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Biomolecular condensates are dynamic intracellular entities defined by their sequence- and composition-encoded material properties. During aging, these properties can change dramatically, potentially leading to pathological solidlike states, the mechanisms of which remain poorly understood. Recent experiments reveal that the aging of condensates involves a complex interplay of solvent depletion, strengthening of sticker links, and the formation of rigid structural segments such as beta fibrils.

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The BioSentinel CubeSat was deployed on the Artemis-I mission in November 2022 and has been continuously transmitting physical measurements of the space radiation environment since that time. Just before mission launch, we published computational model predictions of the galactic cosmic ray exposure expected inside BioSentinel for multiple locations and configurations. The predictions utilized models for the ambient galactic cosmic ray environment, radiation physics and transport, and BioSentinel geometry.

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Proteases are overexpressed at various stages of conditions such as cancers and thus can serve as biomarkers for disease diagnosis. Electrochemical techniques to detect the activity of extracellular proteases have gained attraction due to their multiplexing capability. Here we employ an electrochemical approach based on a 3 × 3 gold (Au) microelectrode array (MEA) functionalized with (2-aminoethyl)ferrocene (AEF) tagged specific peptide substrates to monitor cathepsin B (CB) protease activity.

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Major advances in protein function assignment by remote homolog detection with protein language models - A review.

Curr Opin Struct Biol

January 2025

Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA; Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA. Electronic address:

There is an ever-increasing need for accurate and efficient methods to identify protein homologs. Traditionally, sequence similarity-based methods have dominated protein homolog identification for function identification, but these struggle when the sequence identity between the pairs is low. Recently, transformer architecture-based deep learning methods have achieved breakthrough performances in many fields.

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