Unsupervised Graph-Based Learning Predicts Mutations That Alter Protein Dynamics

Abstract

Proteins exhibit complex dynamics across a vast range of time and length scales, from the atomistic to the conformational. Adenylate kinase (ADK) showcases the biological relevance of such inherently coupled dynamics across scales: single mutations can affect large-scale protein motions and enzymatic activity. Here we present a combined computational and experimental study of multiscale structure and dynamics in proteins, using ADK as our system of choice. We show how a computationally efficient method for unsupervised graph partitioning can be applied to atomistic graphs derived from protein structures to reveal intrinsic, biochemically relevant substructures at all scales, without re-parameterisation or a priori coarse-graining. We subsequently perform full alanine and arginine in silico mutagenesis scans of the protein, and score all mutations according to the disruption they induce on the large-scale organisation. We use our calculations to guide Förster Resonance Energy Transfer (FRET) experiments on ADK, and show that mutating residue D152 to alanine or residue V164 to arginine induce a large dynamical shift of the protein structure towards a closed state, in accordance with our predictions. Our computations also predict a graded effect of different mutations at the D152 site as a result of increased coherence between the core and binding domains, an effect confirmed quantitatively through a high correlation (R 2 = 0.93) with the FRET ratio between closed and open populations measured on six mutants.

Relative, local and global dimension in complex networks

“…The AMP and lid domains are known to open and close around substrate, agreeably we find both have a lower local dimension relative to the core domain (Figure 3e). Furthermore, we find the AMP domain to have a lower average local dimension than the lid domain in both conformations, a result that we validated using experimental fluorescence correlation spectroscopy that shows the AMP domain to open and close at a faster rate (16.2µs) than the lid domain (46.6µs) [19,20].…”

Network memory in the movement of hospital patients carrying antimicrobial-resistant bacteria

“…One such example for such a dynamical approach to community detection is Markov stability (MS) (Delvenne et al 2010), which is the focus for this study. MS exploits diffusion dynamics over an underlying graph structure to reveal a multi-scale community organisation and has been show to be effective in a variety of applications in which multiple scales are expected to exist such as protein sub-structures (Peach et al 2019a) or social behaviours (Peach et al 2019b). Given a partition P of nodes into C non-overlapping communities with a N × C community indicator matrix H P the time-dependent clustered autocovariance matrix in MS is given by, where the elements of the matrix [R(t, H P )] correspond to the probability of a random walker starting at node i and ending up in community c at Markov time t minus the probability of that happening by chance.…”

Network memory in the movement of hospital patients carrying drug-resistant bacteria

“…One such example for such a dynamical approach to community detection is Markov stability (MS) [39], which is the focus for this study. MS exploits diffusion dynamics over an underlying graph structure to reveal a multi-scale community organisation and has been show to be effective in a variety of applications in which multiple scales are expected to exist such as protein sub-structures [44] or social behaviours [45]. Given…”