SearcharxivSearch

arXiv · q-bio/0511035

On the Fibril Elongation Mechanism of the Prion Protein Fragment PrP106-126

Abstract

Mouse prion protein PrP106-126 is a peptide corresponding to the residues 107-127 of human prion protein. It has been shown that PrP106-126 can reproduce the main neuropathological features of prionrelated transmissible spongiform encephalopathies and can form amyloid-like fibrils in vitro. The conformational characteristics of PrP106-126 fibril have been investigated by electron microscopy, CD spectroscopy, NMR and molecular dynamics simulations. Recent researches have found out that PrP106-126 in water assumes a stable structure consisting of two parallel beta-sheets that are tightly packed against each other. In this work we perform molecular dynamics simulation to reveal the elongation mechanism of PrP106-126 fibril. Influenced by the edge strands of the fibril which already adopt beta-sheets conformation, single PrP106-126 peptide forms beta-structure and becomes a new element of the fibril. Under acidic condition, single PrP106-126 peptide adopts a much larger variety of conformations than it does under neural condition, which makes a peptide easier to be influenced by the edge strands of the fibril. However, acidic condition dose not largely affect the stability of PrP106-126 peptide fibril. Thus, the speed of fibril elongation can be dramatically increased by lowering the pH value of the solution. The pH value was adjusted by either changing the protonation state of the residues or adding hydronium ions (acidic solution) or hydroxyl ions (alkaline solution). The differences between these two approaches are analyzed here.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xin Zhao, Shuo-Xing Dou, Ping Xie, Peng-Ye Wang. 2005-11-22. On the Fibril Elongation Mechanism of the Prion Protein Fragment PrP106-126. https://arxiv.org/abs/q-bio/0511035

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