How Fast-Folding Proteins Fold

Lindorff‐Larsen, Kresten; Piana, Stefano; Dror, Ron O.; Shaw, David E.

doi:10.1126/science.1208351

Cited by 1,754 publications

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“…Recently, by using massively parallel supercomputer Anton, the atomic-level molecular dynamics simulations were performed for 12 fast-folding proteins and the folding times of these proteins were predicted [60]. The experimental measurements of the folding rate were carried out under higher temperatures.…”

Section: Comparison With Molecular Dynamics Predictionsmentioning

confidence: 99%

Statistical analyses of protein folding rates from the view of quantum transition

Luo

2014

Sci. China Life Sci.

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Understanding protein folding rate is the primary key to unlock the fundamental physics underlying protein structure and its folding mechanism. Especially, the temperature dependence of the folding rate remains unsolved in the literature. Starting from the assumption that protein folding is an event of quantum transition between molecular conformations, we calculated the folding rate for all two-state proteins in a database and studied their temperature dependencies. The non-Arrhenius temperature relation for 16 proteins, whose experimental data had previously been available, was successfully interpreted by comparing the Arrhenius plot with the first-principle calculation. A statistical formula for the prediction of two-state protein folding rate was proposed based on quantum folding theory. The statistical comparisons of the folding rates for 65 two-state proteins were carried out, and the theoretical vs. experimental correlation coefficient was 0.73. Moreover, the maximum and the minimum folding rates given by the theory were consistent with the experimental results. quantum folding, protein folding rate, temperature dependence, number of torsion mode, folding free energy Citation:Lv J, Luo LF. Statistical analyses of protein folding rates from the view of quantum transition. Sci China Life Sci, 2014Sci, , 57: 1197Sci, -1212Sci, , doi: 10.1007 It is well known that a protein chain can spontaneously fold into its unique native structure [1,2]. To paraphrase Levinthal's paradox, if a protein were to attain its correctly folded configuration by sequentially sampling all the possible conformations, it would require a period of time longer than the age of the universe to arrive at its correct native conformation [3]. Apart from theory, it is a fact that experimentally measured times for spontaneous folding of single-domain globular proteins range from microseconds [46] to tens of minutes [7]. Thus, how configurations of proteins are determined and what makes them fold so quickly are questions that constitute a longstanding puzzle in molecular biology. While two prominent models have been proposed to study the protein folding mechanism, folding nucleus [8,9] and folding tunnel [1014], the importance of topology and contact order in protein folding has been recognized over the last 15 years, and many new models to predict the protein folding rate have been published [1529]. Curiously, it is notable that the rate at which proteins fold is highly sensitive to temperature, showing non-Arrhenius behavior, i.e., the temperature dependence of the rate constant is, in fact, not exponential for these reactions. The nonlinearity of logarithm folding rate on temperature 1/T has been conventionally interpreted by the nonlinear temperature dependence of the configurational diffusion constant on rough energy landscapes [30] or by the temperature dependence of hydrophobic interaction [31,32]. Another model was proposed more recently to interpret the difference between folding and unfolding by introducing the number of dena...

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Section: Comparison With Molecular Dynamics Predictionsmentioning

confidence: 99%

Statistical analyses of protein folding rates from the view of quantum transition

Luo

2014

Sci. China Life Sci.

