To investigate the molecular mechanism of Qianlong Shutong formula (QLSTF) in treating benign prostatic hyperplasia (BPH) through the regulation of metabolism and protein function. An integrated metabolomics and proteomics analysis was conducted using a BPH animal model. The animals were divided into a control group, a model group, and a QLSTF intervention group. Following drug intervention, metabolite extraction and protein sample processing were performed. Metabolomics results indicated the presence of metabolic disorders in the model group, which were significantly ameliorated by QLSTF through the regulation of relevant metabolic pathways. Quantitative proteomic analysis identified multiple differentially expressed proteins. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Clusters of Orthologous Groups (COG) enrichment analyses revealed that these proteins are involved in biological processes such as cell proliferation, apoptosis, and inflammatory responses. Correlation analysis demonstrated a synergistic relationship between metabolite levels and protein expression. Integrated analyses revealed metabolic disorders and differential protein expression, providing multiomics evidence for QLSTF's mechanism. QLSTF alleviates BPH by modulating metabolic pathways and protein networks, providing a molecular basis for TCM in BPH treatment and suggesting future research.

POGLUT2 and POGLUT3: Two essential protein O-glucosyltransferases modifying EGF repeats in extracellular matrix proteins.

Tandem repeats in human brain evolution and disease susceptibility.

Nuclear localization signals (NLSs) endowed the dual-specificity protein phosphatase-4 (DUSP4) of Crassostrea gigas with a novel dephosphorylation function.

The association of high expression levels of the CELSR1 protein with tubal pregnancy.

The rok1 gene encodes the ATP-dependent RNA helicase Rok1, which is involved in regulating the maturation of small subunit ribosomal RNA and thus ribosome biogenesis. However, the regulation of cellular mitotic dynamics by the rok1 gene deletion is currently unclear. In the present study, fluorescent protein labeling and live cell imaging techniques were used to investigate the effects of rok1 deletion on the dynamics of microtubules, actin and kinetochores during mitosis at 25 and 37˚C, and RNA-sequencing and bioinformatics analyses were used to reveal the key genes. Analysis of the live cell imaging results revealed that, in mitosis, the initiation length and contraction length of actin rings were both shortened and the contraction rate was decreased at 25 and 37˚C. The separation process of kinetochores was inhibited at 25 and 37˚C, and the inhibition was more severe at the higher temperature of 37˚C. Analysis of RNA sequencing results showed that upregulation of myo51 and blt1 resulted in delayed actin ring assembly and slowed actin ring contraction in the rok1Δ strain. In addition, psm1 and psc3 were upregulated and are key genes affecting the ability of kinetochores to move on the spindle and the cohesion of sister chromatids. The present study revealed that the Rok1 protein not only influences the actin polymerization process, participate in the regulation of actin ring assembly and contraction, and cytoplasmic division, but also affects the migration ability of kinetochores on the spindle and participate in the regulation of the formation and maintenance of cohesion between sister chromatids, which provides a certain scientific basis for further exploring the function of the Rok1 protein in cell division.

Applying nephelometry for analyzing liquid yeast cultures.

Lactate beyond waste: lactylation as a therapeutic target in glioma.

Advanced nanotechnologies for protein modulation: From Nano-PROTAC to Nano-APROM.

The evolving role of single-molecule force spectroscopy in protein biophysics.

Accurate protein domain annotation is essential for inferring protein function, and databases such as Pfam provide sequence-derived signatures for thousands of domain families. Because protein structure is more evolutionarily conserved than sequence, structure-based searches can detect homologous relationships even at low sequence identity (typically below 30%), where pairwise sequence aligners often lose sensitivity. Here, we leverage AlphaFold-derived structures of Pfam domain instances to systematically evaluate structure-based versus sequence-based methods for Pfam annotation. We benchmarked three structural aligners (Reseek, Foldseek, TM-align) against sequence-based methods (MMseqs, HMMER) using both exhaustive all-against-all searches and a split-family design that enables direct comparison of pairwise and profile-based ranking performance. We also evaluated residue-level alignment accuracy using Pfam multiple sequence alignments as reference and investigated whether profile-derived information can improve structural hit ranking. In all-against-all searches, Reseek achieved the highest sensitivity up to the first false positive (AUC = 0.85), outperforming Foldseek (0.81), TM-align (0.76), and MMseqs (0.46). In split-family evaluation, HMMER remained superior (maximum F1 = 0.991), highlighting the continued strength of sequence-profile approaches for family-level annotation. Performance varied substantially across domain families, with average sequence identity emerging as the strongest predictor of success. Structural aligners consistently produced more accurate residue-level mappings than pairwise sequence methods. Finally, incorporating profile-derived information via rescoring improved structural annotation performance for short domains, suggesting a path toward profile-informed structure-based domain annotation.

