Membrane Interaction of the Factor VIIIa Discoidin Domains in Atomistic Detail

Madsen, Jesper J.; Ohkubo, Y. Zenmei; Peters, Günther H.J.; Faber, Johan H.; Tajkhorshid, Emad; Olsen, Ole H.

doi:10.1021/acs.biochem.5b00417

Cited by 27 publications

(33 citation statements)

References 73 publications

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“…This strategy provides enhanced lipid mobility and thereby accelerates association of peripheral proteins with the lipid bilayer although still maintaining the relevant lipid structural details in the region accessed by the protein (22)(23)(24)43,44). This approach has been effectively employed in previous studies to capture unbiased membrane association of diverse proteins, peptides, and other species (22)(23)(24)39,40,(45)(46)(47)(48)(49)(50)(51). In this study, the HMMM model was employed as a first step to generate an unbiased membrane-bound configuration of Tim1, which could then be further validated using both conventional bilayer MD simulations and interfacial x-ray scattering experiments.…”

Section: Unbiased Tim1 Membrane Binding Simulations With Hmmm Membranesmentioning

confidence: 99%

Coupling X-Ray Reflectivity and In Silico Binding to Yield Dynamics of Membrane Recognition by Tim1

Tietjen

Baylon

Kerr

et al. 2017

Biophysical Journal

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The dynamic nature of lipid membranes presents significant challenges with respect to understanding the molecular basis of protein/membrane interactions. Consequently, there is relatively little known about the structural mechanisms by which membrane-binding proteins might distinguish subtle variations in lipid membrane composition and/or structure. We have previously developed a multidisciplinary approach that combines molecular dynamics simulation with interfacial x-ray scattering experiments to produce an atomistic model for phosphatidylserine recognition by the immune receptor Tim4. However, this approach requires a previously determined protein crystal structure in a membrane-bound conformation. Tim1, a Tim4 homolog with distinct differences in both immunological function and sensitivity to membrane composition, was crystalized in a closed-loop conformation that is unlikely to support membrane binding. Here we have used a previously described highly mobile membrane mimetic membrane in combination with a conventional lipid bilayer model to generate a membrane-bound configuration of Tim1 in silico. This refined structure provided a significantly improved fit of experimental x-ray reflectivity data. Moreover, the coupling of the x-ray reflectivity analysis with both highly mobile membrane mimetic membranes and conventional lipid bilayer molecular dynamics simulations yielded a dynamic model of phosphatidylserine membrane recognition by Tim1 with atomic-level detail. In addition to providing, to our knowledge, new insights into the molecular mechanisms that distinguish the various Tim receptors, these results demonstrate that in silico membrane-binding simulations can remove the requirement that the existing crystal structure be in the membrane-bound conformation for effective x-ray reflectivity analysis. Consequently, this refined methodology has the potential for much broader applicability with respect to defining the atomistic details of membrane-binding proteins.

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Section: Unbiased Tim1 Membrane Binding Simulations With Hmmm Membranesmentioning

confidence: 99%

Coupling X-Ray Reflectivity and In Silico Binding to Yield Dynamics of Membrane Recognition by Tim1

Tietjen

Baylon

Kerr

et al. 2017

Biophysical Journal

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“…As a result, insertion processes on the membrane interface will be significantly accelerated [75] while accurately reproducing the energetics of molecular interactions at the interface [76]. Membrane-bound forms of peripheral proteins, peptides and other membrane–associated molecular species, can be captured using an ensemble of relatively short but convergent MD simulations [74, 77–83]. Thus the HMMM can reveal specific protein-membrane interactions at atomic detail with a substantially reduced computational cost, making it a great companion to experimental techniques aiming at understanding membrane–bound configurations of proteins, and, in particular, lipid dependence of their binding.…”

Section: Complementing Experiments With Simulation At the Membrane mentioning

confidence: 99%

“…There are many examples of the application of the HMMM to real biological systems now in the literature by both our own lab [74, 77–81, 83, 84] and others [85, 86], and we have chosen a subset to review here that particularly highlight the potential impact of the HMMM to react to as well as guide experiment. Specifically, we will highlight recent work on identifying the binding face of hemagglutinin fusion peptide [81] and human cytochrome P450 [77, 84], conformational changes induced by membrane binding in talin [80] and α -Synuclein [78], as well as ongoing work in assembling blood coagulation factors on the membrane surface [74, 83]. …”

