UniProt: the Universal Protein Knowledgebase in 2023

CNKSR2 interactome analysis indicates its association with the centrosome/microtubule system

JOURNAL/nrgr/04.03/01300535-202508000-00031/figure1/v/2024-09-30T120553Z/r/image-tiff The protein connector enhancer of kinase suppressor of Ras 2 (CNKSR2), present in both the postsynaptic density and cytoplasm of neurons, is a scaffolding protein with several protein-binding domains. Variants of the CNKSR2 gene have been implicated in neurodevelopmental disorders, particularly intellectual disability, although the precise mechanism involved has not yet been fully understood. Research has demonstrated that CNKSR2 plays a role in facilitating the localization of postsynaptic density protein complexes to the membrane, thereby influencing synaptic signaling and the morphogenesis of dendritic spines. However, the function of CNKSR2 in the cytoplasm remains to be elucidated. In this study, we used immunoprecipitation and high-resolution liquid chromatography-mass spectrometry to identify the interactors of CNKSR2. Through a combination of bioinformatic analysis and cytological experiments, we found that the CNKSR2 interactors were significantly enriched in the proteome of the centrosome. We also showed that CNKSR2 interacted with the microtubule protein DYNC1H1 and with the centrosome marker CEP290. Subsequent colocalization analysis confirmed the centrosomal localization of CNKSR2. When we downregulated CNKSR2 expression in mouse neuroblastoma cells (Neuro 2A), we observed significant changes in the expression of numerous centrosomal genes. This manipulation also affected centrosome-related functions, including cell size and shape, cell proliferation, and motility. Furthermore, we found that CNKSR2 interactors were highly enriched in de novo variants associated with intellectual disability and autism spectrum disorder. Our findings establish a connection between CNKSR2 and the centrosome, and offer new insights into the underlying mechanisms of neurodevelopmental disorders.

NECAB family of neuronal calcium-binding proteins in health and disease

The N-terminal EF-hand calcium-binding proteins 1-3 (NECAB1-3) constitute a family of predominantly neuronal proteins characterized by the presence of at least one EF-hand calcium-binding domain and a functionally less well characterized C-terminal antibiotic biosynthesis monooxygenase domain. All three family members were initially discovered due to their interactions with other proteins. NECAB1 associates with synaptotagmin-1, a critical neuronal protein involved in membrane trafficking and synaptic vesicle exocytosis. NECAB2 interacts with predominantly striatal G-protein-coupled receptors, while NECAB3 partners with amyloid-β A4 precursor protein-binding family A members 2 and 3, key regulators of amyloid-β production. This demonstrates the capacity of the family for interactions with various classes of proteins. NECAB proteins exhibit distinct subcellular localizations: NECAB1 is found in the nucleus and cytosol, NECAB2 resides in endosomes and the plasma membrane, and NECAB3 is present in the endoplasmic reticulum and Golgi apparatus. The antibiotic biosynthesis monooxygenase domain, an evolutionarily ancient component, is akin to atypical heme oxygenases in prokaryotes but is not well-characterized in vertebrates. Prokaryotic antibiotic biosynthesis monooxygenase domains typically form dimers, suggesting that calcium-mediated conformational changes in NECAB proteins may induce antibiotic biosynthesis monooxygenase domain dimerization, potentially activating some enzymatic properties. However, the substrate for this enzymatic activity remains uncertain. Alternatively, calcium-mediated conformational changes might influence protein interactions or the subcellular localization of NECAB proteins by controlling the availability of protein-protein interaction domains situated between the EF hands and the antibiotic biosynthesis monooxygenase domain. This review summarizes what is known about genomic organization, tissue expression, intracellular localization, interaction partners, and the physiological and pathophysiological role of the NECAB family.

In silico study suggests potential drugs that target CD151 to treat breast cancer and glioblastoma

Recently tetraspanin CD151 has been identified as an important biological target involved in metastatic processes which include cell adhesion, tumor progression processes, and so forth in different types of cancers, such as breast cancer and glioblastoma. This in Silico study considered 1603 compounds from the Food and Drug Administration database, after performing an ADMET analysis; we selected 853 ligands, which were used for docking analysis. The most promising ligands were selected from docking studies, based on two criteria: (a) showed lowest affinity to the CD151 protein and (b) they interact with the QRD motif, located in the second extracellular loop. Furthermore, we investigate the stability of the protein-ligand complexes through MD simulations as well as free energy MM-PBSA calculations. From these results, loperamide and glipizide were identified as the best evaluated drugs. We suggest an in vitro analysis is needed to confirm our in silico prediction studies.

Study of the multitarget mechanism of Astragalus (HUANGQI) in the treatment of Alzheimer's disease based on network pharmacology and molecular docking technology

CONTEXT

In China, HUANGQI is widely used for the treatment of Alzheimer's disease (AD). However, a comprehensive understanding of its mechanism of anti-AD effects is lacking.

OBJECTIVE

To explore the active ingredients of HUANGQI and its potential targets and mechanisms of action in AD.

MATERIALS AND METHODS

The active ingredients and targets of HUANGQI were screened from databases (TCSMP, ETCM, and BATMan), and AD-related genes were obtained from DrugBank and GeneCards. The same target genes were screened, and a drug-target disease network was constructed. The PPI network was constructed and GO and KEGG pathway enrichment analyses of the targets. The Cell Counting Kit-8 (CCK-8) assay was used to determine suitable HUANGQI treatment concentrations for HT-22 cells between 0-480 μg/mL. CCK-8, FITC-phalloidin and propidium iodide (PI) assays were used to examine the protective effect of (0, 60, 120, 240 μg/mL) of HUANGQI on 20 μM Aβ1-42-induced HT-22 cell cytotoxicity.

RESULTS

Twelve active ingredients of HUANGQI were selected, with 679 common targets associated with AD. GO and KEGG analysis revealed that the therapeutic mechanisms of HUANGQI involve TNF, AGE, the NF-κB pathway, and nuclear receptor activity-related processes. The CCK-8 assay indicated that HUANGQI was not cytotoxic to HT-22 cells at concentrations less than 240 μg/mL and was able to attenuate Aβ1-42-induced cellular damage (EC50 = 83.46 μg/mL). FITC-phalloidin and PI assays suggested that HUANGQI could alleviate 20 μM Aβ1-42-induced neuronal cell cytotoxicity in a dose-dependent manner.

CONCLUSION

HUANGQI has a protective effect on Aβ1-42-induced nerve cell injury; further mechanism research was needed.

Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review

Recent breakthroughs in deep learning have revolutionized protein sequence and structure prediction. These advancements are built on decades of protein design efforts, and are overcoming traditional time and cost limitations. Diffusion models, at the forefront of these innovations, significantly enhance design efficiency by automating knowledge acquisition. In the field of de novo protein design, the goal is to create entirely novel proteins with predetermined structures. Given the arbitrary positions of proteins in 3-D space, graph representations and their properties are widely used in protein generation studies. A critical requirement in protein modelling is maintaining spatial relationships under transformations (rotations, translations, and reflections). This property, known as equivariance, ensures that predicted protein characteristics adapt seamlessly to changes in orientation or position. Equivariant graph neural networks offer a solution to this challenge. By incorporating equivariant graph neural networks to learn the score of the probability density function in diffusion models, one can generate proteins with robust 3-D structural representations. This review examines the latest deep learning advancements, specifically focusing on frameworks that combine diffusion models with equivariant graph neural networks for protein generation.

RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes

RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.

One-carbon metabolism nutrients impact the interplay between DNA methylation and gene expression in liver, enhancing protein synthesis in Atlantic salmon

Supplementation of one-carbon (1C) metabolism micronutrients, which include B-vitamins and methionine, is essential for the healthy growth and development of Atlantic salmon (Salmo salar). However, the recent shift towards non-fish meal diets in salmon aquaculture has led to the need for reassessments of recommended micronutrient levels. Despite the importance of 1C metabolism in growth performance and various cellular regulations, the molecular mechanisms affected by these dietary alterations are less understood. To investigate the molecular effect of 1C nutrients, we analysed gene expression and DNA methylation using two types of omics data: RNA sequencing (RNA-seq) and reduced-representation bisulphite sequencing (RRBS). We collected liver samples at the end of a feeding trial that lasted 220 days through the smoltification stage, where fish were fed three different levels of four key 1C nutrients: methionine, vitamin B6, B9, and B12. Our results indicate that the dosage of 1C nutrients significantly impacts genetic and epigenetic regulations in the liver of Atlantic salmon, particularly in biological pathways related to protein synthesis. The interplay between DNA methylation and gene expression in these pathways may play an important role in the mechanisms underlying growth performance affected by 1C metabolism.

Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold

Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel - CaV1.1, and human voltage-gated potassium (KV) channel - KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.

kmerDB: A database encompassing the set of genomic and proteomic sequence information for each species

The decrease in sequencing expenses has facilitated the creation of reference genomes and proteomes for an expanding array of organisms. Nevertheless, no established repository that details organism-specific genomic and proteomic sequences of specific lengths, referred to as kmers, exists to our knowledge. In this article, we present kmerDB, a database accessible through an interactive web interface that provides kmer-based information from genomic and proteomic sequences in a systematic way. kmerDB currently contains 202,340,859,107 base pairs and 19,304,903,356 amino acids, spanning 54,039 and 21,865 reference genomes and proteomes, respectively, as well as 6,905,362 and 149,305,183 genomic and proteomic species-specific sequences, termed quasi-primes. Additionally, we provide access to 5,186,757 nucleic and 214,904,089 peptide sequences absent from every genome and proteome, termed primes. kmerDB features a user-friendly interface offering various search options and filters for easy parsing and searching. The service is available at: www.kmerdb.com.

Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses

Intrinsic disorder predictors were evaluated in several studies including the two large CAID experiments. However, these studies are biased towards eukaryotic proteins and focus primarily on the residue-level predictions. We provide first-of-its-kind assessment that comprehensively covers the taxonomy and evaluates predictions at the residue and disordered region levels. We curate a benchmark dataset that uniformly covers eukaryotic, archaeal, bacterial, and viral proteins. We find that predictive performance differs substantially across taxonomy, where viruses are predicted most accurately, followed by protists and higher eukaryotes, while bacterial and archaeal proteins suffer lower levels of accuracy. These trends are consistent across predictors. We also find that current tools, except for flDPnn, struggle with reproducing native distributions of the numbers and sizes of the disordered regions. Moreover, analysis of two variants of disorder predictions derived from the AlphaFold2 predicted structures reveals that they produce accurate residue-level propensities for archaea, bacteria and protists. However, they underperform for higher eukaryotes and generally struggle to accurately identify disordered regions. Our results motivate development of new predictors that target bacteria and archaea and which produce accurate results at both residue and region levels. We also stress the need to include the region-level assessments in future assessments.

BioTextQuest v2.0: An evolved tool for biomedical literature mining and concept discovery

The process of navigating through the landscape of biomedical literature and performing searches or combining them with bioinformatics analyses can be daunting, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related repositories. Herein, we present BioTextQuest v2.0, a tool for biomedical literature mining. BioTextQuest v2.0 is an open-source online web portal for document clustering based on sets of selected biomedical terms, offering efficient management of information derived from PubMed abstracts. Employing established machine learning algorithms, the tool facilitates document clustering while allowing users to customize the analysis by selecting terms of interest. BioTextQuest v2.0 streamlines the process of uncovering valuable insights from biomedical research articles, serving as an agent that connects the identification of key terms like genes/proteins, diseases, chemicals, Gene Ontology (GO) terms, functions, and others through named entity recognition, and their application in biological research. Instead of manually sifting through articles, researchers can enter their PubMed-like query and receive extracted information in two user-friendly formats, tables and word clouds, simplifying the comprehension of key findings. The latest update of BioTextQuest leverages the EXTRACT named entity recognition tagger, enhancing its ability to pinpoint various biological entities within text. BioTextQuest v2.0 acts as a research assistant, significantly reducing the time and effort required for researchers to identify and present relevant information from the biomedical literature.

Profiling of collagen and extracellular matrix deposition from cell culture using in vitro ExtraCellular matrix mass spectrometry imaging (ivECM-MSI)

While numerous approaches have been reported towards understanding single cell regulation, there is limited understanding of single cell production of extracellular matrix phenotypes. Collagens are major proteins of the extracellular microenvironment extensively used in basic cell culture, tissue engineering, and biomedical applications. However, identifying compositional regulation of collagen remains challenging. Here, we report the development of In vitro ExtraCellular Matrix Mass Spectrometry Imaging (ivECM-MSI) as a tool to rapidly and simultaneously define collagen subtypes from coatings and basic cell culture applications. The tool uses the mass spectrometry imaging platform with reference libraries to produce visual and numerical data types. The method is highly integrated with basic in vitro strategies as it may be used with conventional cell chambers on minimal numbers of cells and with minimal changes to biological experiments. Applications tested include semi-quantitation of collagen composition in culture coatings, time course collagen deposition, deposition altered by gene knockout, and changes induced by drug treatment. This approach provides new access to proteomic information on how cell types respond to and change the extracellular microenvironment and provides a holistic understanding of both the cell and extracellular response.

