Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1009.3161

Arrangement of Annexin A2 tetramer and its impact on the structure and diffusivity of supported lipid bilayers

Kirstin Fritz·Georg Fritz·Barbara Windschiegl·Claudia Steinem·Bert Nickel
View original source ↗Read source document ↗Export metadata ↓

Abstract

Annexins are a family of proteins that bind to anionic phospholipid membranes in a Ca2+-dependent manner. Annexin A2 forms heterotetramers (Anx A2t) with the S100A10 (p11) protein dimer. The tetramer is capable of bridging phospholipid membranes and it has been suggested to play a role in Ca2+-dependent exocytosis and cell-cell adhesion of metastatic cells. Here, we employ x-ray reflectivity measurements to resolve the conformation of Anx A2t upon Ca2+-dependent binding to single supported lipid bilayers (SLBs) composed of different mixtures of anionic (POPS) and neutral (POPC) phospholipids. Based on our results we propose that Anx A2t binds in a side-by-side configuration, i.e., both Anx A2 monomers bind to the bilayer with the p11 dimer positioned on top. Furthermore, we observe a strong decrease of lipid mobility upon binding of Anx A2t to SLBs with varying POPS content. X-ray reflectivity measurements indicate that binding of Anx A2t also increases the density of the SLB. Interestingly, in the protein-facing leaflet of the SLB the lipid density is higher than in the substrate-facing leaflet. This asymmetric densification of the lipid bilayer by Anx A2t and Ca2+ might have important implications for the biochemical mechanism of Anx A2t-induced endo- and exocytosis.

Explore related subjects

q-bio.BMphysics.bio-phq-bio.SC

Keep this discovery

BibTeXRIS

Kirstin Fritz, Georg Fritz, Barbara Windschiegl, Claudia Steinem, Bert Nickel. 2010-09-16. Arrangement of Annexin A2 tetramer and its impact on the structure and diffusivity of supported lipid bilayers. https://doi.org/10.1039/c0sm00047g

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Sequence-Informed Geometric Evaluation of RNA 3D Structures

Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator that conditions structural representations on nucleotide embeddings from a pretrained RNA language model. Early results show that SIRGE outperforms established evaluators in Kendall--$\tau$ alignment, Top-1 selection, and Top-3 ranking. Controlled comparisons further show that sequence conditioning corrects errors made by an otherwise matched geometric model and improves target-level rank structure. These findings provide initial evidence that pretrained sequence representations supply ranking information that complements geometric reasoning.

q-bio.BM↗
arXiv2026

PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion

Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, molecular properties, and physical geometry. We propose \textbf{PocketVE}, a protein-pocket-conditioned variance-exploding (VE) diffusion framework that couples stable coordinate denoising with inference-time property guidance. Specifically, PocketVE combines an EDM-style training and sampling setup for 3D denoising, classifier-free guidance for multi-property steering without external property classifiers, and adaptive protein perturbation as a training-time pocket regularizer. Evaluated on CrossDocked2020 under the GenBench3D protocol, PocketVE improves Valid$_{3\text{D}}$ from 58.6 to 80.6 and reduces strain energy from 457.4 to 127.9 relative to its TAGMol architectural baseline, while retaining competitive docking and molecular-property scores under moderate guidance. A guidance-scale study shows that moderate guidance gives a favorable balance between target-related objectives and geometric quality, whereas stronger guidance can degrade geometry and distributional fidelity. Pocket-permutation and PoseCheck diagnostics further support pocket-specific spatial compatibility with reduced steric conflicts. Overall, the results suggest that geometric stability and inference-time property guidance should be considered as coupled design objectives.

q-bio.BM↗
arXiv2026

Predicting directional flexibility in proteins

Predicting protein dynamics is a long-standing problem in computational structural biology. Often, protein function critically depends on local directed motions, such as hinge movements, catalytic loop rearrangements and domain reorientations, which can be characterized by directional flexibility and correlated structural motions of the protein backbone. While Molecular Dynamics (MD) simulations provide an established but often prohibitively expensive approach, recent deep generative models aim to reduce this cost by directly predicting conformational ensembles, emulating MD. However, due to their large size and the need to generate several states until the derived dynamical properties converge, these models remain expensive. In this work, we propose BackFlip-2: a fast SE(3)-equivariant graph neural network trained to directly predict dynamical descriptors, such as directional backbone flexibility and pairwise dynamic correlations, from an equilibrium structure. In a series of experiments, we show that our model matches the accuracy of substantially larger ensemble generation models while being orders of magnitude faster, and demonstrate that the proposed equivariant architecture is especially well-suited for capturing anisotropic motions in proteins. BackFlip-2 model weights, training and inference code are available at https://github.com/graeter-group/backflip.

q-bio.BM↗