Blood is a specialized connective fluid tissue composed of plasma and cellular components. It circulates throughout the vascular system as a mixture of about 55% plasma and 45% blood cells, ensuring the maintenance of physiological homeostasis. The cellular fraction of blood mainly includes erythrocytes, leukocytes, and platelets, each originating from hematopoietic stem cells in the bone marrow through tightly regulated differentiation processes. Collectively, these cells contribute to essential biological functions, such as oxygen transport, immune defense, hemostasis, and tissue repair. In addition to their classical functions, blood cells actively interact with endothelial cells and circulating mediators, allowing them to participate in systemic responses to infection, injury, metabolic disturbances, and have emerged as key contributors to inflammatory and oxidative stress-related processes. Together, the coordinated activity and dysfunction of these circulating cells contribute significantly to the propagation of inflammatory responses and oxidative stress, processes that are increasingly recognized as central drivers in the pathophysiology of numerous chronic diseases, including cardiovascular disorders such as arterial hypertension. This review provides a comprehensive overview of how oxidative stress alters ENaC channel activity and adhesion protein function in megakaryocytes, erythrocytes, neutrophils, and platelets, highlighting shared mechanistic pathways that may contribute to vascular dysfunction and chronic inflammatory diseases.

Synthetic nanobodies-also called sybodies-have proven valuable for stabilizing conformations of purified proteins, advancing structural and functional studies for example of transmembrane proteins. However, their utility in modulating protein function in living cells has remained less well explored. Structural Maintenance of Chromosomes (SMC) complexes facilitate chromosome organization, a fundamental process in all domains of life. In this study, we target the bacterial SMC complex, Smc-ScpAB, in Bacillus subtilis with synthetic nanobodies, aiming to identify key functional regions of the protein complex in a largely unbiased manner. We first isolate sybodies that specifically bind purified Smc-ScpAB and then express them in B. subtilis to select binders capable of disrupting Smc-ScpAB function, leading to chromosome segregation defects and cell death. Mapping and biochemical characterization show that the 14 disruptive sybodies belong to one of three library designs, target the Smc subunit near the same coiled coil arm interface and modulate its ATPase activity in two principal ways, highlighting the mid-region of the Smc coiled coil as critical feature of the SMC-DNA folding process. These findings underscore the potential of sybodies-and, by extension, designed binders-as versatile tools for probing dynamic protein function in living cells.

The utility of intrinsically disordered regions (IDRs) in protein function has become an area of broad interest in recent years given their known roles in signaling, biomolecular condensates, and protein activity regulation. The central challenge, however, is that disorder is difficult to characterize, and thus, there is a need to develop approaches to monitor how IDRs influence proteins. Here, we employ a strategy to restrict the freedom of the intrinsically disordered N-terminal tail (ID-tail) of human thymidylate synthase (hTS), which, together with tail truncation, enables testing aspects of how the ID-tail modulates enzyme function. The highly flexible hTS tail is known to impact allosteric substrate cooperativity and conformational switching dynamics, although how the tail brings about these effects has not been determined. We attempted to restrict native tail dynamics by chemically linking the dimer tails and tested linked forms with activity, binding, and nuclear magnetic resonance heteronuclear single quantum coherence (NMR HSQC) spectra. Truncating four N-terminal residues eliminated cooperativity in dUMP binding to apo-hTS, but because a similar result was obtained from the linked full-length protein, complete tail freedom appeared to be important for intersubunit communication. NMR analysis was aided by referencing the inactive M190K mutant, which roughly defined regions of active-inactive conformational switching that were disrupted by tail linkage. Collectively, these results point toward a role of the ID-tail to influence the ordered core of the protein. Moreover, while the natural ID-tail is extremely flexible, it likely possesses specific physiochemical movement dynamics and positioning attributes that facilitate its impact on allosteric cooperativity and conformational dynamics.

