scholarly journals Computational methods for exploring protein conformations

2020 ◽  
Vol 48 (4) ◽  
pp. 1707-1724
Author(s):  
Jane R. Allison
Keyword(s):  
Free Energy ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Conformational Sampling ◽  
Conformational Ensemble ◽  
Collective Variables ◽  
Conformational States ◽  
Simulation Techniques ◽  
Wide Range ◽  
Dynamics Simulations

Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.

scholarly journals Free-energy landscape of a hyperstable RNA tetraloop

2016 ◽  
Vol 113 (24) ◽  
pp. 6665-6670 ◽  
Author(s):  
Jacob C. Miner ◽  
Alan A. Chen ◽  
Angel E. García
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
High Performance ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Left Handed ◽  
Rna Conformation ◽  
Dynamics Simulations ◽  
Secondary Structure Motif

We report the characterization of the energy landscape and the folding/unfolding thermodynamics of a hyperstable RNA tetraloop obtained through high-performance molecular dynamics simulations at microsecond timescales. Sampling of the configurational landscape is conducted using temperature replica exchange molecular dynamics over three isochores at high, ambient, and negative pressures to determine the thermodynamic stability and the free-energy landscape of the tetraloop. The simulations reveal reversible folding/unfolding transitions of the tetraloop into the canonical A-RNA conformation and the presence of two alternative configurations, including a left-handed Z-RNA conformation and a compact purine Triplet. Increasing hydrostatic pressure shows a stabilizing effect on the A-RNA conformation and a destabilization of the left-handed Z-RNA. Our results provide a comprehensive description of the folded free-energy landscape of a hyperstable RNA tetraloop and highlight the significant advances of all-atom molecular dynamics in describing the unbiased folding of a simple RNA secondary structure motif.

scholarly journals The histone H3 N-terminal tail: a computational analysis of the free energy landscape and kinetics

2015 ◽  
Vol 17 (20) ◽  
pp. 13689-13698 ◽  
Author(s):  
Yuqing Zheng ◽  
Qiang Cui
Keyword(s):  
Free Energy ◽  
Energy Landscape ◽  
Critical Role ◽  
Histone H3 ◽  
Free Energy Landscape ◽  
Chromatin Dynamics ◽  
Markov State Models ◽  
State Models ◽  
Dynamics Simulations ◽  
Kinetics Of

Extensive molecular dynamics simulations and Markov State models are used to characterize the free energy landscape and kinetics of the histone H3 N-terminal tail, which plays a critical role in regulating chromatin dynamics and gene activity.

scholarly journals Enhanced conformational sampling to visualize a free-energy landscape of protein complex formation

2016 ◽  
Vol 473 (12) ◽  
pp. 1651-1662 ◽  
Author(s):  
Shinji Iida ◽  
Haruki Nakamura ◽  
Junichi Higo
Keyword(s):  
Free Energy ◽  
Room Temperature ◽  
Dimensional Space ◽  
Energy Landscape ◽  
Complex Structure ◽  
Complex Form ◽  
Ensemble Methods ◽  
Ensemble Method ◽  
Free Energy Landscape ◽  
Conformational Sampling

We introduce various, recently developed, generalized ensemble methods, which are useful to sample various molecular configurations emerging in the process of protein–protein or protein–ligand binding. The methods introduced here are those that have been or will be applied to biomolecular binding, where the biomolecules are treated as flexible molecules expressed by an all-atom model in an explicit solvent. Sampling produces an ensemble of conformations (snapshots) that are thermodynamically probable at room temperature. Then, projection of those conformations to an abstract low-dimensional space generates a free-energy landscape. As an example, we show a landscape of homo-dimer formation of an endothelin-1-like molecule computed using a generalized ensemble method. The lowest free-energy cluster at room temperature coincided precisely with the experimentally determined complex structure. Two minor clusters were also found in the landscape, which were largely different from the native complex form. Although those clusters were isolated at room temperature, with rising temperature a pathway emerged linking the lowest and second-lowest free-energy clusters, and a further temperature increment connected all the clusters. This exemplifies that the generalized ensemble method is a powerful tool for computing the free-energy landscape, by which one can discuss the thermodynamic stability of clusters and the temperature dependence of the cluster networks.

scholarly journals Variation of free-energy landscape of the p53 C-terminal domain induced by acetylation: Enhanced conformational sampling

2016 ◽  
Vol 37 (31) ◽  
pp. 2687-2700 ◽  
Author(s):  
Shinji Iida ◽  
Tadaaki Mashimo ◽  
Takashi Kurosawa ◽  
Hironobu Hojo ◽  
Hiroya Muta ◽  
...  
Keyword(s):  
Free Energy ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Conformational Sampling ◽  
Terminal Domain

scholarly journals Assessment and Optimization of Collective Variables for Protein Conformational Landscape: GB1 β-hairpin as a Case Study

2018 ◽  
Author(s):  
Navjeet Ahalawat ◽  
Jagannath Mondal
Keyword(s):  
Free Energy ◽  
Molten Globule ◽  
Energy Landscape ◽  
Model System ◽  
Metastable States ◽  
Free Energy Landscape ◽  
Collective Variables ◽  
Folding Mechanism ◽  
Biochemical Systems ◽  
Conformational Landscape

Collective variables (CV), when chosen judiciously, can play an important role in recognizing rate-limiting processes and rare events in any biomolecular systems. However, high dimensionality and inherent complexities associated with such biochemical systems render the identification of an optimal CV a challenging task, which in turn precludes the elucidation of underlying conformational landscape in sufficient details. In this context, a relevant model system is presented by 16residue, β hairpin of GB1 protein. Despite being the target of numerous theoretical and computational studies for understanding the protein folding, the set of CVs optimally characterizing the conformational landscape of, β hairpin of GB1 protein has remained elusive, resulting in a lack of consensus on its folding mechanism. Here we address this by proposing a pair of optimal CVs which can resolve the underlying free energy landscape of GB1 hairpin quite efficiently. Expressed as a linear combination of a number of traditional CVs, the optimal CV for this system is derived by employing recently introduced Timestructured Independent Component Analysis (TICA) approach on a large number of independent unbiased simulations. By projecting the replica-exchange simulated trajectories along these pair of optimized CVs, the resulting free energy landscape of this system are able to resolve four distinct wellseparated metastable states encompassing the extensive ensembles of folded,unfolded and molten globule states. Importantly, the optimized CVs were found to be capable of automatically recovering a novel partial helical state of this protein, without needing to explicitly invoke helicity as a constituent CV. Furthermore, a quantitative sensitivity analysis of each constituent in the optimized CV provided key insights on the relative contributions of the constituent CVs in the overall free energy landscapes. Finally, the kinetic pathways con necting these metastable states, constructed using a Markov State Model, provide an optimum description of underlying folding mechanism of the peptide. Taken together, this work oers a quantitatively robust approach towards comprehensive mapping of the underlying folding landscape of a quintessential model system along its optimized collective variables.

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