Biology subjects

Sosnick, T. R.

Publications and source records attributed to Sosnick, T. R..

3 recordsLinked to original sources

bioRxiv2018

Aβ Fibrils Can Act as Aqueous Pores: a Molecular Dynamics Study

Aggregation of A{beta} peptides is important in the etiology of Alzheimers Disease (AD), an increasingly prevalent neurodegenerative disease. We ran multiple [~] 300 ns all-atom explicit solvent molecular dynamics (MD) simulations starting from three NMR-based structural models of A{beta}(1-40 residues) fibrils having 2-fold (pdb code 2LMN) or 3-fold rotational symmetry (2LMP, and 2M4J). The 2M4J structure is based on an AD brain-seeded fibril whereas 2LMP and 2LMN represent two all-synthetic fibrils. Fibrils are constructed to contain either 6 or an infinite number of layers made using periodic images. The 6 layer fibrils partially unravel over the simulation time, mainly at their ends, while infinitely long fibrils do not. Once formed, the D23-K28 salt bridges are very stable and form within and between chains. Fibrils tend to retain (2LMN and 2LMP) or develop (2M4J) a \"stagger\" or register shift of {beta}-strands along the fibril axis. The brain-seeded fibril rapidly develops gaps at the sides of the fibril, which allows bidirectional flow of water and ions from the bulk phase in and out the central longitudinal core of the fibril. Similar but less marked changes were also observed for the 2LMP fibrils. The residues defining the gaps largely coincide with those demonstrated to have relatively rapid Hydrogen-Deuterium exchange in solid state NMR studies. These observations suggest that A{beta}(1-40 residues) fibrils may act as aqueous pores that might disrupt water and ion fluxes if inserted into a cell membrane.

neuroscience↗

bioRxiv2018

Commonly-used FRET fluorophores promote collapse of an otherwise disordered protein.

The dimensions that unfolded proteins, including intrinsically disordered proteins (IDPs), adopt at low or no denaturant remains controversial. We recently developed an innovative analysis procedure for small-angle X-ray scattering (SAXS) profiles and found that even relatively hydrophobic IDPs remain nearly as expanded as the chemically denatured ensemble, rendering them significantly more expanded than generally inferred using fluorescence resonance energy transfer (FRET) measurements. We show here that fluorophores typical of those employed in FRET can contribute to this discrepancy. Specifically, we find that addition of Alexa488 to a normally expanded IDP causes contraction of its ensemble. In parallel, we also tested the recent suggestion that FRET and SAXS results can be reconciled if, in contrast to homopolymers, the radius of gyration (Rg) of an unfolded protein chain can vary independently from its end-to-end distance (Ree). To do so, we developed an analysis procedure that can accurately extract both Rg and Ree from SAXS profiles even if they are decoupled. Using this procedure, we find that Rg and Ree remain tightly coupled even for heteropolymeric IDPs. We thus conclude that, when combined with improved analysis procedures for both SAXS and FRET, fluorophore-driven interactions are sufficient to explain the preponderance of existing data regarding the nature of polypeptide chains unfolded in the absence of denaturant.

biophysics↗

bioRxiv2017

Trajectory-Based Parameterization of a Coarse-Grained Forcefield for High-Throughput Protein Simulation

The traditional trade-off in biomolecular simulation between accuracy and computational efficiency is predicated on the assumption that detailed forcefields are typically well-parameterized (i.e. obtaining a significant fraction of possible accuracy). We re-examine this trade-off in the more realistic regime in which parameterization is a greater source of bias than the level of detail in the forcefield. To address parameterization of coarse-grained forcefields, we use the contrastive divergence technique from machine learning to train directly from simulation trajectories on 450 proteins. In our scheme, the computational efficiency of the model enables high accuracy through precise tuning of the Boltzmann ensemble over a large collection of proteins. This method is applied to our recently developed Upside model [1], where the free energy for side chains are rapidly calculated at every time-step, allowing for a smooth energy landscape without steric rattling of the side chains. After our contrastive divergence training, the model is able to fold proteins up to approximately 100 residues de novo on a single core in CPU core-days. Additionally, the improved Upside model is a strong starting point both for investigation of folding dynamics and as an inexpensive Bayesian prior for protein physics that can be integrated with additional experimental or bioinformatic data.

biophysics↗