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.

⚛️ biophysics

DynMoCo: a Novel AI Framework to Reveal Modular Substructures of Protein From Molecular Dynamics

DynMoCo is a novel deep learning framework that utilizes graph convolutional and recurrent networks to perform dynamic community detection on molecular dynamics simulations, transforming high-dimensional protein motion data into interpretable, time-evolving modular substructures.

Mao, L., Kwak, M., Ashkezari, A. H. K., Li, Z., Chen, Y., Cong, P., Phee, J. H., Kang, S., Li, J., Zhu, C.2026-02-10
⚛️ biophysics

Binding Paths: Describing Small Molecule Interactions with Disordered Proteins via a Markov State Model

This paper introduces a Markov State Model-based framework that characterizes small molecule interactions with disordered proteins through dynamic "binding paths" rather than static pockets, thereby rationalizing recognition mechanisms and identifying druggable regions to facilitate drug discovery for previously intractable targets.

Louet, A. A. B., Hummer, G., Vendruscolo, M.2026-02-09
⚛️ biophysics

Early-Enrichment Hit Discovery via Reversible-Work c(t) Estimation in Metadynamics (CTMD)

This paper introduces CTMD, a fast, physics-based, and open-source metadynamics protocol that utilizes the reversible-work estimator c(t) to achieve robust early enrichment in virtual screening, effectively bridging the gap between approximate docking/AI methods and expensive free energy calculations while avoiding the memorization artifacts and training-set biases observed in recent AI co-folding approaches.

Adury, V. S. S., Tiwary, P., Gu, X., Shekhar, M.2026-02-08
⚛️ biophysics

Active Site Local Environment Allows Acidic and Basic Synergy in Enzymatic Ester Hydrolysis by PETase

This paper reveals that the PETase enzyme achieves synergistic acidic and basic catalysis for ester hydrolysis through its active site's local environment, which utilizes a proton shuttle for nucleophile generation and hydrogen bonding for transition state stabilization, thereby suggesting specific protein engineering strategies to enhance its plastic-degrading reactivity.

Fan, J., Ha, Y.2026-02-05