This study identified the LARP6 La Module from Tetrabaena socialis (T. socialis), a four-celled green algae, in an effort to better understand the evolution of LARP6 structure and RNA-binding activity in multicellular eukaryotes. Using a combination of sequence alignments, domain boundary screens, and structural modelling, we recombinantly expressed and isolated the TsLARP6 La Module to > 98% purity for in vitro biochemical characterization. The La Module is stably folded and exerts minimal RNA binding activity against single-stranded homopolymeric RNAs. Surprisingly, it exhibits low micromolar binding affinity for the vertebrate LARP6 cognate ligand, a bulged-stem loop found in the 5'UTR of collagen type I mRNA, but does not bind double-stranded RNAs of similar size. These result suggests that the TsLARP6 La Module may prefer structured RNA ligands. In contrast, however, the TsLARP6 La Module does not exhibit the RNA chaperone activity that is observed in vertebrate homologs. Therefore, we conclude that protist LARP6 may have both distinct RNA ligands and binding mechanisms from the previously characterized LARP6 proteins of animals and vascular plants, thus establishing a distinct third class of the LARP6 protein family.

Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophysical properties that affect the formulation of the molecule into a product, is usually evaluated only toward the end of the drug development pipeline. The ability to evaluate developability properties early in the process of antibody therapeutic development could accelerate the timeline from discovery to clinic and save considerable resources. In silico predictive approaches, such as machine learning models, which map molecular features to predictions of developability properties could offer a cost-effective and high-throughput alternative to experiments for antibody developability assessment. We developed a computational framework, PROPERMAB (PROPERties of Monoclonal AntiBodies), for large-scale and efficient in silico prediction of developability properties for monoclonal antibodies, using custom molecular features and machine learning modeling. We demonstrate the power of PROPERMAB by using it to develop models to predict antibody hydrophobic interaction chromatography retention time and high-concentration viscosity. We further show that structure-derived features can be rapidly and accurately predicted directly from sequences by pre-training simple models for molecular features, thus providing the ability to scale these approaches to repertoire-scale sequence datasets.

The emergence of antibiotic-resistant bacteria has attracted interest in the field of endolysins. Here, we analyzed the diversity of Streptococcus endolysins and identified a new endolysin, Ply2741, that exhibited broad-spectrum bactericidal activity. Our results demonstrated that Ply2741 could effectively eradicate multidrug-resistant gram-positive pathogens in vitro and in vivo. Structural analysis revealed that the bactericidal activity of Ply2741 depends on the classic "Cys-His-Asn" catalytic triad. Site-directed mutagenesis results further identified that the conserved residue Gln29, located near the catalytic triad, also contributes to the lytic activity of Ply2741. Furthermore, the key residues (R189 and W250) in the Ply2741 cell wall binding domain (CBD) responsible for binding to peptidoglycan were revealed by molecular docking and fluorescence-activated cell sorting (FACS) analysis. Ply2741 demonstrates a broad lytic spectrum, with significant bactericidal activity against Enterococcus, Staphylococcus, and Streptococcus and species. To the best of our knowledge, we found that residue Gln29 participated in the lytic activity of endolysin for the first time. Additionally, we systematically elucidate the binding mode and key residues of the Ply2741CBD. This study proposes Ply2741 as a potential antibiotic substitute and provides a structural basis for the modification and design of endolysins.

In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalizability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported. Here, we present a comprehensive study exploring various approaches for protein fitness prediction from limited data, leveraging pre-trained embeddings, repeated stratified nested cross-validation, and ensemble learning to ensure an unbiased assessment of the performances. We applied our framework to introduce NanoMelt, a predictor of nanobody thermostability trained with a dataset of 640 measurements of apparent melting temperature, obtained by integrating data from the literature with 129 new measurements from this study. We find that an ensemble model stacking multiple regression using diverse sequence embeddings achieves state-of-the-art accuracy in predicting nanobody thermostability. We further demonstrate NanoMelt's potential to streamline nanobody development by guiding the selection of highly stable nanobodies. We make the curated dataset of nanobody thermostability freely available and NanoMelt accessible as a downloadable software and webserver.

Circular RNAs (circRNAs) are a unique class of covalently closed single-stranded RNA molecules that play diverse roles in normal physiology and pathology. Among the major types of circRNA, exon-intron circRNA (EIciRNA) distinguishes itself by its sequence composition and nuclear localization. Recent RNA-seq technologies and computational methods have facilitated the detection and characterization of EIciRNAs, with features like circRNA intron retention (CIR) and tissue-specificity being characterized. EIciRNAs have been identified to exert their functions via mechanisms such as regulating gene transcription, and the physiological relevance of EIciRNAs has been reported. Within this review, we present a summary of the current understanding of EIciRNAs, delving into their identification and molecular functions. Additionally, we emphasize factors regulating EIciRNA biogenesis and the physiological roles of EIciRNAs based on recent research. We also discuss the future challenges in EIciRNA exploration, underscoring the potential for novel functions and functional mechanisms of EIciRNAs for further investigation.