Targeting the glutamine-arginine-proline metabolism axis in cancer

Metabolic abnormalities are an important feature of tumours. The glutamine-arginine-proline axis is an important node of cancer metabolism and plays a major role in amino acid metabolism. This axis also acts as a scaffold for the synthesis of other nonessential amino acids and essential metabolites. In this paper, we briefly review (1) the glutamine addiction exhibited by tumour cells with accelerated glutamine transport and metabolism; (2) the methods regulating extracellular glutamine entry, intracellular glutamine synthesis and the fate of intracellular glutamine; (3) the glutamine, proline and arginine metabolic pathways and their interaction; and (4) the research progress in tumour therapy targeting the glutamine-arginine-proline metabolic system, with a focus on summarising the therapeutic research progress of strategies targeting of one of the key enzymes of this metabolic system, P5CS (ALDH18A1). This review provides a new basis for treatments targeting the metabolic characteristics of tumours.

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Evaluation of AlphaFold3 for the fatty acids docking to human fatty acid-binding proteins

Human fatty acid-binding proteins (FABPs) are involved in many aspects of lipid metabolism, such as the uptake, transport, and storage of lipophilic molecules, as well as cellular functions. Understanding how FABPs recognize fatty acids (FAs) is crucial for identifying FABP function and applications, such as in inhibitor design or biomarker development. The recently developed AlphaFold3 (AF3) demonstrates significantly higher accuracy than other prediction tools, particularly in predicting protein-ligand interactions with state-of-the-art docking tools. Studies on whether AF3 can be used to identify the FAs of FABP are lacking. To assess the accuracy of FA docking to FABPs using AF3, models of FA docked into FABP generated using AF3 were compared with experimentally determined FA-bound FABP structures. FA ligands in AF3 structures docked reliably into the FA-binding pocket of FABPs; however, the detailed binding configuration of most FA ligands docked into FABPs and the interaction between FA and FABP determined using AF3 and experimentally differed. These results will aid in understanding FA docking to FABPs and other FA-binding proteins using AF3.

Nuclear pore dysfunction and disease: a complex opportunity

The separation of genetic material from bulk cytoplasm has enabled the evolution of increasingly complex organisms, allowing for the development of sophisticated forms of life. However, this complexity has created new categories of dysfunction, including those related to the movement of material between cellular compartments. In eukaryotic cells, nucleocytoplasmic trafficking is a fundamental biological process, and cumulative disruptions to nuclear integrity and nucleocytoplasmic transport are detrimental to cell survival. This is particularly true in post-mitotic neurons, where nuclear pore injury and errors to nucleocytoplasmic trafficking are strongly associated with neurodegenerative disease. In this review, we summarize the current understanding of nuclear pore biology in physiological and pathological contexts and discuss potential therapeutic approaches for addressing nuclear pore injury and dysfunctional nucleocytoplasmic transport.

PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction

X-ray diffraction crystallography has been most widely used for protein three-dimensional (3D) structure determination for which whether proteins are crystallizable is a central prerequisite. Yet, there are a number of procedures during protein crystallization, including protein material production, purification, and crystal production, which take turns affecting the crystallization outcome. Due to the expensive and laborious nature of this multi-stage process, various computational tools have been developed to predict protein crystallization propensity, which is then used to guide the experimental determination. In this study, we presented a novel deep learning framework, PLMC, to improve multi-stage protein crystallization propensity prediction by leveraging a pre-trained protein language model. To effectively train PLMC, two groups of features of each protein were integrated into a more comprehensive representation, including protein language embeddings from the large-scale protein sequence database and a handcrafted feature set consisting of physicochemical, sequence-based and disordered-related information. These features were further separately embedded for refinement, and then concatenated for the final prediction. Notably, our extensive benchmarking tests demonstrate that PLMC greatly outperforms other state-of-the-art methods by achieving AUC scores of 0.773, 0.893, and 0.913, respectively, at the aforementioned individual stages, and 0.982 at the final crystallization stage. Furthermore, PLMC is shown to be superior for predicting the crystallization of both globular and membrane proteins, as demonstrated by an AUC score of 0.991 for the latter. These results suggest the significant potential of PLMC in assisting researchers with the experimental design of crystallizable protein variants.

Autotrophic adaptive laboratory evolution of the acetogen Clostridium autoethanogenum delivers the gas-fermenting strain LAbrini with superior growth, products, and robustness

Microbes able to convert gaseous one-carbon (C1) waste feedstocks are increasingly important to transition to the sustainable production of renewable chemicals and fuels. Acetogens are interesting biocatalysts since gas fermentation using Clostridium autoethanogenum has been commercialised. However, most acetogen strains need complex nutrients, display slow growth, and are not robust for bioreactor fermentations. In this work, we used three different and independent adaptive laboratory evolution (ALE) strategies to evolve the wild-type C. autoethanogenum to grow faster, without yeast extract and to be robust in operating continuous bioreactor cultures. Multiple evolved strains with improved phenotypes were isolated on minimal media with one strain, named "LAbrini", exhibiting superior performance regarding the maximum specific growth rate, product profile, and robustness in continuous cultures. Whole-genome sequencing of the evolved strains identified 25 mutations. Of particular interest are two genes that acquired seven different mutations across the three ALE strategies, potentially as a result of convergent evolution. Reverse genetic engineering of mutations in potentially sporulation-related genes CLAU_3129 (spo0A) and CLAU_1957 recovered all three superior features of our ALE strains through triggering significant proteomic rearrangements. This work provides a robust C. autoethanogenum strain "LAbrini" to accelerate phenotyping and genetic engineering and to better understand acetogen metabolism.

Chlorogenic acid attenuates idiopathic pulmonary fibrosis: An integrated analysis of network pharmacology, molecular docking, and experimental validation

AIMS

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung condition, the cause of which remains unknown and for which no effective therapeutic treatment is currently available. Chlorogenic acid (CGA), a natural polyphenolic compound found in different plants and foods, has emerged as a promising agent due to its anti-inflammatory, antioxidant, and antifibrotic properties. However, the molecular mechanisms underlying the therapeutic effect of CGA in IPF remain unclear. The purpose of this study was to analyze the pharmacological impact and underlying mechanisms of CGA in IPF.

MAIN METHODS

Using network pharmacology analysis, genes associated with IPF and potential molecular targets of CGA were identified through specialized databases, and a protein-protein interaction (PPI) network was constructed. Molecular docking was performed to accurately select potential therapeutic targets. To investigate the effects of CGA on lung histology and key gene expression, a murine model of bleomycin-induced lung fibrosis was used.

KEY FINDINGS

Network pharmacology analysis identified 384 were overlapped between CGA and IPF. Key targets including AKT1, TP53, JUN, CASP3, BCL2, MMP9, NFKB1, EGFR, HIF1A, and IL1B were identified. Pathway analysis suggested the involvement of cancer, atherosclerosis, and inflammatory processes. Molecular docking confirmed the stable binding between CGA and targets. CGA regulated the expression mRNA of EGFR, MMP9, AKT1, BCL2 and IL1B and attenuated pulmonary fibrosis in the mouse model.

