388 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 study demonstrates that infrared molecular profiles derived from blood samples exhibit high individuality and stability over time, offering greater intrinsic information content than standard clinical laboratory panels and significantly improving individual identification accuracy when the two data modalities are combined.
This paper introduces the Flux-Driven Molecular Dynamics (FD-MD) framework to demonstrate that biomolecular condensate interfaces significantly accelerate amyloid nucleation by reducing entropic costs, while molecular flux and segment rigidity jointly dictate non-equilibrium growth morphologies and aging trajectories.
Using solution NMR spectroscopy, this study reveals that the heart disease-associated A14V variant of human TSPO reduces conformational heterogeneity in the dynamic N-terminal transmembrane helix, thereby locally stabilizing the protein's structure near the VDAC interaction interface while preserving its overall fold.
This study demonstrates that the donor substrate specificity of the flexible GT70 enzyme GumK is governed by an interplay between local conformational states and substrate chemistry, a mechanism elucidated through AI-enhanced docking that reveals distinct binding interactions in open versus closed pocket configurations.
This study utilizes FragLite chemical fragments to map the cyclin T2 interaction landscape, successfully identifying known binding sites for CDK9, AFF4, and HIV-1 Tat while discovering and characterizing a novel BRD4 recruitment interface through integrated biophysical and computational analyses.
This paper demonstrates that combining high-resolution structural priors with 2D template matching significantly improves the cryo-EM reconstruction of small macromolecular complexes, successfully resolving a previously intractable ~43 kDa protein kinase and predicting the method's potential to extend single-particle cryo-EM to targets well below 50 kDa.
The paper introduces RNASCAPE, a deep learning framework that accurately quantifies cytosolic mRNA escape efficiency from lipid nanoparticles using minimal expression data and formulation parameters, thereby overcoming current bottlenecks in scalable and reliable therapeutic delivery optimization.
This study presents a computational framework that integrates molecular dynamics simulations with machine learning classifiers to accurately predict KRAS mutation-induced drug resistance by identifying key conformational and solvent-exposure changes driven by specific residues.
This study presents the first MARTINI 3-compatible coarse-grained forcefield for 19 common peptoid residues, derived from all-atom reference simulations and integrated into the martinize2 tool, to enable efficient and accurate mesoscale modeling of peptoid structures, self-assembly, and interactions.
By integrating single-molecule optical tweezers, biochemical assays, and cryo-EM, this study reveals that the central coupler in ClpX facilitates intersubunit communication by positioning key residues to trigger ATP hydrolysis in neighboring subunits, thereby coupling chemical energy into rapid force generation for efficient protein unfolding.