Spectral analysis of molecular dynamics simulations on PDZ: MD sectors

Lakhani, Bharat; Thayer, Kelly M.; Black, Emily; Beveridge, David L.

doi:10.1080/07391102.2019.1588169

Cited by 18 publications

(48 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While this may be challenging for laboratory evolution, Nature probably evolved similar networks to tailor activity, as indicated by phylogenetic analyses that revealed sectors of spatially proximal coevolving residues [32][33][34] . Notably, these sectors can also show correlated dynamics 35 , hinting at a catalytically relevant role of the underlying networks. Other allosteric effects apparently also rely on similar networks [36][37][38] , which may enhance preorganization, reduce non-productive conformations, and tune global scaffold flexibility for thermoadaptation [17][18][19][20]39 .…”

Section: Evolution Of a Dynamical Networkmentioning

confidence: 97%

Evolution of dynamical networks enhances catalysis in a designer enzyme

Bunzel

Anderson

Hilvert

et al. 2020

Preprint

View full text Add to dashboard Cite

Activation heat capacity is emerging as a crucial factor in enzyme thermoadaptation, as shown by non-Arrhenius behaviour of many natural enzymes. However, its physical origin and relationship to evolution of catalytic activity remain uncertain. Here, we show that directed evolution of a computationally designed Kemp eliminase introduces dynamical changes that give rise to an activation heat capacity absent in the original design. Extensive molecular dynamics simulations show that evolution results in the closure of solvent exposed loops and better packing of the active site with transition state stabilising residues. Remarkably, these changes give rise to a correlated dynamical network involving the transition state and large parts of the protein. This network tightens the transition state ensemble, which induces an activation heat capacity and thereby nonlinearity in the temperature dependence. Our results have implications for understanding enzyme evolution (e.g. in explaining the role of distal mutations and evolutionary tuning of dynamical responses) and suggest that integrating dynamics with design and evolution will accelerate the development of efficient novel enzymes.

show abstract

Section: Evolution Of a Dynamical Networkmentioning

confidence: 97%

Evolution of dynamical networks enhances catalysis in a designer enzyme

Bunzel

Anderson

Hilvert

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…1 Where MD sectors begins to differ is in the measure of covariance used; instead of evolutionary covariance, MD sectors analyzes the motional covaraiance of protein residues within MD trajectories, rooting the method in a first principles approach. 3 We believe MD sectors has the ability to The distance covariance between residues x and z would be positive, since both residues deviated from their initial position at the same time. Note that the direction of movement does not affect the covariance; only the magnitude of the movement will have a quantifiable impact.…”

Section: Sectorsmentioning

confidence: 93%

“…The MD simulation provides a trajectory of a protein's motion, which is used by MD sectors to find motional covariance within the residue network, rooting the method in first principles statistics. 3 MD sectors is a relatively new method which may benefit from refinement and affirmation of its biological significance. This study aims to build upon previous works in validating the hypothesis that MD sectors is able find networks of residues which play a substantial role in allosteric signaling.…”

Section: Statistical Coupling Analysis (Sca) Sectors Is a Methods Devementioning

confidence: 99%

“…The method emphasizes physical principles, which produces a sector rooted in statistical mechanics as opposed to evolutionary effects. 3 The Thayer lab has also developed MD based Markov State Models (MD-MSMs). This method reports on the conformational dynamics of a biological system.…”

Section: Contextualizationmentioning

confidence: 99%

“…SCA on PDZ found a sector of 20 residues, 15 of which were also characterized to be mutationally sensitive. 3 The Thayer lab has also analyzed in silico the effects of making changes to sector residues in the Hydrogen bonding network involved in the allosteric activation of the DNA repair enzyme MutS. 50…”

Section: Statistical Coupling Analysis Sectorsmentioning

confidence: 99%

See 2 more Smart Citations

Network Analysis of Molecular Dynamics Sectors in the p53 Protein

Fabry¹

View full text Add to dashboard Cite

Target-specific drug design necessitates the manipulation of multifaceted networks of biomolecules. At the level of proteins, such networks are composed of amino acids, and can be affected by biological processes such as allostery, point mutations and ligand binding. Binding an effector to an allosteric site, for instance, may limit the active site's binding affinity, thus inhibiting the protein's function. The complexity of amino acid interactions makes difficult the process of predicting protein behavior and network dynamics in the face of such intricate biological processes. In this study, we show that a combination of molecular dynamics (MD) simulations, network analysis, and spectral decomposition of pairwise interaction matrices progresses the ability to pick out targets residues with characteristics beneficial to the design of therapeutics. We observed in silico that the p53 tumor suppressor protein contains a dense network of cohesive residues which may have the ability to transduce longrange allosteric signals. By engineering point mutations to residues within this cohesive network, we found that disturbing the network's connectivity impacted the overall function of p53. The network was outputted via an updated implementation MD sectors, which analyzes and spectrally decomposes correlated motions between residues within an MD trajectory. The application of MD sectors to p53 could aid in the of design drugs which interact directly with sector residues. MD sectors has the potential to successfully pick out allosteric drug targets in a wide variety of biological systems in an accurate and costeffective manner. VI Contents ACKNOWLEDGEMENTS .

show abstract

Navigating the complexity of p53-DNA binding: implications for cancer therapy

Thayer,

Stetson,

Caballero

et al. 2024

Biophys Rev

View full text Add to dashboard Cite

The tumor suppressor protein p53, a transcription factor playing a key role in cancer prevention, interacts with DNA as its primary means of determining cell fate in the event of DNA damage. When it becomes mutated, it opens damaged cells to the possibility of reproducing unchecked, which can lead to formation of cancerous tumors. Despite its critical role, therapies at the molecular level to restore p53 native function remain elusive, due to its complex nature. Nevertheless, considerable information has been amassed, and new means of investigating the problem have become available. Objectives We consider structural, biophysical, and bioinformatic insights and their implications for the role of direct and indirect readout and how they contribute to binding site recognition, particularly those of low consensus. We then pivot to consider advances in computational approaches to drug discovery. Materials and methods We have conducted a review of recent literature pertinent to the p53 protein. Results Considerable literature corroborates the idea that p53 is a complex allosteric protein that discriminates its binding sites not only via consensus sequence through direct H-bond contacts, but also a complex combination of factors involving the flexibility of the binding site. New computational methods have emerged capable of capturing such information, which can then be utilized as input to machine learning algorithms towards the goal of more intelligent and efficient de novo allosteric drug design. Conclusions Recent improvements in machine learning coupled with graph theory and sector analysis hold promise for advances to more intelligently design allosteric effectors that may be able to restore native p53-DNA binding activity to mutant proteins. Clinical relevance The ideas brought to light by this review constitute a significant advance that can be applied to ongoing biophysical studies of drugs for p53, paving the way for the continued development of new methodologies for allosteric drugs. Our discoveries hold promise to provide molecular therapeutics which restore p53 native activity, thereby offering new insights for cancer therapies. Graphical Abstract

show abstract

Spectral analysis of molecular dynamics simulations on PDZ: MD sectors

Cited by 18 publications

References 71 publications

Evolution of dynamical networks enhances catalysis in a designer enzyme

Evolution of dynamical networks enhances catalysis in a designer enzyme

Network Analysis of Molecular Dynamics Sectors in the p53 Protein

Navigating the complexity of p53-DNA binding: implications for cancer therapy

Contact Info

Product

Resources

About