SIGNIFICANCE

CGA is a promising multi-target therapeutic agent for IPF, which is supported by its efficacy in reducing fibrosis through the modulation of key pathways. This evidence provides a basis to further investigate CGA as an IPF potential treatment.

Taxus chinensis var. mairei (Lemée et Lévl) Cheng et L.K. Fu overcomes the resistance to osimertinib in EGFR-mutant non-small-cell lung cancer via suppression of ERK1/2-related cholesterol biosynthesis

ETHNOPHARMACOLOGICAL RELEVANCE

Acquired resistance to osimertinib limits its clinical efficacy in non-small cell lung cancer (NSCLC) with EGFR mutations. The widespread recognition of Taxus chinensis var. Mairei (Lemée et Lévl) Cheng et L.K. Fu (Chinese yew) as a natural anti-cancer medication is well-established. However, the specific contribution of Taxus chinensis var. Mairei (Lemée et Lévl) Cheng et L.K. Fu in addressing resistance to osimertinib is still uncertain.

AIM OF THE STUDY

Based on the biological behaviors and lipid metabolism, we investigated whether aqueous extract of Taxus chinensis var. Mairei (Lemée et Lévl) Cheng et L.K. Fu (AETC) could enhance the antitumor effect of osimertinib in NSCLC with an investigation on the precise mechanisms.

MATERIALS AND METHODS

The effect of AETC on enhancing osimertinib sensitivity was assessed via cell viability measurements, levels of reactive oxygen species (ROS), apoptosis, and lipid levels. Western blotting was used to verify the mechanisms of AETC responsible for overcoming the resistance to osimertinib via ERK1/2 overexpression and knockdown models. In vivo validation was conducted using subcutaneous xenografts from osimertinib-resistant cells in nude mice.

RESULTS

Osimertinib-resistant cells exhibited altered cholesterol biosynthesis, which was induced by ERK1/2 activation. The combination of AETC and osimertinib can synergistically decrease the levels of ROS in cells, enhance apoptosis, and inhibit the growth of osimertinib-resistant cells. Mechanistic experiments demonstrated that AETC can downregulate the key regulators of cholesterol biosynthesis by regulating ERK1/2, inhibiting the endogenous synthesis rate of cholesterol, and suppressing the level of lipids in osimertinib-resistant cells and xenograft tumors when combined with osimertinib, ultimately reversing resistance to osimertinib.

CONCLUSIONS

The resistance to osimertinib is significantly influenced by cholesterol biosynthesis, highlighting its pivotal role in this context. AETC can enhance osimertinib sensitivity via ERK/SREBP-2/HMGCR-mediated cholesterol biosynthesis. These results provide a promising therapeutic target and potential treatment option for resistance to osimertinib.

Uncovering the protective mechanism of baicalin in treatment of fatty liver based on network pharmacology and cell model of NAFLD

Excessive nonesterified fatty acids (NEFA) impair cellular metabolism and will induce fatty liver formation in dairy cows during the periparturient. Baicalin, an active flavonoid, has great potential efficacy in alleviating lipid accumulation and ameliorating the development of fatty liver disease. Nevertheless, its mechanism remains unclear. Here, the potential mechanism of baicalin on system levels was explored using network pharmacology and in vitro experiments. Firstly, the target of baicalin and fatty liver disease was predicted, and then the protein-protein interaction (PPI) network was constructed. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) (q-value) pathway enrichment is performed through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server. Finally, the results of the network analysis of the in vitro treatment of bovine hepatocytes by NEFA were confirmed. The results showed that 33 relevant targets of baicalin in the treatment of liver fatty were predicted by network pharmacology, and the top 20 relevant pathways were extracted by KEGG database. Baicalin treatment can reduce triglyceride (TAG) content and lipid droplet accumulation in NEFA-treated bovine hepatocytes, and the mechanism is related to inhibiting lipid synthesis and promoting lipid oxidation. The alleviating effect of baicalin on fatty liver may be related to the up-regulation of solute vector family member 4 (SLC2A4), Down-regulated AKT serine/threonine kinase 1 (AKT1), Peroxisome proliferator-activated receptor gamma (PPARG), Epidermal growth factor receptor (EGFR), tumor necrosis factor (TNF), Interleukin 6 (IL-6) were associated. These results suggested that baicalin may modulate key inflammatory markers, and lipogenesis processes to prevent fatty liver development in dairy cows.

Multi-step HPLC fractionation enabled in-depth and unbiased characterization of histone PTMs

Histone post-translational modifications (PTMs) are critical epigenetic regulatory factors. Histone PTMs are highly dynamic and complicated, encompassing over 30 structurally diverse modifications across nearly 180 amino acid residues, which generated extensive information regarding histone marks. In proteomics-based characterization of histone PTMs, chemical derivatization and antibody-based affinity enrichment were frequently utilized to improve the identification depth. However, chemical derivatization suffered from the occurrence of side reactions, and antibody-based affinity enrichment focused on specific PTM types of interest. In this research, we developed a multi-step fractionation strategy for comprehensively unbiased detection of histone PTM sites. By combining protein-level fractionation with peptide-level alkaline and acid phase fractionation, we developed the Multidimensional Fractionation based Histone Mark Identification Technology (MudFIT) and increased PTM identification to a total of 264 histone PTM sites. To the best of our knowledge, this strategy achieved the most comprehensive characterization of histone PTM sites in a single proteomics study. Using the same starting amount of sample, MudFIT identified more Kac sites and Kac peptides than those in antibody-based acetylated peptide enrichment. Moreover, in addition to well-studied histone marks, we discovered 36 potential new histone PTM sites including H2BK116bu, H4R45me2, H1K63pr, and uncovered unknown histone PTM types like aminoadipic on lysine and nitrosylation on tyrosine. Our data provided a method and resource for in-depth characterization of histone PTM sites, facilitating further biological understanding of histone marks.

Nesprin-2 coordinates opposing microtubule motors during nuclear migration in neurons

Nuclear migration is critical for the proper positioning of neurons in the developing brain. It is known that bidirectional microtubule motors are required for nuclear transport, yet the mechanism of the coordination of opposing motors is still under debate. Using mouse cerebellar granule cells, we demonstrate that Nesprin-2 serves as a nucleus-motor adaptor, coordinating the interplay of kinesin-1 and dynein. Nesprin-2 recruits dynein-dynactin-BicD2 independently of the nearby kinesin-binding LEWD motif. Both motor binding sites are required to rescue nuclear migration defects caused by the loss of function of Nesprin-2. In an intracellular cargo transport assay, the Nesprin-2 fragment encompassing the motor binding sites generates persistent movements toward both microtubule minus and plus ends. Nesprin-2 drives bidirectional cargo movements over a prolonged period along perinuclear microtubules, which advance during the migration of neurons. We propose that Nesprin-2 keeps the nucleus mobile by coordinating opposing motors, enabling continuous nuclear transport along advancing microtubules in migrating cells.

