Conformational Plasticity in α-Synuclein and How Crowded Environment Modulates It

Abstract

A 140-residue intrinsically disordered protein (IDP), α-synuclein (αS), is known to adopt conformations that are vastly plastic and susceptible to environmental cues and crowders. However, the inherently heterogeneous nature of αS has precluded a clear demarcation of its monomeric precursor between aggregation-prone and functionally relevant aggregation-resistant states and how a crowded environment could modulate their mutual dynamic equilibrium. Here, we identify an optimal set of distinct metastable states of αS in aqueous media by dissecting a 73 μs-long molecular dynamics ensemble via building a comprehensive Markov state model (MSM). Notably, the most populated metastable state corroborates with the dimension obtained from PRE-NMR studies of αS monomer, and it undergoes kinetic transition at diverse time scales with a weakly populated random-coil-like ensemble and a globular protein-like state. However, subjecting αS to a crowded environment results in a nonmonotonic compaction of these metastable conformations, thereby skewing the ensemble by either introducing new tertiary contacts or by reinforcing the innate contacts. The early stage of dimerization process is found to be considerably expedited in the presence of crowders, albeit promoting nonspecific interactions. Together with this, using an extensively sampled ensemble of αS, this exposition demonstrates that crowded environments can potentially modulate the conformational preferences of IDP that can either promote or inhibit aggregation events.

Modulation ofα-Synuclein Aggregation Amid Diverse Environmental Perturbation

“…Using the cutoff defined, we calculate the percentage bound between the two α S monomers for different values of λ in the coarse-grained model. We also calculate the same from atomistic simulations reported in 23 as the reference. From Figure 9b, we can see that for multiple values of λ , we observe a close agreement in percentage bound values between coarse-grained and atomistic simulations.…”

“…A recent study used Markov State models to delineate the metastable states based on the extent of compaction (R g ) and identified 3 macrostates and their relative populations. 23 Therefore, in the multi-chain simulations, we maintain similar relative populations of these macrostates. (Figure S7) .…”

“…In particular, to understand the influence of environmental factors on the inter-protein interactions within a phase separated droplet, we target to computationally simulate the aggregation process of αS under different conditions, emphasizing the roles of crowders and salt. While recent progress in computational forcefields and hardware has enabled the simulation of individual Intrinsically Disordered Proteins (IDPs) especially αS, using All-Atom Molecular Dynamics (AAMD), [18][19][20][21][22][23][24] these simulations can be extremely time-consuming and resource-intensive, making multi-chain AAMD simulations, even with cutting-edge software and hardware, impractical. Therefore, to simulate the the aggregation process, we resort to coarse-grained molecular dynamics (CGMD) simulations.…”

Bridging Soft Interaction and Excluded Volume in Crowded Milieu through Subtle Protein Dynamics

“…The cellular interior presents a highly congested and heterogeneous environment with the concentration of macromolecules reaching as high as 400 g/L. − The underlying volume exclusion caused by the impenetrability of nearby macromolecules at high concentrations has been reported to alter the structure and function of proteins and enzymes, − although less crowded environments have also been shown to bring about considerable modulation. − Proteins can exhibit a hierarchy of dynamics spanning a range of time scales due to large-scale domain movements and local fluctuations, providing deeper insights into the structure–function correlation . Water molecules in the hydration layer of proteins, referred to as biological water, are intimately associated with biomolecular dynamics, oftentimes driving the same, an aspect that has been termed as solvent slaving of motions. , To mimic the cellular environment, researchers have been using polymer-based crowders with sugar (Dextran and Ficoll) and poly ethylene glycol (PEG) as the monomeric units since performing experiments in cellular environments can be quite challenging.…”

Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding

“…Theoretically well-grounded dimensionality reduction (DR) techniques are now commonly being used in protein conformation analysis to extract the latent low-dimensional features, and the quantum of information lost during the projection depends heavily on the kind of data set under consideration. − The time-lagged independent component analysis (TICA) is a commonly used linear transformation method that identifies coordinates with maximal correlation given an appropiate lag time. − However, the featurization of data remains critical and must be considered to minimize statistical error. A highly heterogeneous data set that lies on a high-dimensional manifold, as in the case of IDPs, is best handled with nonlinear dimension reduction (NLDR) techniques, which generally attempt to keep the nearest neighbors close together.…”