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 paper introduces the alpha kissing loop (alphaKL), a programmable RNA connector that enables the edge-to-edge assembly of complex three-dimensional RNA nanostructures by combining kissing loops with triplex interactions to overcome the geometric limitations of traditional end-to-end joining methods.
This study introduces ICed-ENM, a computationally efficient coarse-grained modeling framework that refines elastic network models to quantify how disease-related missense mutations reshape protein conformational energy landscapes and identify mutation-sensitive regions through vibrational entropy changes.
This study elucidates the structural and evolutionary determinants of metal import specificity in the *Deinococcus radiodurans* Nramp transporter, revealing that while Mn²⁺ import follows a global epistasis model, Mg²⁺ specificity is governed by a combination of core positions and modulator mutations that alter conformational balance, thereby linking long-range epistasis to specificity modulation.
This paper introduces Independent Fluorescence Component Analysis (IFCA), a novel framework utilizing third-order cumulant tensor decomposition to robustly unmix and quantitatively characterize complex, heterogeneous signals from freely diffusing single molecules, even under low photon rates and rapid sub-millisecond dynamics.
This paper presents Monte Carlo simulations using McStas to optimize the upcoming NMX instrument at the European Spallation Source by implementing ray-splitting techniques and a new sampling method to improve event formation and assess environmental scattering effects for neutron protein crystallography.
By combining experiments on confined embryonic cells with a continuum model, this study reveals that aggregation-driven patterning is subject to a developmental time-size tradeoff, where the ability to maintain robust scaling is limited in larger systems due to the delayed coarsening dynamics required to form scaled patterns.
By applying the Mutation-Stability-Activity (MSA) model to 34 enzyme families, this study demonstrates that residue-dependent structural divergence profiles arise from a variable balance of mutation, stability, and activity constraints, revealing that these profiles encode specific information about the selective regimes governing enzyme evolution.
By reanalyzing hyperthermal sarcomeric oscillations through a shared rephasing compass, this study reveals that local timing mismatches between neighboring sarcomeres are not random but follow a structured, edge-biased pattern that becomes clearly visible only after aligning cell-specific circular coordinates.
Using cryo-electron tomography, this study reveals that RNA polymerases and DNA-binding proteins preferentially localize at plectoneme apices to act as torsional blocks that segregate twin-supercoiling domains, where the resulting load-and-release mechanism driven by topoisomerase I provides a structural basis for transcriptional bursting.
This paper presents a physics-grounded evaluation revealing that state-of-the-art protein structure prediction models, while capturing basic energetic principles, exhibit pervasive biases in atomic interactions and conformational preferences that limit their accuracy and generalizability, thereby highlighting the need for such frameworks to guide the development of next-generation models capable of reliable biomolecular function prediction.