Section: Complementing Experiments With Simulation At the Membrane mentioning

confidence: 99%

“…Employing a similar approach, it was also possible to develop a model for the FVIII C2 and C1 membrane binding domains and propose possible membrane bound conformations for the FVIIa:FIXa complex which activates FX [83]. Converged membrane binding depth was reached within 15 ns for binding trajectories.…”

Section: Towards Understanding Regulation and Lipid Specificity Inmentioning

confidence: 99%

“…Insertion depth relative to the membrane converged for both the C1 and C2 domains to similar values in different simulations. Using the resulting structures of bound C1 and C2 domains, a putative model was constructed for the FVIIIa:FIXa complex [83]. This complex has never been crystallized, but the homologous FVa:FXa complex has [169].…”

Section: Towards Understanding Regulation and Lipid Specificity Inmentioning

confidence: 99%

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Atomic-level description of protein–lipid interactions using an accelerated membrane model

Baylon

Vermaas

Müller

et al. 2016

Biochimica et Biophysica Acta (BBA) - Biomembranes

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Peripheral membrane proteins are structurally diverse proteins that are involved in fundamental cellular processes. Their activity of these proteins is frequently modulated through their interaction with cellular membranes, and as a result techniques to study the interfacial interaction between peripheral proteins and the membrane are in high demand. Due to the fluid nature of the membrane and the reversibility of protein–membrane interactions, the experimental study of these systems remains a challenging task. Molecular dynamics simulations offer a suitable approach to study protein–lipid interactions; however, the slow dynamics of the lipids often prevents sufficient sampling of specific membrane–protein interactions in atomistic simulations. To increase lipid dynamics while preserving the atomistic detail of protein–lipid interactions, in the highly mobile membrane-mimetic (HMMM) model the membrane core is replaced by an organic solvent, while short-tailed lipids provide a nearly complete representation of natural lipids at the organic solvent/water interface. Here, we present a brief introduction and a summary of recent applications of the HMMM to study different membrane proteins, complementing the experimental characterization of the presented systems, and we offer a perspective of future applications of the HMMM to study other classes of membrane proteins.

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Binding mode of SARS-CoV-2 fusion peptide to human cellular membrane

Gorgun

Lihan

Kapoor

et al. 2021

Biophysical Journal

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Infection of human cells by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) relies on its binding to a specific receptor and subsequent fusion of the viral and host cell membranes. The fusion peptide (FP), a short peptide segment in the spike protein, plays a central role in the initial penetration of the virus into the host cell membrane, followed by the fusion of the two membranes. Here, we use an array of molecular dynamics simulations that take advantage of the highly mobile membrane mimetic model to investigate the interaction of the SARS-CoV2 FP with a lipid bilayer representing mammalian cellular membranes at an atomic level and to characterize the membrane-bound form of the peptide. Six independent systems were generated by changing the initial positioning and orientation of the FP with respect to the membrane, and each system was simulated in five independent replicas, each for 300 ns. In 73% of the simulations, the FP reaches a stable, membrane-bound configuration, in which the peptide deeply penetrated into the membrane. Clustering of the results reveals three major membrane-binding modes (binding modes 1-3), in which binding mode 1 populates over half of the data points. Taking into account the sequence conservation among the viral FPs and the results of mutagenesis studies establishing the role of specific residues in the helical portion of the FP in membrane association, the significant depth of penetration of the whole peptide, and the dense population of the respective cluster, we propose that the most deeply inserted membrane-bound form (binding mode 1) represents more closely the biologically relevant form. Analysis of FP-lipid interactions shows the involvement of specific residues, previously described as the ''fusion-active core residues,'' in membrane binding. Taken together, the results shed light on a key step involved in SARS-CoV2 infection, with potential implications in designing novel inhibitors.

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Membrane Interaction of the Factor VIIIa Discoidin Domains in Atomistic Detail

Cited by 27 publications

References 73 publications

Coupling X-Ray Reflectivity and In Silico Binding to Yield Dynamics of Membrane Recognition by Tim1

Coupling X-Ray Reflectivity and In Silico Binding to Yield Dynamics of Membrane Recognition by Tim1

Atomic-level description of protein–lipid interactions using an accelerated membrane model

Binding mode of SARS-CoV-2 fusion peptide to human cellular membrane

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