The emergence and spread of antifungal-resistant Trichophyton indotineae pose an increasing public health concern worldwide. Multidrug-resistant T. indotineae infections have been reported in China in the past few years. To understand the genetic relationship and the origin of these Chinese isolates, as well as their relationship to the global collections, we sequenced the whole genomes of 31 isolates using the Illumina platforms. Genomic epidemiology was performed on a dataset of 181 T. indotineae isolates from China and 8 other countries, representing the largest genome-wide analysis. Single nucleotide polymorphism analysis revealed that T. indotineae can be divided into four distinct phylogenetic groups (I, II, III, IV), with regional clonal transmission clusters identified in eastern China; T. indotineae was introduced into China more than once given the genetic variability. The isolates from South Asia may be the source of Chinese isolates based on epidemiological information. There were differences in the prevalence and resistance profiles among four phylogenetic groups, with Group III being predominant and exhibiting a higher terbinafine resistance rate of 88.24% and azole resistance. Also, we characterized the role of gene mutation, copy number variation, and gene expression in antifungal drug resistance. Terbinafine resistance could be mainly associated with Phe397Leu substitution in SQLE, and azole resistance might be related to increased copy number of CYP51B, as well as elevated MDR2 and MDR3 expression. Given the clinical challenges posed by T. indotineae, this emerging dermatophyte should be recognized as a global threat, necessitating urgent collaborative surveillance and management strategies.

Sulfur metabolism is an essential aspect of fungal physiology and pathogenicity. Fungal sulfur metabolism comprises anabolic and catabolic routes that are not well conserved in mammals, therefore is considered a promising source of prospective novel antifungal targets. To gain insight into Aspergillus fumigatus sulfur-related metabolism during infection, we used a NanoString custom nCounter-TagSet and compared the expression of 68 key metabolic genes in different murine models of invasive pulmonary aspergillosis, at 3 time-points, and under a variety of in vitro conditions. We identified a set of 15 genes that were consistently expressed at higher levels in vivo than in vitro, suggesting that they may be particularly relevant for intrapulmonary growth and thus constitute promising drug targets. Indeed, the role of 5 of the 15 genes has previously been empirically validated, supporting the likelihood that the remaining candidates are relevant. In addition, the analysis of gene expression dynamics at early (16 h), mid (24 h), and late (72 h) time-points uncovered potential disease initiation and progression factors. We further characterized one of the identified genes, encoding the cytosolic serine hydroxymethyltransferase ShmB, and demonstrated that it is an essential gene of A. fumigatus, also required for virulence in a murine model of established pulmonary infection. We further showed that the structure of the ligand-binding pocket of the fungal enzyme differs significantly from its human counterpart, suggesting that specific inhibitors can be designed. Therefore, in vivo transcriptomics is a powerful tool for identifying genes crucial for fungal pathogenicity that may encode promising antifungal target candidates.

Type 2 diabetes mellitus (T2DM) and cancers are two globally prevalent diseases which can increase the incidence of each other. Intestinal α-glucosidase and β-glucuronidase are key targets for glycaemic control and chemotherapy detoxification, respectively. This study first found that the leaf methanol extract of Millettia pachycarpa displayed dual inhibition to the two enzymes. The dually active constituents were then isolated and identified as two prenylated isoflavones of 6,8-diprenylorobol and 6,8-diprenylgenistein. Diprenylorobol exhibits competitive inhibition to both the two enzymes with Ki values of 21.6 μM (α-glucosidase) and 1.41 μM (β-glucuronidase). Diprenylgenistein is an uncompetitive inhibitor of α-glucosidase (Ki = 11.4 μM) but a competitive inhibitor of β-glucuronidase (Ki = 1.69 μM). Molecular docking studies showed that both the two isoflavones tightly bind into the active pockets via various hydrogen bonds and hydrophobic interactions. In summary, the current study identifies two promising dual inhibitors of α-glucosidase and β-glucuronidase from the leaves of Millettia pachycarpa.

ISB 1442 is a bispecific biparatopic antibody in clinical development to treat hematological malignancies. It consists of two adjacent anti-CD38 arms targeting non-overlapping epitopes that preferentially drive binding to tumor cells and a low-affinity anti-CD47 arm to enable avidity-induced blocking of proximal CD47 receptors. We previously reported the pharmacology of ISB 1442, designed to reestablish synthetic immunity in CD38+ hematological malignancies. Here, we describe the discovery, optimization and characterization of the ISB 1442 antigen binding fragment (Fab) arms, their assembly to 2 + 1 format, and present the high-resolution co-crystal structures of the two anti-CD38 Fabs, in complex with CD38. This, with biophysical and functional assays, elucidated the underlying mechanism of action of ISB 1442. In solution phase, ISB 1442 forms a 2:2 complex with CD38 as determined by size-exclusion chromatography with multi-angle light scattering and electron microscopy. The predicted antibody-antigen stoichiometries at different CD38 surface densities were experimentally validated by surface plasmon resonance and cell binding assays. The specific design and structural features of ISB 1442 enable: 1) enhanced trans binding to adjacent CD38 molecules to increase Fc density at the cancer cell surface; 2) prevention of avid cis binding to monomeric CD38 to minimize blockade by soluble shed CD38; and 3) greater binding avidity, with a slower off-rate at high CD38 density, for increased specificity. The superior CD38 targeting of ISB 1442, at both high and low receptor densities, by its biparatopic design, will enhance proximal CD47 blockade and thus counteract a major tumor escape mechanism in multiple myeloma patients.

