scholarly journals Pervasive cooperative mutational effects on multiple catalytic enzyme traits emerge via long-range conformational dynamics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Carlos G. Acevedo-Rocha ◽  
Aitao Li ◽  
Lorenzo D’Amore ◽  
Sabrina Hoebenreich ◽  
Joaquin Sanchis ◽  
...  
Keyword(s):  
Long Range ◽  
Fitness Landscape ◽  
Conformational Dynamics ◽  
P450 Monooxygenase ◽  
Access Channel ◽  
Long Range Interactions ◽  
Limited Protein ◽  
Dynamics Simulations ◽  
The Relationship ◽  
Single Enzyme

AbstractMultidimensional fitness landscapes provide insights into the molecular basis of laboratory and natural evolution. To date, such efforts usually focus on limited protein families and a single enzyme trait, with little concern about the relationship between protein epistasis and conformational dynamics. Here, we report a multiparametric fitness landscape for a cytochrome P450 monooxygenase that was engineered for the regio- and stereoselective hydroxylation of a steroid. We develop a computational program to automatically quantify non-additive effects among all possible mutational pathways, finding pervasive cooperative signs and magnitude epistasis on multiple catalytic traits. By using quantum mechanics and molecular dynamics simulations, we show that these effects are modulated by long-range interactions in loops, helices and β-strands that gate the substrate access channel allowing for optimal catalysis. Our work highlights the importance of conformational dynamics on epistasis in an enzyme involved in secondary metabolism and offers insights for engineering P450s.

scholarly journals Pervasive cooperative mutational effects on multiple catalytic enzyme traits emerge via long-range conformational dynamics

2020 ◽  
Author(s):  
Carlos G. Acevedo-Rocha ◽  
Aitao Li ◽  
Lorenzo D’Amore ◽  
Sabrina Hoebenreich ◽  
Joaquin Sanchis ◽  
...  
Keyword(s):  
Long Range ◽  
Fitness Landscape ◽  
Conformational Dynamics ◽  
P450 Monooxygenase ◽  
Access Channel ◽  
Long Range Interactions ◽  
Limited Protein ◽  
Dynamics Simulations ◽  
The Relationship ◽  
Single Enzyme

AbstractMultidimensional fitness landscapes provide insights into the molecular basis of laboratory and natural evolution. Yet such efforts are rare and focus only on limited protein families and a single enzyme trait, with little concern about the relationship between protein epistasis and conformational dynamics. Here, we report the first multiparametric fitness landscape for a cytochrome P450 monooxygenase that was engineered for the regio- and stereoselective hydroxylation of a steroid. We developed a computational program to automatically quantify non-additive effects among all possible mutational pathways, finding pervasive cooperative sign and magnitude epistasis on multiple catalytic traits. By using quantum mechanics and molecular dynamics simulations, we show that these effects are modulated by long-range interactions in loops, helices and beta-strands that gate the substrate access channel allowing for optimal catalysis. Our work highlights the importance of conformational dynamics on epistasis in an enzyme involved in secondary metabolism and offers lessons for engineering P450s.

Local order and long range correlations in imidazolium halide ionic liquids: a combined molecular dynamics and XAS study

2015 ◽  
Vol 17 (25) ◽  
pp. 16443-16453 ◽  
Author(s):  
Valentina Migliorati ◽  
Alessandra Serva ◽  
Giuliana Aquilanti ◽  
Sakura Pascarelli ◽  
Paola D'Angelo
Keyword(s):  
Molecular Dynamics ◽  
Ionic Liquids ◽  
Molecular Dynamics Simulations ◽  
Long Range ◽  
Exafs Spectroscopy ◽  
Local Order ◽  
Long Range Correlations ◽  
Long Range Interactions ◽  
Dynamics Simulations

EXAFS spectroscopy and molecular dynamics simulations have been combined to unveil the effect of the cation and anion nature on the local order and long range interactions of imidazolium halide ionic liquids.

Mapping Long-Range Interactions in α-Synuclein using Spin-Label NMR and Ensemble Molecular Dynamics Simulations

2005 ◽  
Vol 127 (2) ◽  
pp. 476-477 ◽  
Author(s):  
Matthew M. Dedmon ◽  
Kresten Lindorff-Larsen ◽  
John Christodoulou ◽  
Michele Vendruscolo ◽  
Christopher M. Dobson
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Long Range ◽  
Spin Label ◽  
Long Range Interactions ◽  
Dynamics Simulations

scholarly journals Neural relational inference to learn allosteric long-range interactions in proteins from molecular dynamics simulations

2021 ◽  
Author(s):  
Jingxuan Zhu ◽  
Juexin Wang ◽  
Weiwei Han ◽  
Dong Xu
Keyword(s):  
Molecular Dynamics ◽  
Long Range ◽  
Md Simulation ◽  
Biological Process ◽  
Dynamic Networks ◽  
Md Simulations ◽  
Amino Acid Mutation ◽  
Long Range Interactions ◽  
Decoder Architecture ◽  
Dynamics Simulations

Abstract Protein allostery is a biological process facilitated by spatially long-range intra-protein communication, whereby ligand binding or amino acid mutation at a distant site affects the active site remotely. Molecular dynamics (MD) simulation provides a powerful computational approach to probe the allosteric effect. However, current MD simulations cannot reach the time scales of whole allosteric processes. The advent of deep learning made it possible to evaluate both spatially short and long-range communications for understanding allostery. For this purpose, we applied a neural relational inference (NRI) model based on a graph neural network (GNN), which adopts an encoder-decoder architecture to simultaneously infer latent interactions to probe protein allosteric processes as dynamic networks of interacting residues. From the MD trajectories, this model successfully learned the long-range interactions and pathways that can mediate the allosteric communications between the two distant sites in the Pin1, SOD1, and MEK1 systems.

scholarly journals Insights into Correlated Motions and Long-Range Interactions in CheY Derived from Molecular Dynamics Simulations

2007 ◽  
Vol 92 (6) ◽  
pp. 2062-2079 ◽  
Author(s):  
Michael H. Knaggs ◽  
Freddie R. Salsbury ◽  
Marshall Hall Edgell ◽  
Jacquelyn S. Fetrow
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Long Range ◽  
Correlated Motions ◽  
Long Range Interactions ◽  
Dynamics Simulations

scholarly journals Neural relational inference to learn allosteric long-range interactions in proteins from molecular dynamics simulations

2021 ◽  
Author(s):  
Jingxuan Zhu ◽  
Juexin Wang ◽  
Weiwei Han ◽  
Dong Xu
Keyword(s):  
Molecular Dynamics ◽  
Long Range ◽  
Binding Sites ◽  
Md Simulation ◽  
Dynamic Networks ◽  
Md Simulations ◽  
Amino Acid Mutation ◽  
Long Range Interactions ◽  
Decoder Architecture ◽  
Dynamics Simulations

AbstractProtein allostery is a biological process facilitated by spatially long-range intra-protein communication, whereby ligand binding or amino acid mutation at a distant site affects the active site remotely. Molecular dynamics (MD) simulation provides a powerful computational approach to probe the allostery effect. However, current MD simulations cannot reach the time scales of whole allostery processes. The advent of deep learning made it possible to evaluate both spatially short and long-range communications for understanding allostery. For this purpose, we applied a neural relational inference (NRI) model based on a graph neural network (GNN), which adopts an encoder-decoder architecture to simultaneously infer latent interactions to probe protein allosteric processes as dynamic networks of interacting residues. From the MD trajectories, this model successfully learned the long-range interactions and pathways that can mediate the allosteric communications between the two distant binding sites in the Pin1, SOD1, and MEK1 systems.

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