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A fundamental understanding of how the HIV-1 envelope (Env) protein facilitates fusion is still lacking. The HIV-1 fusion peptide, consisting of 15 to 22 residues, is the N-terminus of the gp41 subunit of the Env protein. Further, this peptide, a promising vaccine candidate, initiates viral entry into target cells by inserting and anchoring into human immune cells. The influence of membrane lipid reorganization and the conformational changes of the fusion peptide during the membrane insertion and anchoring processes, which can significantly affect HIV-1 cell entry, remains largely unexplored due to the limitations of experimental measurements. In this work, we investigate the insertion of the fusion peptide into an immune cell membrane mimic through multiscale molecular dynamics simulations. We mimic the native T-cell by constructing a nine-lipid asymmetric membrane, along with geometrical restraints accounting for insertion in the context of gp41. To account for the slow time scale of lipid mixing while enabling conformational changes, we implement a protocol to go back and forth between atomistic and coarsegrained simulations. Our study provides a molecular understanding of the interactions between the HIV-1 fusion peptide and the Tcell membrane, highlighting the importance of the conformational flexibility of fusion peptides and local lipid reorganization in stabilizing the anchoring of gp41 into the targeted host membrane during the early events of HIV-1 cell entry. Importantly, we identify a motif within the fusion peptide critical for fusion that can be further manipulated in future immunological studies.
A fundamental understanding of how the HIV-1 envelope (Env) protein facilitates fusion is still lacking. The HIV-1 fusion peptide, consisting of 15 to 22 residues, is the N-terminus of the gp41 subunit of the Env protein. Further, this peptide, a promising vaccine candidate, initiates viral entry into target cells by inserting and anchoring into human immune cells. The influence of membrane lipid reorganization and the conformational changes of the fusion peptide during the membrane insertion and anchoring processes, which can significantly affect HIV-1 cell entry, remains largely unexplored due to the limitations of experimental measurements. In this work, we investigate the insertion of the fusion peptide into an immune cell membrane mimic through multiscale molecular dynamics simulations. We mimic the native T-cell by constructing a nine-lipid asymmetric membrane, along with geometrical restraints accounting for insertion in the context of gp41. To account for the slow time scale of lipid mixing while enabling conformational changes, we implement a protocol to go back and forth between atomistic and coarsegrained simulations. Our study provides a molecular understanding of the interactions between the HIV-1 fusion peptide and the Tcell membrane, highlighting the importance of the conformational flexibility of fusion peptides and local lipid reorganization in stabilizing the anchoring of gp41 into the targeted host membrane during the early events of HIV-1 cell entry. Importantly, we identify a motif within the fusion peptide critical for fusion that can be further manipulated in future immunological studies.
Proteins are inherently dynamic molecules, and their conformational transitions among various states are essential for numerous biological processes, which are often modulated by their interactions with surrounding environments. Although molecular dynamics (MD) simulations are widely used to investigate these transitions, all-atom (AA) methods are often limited by short timescales and high computational costs, and coarse-grained (CG) implicitsolvent Gō-like models are usually incapable of studying the interactions between proteins and their environments. Here, we present an approach called Multiple-basin Gō-Martini, which combines the recent Gō-Martini model with an exponential mixing scheme to facilitate the simulation of spontaneous protein conformational transitions in explicit environments. We demonstrate the versatility of our method through five diverse case studies: GlnBP, Arc, Hinge, SemiSWEET, and TRAAK, representing ligand-binding proteins, fold-switching proteins,de novodesigned proteins, transporters, and mechanosensitive ion channels, respectively. The Multiple-basin Gō-Martini offers a new computational tool for investigating protein conformational transitions, identifying key intermediate states, and elucidating essential interactions between proteins and their environments, particularly protein-membrane interactions. In addition, this approach can efficiently generate thermodynamically meaningful datasets of protein conformational space, which may enhance deep learning-based models for predicting protein conformation distributions.
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