l-serine is a versatile, high value-added amino acid, widely used in food, medicine and cosmetics. However, the low titer of l-serine has limited its industrial production. In this study, a cell factory without plasmid for efficient production of l-serine was constructed based on transport engineering. Firstly, the effects of l-serine exporter SerE overexpression and deletion on the cell growth and l-serine titer were investigated in Corynebacterium glutamicum (C. glutamicum) A36, overexpression of s erE using a plasmid led to a 15.1% increase in l-serine titer but also caused a 15.1% decrease in cell growth. Subsequently, to increase the export capacity of SerE, we conducted semi-rational design and bioinformatics analysis, combined with alanine mutation and site-specific saturation mutation. The mutant E277K was obtained and exhibited a 53.2% higher export capacity compared to wild-type SerE, resulting in l-serine titer increased by 39.6%. Structural analysis and molecular dynamics simulations were performed to elucidate the mechanism. The results showed that the mutation shortened the hydrogen bond distance between the exporter and l-serine, enhanced complex stability, and reduced the binding energy. Finally, Bayesian optimization was employed to further improve l-serine titer of the mutant strain C-E277K. Under the optimized conditions, 47.77 g/L l-serine was achieved in a 5-L bioreactor, representing the highest reported titer for C. glutamicum to date. This study provides a basis for the transformation of l-serine export pathway and offers a new strategy for increasing l-serine titer.

The third subfamily of voltage-gated K+ (Kv) channels includes four members, Kv3.1, Kv3.2, Kv3.3 and Kv3.4. Fast gating and activation at relatively depolarized membrane potentials allows Kv3 channels to be major drivers of fast action potential repolarization in the nervous system. Consequently, they help determine the fast-spiking phenotype of inhibitory interneurons and regulate fast synaptic transmission at glutamatergic synapses and the neuromuscular junction. Recent studies from our group and a team of collaborators have used cryo-EM to demonstrate the surprising gating role of the Kv3.1 cytoplasmic T1 domain, the structural basis of a developmental epileptic encephalopathy caused by the Kv3.2-C125Y variant and the mechanism of action of positive allosteric modulators involving unexpected interactions and conformational changes in Kv3.1 and Kv3.2. Furthermore, our recent work has shown that Kv3.4 regulates use-dependent spike broadening in a manner that depends on gating modulation by phosphorylation of the channel's N-terminal inactivation domain, which can impact activity-dependent synaptic facilitation. Here, we review and integrate these studies to provide a perspective on our current understanding of Kv3 channel function, dysfunction and pain modulation in the nervous system.

X-linked hyper-IgM (X-HIGM), which results from mutations of the CD40 ligand gene (CD40LG) located on chromosome Xq26.3, is characterized by a defective T-B lymphocyte cross talk and class switch recombination (CSR). The present study aimed to evaluate the expression of CD40L and lymphocyte subsets using flow cytometry and to identify the novel genetic defect of CD40LG responsible for X-HIGM in a Chinese family. We reported an X-HIGM case caused by a novel mutation in CD40LG. The expression of CD40L was absent on the surface of activated CD4 + T cells evaluated using flow cytometry. The total number of mature B cells in circulation was normal, but memory B cells were significantly decreased. In helper T cells, Th2 was dominant, and the numbers of Th1 and Th17 were decreased. The results of genetic analysis revealed a new causative mutation in CD40L (NM_000074;exon5;c.505_506del), which leads to a change in amino acids (p.Y169Lfs*31) appearing in the proband. The frame shift mutation led to incorrect amino acid translation and loss of β-pleated sheet and loop region, which produced a mutant dysfunctional protein. This study provides a complete picture of X-HIGM and broadens our knowledge of the pathogenicity of the CD40L variant spectrum.

The casein-derived bitter peptide VAPFPEVF has been shown to stimulate proton secretion in human parietal cells (HGT-1) via bitter taste receptor TAS2R16, confirmed by siRNA knockdown. Since literature evidence is inconclusive, we hypothized that VAPFPEVF binds to TAS2R16, and investigated its effects on G protein-coupled signaling pathways. Exposure of HGT-1 cells to VAPFPEVF altered cAMP signaling without inducing a calcium response. An atomic force microscopy (AFM)-based approach was employed to demonstrate peptide binding to TAS2R16 in cellular and cell-free environments using TAS2R16-reconstituted proteoliposomes. Increased binding events were observed, reduced by the addition of salicin and TAS2R16 antagonist probenecid. AlphaFold multimer and molecular dynamics simulations suggest VAPFPEVF binds the orthosteric site of TAS2R16. These findings reveal (i) VAPFPEVF interacts with TAS2R16 to modulate cAMP levels without triggering calcium mobilization and (ii) the AFM approach as a valuable tool for studying peptide binding to TAS2R16 and possibly other G-protein coupled transmembrane receptors.

