scholarly journals Multi-Funnel Landscape of the Fold-Switching Protein RfaH-CTD

2017 ◽  
Author(s):  
Nathan A. Bernhardt ◽  
Ulrich H.E. Hansmann
Keyword(s):  
Free Energy ◽  
Transcription Factor ◽  
Biological Function ◽  
Double Helix ◽  
Energy Landscape ◽  
Three Dimensional ◽  
Conversion Process ◽  
Free Energy Landscape ◽  
Enhanced Sampling ◽  
Helix Bundle

AbstractProteins such as the transcription factor RfaH can change biological function by switching between distinct three-dimensional folds. RfaH regulates transcription if the C-terminal domain folds into a double helix bundle, and promotes translation when this domain assumes a β-barrel form. This fold-switch has been also observed for the isolated domain, dubbed by us RfaH-CTD, and is studied here with a variant of the RET approach recently introduced by us. We use the enhanced sampling properties of this technique to map the free energy landscape of RfaH-CTD and to propose a mechanism for the conversion process.TOC Image

scholarly journals Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium

2020 ◽  
Author(s):  
Elizabeth Lee ◽  
Thomas Ludwig ◽  
Boyuan Yu ◽  
Aayush Singh ◽  
François Gygi ◽  
...  
Keyword(s):  
Neural Network ◽  
Free Energy ◽  
Heterogeneous Catalysis ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Enhanced Sampling ◽  
Elementary Reactions

<p>Reaction rates in heterogeneous catalysis are predicted using the free energy profiles of elementary reactions. Conventionally, the energetics are computed from critical points of the potential energy surface, with harmonic free energy corrections. Here we use <i>ab initio</i> molecular dynamics and neural network-assisted enhanced sampling simulations to directly calculate the free energy landscape of a prototypical heterogeneous catalysis reaction, the dissociation of molecular nitrogen on ruthenium. We show that accelerating force- and frequency-based enhanced sampling using neural networks can characterize reactive phenomena at density functional theory-level accuracy. A previously reported molecularly adsorbed metastable state is found in the potential energy surface but is absent in the free energy surface. The potential of mean force for the dissociation reaction shows significant temperature-dependent effects beyond the standard harmonic approximation. We demonstrate that these thermodynamic effects can be important for elementary reactions on transition metal surfaces.</p>

scholarly journals Chasing the full free energy landscape of neuroreceptor/ligand unbinding by metadynamics simulations

2019 ◽  
Author(s):  
Riccardo Capelli ◽  
Anna Bochicchio ◽  
GiovanniMaria Piccini ◽  
Rodrigo Casasnovas ◽  
Paolo Carloni ◽  
...  
Keyword(s):  
Free Energy ◽  
Sampling Method ◽  
Energy Landscape ◽  
Membrane Receptors ◽  
Free Energy Landscape ◽  
Neuronal Membrane ◽  
Allosteric Modulator ◽  
Enhanced Sampling ◽  
Reaction Coordinates ◽  
Slowing Down

Predicting the complete free energy landscape associated with protein-ligand unbinding would greatly help designing drugs with highly optimized pharmacokinetics. Here we investigate the unbinding of the iperoxo agonist to its target human neuroreceptor M2, embedded in a neuronal membrane. By feeding out-of-equilibrium molecular simulations data in a classification analysis, we identify the few essential reaction coordinates of the process. The full landscape is then reconstructed using an exact enhanced sampling method, well-tempered metadynamics in its funnel variant. The calculations reproduce well the measured affinity, provide a rationale for mutagenesis data and show that the ligand can escape via two different routes. The allosteric modulator LY2119620 turns out to hamper both escapes routes, thus slowing down the unbinding process, as experimentally observed. This computationally affordable protocol is totally general and it can be easily applied to determine the full free energy landscape of membrane receptors/drug interactions.

Energetics and structural characterization of the “DFG-flip” conformational transition of B-RAF kinase: a SITS molecular dynamics study

2017 ◽  
Vol 19 (2) ◽  
pp. 1257-1267 ◽  
Author(s):  
Qiang Shao ◽  
Zhijian Xu ◽  
Jinan Wang ◽  
Jiye Shi ◽  
Weiliang Zhu
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Conformational Transition ◽  
Energy Landscape ◽  
Dynamics Simulation ◽  
Free Energy Landscape ◽  
Enhanced Sampling ◽  
Raf Kinase ◽  
Kinase A

A combination of a homology modeling technique and an enhanced sampling molecular dynamics simulation implemented using the SITS method is employed to compute a detailed map of the free-energy landscape and explore the conformational transition pathway of B-RAF kinase.