Identification of potential inhibitors for drug resistance in acute lymphoblastic leukemia through differentially expressed gene analysis and in silico screening

Acute lymphoblastic leukemia (ALL) is a disease of lymphocyte origin predominantly diagnosed in children. While its 5-year survival rate is high, resistance to chemotherapy drugs is still an obstacle. Our aim is to determine differentially expressed genes (DEGs) related to Asparaginase, Daunorubicin, Prednisolone, and Vincristine resistance and identify potential inhibitors via docking. Three datasets were accessed from the Gene Expression Omnibus database; GSE635, GSE19143, and GSE22529. The microarray data was analyzed using R4.2.0 and Bioconductor packages, and pathway and protein-protein interaction analysis were performed. We identified 1294 upregulated DEGs, with 12 genes consistently upregulated in all four resistant groups. KEGG analysis revealed an association with the PI3K-Akt pathway. Among DEGs, 33 hub genes including MDM2 and USP7 were pinpointed. Within common genes, CLDN9 and HS3ST3A1 were subjected to molecular docking against 3556 molecules. Following ADMET analysis, three drugs emerged as potential inhibitors: Flunarizine, Talniflumate, and Eltrombopag. Molecular dynamics analysis for HS3ST3A1 indicated all candidates had the potential to overcome drug resistance, Eltrombopag displaying particularly promising results. This study promotes a further understanding of drug resistance in ALL, introducing novel genes for consideration in diagnostic screening. It also presents potential inhibitor candidates to tackle drug resistance through repurposing.

Mechanisms of assembly and remodelling of the extracellular matrix

The extracellular matrix (ECM) is the complex meshwork of proteins and glycans that forms the scaffold that surrounds and supports cells. It exerts key roles in all aspects of metazoan physiology, from conferring physical and mechanical properties on tissues and organs to modulating cellular processes such as proliferation, differentiation and migration. Understanding the mechanisms that orchestrate the assembly of the ECM scaffold is thus crucial to understand ECM functions in health and disease. This Review discusses novel insights into the compositional diversity of matrisome components and the mechanisms that lead to tissue-specific assemblies and architectures tailored to support specific functions. The Review then highlights recently discovered mechanisms, including post-translational modifications and metabolic pathways such as amino acid availability and the circadian clock, that modulate ECM secretion, assembly and remodelling in homeostasis and human diseases. Last, the Review explores the potential of 'matritherapies', that is, strategies to normalize ECM composition and architecture to achieve a therapeutic benefit.

m6A modification of VEGFA mRNA by RBM15/YTHDF2/IGF2BP3 contributes to angiogenesis of hepatocellular carcinoma

Vascular endothelial growth factor A (VEGFA) plays a critical role as a potent angiogenesis factor and is highly expressed in hepatocellular carcinoma (HCC). Although the expression of VEGFA has been strongly linked to the aggressive nature of HCC, the specific posttranscriptional modifications that might contribute to VEGFA expression and HCC angiogenesis are not yet well understood. In this study, we aimed to investigate the epitranscriptome regulation of VEGFA in HCC. A comprehensive analysis integrating MeRIP-seq, RNA-seq, and crosslinking-immunprecipitation-seq data revealed that VEGFA was hypermethylated in HCC and identified the potential m6A regulators of VEGFA including a m6A methyltransferase complex component RBM15 and the two readers, YTHDF2 and IGF2BP3. Through rigorous cell and molecular biology experiments, RBM15 was validated as a key component of methyltransferase complex responsible for m6A methylation of VEGFA, which was subsequently recognized and stabilized by IGF2BP3 and YTHDF2, leading to enhanced VEGFA expression and VEGFA-related functions such as human umbilical vascular endothelial cells (HUVEC) migration and tube formation. In the HCC xenograft model, knockdown of RBM15, IGF2BP3, or YTHDF2 resulted in reduced expression of VEGFA, accompanied by significant inhibition of tumor growth closely associated with VEGFA expression and angiogenesis. Furthermore, our analysis of HCC clinical samples identified positive correlations between the expression levels of VEGFA and the regulators RBM15, IGF2BP3, and YTHDF2. Collectively, these findings offer novel insights into the posttranscriptional modulation of VEGFA and provide potential avenues for alternative approaches to antiangiogenesis therapy targeting VEGFA.

ProteinFlow: An advanced framework for feature engineering in protein data analysis

In the burgeoning field of proteins, the effective analysis of intricate protein data remains a formidable challenge, necessitating advanced computational tools for data processing, feature extraction, and interpretation. This study introduces ProteinFlow, an innovative framework designed to revolutionize feature engineering in protein data analysis. ProteinFlow stands out by offering enhanced efficiency in data collection and preprocessing, along with advanced capabilities in feature extraction, directly addressing the complexities inherent in multidimensional protein data sets. Through a comparative analysis, ProteinFlow demonstrated a significant improvement over traditional methods, notably reducing data preprocessing time and expanding the scope of biologically significant features identified. The framework's parallel data processing strategy and advanced algorithms ensure not only rapid data handling but also the extraction of comprehensive, meaningful insights from protein sequences, structures, and interactions. Furthermore, ProteinFlow exhibits remarkable scalability, adeptly managing large-scale data sets without compromising performance, a crucial attribute in the era of big data.

Cytochrome P450 monooxygenase systems: Diversity and plasticity for adaptive stress response

Superfamily of cytochromes P450 (CYPs) is composed of heme-thiolate-containing monooxygenase enzymes, which play crucial roles in the biosynthesis, bioactivation, and detoxification of a variety of organic compounds, both endogenic and exogenic. Majority of CYP monooxygenase systems are multi-component and contain various redox partners, cofactors and auxiliary proteins, which contribute to their diversity in both prokaryotes and eukaryotes. Recent progress in bioinformatics and computational biology approaches make it possible to undertake whole-genome and phylogenetic analyses of CYPomes of a variety of organisms. Considerable variations in sequences within and between CYP families and high similarity in secondary and tertiary structures between all CYPs along with dramatic conformational changes in secondary structure elements of a substrate binding site during catalysis have been reported. This provides structural plasticity and substrate promiscuity, which underlie functional diversity of CYPs. Gene duplication and mutation events underlie CYP evolutionary diversity and emergence of novel selectable functions, which provide the involvement of CYPs in high adaptability to changing environmental conditions and dietary restrictions. In our review, we discuss the recent advancements and challenges in the elucidating the evolutionary origin and mechanisms underlying the CYP monooxygenase system diversity and plasticity. Our review is in the view of hypothesis that diversity of CYP monooxygenase systems is translated into the broad metabolic profiles, and this has been acquired during the long evolutionary time to provide structural plasticity leading to high adaptative capabilities to environmental stress conditions.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Conformational Dynamics and Molecular Characterization of Alsin MORN Monomer and Dimeric Assemblies