Previously, a glycoside hydrolase (GH) family 2 β-galactosidase (PbBGal2A) from Paenibacillus barengoltzii is characterized for its high transglycosylation capability. Here, the cryo-electron microscopy (cryo-EM) structure of PbBGal2A was determined, revealing an enlarged acidic catalytic pocket that facilitate the binding of carbohydrate substrates. Three structure-based strategies as well as machine learning MECE platform (method for enhancing the catalytic efficiency) were employed to identify active and distal mutations with enhanced galacto-oligosaccharides (GOS) synthesis and their synergistic effects were evaluated. The best H331V mutation yielded a maximum GOS production of 76.57 % at 4 h when 35 % (w/v) of lactose was used as a substrate. Molecular dynamics (MD) simulation analysis further indicated that distal mutations increase the rigidity of the loops surrounding the catalytic pocket. This research sheds light on the structural and catalytic mechanisms of PbBGal2A, highlighting the importance of both active and distal mutations in the efficient design of customized β-galactosidases.

Cellobiose 2-epimerase (CE) converts lactose into lactulose and epilactose with high added value and prebiotic benefits. However, the low conversion of lactulose by CsCE limits its industrial application. Inspired by previous work introducing engineered disulfide bonds on the loop of CsCE, we performed saturation mutagenesis and high-throughput screening of all residues within 4 Å of the engineered disulfide bond. The best mutant, CsCE-S173C/F231C/L172S/I178V(M4), showed a 1.3-fold increase in isomerization activity over CsCE-S173C/F231C. Molecular dynamics simulations and binding pocket analysis revealed that mutations reshaped the shape and size of the substrate-binding pocket and increased non-covalent interactions of the protein with the ligand. Binding free energy calculations showed a higher binding affinity of M4 for lactose and lactulose. Therefore, we demonstrated that the catalytic characteristics of CE can be modulated by further manipulating the loop region near the pocket, which is important for improving the catalytic efficiency and promiscuity of the enzyme.

RNA molecules play essential roles in numerous biological pathways, making them attractive targets for drug discovery. Despite the challenges in developing small molecules targeting RNA, the success in developing compounds that modulate RNA function underscores its therapeutic potential. 19F NMR spectroscopy has emerged as a powerful tool in structural biology and drug discovery, particularly for studying macromolecular structures and ligand interactions. As RNA continues to gain prominence as a drug target, 19F NMR is expected to play a pivotal role in advancing RNA-focused drug discovery. This review describes the diverse applications of 19F NMR in RNA biology, including its use in characterizing RNA structures, probing molecular dynamics, identifying small-molecule binders, and investigating interaction mechanisms of small-molecule ligands. By providing detailed structural and ligand binding insights, 19F NMR will facilitate the discovery of RNA-targeting therapeutics and deepen our understanding of RNA modulatory mechanisms.

Lysine acetylation (Kace) is one of the most important post-translational modifications. It is key to identify Kace sites for understanding regulation mechanisms in Camellia sinensis. In this study, we defined a mathematical formula, named sequence spatial equation (SSE), which could give each amino acid coordinate in 3-D space by rotating and translating. Based on SSE, an optional network SSE-Net was constructed for representing spatial structure information. Centrality metrics of SSE-Net were used to design structure feature vectors for reflecting the importance of sites. The optimal features were fed into classifier to construct model SSE-ET. The results showed that SSE-ET outperformed the other classifiers. Meanwhile, all MCC results were higher than 0.7 for different machine learning, which indicated that SSE-Net was effective for representing Kace sites in Camellia sinensis. Moreover, we implemented the other models on our dataset. The results of comparison showed that SSE-ET was much more powerful than the others. Specifically, the result of SN was nearly 20 % higher than the other models. These results showed that the proposed SSE was a valuable mathematics concept for reflecting 3-D space Kace site information in Camellia sinensis, and SSE-Net may be an essential complementary for biology and bioinformatics research.

β-Galactosidases are important enzymatic tools for glycosylation, but their properties vary greatly with the source. Here, ten putative β-galactosidase genes, designated as bga1 to bga10, encoding proteins Bga1 to Bga10, were mined from an environmental metagenomic dataset comprising 119,152 sequences. Five of the encoded enzyme proteins exhibited less than 80% sequence similarity to known enzymes, but displayed conserved catalytic sites in their predicted three-dimensional models. After heterologous expression and characterization, two recombinant enzymes showed specific hydrolysis activity toward o-nitrophenyl-β-d-galactopyranoside. One of them, Bga4R, exhibited remarkable activity at pH 7.4 and 50℃, with excellent alkaline stability. Notably, Bga4R tolerated a wide range of acceptors for transglycosylation. It catalyzed galactosyl transfer to various monosaccharides and sugar alcohols, and enabling the synthesis of diverse glycosylated derivatives. This study identifies a novel GH 1 β-galactosidase as a powerful tool for glycosylation engineering, with promising potential for synthesizing galactosides valuable to food and pharmaceutical industries.

