about
Control of membrane fusion mechanism by lipid composition: predictions from ensemble molecular dynamicsEbolavirus entry requires a compact hydrophobic fist at the tip of the fusion loop.Structure of the Neisserial outer membrane protein Opa₆₀: loop flexibility essential to receptor recognition and bacterial engulfment.Improving pandemic influenza risk assessment.Influenza viral membrane fusion is sensitive to sterol concentration but surprisingly robust to sterol chemical identity.GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit.Viral factors in influenza pandemic risk assessment.Excess positional mutual information predicts both local and allosteric mutations affecting beta lactamase drug resistance.Disentangling Viral Membrane Fusion from Receptor Binding Using Synthetic DNA-Lipid ConjugatesMolecular simulation workflows as parallel algorithms: the execution engine of Copernicus, a distributed high-performance computing platform.Hemagglutinin Spatial Distribution Shifts in Response to Cholesterol in the Influenza Viral Envelope.Coupled diffusion in lipid bilayers upon close approach.Lipid converter, a framework for lipid manipulations in molecular dynamics simulationsMultiphasic effects of cholesterol on influenza fusion kinetics reflect multiple mechanistic roles.Dynamic heterogeneity controls diffusion and viscosity near biological interfaces.Probing microscopic material properties inside simulated membranes through spatially resolved three-dimensional local pressure fields and surface tensionsLipid tail protrusion in simulations predicts fusogenic activity of influenza fusion peptide mutants and conformational modelsA bundling of viral fusion mechanisms.Water ordering at membrane interfaces controls fusion dynamics.Receptor binding by influenza virus: using computational techniques to extend structural data.Function and dynamics of macromolecular complexes explored by integrative structural and computational biology.Quantitative imaging of lymphocyte membrane protein reorganization and signaling.A hybrid machine-learning approach for segmentation of protein localization data.Atomic-resolution simulations predict a transition state for vesicle fusion defined by contact of a few lipid tails.Molecular dynamics simulation of lipid reorientation at bilayer edges.Persistent voids: a new structural metric for membrane fusion.Structural basis for influence of viral glycans on ligand binding by influenza hemagglutinin.Ensemble molecular dynamics yields submillisecond kinetics and intermediates of membrane fusionModel for a novel membrane envelope in a filamentous hyperthermophilic virus.Predicting allosteric mutants that increase activity of a major antibiotic resistance enzyme."Cross-graining": efficient multi-scale simulation via Markov state models.Computational biology in the cloud: methods and new insights from computing at scale.Formation of a highly peptide-receptive state of class II MHC.Influenza Hemifusion Phenotype Depends on Membrane Context: Differences in Cell-Cell and Virus-Cell Fusion.Deformable modeling for improved calculation of molecular velocities from single-particle tracking.Predicting structure and dynamics of loosely-ordered protein complexes: influenza hemagglutinin fusion peptide.Structural conservation in a membrane-enveloped filamentous virus infecting a hyperthermophilic acidophileRefinement of highly flexible protein structures using simulation-guided spectroscopyInfluenza hemagglutinin drives viral entry via two sequential intramembrane mechanismsAntibiotic Uptake Across Gram-Negative Outer Membranes: Better Predictions Towards Better Antibiotics
P50
Q21145657-D6826D79-D5BD-4839-B483-297ED63C15F6Q27682816-10842EF1-682A-40D1-BC7C-2306CB777A71Q27683700-3A5EC41C-687E-4174-8EF9-1290C31DA6EAQ28395976-7C9087BA-71B6-457E-8149-FB58A71960B2Q28829816-07F27A04-3A9D-46E0-992E-6B5396D67548Q29615867-DD46AD3D-D1E8-4E8A-B84B-9D8A3F05755DQ30275435-6BDE3934-2ED4-45AF-86AF-EB72C7970254Q30276256-D956DBB0-AFAD-4CB4-9ABE-974279B4B9CBQ30276368-AF961B76-A531-403B-A775-D873870EAD96Q30278076-4481A378-4E1A-42CC-BAFF-B3C786CE4E9BQ30278402-47BF3CE2-8071-4EA1-911D-A894871C841CQ30361955-90AF0EC1-3471-46EA-A3E3-A153A3CCB40EQ30370163-60C9DDD4-4FA1-45B3-A7F8-7262A34C4BDDQ30410686-681F6C17-B575-4ED7-AF65-6D23D6E71750Q30412224-F4CFCA1E-D339-4EA7-9E3F-5F8FE571260EQ30413967-1EC25A9E-49E1-49C3-9892-41D5DF838C96Q30421918-534F9965-82F1-425F-8140-62BD3B30B9F3Q30430768-08CC3066-D695-4385-8489-7B33632FC908Q30452942-E57A0833-B29F-47E4-BCFE-95CF097A9CD8Q30455210-1710AEF5-FBA0-47B6-87D7-5D5FF959FF6BQ30459151-B8D1AE06-C917-416E-8477-D56A9DE84332Q30476473-6AC9B911-908C-40B3-AEDC-F4ECAF813927Q30998990-71AC4CC7-0F71-4543-98ED-3ECE6934CB1EQ33619201-746C098C-EAC9-461E-AD59-A824CB50D083Q34186048-8B3BFB40-110A-4E7B-8E93-136F10C60EECQ34626588-C77680B4-EB3C-414D-B9A7-36427672FBB8Q34798238-E471F862-C733-473F-8512-29CFE30F5C10Q35033090-B66EC27F-1102-4680-B4AC-196FC0F3F51CQ40149841-D2881545-D42A-4A3D-AADE-A73D64858F31Q42057094-B5A1D989-BBF9-496C-AD49-6EA5FF3DBEAFQ43243758-FEB26085-C738-4995-A31B-125BDED4C164Q44382634-D52F3EFF-3C95-4C06-B0D3-17944605BF59Q46139976-FAEED11C-F892-4784-8F84-318A46A09BD4Q47550631-F5FAF71D-F872-431B-80CC-C6EB49DCFA4CQ48939365-09EA2C74-4924-424B-A7B7-728ED9DF69BCQ55045561-D953D49E-CF41-4909-8A7C-333A4753CE87Q56334007-D13AA73E-9B21-4A5C-A8C2-8FBF4DB53955Q58609242-C9F9374E-7C9F-46DE-9D29-3E6C9C512729Q90437854-3567EF43-7143-49A3-95D7-A8470B077904Q90590315-50D5372C-BA7F-45AE-ADBE-D04509B695A1
P50
description
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Peter M Kasson
@ast
Peter M Kasson
@en
Peter M Kasson
@es
Peter M Kasson
@nl
Peter M Kasson
@sl
type
label
Peter M Kasson
@ast
Peter M Kasson
@en
Peter M Kasson
@es
Peter M Kasson
@nl
Peter M Kasson
@sl
altLabel
Peter Kasson
@en
prefLabel
Peter M Kasson
@ast
Peter M Kasson
@en
Peter M Kasson
@es
Peter M Kasson
@nl
Peter M Kasson
@sl
P106
P2002
P21
P31
P496
0000-0002-3111-8103