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.
Using the DNAfold2 coarse-grained model, this study demonstrates that nanoscale spatial confinement acts as a structural selector that reshapes the folding landscape of ion-stabilized DNA three-way junctions by entropically excluding extended intermediates, thereby stabilizing compact topologies and enforcing cooperative transitions across diverse ionic conditions.
This study reveals that inhibitory ligand-gated chloride channels, such as the nematode AVR-14B and human glycine receptors, function as intrinsic thermosensors by switching from phasic to tonic inhibition at specific temperature thresholds via a noncanonical ion permeation pathway, a mechanism that critically influences drug efficacy and organismal heat tolerance.
This paper introduces a suite of new tools in RELION-5.1 for the automated processing and structural determination of amyloid filaments from cryo-EM data, including automated picking, on-the-fly pre-processing, filament type selection via clustering, and a specialized denoising network, which are demonstrated to successfully identify new filament types in human islet amyloid protein datasets.
This study reveals that hyperthermal sarcomeric oscillations in living cardiomyocytes are not unstructured disorder but are instead shaped by a constrained neighboring-sarcomere phase topology, where the mean oscillation amplitude is accurately predicted by the product of local amplitude and weighted synchrony.
This study combines steady-state immune models with dynamical antigenic traveling wave theory to demonstrate that the optimal CRISPR memory size in bacteria is not fixed but dynamically shaped by the interplay between acquisition mechanisms like primed acquisition and cassette expansion, which respectively favor longer arrays with partial matches or shorter arrays with highly effective new spacers.
The paper introduces AlphaSAXS, an end-to-end AI framework that integrates Small Angle X-ray Scattering (SAXS) experimental data directly into the AlphaFold architecture to accurately reconstruct dynamic protein conformational ensembles and resolve limitations of sequence-only models.
This paper presents a computationally efficient, data-driven method for segmenting multi-state Brownian trajectories using optimized Gaussian filtering and Gaussian mixture model fitting, which achieves high accuracy in both synthetic and experimental single-particle tracking data while offering a lighter alternative to deep learning or hidden Markov model approaches.
The paper introduces ProteinEBM, an Energy-Based Model that leverages diffusion models as statistical potentials to effectively address key challenges in protein science, including structure prediction without evolutionary data, conformational landscape modeling, and mutation effect prediction, while outperforming or matching existing state-of-the-art methods.
This study demonstrates that while matching music tempo to running cadence reduces perceived exertion, the addition of motivational content further decreases trunk acceleration variability, suggesting that lyrical distraction may be even more effective than tempo synchronization in delaying running fatigue.
This study develops and validates a coarse-grained model demonstrating that the probability of error-free chromosome segregation is determined by the ratio of the spindle assembly checkpoint failure rate to the error correction rate, providing a quantitative framework where checkpoint disruptions shorten anaphase time while error correction disruptions lengthen it.