Short rib polydactyly syndrome (SRPS), with or without polydactyly, also known as Verma-Naumoff/Saldino-Noonan syndrome, is a type of skeletal ciliopathy. Initially, variants in the IFT80 gene were implicated; however, approximately half of the SRPS cases are associated with variants in the DYNC2H1 gene. Additionally, digenic variants involving DYNC2H1 and NEK1 can contribute to the syndrome.

This case report describes a male patient presenting with characteristic SRPS features, including a constricted thorax and shortened limbs. Exome sequencing was performed to identify causative variants, followed by functional analyses to assess the pathogenicity of the identified variants, including a synonymous variant.

Exome sequencing identified compound heterozygous variants in the DYNC2H1 gene: a novel missense variant c.6439G>T p.(Asp2147Tyr) and a synonymous variant c.6477G>A p.(Gln2159=). Functional analyses confirmed that the synonymous variant triggers nonsense-mediated decay of the affected allele.

This study expands the spectrum of DYNC2H1 variants associated with SRPS and emphasizes the importance of functional analyses in genetic diagnostics. Demonstrating pathogenicity for a synonymous variant highlights the necessity for comprehensive variant assessments to improve diagnostic accuracy and enable early intervention. These findings have significant implications for molecular diagnostics and personalized therapy strategies in skeletal ciliopathies.

Nucleocytoplasmic transport plays a critical role in the activation of immune mechanisms in plant cells. Fluorescence imaging analysis indicated that a high concentration of rapid alkalinization factor (RALF) suppresses the immune response and can induce nuclear envelope (NE) shape deformation. This phenomenon depends on the receptor kinase FERONIA (FER). Consistently, bacterial infection also affects NE shape. This study presents evidence that FER displays functional interactions with NPL4 and that phosphorylated NPL4 promotes its stability. We reported the identification and characterization of the nuclear localization protein NPL4, which is directly involved in general mRNA nuclear export. FER and NPL4 mutations both affected rhizosphere Pseudomonas colonization levels, suggesting that the interactions between FER and NPL4 are largely indispensable for regulating rhizosphere Pseudomonas colonization levels. In addition, NPL4 altered the mRNA nucleocytoplasmic distribution of immune genes in conjunction with the function of RALF-FER in the suppression of plant immunity. In brief, NPL4, which is downstream of FER is required for innate immunity and mRNA nuclear accumulation of resistance genes in Arabidopsis. Overall, through the analysis of RALF-FER and the nuclear localization protein NPL4, this work provides novel insights into the mRNA nucleocytoplasmic transport of immune genes and plant health.

Drug-target affinity prediction is a fundamental task in the field of drug discovery. Extracting and integrating structural information from proteins effectively is crucial to enhance the accuracy and generalization of prediction, which remains a substantial challenge. This paper proposes a pocket-based multimodal deep learning model named PocketDTA for drug-target affinity prediction, based on the principle of "structure determines function". PocketDTA introduces the pocket graph structure that encodes protein residue features pretrained using a biological language model as nodes, while edges represent different protein sequences and spatial distances. This approach overcomes the limitations of lack of spatial information in traditional prediction models with only protein sequence input. Furthermore, PocketDTA employs relational graph convolutional networks at both atomic and residue levels to extract structural features from drugs and proteins. By integrating multimodal information through deep neural networks, PocketDTA combines sequence and structural data to improve affinity prediction accuracy. Experimental results demonstrate that PocketDTA outperforms state-of-the-art prediction models across multiple benchmark datasets by showing strong generalization under more realistic data splits and confirming the effectiveness of pocket-based methods for affinity prediction.

Streptomyces species are vital for producing natural products like antibiotics, with c-di-GMP playing a key role in regulating processes such as differentiation. C-di-GMP metabolism is controlled by diguanylate cyclases (DGCs) and phosphodiesterases (PDEs), which synthesize and hydrolyze c-di-GMP, respectively, to modulate cellular levels. To improve our understanding of c-di-GMP-regulated processes in Streptomyces, we have characterized a c-di-GMP-metabolizing enzyme CdgA from Streptomyces ghanaensis that contains both a diguanylate cyclase and a phosphodiesterase domain. Our studies demonstrate that the enzyme is purified in a form without heme and is only able to degrade c-di-GMP. When reconstituted with heme, it enables c-di-GMP synthesis, and depending on the redox state the synthesis rate is changed. To our knowledge, this is the first heme-dependent activity reported for a c-di-GMP-metabolizing enzyme in Streptomyces and has major implications for understanding the way c-di-GMP is metabolized in vivo in Streptomyces.