Despite the ubiquity of membrane occupation recognition nexus (MORN) motifs across diverse species in both eukaryotic and prokaryotic organisms, these protein domains remain poorly characterized. Their significance is underscored in the context of the Alsin protein, implicated in the debilitating condition known as infantile-onset ascending hereditary spastic paralysis (IAHSP). Recent investigations have proposed that mutations within the Alsin MORN domain disrupt proper protein assembly, precluding the formation of the requisite tetrameric configuration essential for the protein's inherent biological activity. However, a comprehensive understanding of the relationship between the biological functions of Alsin and its three-dimensional molecular structure is hindered by the lack of available experimental structures. In this study, we employed and compared several protein structure prediction algorithms to identify a three-dimensional structure for the putative MORN of Alsin. Furthermore, inspired by experimental pieces of evidence from previous studies, we employed the developed models to predict and investigate two homo-dimeric assemblies, characterizing their stability. This study's insights into the three-dimensional structure of the Alsin MORN domain and the stability dynamics of its homo-dimeric assemblies suggest an antiparallel linear configuration stabilized by a noncovalent interaction network.

Novel telomerase reverse transcriptase gene mutation in a family with aplastic anaemia

Telomerase Reverse Transcriptase (TERT) encodes the telomerase reverse transcriptase enzyme and is the most frequently mutated gene in patients with telomeropathies. Heterozygous variants impair telomerase activity by haploinsufficiency and pathogenic variants are associated with bone marrow failure syndrome and predisposition to acute myeloid leukaemia. Owing to their rarity, telomeropathies are often unrecognised and misdiagnosed. Herein, we report a novel TERT gene variant, c.2605G > A p.(Asp869Asn) in a family with hereditary aplastic anaemia. This report emphasises the importance of routine deep genetic screening for rare TERT variants in patients with a family history of cytopenia or aplastic anaemia, which could identify clinically inapparent telomere disorders.

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SIGNIFICANCE: Liver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Comprehensive genetic analysis uncovers the mutational spectrum of MFRP and its genotype-phenotype correlation in a large cohort of Chinese microphthalmia patients

PURPOSE

Microphthalmia is a severe congenital ocular disease featured by abnormal ocular development. The aim of this study was to detail the genetic and clinical characteristics of a large cohort of Chinese patients with microphthalmia related to MFRP variants, focusing on uncovering genotype-phenotype correlations.

METHODS

Fifty microphthalmia patients from 44 unrelated Chinese families were recruited. Whole-exome sequencing (WES) was conducted to analyze the coding regions and adjacent intronic regions of MFRP. Axial lengths (AL) were measured for all probands and available family members. Protein structures of mutations with high frequency in our cohort were predicted. The genotype-phenotype correlations were explored by statistical analysis.

RESULTS

Sixteen MFRP variants were detected in 17 families, accounting for 38.64 % of all microphthalmia families. There were 9 novel mutations (c.427+1G>C, c.428-2A>C, c.561_575del:p.A188_E192del, c.836G>A:p.C279Y, c.1010_1021del:p.H337_E340del:p.Y479*, c.1516_1517del:p.S506Pfs*66, c.1561T>G:p.C521G, c.1616G>A:p.R539H, and c.1735C>T:p.P579S) and six previously reported variants in MFRP, with p.E496K and p.H337_E340del being highly frequent, found in eight (47.06 %) and two families (11.76 %), respectively. Seven variants (43.75 %) were located in the C-terminal cysteine-rich frizzled-related domain (CRD) (7/16, 43.75 %). Protein prediction implicated p.E496K and p.H337_E340del mutations might lead to a destabilization of the MFRP protein. The average AL of all 42 eyes was 16.02 ± 1.05 mm, and 78.36 % of eyes with AL < 16 mm harbored p.E496K variant. Twenty-six eyes with variant variant had shorter AL than that of the other 16 eyes without this variant (p = 0.006), highlighting a novel genotype-phenotype correlation.

CONCLUSIONS

In this largest cohort of Chinese patients with microphthalmia, the 9 novel variants, high frequency of p.E496W, and mutation hotspots in CRD reveals unique insights into the MFRP mutation spectrum among Chinese patients, indicating ethnic variability. A new genotype-phenotype correlation that p.E496K variant associated with a shorter AL is unveiled. Our findings enhance the current knowledge of MFRP-associated microphthalmia and provide valuable information for prenatal diagnosis as well as future therapy.

AlphaFold-Multimer accurately captures interactions and dynamics of intrinsically disordered protein regions

Interactions mediated by intrinsically disordered protein regions (IDRs) pose formidable challenges in structural characterization. IDRs are highly versatile, capable of adopting diverse structures and engagement modes. Motivated by recent strides in protein structure prediction, we embarked on exploring the extent to which AlphaFold-Multimer can faithfully reproduce the intricacies of interactions involving IDRs. To this end, we gathered multiple datasets covering the versatile spectrum of IDR binding modes and used them to probe AlphaFold-Multimer's prediction of IDR interactions and their dynamics. Our analyses revealed that AlphaFold-Multimer is not only capable of predicting various types of bound IDR structures with high success rate, but that distinguishing true interactions from decoys, and unreliable predictions from accurate ones is achievable by appropriate use of AlphaFold-Multimer's intrinsic scores. We found that the quality of predictions drops for more heterogeneous, fuzzy interaction types, most likely due to lower interface hydrophobicity and higher coil content. Notably though, certain AlphaFold-Multimer scores, such as the Predicted Aligned Error and residue-ipTM, are highly correlated with structural heterogeneity of the bound IDR, enabling clear distinctions between predictions of fuzzy and more homogeneous binding modes. Finally, our benchmarking revealed that predictions of IDR interactions can also be successful when using full-length proteins, but not as accurate as with cognate IDRs. To facilitate identification of the cognate IDR of a given partner, we established "minD," which pinpoints potential interaction sites in a full-length protein. Our study demonstrates that AlphaFold-Multimer can correctly identify interacting IDRs and predict their mode of engagement with a given partner.

Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions

Assessing mutation impact on the binding affinity change (ΔΔG) of protein-protein interactions (PPIs) plays a crucial role in unraveling structural-functional intricacies of proteins and developing innovative protein designs. In this study, we present a deep learning framework, PIANO, for improved prediction of ΔΔG in PPIs. The PIANO framework leverages a graph masked self-distillation scheme for protein structural geometric representation pre-training, which effectively captures the structural context representations surrounding mutation sites, and makes predictions using a multi-branch network consisting of multiple encoders for amino acids, atoms, and protein sequences. Extensive experiments demonstrated its superior prediction performance and the capability of pre-trained encoder in capturing meaningful representations. Compared to previous methods, PIANO can be widely applied on both holo complex structures and apo monomer structures. Moreover, we illustrated the practical applicability of PIANO in highlighting pathogenic mutations and crucial proteins, and distinguishing de novo mutations in disease cases and controls in PPI systems. Overall, PIANO offers a powerful deep learning tool, which may provide valuable insights into the study of drug design, therapeutic intervention, and protein engineering.

Construction of Protein Sequence Databases for Metaproteomics: A Review of the Current Tools and Databases

In metaproteomics studies, constructing a reference protein sequence database that is both comprehensive and not overly large is critical for the peptide identification step. Therefore, the availability of well-curated reference databases and tools for custom database construction is essential to enhance the performance of metaproteomics analyses. In this review, we first provide an overview of metaproteomics by presenting a concise historical background, outlining a typical experimental and bioinformatics workflow, emphasizing the crucial step of constructing a protein sequence database for metaproteomics. We then delve into the current tools available for building such databases, highlighting their individual approaches, utility, and advantages and limitations. Next, we examine existing protein sequence databases, detailing their scope and relevance in metaproteomics research. Then, we provide practical recommendations for constructing protein sequence databases for metaproteomics, along with an overview of the current challenges in this area. We conclude with a discussion of anticipated advancements, emerging trends, and future directions in the construction of protein sequence databases for metaproteomics.

Biomarker and adverse outcome pathway responses of Tubifex tubifex (sludge worm) exposed to environmentally-relevant levels of acenaphthene: insights from behavioral, physiological, and chemical structure-activity analyses

Polycyclic aromatic hydrocarbons (PAHs), including acenaphthene, pose a significant threat to aquatic ecosystems by harming vital organisms such as benthic invertebrates. This study evaluated the impact of environmentally relevant concentrations of acenaphthene on Tubifex tubifex, focusing on sublethal acute toxicity and subchronic biomarker responses. Key biomarkers assessed included histopathological changes and the modulation of antioxidant enzymes: catalase (CAT), superoxide dismutase (SOD), glutathione S-transferase (GST), and malondialdehyde (MDA). Additionally, the study examined structure-activity relationships and species sensitivity distribution (SSD). Concentrations exceeding the solubility threshold of acenaphthene (3.9 mg/L) triggered distinct, concentration-dependent behavioral responses in Tubifex tubifex, such as clumping, mucus secretion, and body wrinkling. Prolonged exposure exacerbated these behavioral dysfunctions, while subchronic exposure resulted in significant histopathological alterations, including epithelial hyperplasia, inflammation, edema, fibrosis, and degenerative changes. The edematic appearance of the body wall suggested a potential immune response to exposure. Furthermore, increased activities of CAT, SOD, and GST indicated oxidative stress in the worms. The study found a 1.5-fold increase in CAT and GST activity, a fivefold increase in SOD, and a striking 100-fold increase in MDA levels compared to controls, signifying an overwhelmed antioxidant defense system and potential cellular disruption. The SSD curve revealed hazard concentrations (HC50 and HC90), indicating that Tubifex tubifex exhibited lower sensitivity to acenaphthene compared to other taxa. In silico analysis and read-across models confirmed the potential of acenaphthene to induce significant oxidative stress upon exposure. The correlation between biomarker responses and structure-activity relationship analysis highlighted the aromatic nature of acenaphthene as a key factor in generating reactive metabolites, inhibiting antioxidant enzymes, and promoting redox cycling, ultimately contributing to adverse outcomes. These findings, coupled with behavioral responses and SSD curve inferences, underscore the importance of the solubility threshold of acenaphthene as a critical benchmark for evaluating its ecological impact in aquatic environments.

Forward genetic approach identifies a phylogenetically conserved serine residue critical for the catalytic activity of UBIQUITIN-SPECIFIC PROTEASE 12 in Arabidopsis

Circadian clocks rely on transcriptional/translational feedback loops involving clock genes and their corresponding proteins. While the primary oscillations originate from gene expression, the precise control of clock protein stability plays a pivotal role in establishing the 24-hour circadian rhythms. Most clock proteins are degraded through the ubiquitin/26S proteasome pathway, yet the enzymes responsible for ubiquitination and deubiquitination remain poorly characterised. We identified a missense allele (ubp12-3, S327F) of the UBP12 gene/protein in Arabidopsis. Despite ubp12-3 exhibited a short period phenotype similar to that of a loss-of-function allele, molecular analysis indicated elevated protease activity in ubp12-3. We demonstrated that early flowering of ubp12 mutants is a result of the shortened circadian period rather than a direct alteration of UBP12 function. Analysis of protease activity of non-phosphorylatable (S327A, S327F) and phosphomimetic (S327D) derivatives in bacteria suggested that phosphorylation of serine 327 inhibits UBP12 enzymatic activity, which could explain the over-functioning of S327F in vivo. We showed that phosphomimetic mutations of the conserved serine in the Neurospora and human orthologues reduced ubiquitin cleavage activity suggesting that not only the primary structures of UBP12-like enzymes are phylogenetically conserved across a wide range of species, but also the molecular mechanisms governing their enzymatic activity.

Discovery of Therapeutic Antibodies Targeting Complex Multi-Spanning Membrane Proteins

Complex integral membrane proteins, which are embedded in the cell surface lipid bilayer by multiple transmembrane spanning polypeptides, encompass families of proteins that are important target classes for drug discovery. These protein families include G protein-coupled receptors, ion channels, transporters, enzymes, and adhesion molecules. The high specificity of monoclonal antibodies and the ability to engineer their properties offers a significant opportunity to selectively bind these target proteins, allowing direct modulation of pharmacology or enabling other mechanisms of action such as cell killing. Isolation of antibodies that bind these types of membrane proteins and exhibit the desired pharmacological function has, however, remained challenging due to technical issues in preparing membrane protein antigens suitable for enabling and driving antibody drug discovery strategies. In this article, we review progress and emerging themes in defining discovery strategies for a generation of antibodies that target these complex membrane protein antigens. We also comment on how this field may develop with the emerging implementation of computational techniques, artificial intelligence, and machine learning.

