title: Discerning intersecting fusion-activation pathways in the Nipah virus using machine learning.
abstract: The fusion of Nipah with host cells is facilitated by two of their glycoproteins, the G and the F proteins. The binding of cellular ephrins to the G head domain causes the G stalk domain to interact differently with F, which activates F to mediate virus-host fusion. To gain insight into how the ephrin-binding signal transduces from the head to the stalk domain of G, we examine quantitatively the differences between the conformational ensembles of the G head domain in its ephrin-bound and unbound states. We consider the human ephrins B2 and B3, and a double mutant of B2, all of which trigger fusion. The ensembles are generated using molecular dynamics, and the differences between them are quantified using a new machine learning method. We find that the portion of the G head domain whose conformational density is altered equivalently by the three ephrins is large, and comprises ∼25% of the residues in the G head domain. This subspace also includes the residues that are known to be important to F activation, which suggests that it contains at least one common signaling pathway. The spatial distribution of the residues constituting this subspace supports the model of signal transduction in which the signal transduces via the G head dimer interface. This study also adds to the growing list of examples where signaling does not depend solely on backbone deviations. In general, this study provides an approach to filter out conserved patterns in protein dynamics.
authors.affiliation: Department of Cell Biology, Microbiology and Molecular Biology, University of South Florida, Tampa, Florida, 33620.
authors.forename: Sameer
authors.initials: S
authors.lastname: Varma
authors.forename: Mohsen
authors.initials: M
authors.lastname: Botlani
authors.forename: Ralph E
authors.initials: RE
authors.lastname: Leighty
mesh_terms: D000494:Allosteric Regulation; D019943:Amino Acid Substitution; D001185:Artificial Intelligence; D030562:Databases, Protein; D036388:Ephrin-B2 / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D036389:Ephrin-B3 / Q000737:chemistry / Q000378:metabolism*; D054884:Host-Pathogen Interactions; D006801:Humans; D008024:Ligands; D008954:Models, Biological; D056004:Molecular Dynamics Simulation; D009154:Mutation; D045405:Nipah Virus / Q000502:physiology*; D011487:Protein Conformation; D054730:Protein Interaction Domains and Motifs; D015398:Signal Transduction; D060388:Support Vector Machine; D014759:Viral Envelope Proteins / Q000819:agonists / Q000737:chemistry / Q000378:metabolism*; D014775:Virus Activation; D053585:Virus Attachment; D016662:Virus Integration
chemical_list: D036388:Ephrin-B2; D036389:Ephrin-B3; C510755:F protein, Nipah virus; D008024:Ligands; D014759:Viral Envelope Proteins; C116204:attachment protein G
keywords: allosteric signaling; machine learning; molecular dynamics; protein-protein interactions; support vector machines; viral fusion