Simulating Biomolecular Folding and Function by Native‐Structure‐Based/Go‐Type Models

Abstract

The 2013 Nobel Prize in Chemistry highlights how crucial computer simulations have become for many scientific and engineering fields. Nowadays, scientific progress is not only driven by the interplay of new experimental measurements and increasingly sophisticated theoretical frameworks, but also by an incredible toolbox of complex computational models meeting ubiquitously available computing power and data storage facilities. Quantum mechanical (QM) calculations can be condensed into molecular mechanics (MM) force fields and coupled QM/MM calculations can derive atomic and molecular properties of biomolecular or materials science systems with high accuracy. Pure MM simulations driven by Monte Carlo or molecular dynamics algorithms are widely applied in biological chemistry/physics and can investigate large biomolecular systems, such as proteins, DNA, or RNA. One coarse‐grained class of these models, native‐structure‐based or Go models, are based on energy landscape theory and the principle of minimal frustration. Herein, an ensemble of converging pathways guide protein folding on a funnel‐like shape of the entire energy landscape towards the native state. Simulations based on these ideas have been tremendously successful in explaining protein folding and function. Their history and recent application highlights are reviewed.

Simulation of FRET dyes allows quantitative comparison against experimental data

“…29 They are based on energy landscape theory and the principle of minimal frustration. [30][31][32][33] SBMs are used successfully to study a wide range of phenomena 34 ranging from, e.g., protein folding, 35,36 misfolding, 37,38 structure prediction, 39 and conformational dynamics 40,41 to large biomolecules such as the ribosome 42 or RNA. 43 SBM simulations show good agreement with experimental results, e.g., they are used to reproduce transition state ensembles and "en-route" intermediates, 44 and folding rates comparable to experimental measurements.…”

Rapid interpretation of small-angle X-ray scattering data

“…Thus it is even possible to perform instructive atomistic simulations [29, 30] on desktop PCs. Successful applications cover a wide range of protein dynamics, including folding pathways [31–34] and kinetics [35]. SBMs are also employed for structure prediction [36–38], integrative structural modeling of experimental data from e.g.…”

Native structure-based modeling and simulation of biomolecular systems per mouse click

“…protein folding simulation [ 16 ] on the all-atom level on standard desktop computers. In many recent studies of protein dynamics SBM has become the tool of choice to rationalize experimental observations by means of computer simulations [ 17 ]. Structure based modeling provides now an established method for physical understanding of, e.g.…”

“…Still, one might easily expect that focusing on native interactions and neglecting non-native interactions within SBM distorts simulation results. Significant effort has therefore been invested to examine to which extend non-native interactions play critical roles [ 17 , 25 – 28 ]. In particular, recent work has carefully analyzed the role of native interactions in prior atomically resolved simulations [ 29 , 30 ].…”

“…Overall, SBM is accurate for the extreme case of minimal or no frustration, with all-atom models realizing all possible non-native interactions [ 32 ]. Somewhat surprisingly, neglecting non-native interactions does not significantly distort simulations results [ 17 , 31 ]. This not only makes the minimalistic SBM very useful, but also suggests that naturally occurring proteins seem to possess low frustration [ 8 , 33 ].…”