Interplay of cholesterol, membrane bilayers and the AT1R: A cholesterol consensus motif on AT1R is revealed

Kiriakidi, Sofia; Chatzigiannis, Christos; Papaemmanouil, Christina; Tzakos, Andreas G.; Cournia, Zoe; Mavromoustakos, Thomas

doi:10.1016/j.csbj.2020.11.042

Cited by 10 publications

(5 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The diffusion of K-Ras4B, DOPC, DOPS, and PIP2 molecules was calculated by means of Mean Square Displacement (MSD) using GROMACS module gmx_msd . The diffusion coefficients were calculated using the Einstein relation and fitting was performed in the linear regime of the log–log MSD( t ) plot as described in ref . Errors represent standard deviations calculated by block averaging with the gmx_analyze module.…”

Section: Methodsmentioning

confidence: 99%

Membrane Composition and Raf[CRD]-Membrane Attachment Are Driving Forces for K-Ras4B Dimer Stability

et al. 2022

Self Cite

View full text Add to dashboard Cite

Ras proteins are membrane-anchored GTPases that regulate key cellular signaling networks. It has been recently shown that different anionic lipid types can affect the properties of Ras in terms of dimerization/clustering on the cell membrane. To understand the effects of anionic lipids on key spatiotemporal properties of dimeric K-Ras4B, we perform all-atom molecular dynamics simulations of the dimer K-Ras4B in the presence and absence of Raf[RBD/CRD] effectors on two model anionic lipid membranes: one containing 78% mol DOPC, 20% mol DOPS, and 2% mol PIP2 and another one with enhanced concentration of anionic lipids containing 50% mol DOPC, 40% mol DOPS, and 10% mol PIP2. Analysis of our results unveils the orientational space of dimeric K-Ras4B and shows that the stability of the dimer is enhanced on the membrane containing a high concentration of anionic lipids in the absence of Raf effectors. This enhanced stability is also observed in the presence of Raf[RBD/CRD] effectors although it is not influenced by the concentration of anionic lipids in the membrane, but rather on the ability of Raf[CRD] to anchor to the membrane. We generate dominant K-Ras4B conformations by Markov state modeling and yield the population of states according to the K-Ras4B orientation on the membrane. For the membrane containing anionic lipids, we observe correlations between the diffusion of K-Ras4B and PIP2 and anchoring of anionic lipids to the Raf[CRD] domain. We conclude that the presence of effectors with the Raf[CRD] domain anchoring on the membrane as well as the membrane composition both influence the conformational stability of the K-Ras4B dimer, enabling the preservation of crucial interface interactions.

show abstract

Section: Methodsmentioning

confidence: 99%

Membrane Composition and Raf[CRD]-Membrane Attachment Are Driving Forces for K-Ras4B Dimer Stability

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…In eukaryotes, sterols such as cholesterol are ubiquitous in modulating membrane order, yet they are usually absent or rare in prokaryotes and intracellular membranes. The effects of cholesterol on mammalian lipid bilayer or membrane proteins have been well studied both computationally [185][186][187][188][189][190][191] and experimentally, [192][193][194][195] suggesting an essential role of cholesterol in regulating the order, dynamics, and other physicochemical properties of membranes. For example, the binary and ternary phase diagrams of bilayers in the presence of varying concentrations of cholesterol have been well studied by using CG simulations with the MARTINI force field.…”

Section: Membrane Compositional and Structural Complexitymentioning

confidence: 99%

Application of computational approaches in biomembranes: From structure to function

Guo

Bao

et al. 2023

WIREs Comput Mol Sci

View full text Add to dashboard Cite

Biological membranes (biomembranes) are one of the most complicated structures that allow life to exist. Investigating their structure, dynamics, and function is crucial for advancing our knowledge of cellular mechanisms and developing novel therapeutic strategies. However, experimental investigation of many biomembrane phenomena is challenging due to their compositional and structural complexity, as well as the inherently multi‐scalar features. Computational approaches, particularly molecular dynamics (MD) simulations, have emerged as powerful tools for addressing the atomic details of biomembrane systems, driving breakthroughs in our understanding of biomembranes and their roles in cellular function. This review presents an overview of the latest advancements in related computational approaches, from force fields and model construction to MD simulations and trajectory analysis. We also discussed current hot research topics and challenges. Finally, we outline future directions, emphasizing the integration of force field development, enhanced sampling techniques, and data‐driven approaches to accelerate the growth of this field in the years to come. We aim to equip readers with an understanding of the promise and limitations of emerging computational technologies in biomembrane systems and offer valuable recommendations for future research endeavors.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

show abstract

“…Moreover, these conformational ensembles can be allosterically modulated by their environment (e.g., ligand or lipid binding) and mutations . In a recent study, Kiriakidi et al used MD simulations of the AT1 receptor to show that conformational changes can be observed upon binding of cholesterol . In the case of PI3Kalpha, the most mutated kinase in human cancer, the E545K and H1047R point mutations, despite being distant from the active site, alter the conformational landscape of PI3Kalpha, abolish the autoinhibitory role of its C-terminal, modify protein–membrane interactions, and perturb allosteric pathways. ,− ML folding models can hardly predict these conformational changes as they are trained on static structures and evolutionary information, although attempts have been made more recently …”

Section: Membrane Proteinsmentioning

confidence: 99%

A Perspective on the Prospective Use of AI in Protein Structure Prediction

Versini,

Sritharan,

Aykac Fas

et al. 2023

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

show abstract

Interplay of cholesterol, membrane bilayers and the AT1R: A cholesterol consensus motif on AT1R is revealed

Abstract: Graphical abstract

Cited by 10 publications

References 68 publications

Membrane Composition and Raf[CRD]-Membrane Attachment Are Driving Forces for K-Ras4B Dimer Stability

Membrane Composition and Raf[CRD]-Membrane Attachment Are Driving Forces for K-Ras4B Dimer Stability

Application of computational approaches in biomembranes: From structure to function

A Perspective on the Prospective Use of AI in Protein Structure Prediction

Contact Info

Product

Resources

About