The high-sensitivity detection of low-concentration multicomponent biomarkers in the blood of heart failure (HF) patients using surface-enhanced Raman spectroscopy (SERS) remains a significant challenge. In this study, an ultrasensitive biosensor for the detection of multicomponent HF biomarkers was designed. This biosensor utilizes Au@Ag nanoparticles (Au@Ag NPs) functionalized with Raman reporter molecules (RaRs) as SERS probes, and Ag-coated Fe3O4 nanoparticles (Fe3O4-Ag NPs) modified with internal standard (IS) molecules as the capture substrate, offering the dual advantages of magnetic enrichment and SERS enhancement. Additionally, specific aptamers or antibodies were conjugated to the surfaces of Au@Ag NPs and Fe3O4-Ag NPs to specifically recognize target proteins to construct a three-layer composite structure (Fe3O4-Ag/HF biomarkers/Au@Ag). The limit of detection (LOD) of HF markers for cTnI, NT-proBNP, and sST2 is 0.1 pg/mL, 1.0 fg/mL, and 1.0 fg/mL, respectively, surpassing most reported methods. Additionally, the analysis of 45 clinical serum samples revealed no statistically significant differences between the SERS-based results and those obtained by conventional clinical methods, as confirmed by the Shapiro-Wilk test (p > 0.05). In conclusion, this SERS biosensor successfully developed an easy-to-operate accurate diagnosis method capable of simultaneous, quantitative detection of multiple HF biomarkers and provided a new technique for accurate diagnosis of other diseases in clinical testing.

Protein structure prediction has undergone significant advancements, driven by the limitations of experimental techniques like X-ray crystallography, NMR, and cryo-EM, which are costly and time-consuming. To bridge the gap between protein sequences and their structures, computational methods have emerged as essential tools. Traditional approaches such as homology modeling, threading, and ab initio folding made progress but often lacked atomic-level precision. The field has been revolutionized by deep learning-based models such as AlphaFold2, RoseTTAFold, and OpenFold, which have demonstrated unprecedented accuracy in predicting protein structures. These AI-driven models leverage vast datasets and neural networks to generate highly reliable structural predictions, sometimes rivaling experimental methods. This review explores the historical evolution of computational protein structure prediction, analyzing the strengths and weaknesses of state-of-the-art models. These models have broad applications in fields such as drug discovery, enzyme engineering, and disease-related protein modeling. However, challenges remain, including the need for extensive training data, computational resource requirements, and difficulties in modeling protein dynamics, intrinsically disordered regions, and protein-protein interactions. Future directions in the field include improving AI models to address current limitations, better integration with experimental techniques, and extending predictions to protein complexes and post-translational modifications. By continuing to refine these methods, computational protein structure prediction will further enhance biomedical research and therapeutic design, reshaping the landscape of structural biology and computational biophysics.

Dipeptidyl peptidases 8 and 9 (DPP8/9) are critical for the quality control of mitochondrial and endoplasmic reticulum protein import, immune regulation, cell adhesion, and cell migration. Dysregulation of DPP8/9 is associated with pathologies including tumorigenesis and inflammation. Commonly, DPP8/9 activity is analysed by in vitro assays using artificial substrates, which allow neither continuously monitoring DPP8/9 activity in individual, living cells nor detecting effects from endogenous interactors and posttranslational modifications. Here, we developed DiPAK (for DPP8/9 activity sensor based on AK2), a ratiometric genetically encoded fluorescent sensor, which enables studying DPP8/9 activity in living cells. Using DiPAK, we determined the dynamic range of DPP8/9 activity in cells overexpressing or lacking DPP9. We identified distinct activity levels among melanoma cell lines and found that LPS-induced primary B-cell activation depends on DPP8/9 as the absence of DPP8/9 activity results in apoptotic but not pyroptotic cell death. Consistently, we observed increasing DPP8/9 activity during B-cell maturation. Overall, DiPAK is a versatile tool for real-time single-cell monitoring of DPP8/9 activity in a broad range of cells and organisms.

Accurately identifying protein-DNA binding sites is essential for understanding the molecular mechanisms underlying biological processes, which in turn facilitates advancements in drug discovery and design. While biochemical experiments provide the most accurate way to locate DNA-binding sites, they are generally time-consuming, resource-intensive, and expensive. There is a pressing need to develop computational methods that are both efficient and accurate for DNA-binding site prediction. This study thoroughly reviews and categorizes major computational approaches for predicting DNA-binding sites, including template detection, statistical machine learning, and deep learning-based methods. The 14 state-of-the-art DNA-binding site prediction models have been benchmarked on 136 non-redundant proteins, where the deep learning-based, especially pre-trained large language model-based, methods achieve superior performance over the other two categories. Applications of these DNA-binding site prediction methods are also involved.

Mirid plant bugs (Hemiptera: Miridae), including Lygus hesperus (western tarnished plant bug), are key pests of numerous agricultural crops. While management of this pest relies heavily on chemical insecticides, the evolution of resistance and environmental concerns underscore the need for new and more effective approaches. Genetic-based strategies that target male fertiliy are currently being evaluated for population suppression. However, a lack of candidate gene targets with appropriate function, specifically in non-model species like L. hesperus, has hindered progress in the development and application of such approaches. Given their conserved role in stabilization of the flagella axoneme and association with sperm motility in many organisms, members of the tektin gene family represent logical targets for genetic-based sterilization. Here, we identified four homologs of the non-vertebrate tektin family from L. hesperus and used RNA interference-mediated knockdown to assess their roles in male fertility. Although transcription of the four tektins was predominantly in the testis, knockdown had negligible effects on either sperm abundance or male fertility. Our results suggest that tektins do not play a critical role in sperm fertilization of eggs in L. hesperus and are thus likely poor targets for genetic-based sterilization approaches in this species.