Accurate protein function prediction is fundamental to advancing drug discovery and precision medicine and understanding complex biological systems. Although Gene Ontology (GO) provides a standardized framework for protein annotation, a critical challenge persists: the imbalance between low-specificity GO terms and high-specificity GO terms. This imbalance creates blind spots in our understanding of protein function landscapes, particularly in clinically relevant pathways. Here, we present ProGO-PSL, a novel large graph architecture designed to resolve this imbalance. ProGO-PSL simultaneously leverages explicit domain identifiers from InterPro and implicit evolutionary contexts from multiple sequence alignments, fusing these complementary data sources within a powerful imbalance learning framework. Our model consistently outperforms state-of-the-art methods by 5%-15% across all specificity levels and on both a benchmark data set and an independent test set, demonstrating robust generalization. Furthermore, ProGO-PSL generates interpretable representations that clarify relationships between low- and high-specificity GO terms, enabling a more complete functional characterization of the proteome. This work accelerates the identification of therapeutic targets in previously uncharacterized biological pathways.

Protein sequences encode rich structural and functional information that governs how organisms respond to genetic variation, environmental challenge, and disease. However, existing computational methods typically rely on a single information source, whether sequence, structure, or functional annotation, and their predictive power is substantially reduced for low-homology proteins or orphan proteins. Here we present ProSSF (Protein Sequence-Structure-Function), a unified multimodal pretraining framework that performs masked pretraining on large-scale protein sequences, encodes three-dimensional structural information via a Geometric Vector Perceptron Graph Neural Network (GVP-GNN), integrates Gene Ontology (GO) semantics through a dual-path hierarchical encoder, and aligns all three modalities into a shared representation space via cross-modal attention. Evaluated across three downstream tasks, ProSSF achieves a Spearman correlation of 0.74 ± 0.009 on the TAPE protein stability benchmark, a mean Micro-F1 of 84.60% ± 0.9% on the SHS148K protein-protein interaction dataset under the stringent DFS partition, and comparable or superior Fmax and AUPR relative to state-of-the-art baselines across all three GO sub-ontologies. Ablation analyses demonstrate that structural geometry and GO functional semantics contribute complementary and task-dependent information, with the largest performance gains observed under low-homology conditions. Attention-based interpretability analyses further reveal that the model preferentially attends to biologically meaningful regions, such as kinase catalytic domains, without explicit supervision. This study provides a unified multimodal pretraining framework and demonstrates that jointly encoding sequence, structure, and functional semantics substantially improves the generalizability of protein property prediction. Future studies should validate this framework on larger, taxonomically diverse protein datasets and explore its potential applications in the functional annotation of disease-associated proteins and the identification of novel drug targets.

Phosphorylation is a central post-translational mechanism for on-demand regulation of protein function. In the ATP-dependent molecular chaperone Hsp90, multiple phosphorylation sites have been implicated in activity control, yet how individual sites encode regulatory instructions remains unclear. Here, using solution NMR spectroscopy, we delineate how site-specific phosphorylation distinctly reshapes the conformational energy landscape of Hsp90 across its ATPase cycle. The phospho-mimetic mutation T115E induces a global redistribution of the N-terminal domain energy landscape, flattening conformational barriers, pre-populating a lid-closed-like excited state in the apo form, weakening ATP-driven stabilization, and impairing ADP-mediated resetting. In contrast, T36E preserves the overall structure but selectively rewires dynamics at the ATP-bound step, where accelerated exchange and a reduced excited-state population bias the ensemble toward the ground state. Together, these findings reveal that phosphorylation sites engage different dynamic allosteric routes, yet converge on a common functional outcome by suppressing productive progression through the Hsp90 ATPase cycle.

