329 papers
Biophysics sits at the fascinating intersection where the laws of physics meet the complexity of living systems. This field uses tools like light, electricity, and mechanical forces to decode how cells move, how proteins fold, and how our senses translate the world around us. Rather than just observing biology, biophysicists measure and model life to understand the fundamental machinery that powers every organism.
On Gist.Science, we make these discoveries accessible by curating the latest preprints directly from bioRxiv. Our team processes every new submission in this category, providing both clear, plain-language overviews and detailed technical summaries so readers of all backgrounds can grasp the cutting-edge science. Below are the most recent biophysics papers from bioRxiv, ready for you to explore.
This paper presents a novel high-throughput Surface Plasmon Resonance (SPR) array capable of quantitatively profiling interactions between 16 diverse glycosaminoglycan preparations and proteins, successfully validating known binding specificities and discovering previously unreported interactions such as Slit2 binding to chondroitin and dermatan sulphates.
This paper demonstrates that deep learning tools, guided by an RNet foundation model trained on chemical mapping data, can successfully de novo design RNA pseudoknots with accurate secondary structures and well-ordered 3D folds, matching human performance in an Eterna competition without requiring prior solutions to the general RNA 3D structure prediction problem.
This paper presents a practical protocol for using the open-source, self-supervised deep-learning tool spIsoNet to mitigate preferred-orientation artifacts in cryoEM reconstructions through two complementary workflows: map anisotropy correction and particle misalignment correction.
The paper introduces cryoGO, a simple and scalable mechanical method for opening cells directly on EM grids, which enables high-resolution, time-resolved structural profiling of diverse endogenous macromolecules in their native cellular environment using minimal sample quantities.
By leveraging coevolution and machine learning to derive a sequence-independent collective variable, this study establishes a unified, evolutionarily conserved mechanism for class A GPCR activation that enables the prediction of conformational transitions and ligand effects across diverse family members, including orphan receptors.
This study demonstrates that decomposed energy terms in MM-GBSA binding free energy calculations are highly collinear rather than independent, revealing that traditional assumptions of separability are invalid and urging the combination of correlated terms into effective polar, nonpolar, and entropic contributions for more robust predictive modeling.
This study presents cryo-EM structures of heteromeric GluK2/GluK5 kainate receptors in apo, agonist-bound, and antagonist-bound states, revealing that heterotetramers exhibit reduced conformational dynamics compared to homomers and demonstrating that targeting the GluK5 subunit with UBP310 is more effective than targeting GluK2 by locking the receptor in a pore-occluded conformation.
This paper introduces a 3D mechanically adaptive viscoelastic cell-network model that links discrete single-cell interactions and connection remodeling to emergent tissue-scale rheology, successfully validating its ability to reproduce complex phenomena like swirling and jamming against experimental micropipette aspiration and compression assays.
This study demonstrates that stereochemical modifications in oligopeptides significantly control electron transport by altering aromatic side-chain stacking in tyrosine-based sequences and secondary structure conformations in alanine-based sequences, offering new pathways for designing peptide-based electronic materials.
This paper proposes a unified conceptual framework and biophysical model demonstrating how geometrical effects, parallel amplification, and resource-sharing polarization mechanisms collectively establish the distinct microtubule orientations that define axon-dendrite differentiation in developing neurons.