Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 0901.4850

Anomalous scaling in nanopore translocation of structured heteropolymers

Malcolm McCauley·Robert Forties·Ulrich Gerland·Ralf Bundschuh
View original source ↗Read source document ↗Export metadata ↓

Abstract

Translocation through a nanopore is a new experimental technique to probe physical properties of biomolecules. A bulk of theoretical and computational work exists on the dependence of the time to translocate a single unstructured molecule on the length of the molecule. Here, we study the same problem but for RNA molecules for which the breaking of the secondary structure is the main barrier for translocation. To this end, we calculate the mean translocation time of single-stranded RNA through a nanopore of zero thickness and at zero voltage for many randomly chosen RNA sequences. We find the translocation time to depend on the length of the RNA molecule with a power law. The exponent changes as a function of temperature and exceeds the naively expected exponent of two for purely diffusive transport at all temperatures. We interpret the power law scaling in terms of diffusion in a one-dimensional energy landscape with a logarithmic barrier.

Explore related subjects

q-bio.BM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Malcolm McCauley, Robert Forties, Ulrich Gerland, Ralf Bundschuh. 2009-01-30. Anomalous scaling in nanopore translocation of structured heteropolymers. https://doi.org/10.1088/1478-3975%2F6%2F3%2F036006

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

KEEP EXPLORING

Related papers

arXiv2026

Synthesizing State-of-the-Art Structure Predictions from Soup of Co-folding Models

Co-folding models have advanced rapidly, yet no single model consistently performs best across all biomolecular complexes. This raises the question of whether independently trained co-folding models encode complementary information that can be transferred across co-folding models. We introduce SoupFold, which improves co-folding predictions by learning simple mappings between the representation spaces of co-folding models. At inference time, SoupFold transfers and incorporates representations from other co-folding models to update the representation used for structure prediction. Importantly, this does not re-train the co-folding models. We evaluate SoupFold on protein-protein and protein-ligand prediction tasks of FoldBench using AlphaFold3, Protenix, ESMFold2, and OpenDDE. By combining their representations, SoupFold achieves state-of-the-art performance on both protein-protein and protein-ligand structure prediction, showing that independently trained co-folding models encode complementary information that can be effectively transferred across models.

q-bio.BM↗
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--$τ$ 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↗