protein folding

Integrated proteomic-transcriptomic profiling identifies NR3C2 up-regulation and a candidate plasma-protein-handling signature in chronic podocyte injury.

Findings show that the late phase of podocyte-driven CKD involves distributed protein-level remodelling, including an RNA-discordant increase in NR3C2 protein and a candidate plasma-protein-handling signature.

Simplifying in silico protein evolution with minimal screening by unZipro.

UnZipro is presented, an efficient, scalable, and generalizable framework for zero-shot, in silico protein evolution, that integrates a compact, pre-trained inverse folding model with meta-learning to derive family-specific fitness landscapes.

Screening strong anaerobic E. coli promoters to enhance therapeutical protein expression in tumors.

This study reports the first systematic screening of strong constitutive anaerobic promoters in the probiotic Escherichia coli Nissle 1917 (EcN), establishing a precision delivery framework for potent yet highly toxic anticancer agents, facilitating the development of bacterial biotherapeutics.

The molecular interplay between USP32 and ERP44 in driving multiple myeloma progression and tumor growth.

Findings identify ERP44 as a pro-proliferative factor in MM and establish a USP32-ERP44-PI3K-AKT regulatory axis.

Cannabinoids as Context-Dependent Modulators of the Adenosine A3 Receptors: Potential Orthosteric and Allosteric Mechanisms.

CBG is identified as an effective enhancer of adenosine potency at human A3R, suggesting it stabilizes an occupancy-dependent secondary pose that fine-tunes the orthosteric agonist-receptor interaction network.

Seryl phosphorylation modulates chaperonin function of Mycobacterial Chaperonin, Cpn60.1.

It is shown that canonical oligomerization residues are replaced by serines in Cpn60.1 homologues, enabling a phosphorylation‑switch and support a model in which phosphorylation contributes to higher-order oligomerization and partial chaperonin activity of Cpn60.1.

MultiSpaceNet: graph-based joint representation learning for paired spatial transcriptome–proteome data

MultiSpaceNet is presented, a graph-based framework for joint representation learning from paired spatial transcriptomic and proteomic data that outperforms nine published state-of-the-art methods in spatial domain identification and preserves biological structure across replicate sections better than all compared alte...

Quantification of Amlexanox in Beagle Dog Plasma by LC-MS/MS and Its Application in a Comparative Pharmacokinetic Study of Conventional and Sustained-Release Tablets.

The clinical development of a sustained-release amlexanox formulation with reduced dosing frequency and improved patient compliance is supported and support the clinical development of a sustained-release amlexanox formulation with reduced dosing frequency and improved patient compliance.

Peanut Protein Hydrolysates from Oil Cake: Enhanced Antioxidant Activity, Techno-Functional Properties, and Safety Evaluation

Modern Solvation Thermodynamics from Macroscopic to Microscopic Approach

The concept of solvation was originally studied within thermodynamics. This may be referred to as the macroscopic approach to solvation. It involved the transfer of solute from the gaseous phase into the solvent under well-specified thermodynamic conditions. In 1978, a new definition of the solvation process was introd...

Topological Protection as the Organizing Principle of Threshold-Gated Regulation: From Quantum Integers to Macroscopic Coherence (CMF Paper 3)

The Coherence Maintenance Framework (CMF), established in Papers 1 and 2, demonstrates that threshold-gated regulatory architecture is a universal feature of driven non-linear systems across 29 orders of magnitude in scale. While R_mode = f_pert * tau_relax quantitatively categorizes regulatory dynamics into discrete o...

eLife Assessment: Large scale prospective evaluation of co-folding across hundreds of Mac1-ligand complexes and three virtual screens

Accurate prediction of ligand-bound protein complexes and ranking them by affinity are central problems in drug discovery. While deep learning co-folding methods can help address these challenges, their evaluation has been hampered by the difficulties in assessing independence from training data and insufficiently larg...