SearcharxivSearch

arXiv · 1910.13576

Single-molecule stretching shows glycosylation sets tension in the hyaluronan-aggrecan bottlebrush

Abstract

Large bottlebrush complexes formed from the polysaccharide hyaluronan (HA) and the proteoglycan aggrecan contribute to cartilage compression resistance and are necessary for healthy joint function. A variety of mechanical forces act on these complexes in the cartilage extracellular matrix, motivating the need for a quantitative description which links their structure and mechanical response. Studies using electron microscopy have imaged the HA-aggrecan brush but require adsorption to a surface, dramatically altering the complex from its native conformation. We use magnetic tweezers force spectroscopy to measure changes in extension and mechanical response of an HA chain as aggrecan monomers bind and form a bottlebrush. This technique directly measures changes undergone by a single complex with time and under varying solution conditions. Upon addition of aggrecan, we find a large swelling effect manifests when the HA chain is under very low external tension (i.e. stretching forces less than ~1 pN). We use models of force-extension behavior to show that repulsion between the aggrecans induces an internal tension in the HA chain. Through reference to theories of bottlebrush polymer behavior, we demonstrate that the experimental values of internal tension are consistent with a polydisperse aggrecan population, likely caused by varying degrees of glycosylation. By enzymatically deglycosylating aggrecan, we show that aggrecan glycosylation is the structural feature which causes HA stiffening. We then construct a simple stochastic binding model to show that variable glycosylation leads to a wide distribution of internal tensions in HA, causing variations in the mechanics at much longer length-scales. Our results provide a mechanistic picture of how flexibility and size of HA and aggrecan lead to the brush architecture and mechanical properties of this important component of cartilage.

Explore related subjects

Keep this discovery

BibTeXRIS

Sarah N. Innes-Gold, John P. Berezney, Omar A. Saleh. 2019-10-29. Single-molecule stretching shows glycosylation sets tension in the hyaluronan-aggrecan bottlebrush. https://doi.org/10.1016/j.bpj.2019.11.1205

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

KEEP EXPLORING

Related papers

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

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

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