The genetic architecture of lung cancer (LC) varies substantially across populations. However, the limited ancestral diversity of existing discovery cohorts hampers comprehensive characterization of ancestry-specific risk mechanisms and limits the transferability of polygenic risk scores (PRSs) across populations. In addition, the interplay between germline variants and somatic mutations in LC remains poorly characterized. We performed a cross-ancestry genome-wide association meta-analysis (GWMA) studies comprising 53,650 LC cases and 486,210 controls of East Asian (EAS) and European (EUR) ancestry. PRSs of cross-ancestry were developed and validated in the UK Biobank (UKB) and China Kadoorie Biobank (CKB). Fine-mapping and expression quantitative trait locus (eQTL) colocalization were conducted to prioritize candidate susceptibility genes. Gene-set based PRSs were calculated based on pathway enrichment analyses, telomere and smoking-related GWASs and supporting literature, and their association with somatic mutations and prognostic metrics were studied. Functional validation of novel locus was performed to elucidate the mechanisms of LC. Cross-ancestry GWMA identified 36 independent variants across 30 loci, including one novel locus, and revealed remarkable differences in genetic correlations with smoking behaviors between East Asians and Europeans. PRS-CSx derived from the meta-analysis outperformed previous models in both the two biobanks (UKB: HR = 1.27, 95%CI: 1.23-1.31; CKB: HR = 1.26, 95%CI: 1.19-1.34). Susceptibility genes were grouped into four pathways: telomere maintenance, smoking behavior, epithelial cell biology, and DNA damage response. Corresponding gene-set-based PRSs were significantly associated with tumor mutation burden, mutational signatures, key driver mutations, and patient survival. Finally, functional assays further demonstrated that two variants at the novel locus at 16q23.3 regulate MPHOSPH6 expression and protein function, influencing telomere length and LC risk. Our study systematically elucidates the shared and distinct genetic architectures of LC between EAS and EUR populations, introduces PRS-CSx for effective LC risk stratification underscoring its clinical implications across diverse ancestries. Additionally, we uncover a novel mechanism by which functional genetic variants located in MPHOSPH6 contribute to LC susceptibility, and provide comprehensive insights into the interplay between germline and somatic mutations in LC.

Atherosclerosis (AS) is a progressive disease of the arterial wall characterized by metabolic dysregulation, inflammatory activation, and genetic susceptibility. Given the complex interactions across molecular layers, this review aims to summarize the key applications of multi-omics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, single-cell omics, spatial omics, plasma proteomics, and radiomics, in elucidating AS pathogenesis and clinical relevance. Recent multi-omics studies have enabled the construction of functional networks linking genetic variation, epigenetic regulation, gene expression, protein function, and metabolic imbalance, thereby providing complementary insights into AS mechanisms. These approaches have advanced the understanding of distinct pathological phenotypes, such as calcified versus non-calcified plaques and stable versus unstable lesions. Emerging evidence also highlights the clinical relevance of underexplored areas, including molecular subtyping, and plasma biomarker prediction. Furthermore, the integration of artificial intelligence (AI) has enhanced multi-omics data mining, particularly in radiomics-based phenotypic profiling and multidimensional risk modeling. This review synthesizes current advances in multi-omics strategies for AS research and discusses the sources and application status of human samples in representative studies, emphasizing differences in acquisition methods, utilization rates, and omics preferences across vascular beds. Collectively, these integrative approaches support systems biology frameworks and hold promise for informing precision strategies for early detection, risk stratification, and targeted intervention in AS.

A protein's function depends critically on its conformational ensemble, a collection of energy-weighted structures whose balance depends on temperature and environment. Though recent deep learning methods have substantially advanced predictions of single protein structures, computationally modeling conformational ensembles remains a challenge. Here, we focus on modeling fold-switching proteins, which remodel their secondary and/or tertiary structures and change their functions in response to cellular stimuli. These underrepresented members of the protein universe serve as test cases for a method's generalizability. They reveal that deep learning models often predict conformational ensembles by association with training set structures, which limits generalizability. These observations suggest use cases for when deep learning methods will likely succeed or fail. Developing computational methods that successfully identify new fold-switching proteins from large pools of candidates may advance modeling conformational ensembles more broadly.