Atomistic Folding Simulations of the Five-Helix Bundle Protein λ₆₋₈₅

Abstract

Protein folding is a classic grand challenge that is relevant to numerous human diseases, such as protein misfolding diseases like Alzheimer's. Solving the folding problem will ultimately require a combination of theory, simulation, and experiment; with theory and simulation providing an atomically-detailed picture of both the thermodynamics and kinetics of folding and experimental tests grounding these models in reality. However, theory and simulation generally fall orders of magnitude short of biologically relevant timescales. Here we report significant progress towards closing this gap: an atomistic model of the folding of an 80-residue fragment of the λ repressor protein with explicit solvent that captures dynamics on 10 millisecond timescales. In addition, we provide a number of predictions that warrant further experimental investigation. For example, our model's native state is a kinetic hub and biexponential kinetics arise from the presence of many free energy basins separated by barriers of different heights rather than a single low barrier along one reaction coordinate (the previously proposed incipient downhill scenario).Understanding protein folding is a long-standing problem with important medical applications, such as elucidating the role of protein misfolding in diseases like Alzheimer's. Solving the folding problem will ultimately require a combination of theory, simulation, and experiment; with theory and simulation providing an atomically-detailed picture of both the thermodynamics and kinetics of folding and experimental tests grounding these models in reality. However, modeling long timescale dynamics (e.g. microseconds, milliseconds, and beyond) with sufficient statistical accuracy and chemical detail to make a quantitative connection with experiments is extremely challenging. Much progress has been made with small, fast-folding proteins (less than 40 residues and one millisecond folding timescales 1 ) but can the methods used scale to larger, slower systems? Here we report significant progress in this direction: an atomistic model of the folding of an 80-residue fragment of the λ repressor protein (λ 6-85 ) with explicit solvent that captures dynamics on a 10 millisecond timescale.This advance builds on a growing body of work on describing molecular kinetics with network models called Markov state models (MSMs). MSMs are discrete time master equation models that essentially serve as maps of a molecule's conformational space. [1][2][3] The states in an MSM come from kinetic clustering of atomistic simulations (i.e. conformations that can interconvert rapidly are grouped together into what is called a metastable state). Thus, these models are an important advance over previous approaches, like diffusion-collision models, 4,5 as an MSM's states are derived from dynamics in detailed pande@stanford.edu. Supporting Information Available: Methods, Figure S1 to S14, To test whether the MSM approach can scale to larger systems, we have built MSMs for the D14A mutant of λ 6-85 ( Figure 1A). 12...

Misplaced helix slows down ultrafast pressure-jump protein folding

“…The T-jump unfolding relaxation kinetics from Prigozhin and Gruebele (23) at 339 K are also shown for comparison. A ≈60-μs phase of increasing lifetime was observed in that case, followed by a small millisecond phase attributed to a β-sheet-rich trap in the studies by Bowman et al (22) and Prigozhin and Gruebele (23).…”

“…Thus, our pressure experiments can be directly compared with the T-jump results. The motivation to use IR absorbance as a complementary structural probe came from recent computational and experimental reports indicating the presence of transient β-sheet formation in λ*YA at high temperature (17,22,23).…”

Bayesian Energy Landscape Tilting: Towards Concordant Models of Molecular Ensembles

“…A truly quantitative connection to these measurements requires modeling the equilibrium ensemble. Finally, recent advances in atomistic simulation (34,43,44,45), special-purpose hardware (46), and distributed computing analysis (47,48) have enabled atomistic simulations to reach the millisecond timescale (49,50,51,52); the computational cost of ensemble modeling is quickly becoming manageable.…”

Estimation and uncertainty of reversible Markov models

“…7 updates x i j with the exact solution to the quadratic problem arising from (23) while holding all other variables x k l fixed. As shown in Fig.…”

“…[19][20][21] A variety of complex molecular processes have been successfully described using MSMs. Examples include the folding of proteins into their native folded structure, 20,22,23 the dynamics of natively unstructured proteins, 24,25 and the binding of a ligand to a target protein. 21,[26][27][28][29][30] There are two key steps in the construction of a MSM.…”