetting transitions along peptide-protein binding trajectories. Our results provide a solvent-centric view of the hydrophobic effect in action for a realistic peptide-protein binding scenario.

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables

“…Permutational invariance is typically enforced by summing particle-centric basis functions over all identical particles or a permutationally invariant ordering. Examples of the former class include permutation-invariant polynomials (PIPs) generated by summing monomial functions of internal coordinates over all like-particle orderings, , atom-centered symmetry functions (ACSF) that achieve permutational invariance by a similar summation procedure, , many-body tensor representations (MBTR) that define scalar geometry functions (e.g., atomic number, distance, and angle) over small body-order numbers of atoms and arrange these into permutationally invariant distribution functions for each class of interaction, solvent signature vectors that use Gaussian kernel functions to quantify the distribution of solvent molecules around the solute, solvent shell featurizations that quantify the number of water molecules in nested shells around the solute, , and smooth overlap of atomic positions (SOAP) that generate the partial power spectra of an expansion of spherical harmonic representations of the atomic-centered smoothed density fields summed over all identical atoms . In general, the exponential scaling in the number of permutational orderings associated with the summations for each of these approaches must be controlled by defining a cutoff in the range and/or order of the interactions considered .…”