Producing low FODMAP grain-based staple foods is crucial for individuals with irritable bowel syndrome (IBS). This study aimed to resolve the impact of microwave heating on the fructan content in sourdough steamed cake and to elucidate the potential mechanism by which microwave fields activate β-fructosidase FosE. With similar heating rate, microwave treatment induced a significant reduction of fructans in sourdough steamed cakes compared to conventional steaming method, and this was confirmed to be the result of enhanced FosE activity by microwaves irradiation. Molecular docking and molecular dynamics simulations revealed that microwave irradiation improves the structural stability of the enzyme-substrate complex. Analysis of binding free energy indicated that microwaves enhance the coulombic interactions through energy transfer. These findings provide valuable insights into the molecular mechanisms underlying the interactions between FosE and fructan under microwave irradiation, paving the way for the future applications of microwaves in low-FODMAP cereal-based food processing.

G protein-coupled receptors (GPCRs) are membrane proteins crucial to numerous diseases, yet many remain poorly characterized and untargeted by drugs. Chimeric GPCRs have emerged as valuable tools for elucidating GPCR function by facilitating the identification of signaling pathways, resolving structures, and discovering novel ligands of poorly understood GPCRs. Such chimeric GPCRs are obtained by merging a well- and less-well-characterized GPCR at the intracellular limits of their transmembrane regions or intracellular loops, leveraging knowledge transfer from the well-characterized GPCR. However, despite the engineering of over 200 chimeric GPCRs to date, the design process remains largely trial-and-error and lacks a standardized approach. To address this gap, we introduce GPCRchimeraDB (https://www.bio2byte.be/gpcrchimeradb/), the first comprehensive database dedicated to chimeric GPCRs. It catalogs 212 chimeric receptors, identified through literature review, and includes 1,755 class A natural GPCRs, enabling connections between chimeras and their parent receptors while facilitating the exploration of novel parent combinations. Both chimeric and natural GPCR entries are extensively described at the sequence, structural, and biophysical level through a range of visualization tools, with annotations from resources like UniProt and GPCRdb and predictions from AlphaFold2 and b2btools. Additionally, GPCRchimeraDB offers a GPCR sequence aligner and a feature comparator to investigate differences between natural and chimeric receptors. It also provides design guidelines to support rational chimera engineering. GPCRchimeraDB is therefore a resource to facilitate and optimize the design of new chimeras, so helping to gain insights into poorly characterized receptors and contributing to advances in GPCR therapeutic development.

Modified cyclodextrins (CDs) are cyclic oligosaccharides with many applications in drug delivery, catalysis, and as active pharmaceutical ingredients. In general, they exist as distributions of structurally diverse molecules rather than single-isomer compounds. Their performance depends on the number of glucopyranose units (GPUs), and the type, number, and position of chemical substitutions in their hydroxyl groups. Effectively targeting individual species within these distributions is essential for optimizing CDs for specific applications. Computational techniques can generate large datasets to AI-driven structural optimization, but the absence of a standardized nomenclature system for modified CDs presents a major barrier to progress in this direction. This lack of consensus limits effective communication, data sharing, automation, and collaboration. To address this, a clear and extensible nomenclature for modified CDs is proposed. In this framework, GPUs are treated like amino-acid residues, with unsubstituted GPUs as reference building-blocks and substituted ones considered as mutations. This approach precisely defines substitution types and patterns, resolves cyclic permutation ambiguities, and offers versatility for both simple and complex modifications, including chiral center alterations and covalently linked CD oligomers. By introducing this standardized nomenclature, we aim to enhance molecular design, improve reproducibility, and streamline both experimental and computational research in the CD field.

Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial susceptibility testing (AST) by leveraging machine learning models, including Random Forest, Support Vector Machines (SVM), and deep learning architectures such as Convolutional Neural Networks (CNNs) and transformers. The integration of AI into these methods promises to address the current limitations of traditional techniques, offering a path toward more efficient, accessible, and reliable diagnostic solutions. In particular, AI-based approaches have demonstrated significant potential in resource-limited settings by enabling cost-effective and portable diagnostic solutions, reducing dependency on specialized infrastructure, and facilitating remote bacterial detection through smartphone-integrated platforms and telemedicine applications. This review highlights AI's transformative role in automating data analysis, minimizing human error, and delivering real-time diagnostic results, ultimately improving patient outcomes and optimizing healthcare efficiency. In addition, we not only examine the current advances in machine learning and deep learning but also review their applications in plate counting, mass spectrometry, morphology-based and motion-based microscopic detection, holographic microscopy, colorimetric and fluorescence detection, electrochemical sensors, Raman and Surface-Enhanced Raman Spectroscopy (SERS), and Atomic Force Microscopy (AFM) for bacterial diagnostics and AST. Finally, we discuss the future directions and potential advancements in AI-driven bacterial diagnostics.