Knotting pathways in proteins

2013 ◽  
Vol 41 (2) ◽  
pp. 523-527 ◽  
Author(s):  
Joanna I. Sułkowska ◽  
Jeffrey K. Noel ◽  
César A. Ramírez-Sarmiento ◽  
Eric J. Rawdon ◽  
Kenneth C. Millett ◽  
...  
Keyword(s):  
Free Energy ◽  
Biological Function ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Biological Functions ◽  
Loop Formation ◽  
Native State ◽  
Theoretical Approaches ◽  
Knotted Proteins ◽  
Comparison With Experiments

Most proteins, in order to perform their biological function, have to fold to a compact native state. The increasing number of knotted and slipknotted proteins identified suggests that proteins are able to manoeuvre around topological barriers during folding. In the present article, we review the current progress in elucidating the knotting process in proteins. Although we concentrate on theoretical approaches, where a knotted topology can be unambiguously detected, comparison with experiments is also reviewed. Numerical simulations suggest that the folding process for small knotted proteins is composed of twisted loop formation and then threading by either slipknot geometries or flipping. As the size of the knotted proteins increases, particularly for more deeply threaded termini, the prevalence of traps in the free energy landscape also increases. Thus, in the case of longer knotted and slipknotted proteins, the folding mechanism is probably supported by chaperones. Overall, results imply that knotted proteins can be folded efficiently and survive evolutionary pressure in order to perform their biological functions.

scholarly journals Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium

2020 ◽  
Author(s):  
Elizabeth Lee ◽  
Thomas Ludwig ◽  
Boyuan Yu ◽  
Aayush Singh ◽  
François Gygi ◽  
...  
Keyword(s):  
Neural Network ◽  
Free Energy ◽  
Heterogeneous Catalysis ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Enhanced Sampling ◽  
Elementary Reactions

<p>Reaction rates in heterogeneous catalysis are predicted using the free energy profiles of elementary reactions. Conventionally, the energetics are computed from critical points of the potential energy surface, with harmonic free energy corrections. Here we use <i>ab initio</i> molecular dynamics and neural network-assisted enhanced sampling simulations to directly calculate the free energy landscape of a prototypical heterogeneous catalysis reaction, the dissociation of molecular nitrogen on ruthenium. We show that accelerating force- and frequency-based enhanced sampling using neural networks can characterize reactive phenomena at density functional theory-level accuracy. A previously reported molecularly adsorbed metastable state is found in the potential energy surface but is absent in the free energy surface. The potential of mean force for the dissociation reaction shows significant temperature-dependent effects beyond the standard harmonic approximation. We demonstrate that these thermodynamic effects can be important for elementary reactions on transition metal surfaces.</p>

scholarly journals Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium

2020 ◽  
Author(s):  
Elizabeth Lee ◽  
Thomas Ludwig ◽  
Boyuan Yu ◽  
Aayush Singh ◽  
François Gygi ◽  
...  
Keyword(s):  
Neural Network ◽  
Free Energy ◽  
Heterogeneous Catalysis ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Enhanced Sampling ◽  
Elementary Reactions

<p>Reaction rates in heterogeneous catalysis are predicted using the free energy profiles of elementary reactions. Conventionally, the energetics are computed from critical points of the potential energy surface, with harmonic free energy corrections. Here we use <i>ab initio</i> molecular dynamics and neural network-assisted enhanced sampling simulations to directly calculate the free energy landscape of a prototypical heterogeneous catalysis reaction, the dissociation of molecular nitrogen on ruthenium. We show that accelerating force- and frequency-based enhanced sampling using neural networks can characterize reactive phenomena at density functional theory-level accuracy. A previously reported molecularly adsorbed metastable state is found in the potential energy surface but is absent in the free energy surface. The potential of mean force for the dissociation reaction shows significant temperature-dependent effects beyond the standard harmonic approximation. We demonstrate that these thermodynamic effects can be important for elementary reactions on transition metal surfaces.</p>

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