The hydrophobic effect is one of the central organizing principles of molecular biology, underpinning protein folding, molecular recognition, membrane self-assembly and many forms of biomolecular recognition. Contemporary statistical-mechanical frameworks explain much of its thermodynamics, including the dependence of hydration on solute size, density fluctuations and interfacial formation. The remaining conceptual challenge is not the absence of successful theories, but the lack of a common spatial language to describe how heterogeneous non-polar interfaces perturb the collective state of water. In this paper, I propose interfacial frustration as such a descriptor, presenting it as a spatially distributed measure of the mismatch between the preferred state of hydrogen bonded water and the geometric constraints imposed by a non-polar interface. I propose that hydrophobic association can be viewed as a solvent-mediated reduction of this mismatch, while retaining the established thermodynamic description in terms of free energy (G), enthalpy (H), entropy (S) and density fluctuations. Hence, the proposed framework is intended as a complementary representation, and not a replacement of existing theories like Lum-Chandler-Weeks (LCW) theory. The spatial pattern, overlap, and non-additivity of interfacial frustration can provide a common descriptor for geometry dependent association and the crossover from localized hydration to collective interfacial drying. I also develop a more tentative extension to protein dynamics, in which hydration coupled fluctuations at and around hydrophobic cores may modulate distal conformational ensembles. The proposed framework generates explicit operational definitions, comparative predictions and falsification criteria that distinguish it from explanations based solely on surface area, local density or generic conformational entropy
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
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