In recent years, monoclonal antibody drugs have dominated the bio-pharmaceutical market because of their high specificity and low adverse reactions. Antibody purification has always occupied most of the cost of antibody drug production. Protein A affinity chromatogra phy, as the most commonly used antibody purification method, has been explored to improve its service life and reduce the cost of purification. Cleaning in place (CIP) of protein A requires protein A to have a better alkalinity resistance for protein A affinity chromatography reuse. Therefore, in this study, we redesigned a base-tolerant protein A, 4xPA-LPA, by semi-rational analysis based on bio-informatics. Including surface charge analysis, construction of linker peptides, and MSA and FoldX prediction-based conserved site screening and targeting mutagenesis. Analyzed by SDS-PAGE, differential scanning calorimeter and circular dichroism, the results showed that 4xPA-LPA was treated with 0.5 M NaOH and more than 84 % residues remain after 24 h. The Tm value of 4xPA-LPA increased by 2.47 °C compared with that before mutation. Its α-helices number increased, and the protein structure was more stable. DBC retention was approximately 86 % after alkali immersing and 89 % after 100 simulated wash repetitions. The novel alkali-resistant protein A has good application prospects in the field of monoclonal antibody purification, and this study also provides a certain reference strategy for future engineering to improve the alkali-resistance of proteins.

The cell wall of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, is rich in complex lipids. During intracellular stage, Mtb depends on lipids for its survival. Mammalian cell entry (Mce) 1 complex encoded by the mce1 operon is a mycolic/fatty acid importer. mce1 operon also encodes a putative fatty acyl-CoA synthetase (FadD5; Rv0166), potentially responsible for the activation of fatty acids imported through the Mce1 complex by conjugating them to Coenzyme A. Here, we report that FadD5 is associated to membrane although it can be purified as a soluble dimeric protein. ATP and CoA binding influence FadD5's stability and conformation respectively. Enzymatic studies with fatty acids of varying chain lengths show that FadD5 prefers long chain fatty acids as substrates. X-ray crystallographic studies on FadD5 and its variant reveal that the C-terminal domain (∼100 residues) is cleaved off during crystallization. Noteworthy, deletion of this domain renders FadD5 completely inactive. SAXS studies, however, confirm the presence of full length FadD5 as a dimer in solution. Further structural analysis and comparisons with homologs provide insights on the possible mode of membrane association and fatty acyl tail binding.

In eukaryotic cells, communication between organelles and the coordination of their activities depend on membrane contact sites (MCS). How MCS are regulated under the dynamic cellular environment remains poorly understood. Here, we investigate how Pex30, a membrane protein localized to the endoplasmic reticulum (ER), regulates multiple MCS in budding yeast. We show that Pex30 is critical for the integrity of ER MCS with peroxisomes and vacuoles. This requires the dysferlin (DysF) domain on the Pex30 cytosolic tail. This domain binds to phosphatidic acid (PA) both in vitro and in silico, and it is important for normal PA metabolism in vivo. The DysF domain is evolutionarily conserved and may play a general role in PA homeostasis across eukaryotes. We further show that the ER-vacuole MCS requires a Pex30 C-terminal domain of unknown function and that its activity is controlled by phosphorylation in response to metabolic cues. These findings provide new insights into the dynamic nature of MCS and their coordination with cellular metabolism.

Myo2, a class V myosin motor, is essential for organelle transport in budding yeast. Its association with cargo is regulated by adaptor proteins that mediate both attachment and release. Vac17, a vacuole-specific adaptor, links Myo2 to the vacuole membrane protein Vac8 and plays a key role in assembling and disassembling the Myo2-Vac17-Vac8 complex during vacuole inheritance. Using genetics, cryo-EM, and structure prediction, we find that Vac17 interacts with Myo2 at two distinct sites rather than a single interface. Similarly, the peroxisome adaptor Inp2 engages two separate regions of Myo2, one of which overlaps with a Vac17-binding site. These findings support a "handhold" model, in which cargo adaptors occupy multiple surfaces on the Myo2 tail, which likely enhances motor-cargo associations as well as provide additional regulatory control over motor recruitment.

Capsid protein (CP) is the antigen of nervous necrosis virus (NNV), a fatal microorganism for almost marine fishes. Antigen epitope is a special chemical group in an antigen molecule that determines the specificity of the antigen, usually consisting of 5-15 amino acid residues. However, the antigen epitope of NNV antigen remains unclear. In this study, using immunoinformatic method, we analyzed the antigen epitope of CP and designed a multi-epitopes vaccine of NNV (PA). Furthermore, we evaluated the immune responses induced by PA vaccine. The results showed that three cytotoxic T lymphocyte epitopes and six B-cell lymphocyte epitopes were predicted, with high antigenicity, non-allergen, and non-toxin. Based on these epitopes, a multi-epitopes vaccine of NNV (PA) was designed and prepared. After immunization, the mRNA expression levels of IL-1β, TNF-α, CD4, CD8, MHC-Ⅰ, and MHC-Ⅱ in PA group were significantly up-regulated. Moreover, it has been proven that PA could significantly activate antigen presenting cells. Importantly, PA could induce similar levels of antibodies secretion and immune protection, compared to CP group. The survival rate reached 77.22 % in PA group. This study provides a cheap and effective strategy for aquatic vaccine design, which will be beneficial in application to development of vaccine in aquaculture industry.