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 two-stage physics-based computational framework that combines weighted ensemble simulations with the RiteWeight reweighting algorithm to harmonize diverse AI-generated protein structure ensembles into a consistent, atomically-detailed equilibrium state.
This study demonstrates that multiple sequence alignment perturbation strategies significantly enhance AlphaFold3's ability to sample alternative protein conformational states, often outperforming AlphaFold2 and matching the BioEmu model.
This study employs an integrated computational framework to reveal that broadly neutralizing SARS-CoV-2 antibodies achieve escape-proof efficacy by targeting minimally frustrated, evolutionarily constrained epitope cores, whereas immune escape occurs in neutrally frustrated peripheral regions that serve as energetic playgrounds for mutation.
This study demonstrates that existing ion force fields do not transfer directly to OPC water without significant deviations, leading the authors to propose the new MS/G-LB(OPC) force field, which combines specific cation and anion parameters to accurately reproduce experimental hydration free energies, structural properties, and activity derivatives.
This paper identifies and corrects a fundamental bias in chromatin contact map preprocessing caused by whole-matrix percentile clipping, introducing a statistically consistent framework and a deep learning tool (CCUT) that enables physically interpretable, quantitative reconstruction of genome architecture aligned with polymer physics and loop extrusion models.
This study reveals that the transcription factor YY1 forms mechanically distinct DNA condensates in a concentration-dependent manner, where moderately high concentrations yield weakly-linked, liquid-like assemblies scaffolded by immobile DNA, while high concentrations drive strongly-linked, solid-like condensates through zinc finger-mediated bridging.
This study elucidates the structural basis by which the lead compound CN045 inhibits M1 muscarinic and H3 histamine receptors to promote oligodendrocyte progenitor cell differentiation, revealing stronger and more stable binding to M1 through computational simulations to guide the development of remyelination therapies for multiple sclerosis.
Using cryogenic electron microscopy under acidic conditions, researchers determined the structure of the bacterial ligand-gated ion channel DeCLIC in a previously unreported expanded-pore conformation, identifying it as a functional open state and elucidating how calcium binding and N-terminal domain dynamics drive channel closure.
This paper introduces biophysical fitness landscape design (FLD), a computational framework that uses optimized antibody ensembles to quantitatively reshape viral fitness landscapes, thereby proactively suppressing the evolution of escape variants like those of SARS-CoV-2 before they emerge.
Using mass photometry and kinetic modeling, this study characterizes the binding dynamics of the bispecific antibody ivonescimab, revealing that it predominantly forms stable 2:2 dimers with VEGF and ternary complexes presenting two PD-1 molecules, with distinct dissociation constants for each interaction.