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“…The MD technique with a general-purpose graphics processing unit (GPGPU) (Götz et al 2012;Mashimo et al 2013;Pall et al 2014;SalomonFerrer et al 2013) is growing in importance due to multiple processors being less expensive than CPUs. Hardware architectures specialized for MD, such as Anton (Shaw et al 2009) and MD-GRAPE (Narumi et al 2006), have enabled extremely long-time scale MD simulations (Kikugawa et al 2009;Lindorff-Larsen et al 2011;Shaw et al 2010). These specialized architectures speed up a single MD simulation run and provide an increased number of conformations from the long MD trajectory.…”

Section: Trivial Trajectory Parallelization Of Mcmdmentioning

confidence: 99%

Enhanced sampling simulations to construct free-energy landscape of protein–partner substrate interaction

2016

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Molecular dynamics (MD) simulations using allatom and explicit solvent models provide valuable information on the detailed behavior of protein-partner substrate binding at the atomic level. As the power of computational resources increase, MD simulations are being used more widely and easily. However, it is still difficult to investigate the thermodynamic properties of protein-partner substrate binding and protein folding with conventional MD simulations. Enhanced sampling methods have been developed to sample conformations that reflect equilibrium conditions in a more efficient manner than conventional MD simulations, thereby allowing the construction of accurate free-energy landscapes. In this review, we discuss these enhanced sampling methods using a series of case-by-case examples. In particular, we review enhanced sampling methods conforming to trivial trajectory parallelization, virtual-system coupled multicanonical MD, and adaptive lambda square dynamics. These methods have been recently developed based on the existing method of multicanonical MD simulation. Their applications are reviewed with an emphasis on describing their practical implementation. In our concluding remarks we explore extensions of the enhanced sampling methods that may allow for even more efficient sampling.

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“…But we have found [32][33][34] that an excellent approximative solution can be obtained by discretizing the topological soliton (43).…”

Section: Discretized Solitonsmentioning

confidence: 99%

“…The equations are numerically integrated with a time step around a femtosecond, which is the characteristic time scale of peptide bond vibrations. Using purpose built supercomputers like Anton [43,44] and distributed computing projects like folding@home [45], the speediest MD simulations can reach a few micro-seconds of in vivo folding time, in a day in silico [44]. This enables the modeling of relatively short and very fast folding proteins such as villin headpiece (HP35) and the λ-repressor protein (1LMB in Protein Data Bank PDB [40]), up to time scales that it takes for these proteins to fold.…”

Section: Yearning For the Speed Of Lifementioning

confidence: 99%

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Gauge fields, strings, solitons, anomalies, and the speed of life

Niemi

2014

Theor Math Phys

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It's been said that mathematics is biology's next microscope, only better; biology is mathematics' next physics, only better [1]. Here we aim for something even better. We try to combine mathematical physics and biology into a picoscope of life. For this we merge techniques which have been introduced and developed in modern mathematical physics, largely by Ludvig Faddeev to describe objects such as solitons and Higgs and to explain phenomena such as anomalies in gauge fields. We propose a synthesis that can help to resolve the protein folding problem, one of the most important conundrums in all of science. We apply the concept of gauge invariance to scrutinize the extrinsic geometry of strings in three dimensional space. We evoke general principles of symmetry in combination with Wilsonian universality and derive an essentially unique Landau-Ginzburg energy that describes the dynamics of a generic string-like configuration in the far infrared. We observe that the energy supports topological solitons, that pertain to an anomaly in the manner how a string is framed around its inflection points. We explain how the solitons operate as modular building blocks from which folded proteins are composed. We describe crystallographic protein structures by multi-solitons with experimental precision, and investigate the non-equilibrium dynamics of proteins under varying temperature. We simulate the folding process of a protein at in vivo speed and with close to pico-scale accuracy using a standard laptop computer: With pico-biology as mathematical physics' next pursuit, things can only get better.This article is dedicated to Ludvig Faddeev on the occasion of his 80 th birthday. * Electronic address: Antti.Niemi@physics.uu.se 1 arXiv:1406.7468v3 [math-ph]

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How Fast-Folding Proteins Fold

Cited by 1,754 publications

References 60 publications

Statistical analyses of protein folding rates from the view of quantum transition

Statistical analyses of protein folding rates from the view of quantum transition

Enhanced sampling simulations to construct free-energy landscape of protein–partner substrate interaction

Gauge fields, strings, solitons, anomalies, and the speed of life

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