Luteibacter sahnii sp. nov., A Novel Yellow-Colored Xanthomonadin Pigment Producing Probiotic Bacterium from Healthy Rice Seed Microbiome

To explore the rice seed microbiome, our objective was to isolate novel strains of Xanthomonas, a plant-associated bacterium with diverse lifestyles. Four isolates, anticipated to be Xanthomonas based on morphological features of yellow colonies, were obtained from healthy rice seeds. Phylo-taxono-genomic analysis revealed that these isolates formed monophyletic lineages belonging to a novel species within the genus Luteibacter. Pairwise ortho Average Nucleotide Identity and digital DNA-DNA hybridization confirmed their distinct species status. We propose Luteibacter sahnii sp. nov. as a novel species, with PPL193T = MTCC 13290T = ICMP 24807T = CFBP 9144T as the type strain and PPL201, PPL552, and PPL554 as other constituent members. The fatty acid profile of the type strain is dominated by branched fatty acids like Iso-C15:0, consistent with other members of the genus. The novel species displays non-pathogenic attributes and exhibits plant probiotic properties, protecting rice plants from the leaf blight pathogen X. oryzae pv. oryzae. Production of Indole-3-Acetic Acid (IAA) and genomic regions encoding anti-microbial peptides emphasize its potential contributions to plant hosts. This study underscores the importance of employing a combination of phenotypic and genotypic methods in culturomics to enhance our understanding of rice seed microbiome diversity.

Expanding the β-Lactamase Family in the Human Microbiome

β-lactams, the most common antibiotics globally, have resistance primarily determined by β-lactamases. Human microbiota and β-lactams influence mutually; however, β-lactamase variety and abundance in the human microbiome remain partially understood. This study aimed to elucidate the diversity, abundance, and substrate spectrum of β-lactamases. 1369 characterized β-lactamases and 16 204 putative sequences are collected from protein databases. Upon clustering analysis and biochemical assays, nine proteins exhibiting less than 35% identity to those previously characterized are confirmed as β-lactamases. These newly identified β-lactamases originated from eight distinct clusters comprising 1163 β-lactamases. Quantifying healthy participants (n = 2394) across 19 countries using functionally confirmed clusters revealed that Japan have the highest gut β-lactamase abundance (log2[reads per million (RPM)] = 6.52) and Fiji have the lowest (log2[RPM] = 2.31). The β-lactamase abundance is correlated with β-lactam consumption (R = 0.50, p = 0.029) and income (R = 0.51, p = 0.024). Comparing individuals with ailments with healthy participants, β-lactamase abundance in the gut is increased significantly in patients with colorectal cancer, cardiovascular diseases, breast cancer, and epilepsy. These outcomes provide insights into investigating antibiotic resistance, antibiotic stewardship, and gut microbiome-antibiotic interactions.

Unbiased complexome profiling and global proteomics analysis reveals mitochondrial impairment and potential changes at the intercalated disk in presymptomatic R14Δ/+ mice hearts

Phospholamban (PLN) is a sarco-endoplasmic reticulum (SER) membrane protein that regulates cardiac contraction/relaxation by reversibly inhibiting the SERCA2a Ca2+-reuptake pump. The R14Δ-PLN mutation causes severe cardiomyopathy that is resistant to conventional treatment. Protein complexes and higher-order supercomplexes such as intercalated disk components and Ca+2-cycling domains underlie many critical cardiac functions, a subset of which may be disrupted by R14Δ-PLN. Complexome profiling (CP) is a proteomics workflow for systematic analysis of high molecular weight (MW) protein complexes and supercomplexes. We hypothesize that R14Δ-PLN may alter a subset of these assemblies, and apply CP workflows to explore these changes in presymptomatic R14Δ/+ mice hearts. Ventricular tissues from presymptomatic 28wk-old WT and R14Δ/+ mice were homogenized under non-denaturing conditions, fractionated by size-exclusion chromatography (SEC) with a linear MW-range exceeding 5 MDa, and subjected to quantitative data-independent acquisition mass spectrometry (DIA-MS) analysis. Unfortunately, current workflows for the systematic analysis of CP data proved ill-suited for use in cardiac samples. Most rely upon curated protein complex databases to provide ground-truth for analysis; however, these are derived primarily from cancerous or immortalized cell lines and, consequently, cell-type specific complexes (including cardiac-specific machinery potentially affected in R14Δ-PLN hearts) are poorly covered. We thus developed PERCOM: a novel CP data-analysis strategy that does not rely upon these databases and can, furthermore, be implemented on widely available spreadsheet software. Applying PERCOM to our CP dataset resulted in the identification of 296 proteins with disrupted elution profiles. Hits were significantly enriched for mitochondrial and intercalated disk (ICD) supercomplex components. Changes to mitochondrial supercomplexes were associated with reduced expression of mitochondrial proteins and maximal oxygen consumption rate. The observed alterations to mitochondrial and ICD supercomplexes were replicated in a second cohort of "juvenile" 9wk-old mice. These early-stage changes to key cardiac machinery may contribute to R14Δ-PLN pathogenesis.

A new framework for SubtiWiki, the database for the model organism Bacillus subtilis

Bacillus subtilis is a Gram-positive model bacterium and one of the most-studied and best understood organisms. The complex information resulting from its investigation is compiled in the database SubtiWiki (https://subtiwiki.uni-goettingen.de/v5) in an integrated and intuitive manner. To enhance the utility of SubtiWiki, we have added novel features such as a viewer to interrogate conserved genomic organization, a widget that shows mutant fitness data for all non-essential genes, and a widget showing protein structures, structure predictions and complex structures. Moreover, we have integrated metabolites as new entities. The new framework also includes a documented API, enabling programmatic access to data for computational tasks. Here we present the recent developments of SubtiWiki and the current state of the data for this organism.

CovalentInDB 2.0: an updated comprehensive database for structure-based and ligand-based covalent inhibitor design and screening

The rational design of targeted covalent inhibitors (TCIs) has emerged as a powerful strategy in drug discovery, known for its ability to achieve strong binding affinity and prolonged target engagement. However, the development of covalent drugs is often challenged by the need to optimize both covalent warhead and non-covalent interactions, alongside the limitations of existing compound libraries. To address these challenges, we present CovalentInDB 2.0, an updated online database designed to support covalent drug discovery. This updated version includes 8303 inhibitors and 368 targets, supplemented by 3445 newly added cocrystal structures, providing detailed analyses of non-covalent interactions. Furthermore, we have employed an AI-based model to profile the ligandability of 144 864 cysteines across the human proteome. CovalentInDB 2.0 also features the largest covalent virtual screening library with 2 030 192 commercially available compounds and a natural product library with 105 901 molecules, crucial for covalent drug screening and discovery. To enhance the utility of these compounds, we performed structural similarity analysis and drug-likeness predictions. Additionally, a new user data upload feature enables efficient data contribution and continuous updates. CovalentInDB 2.0 is freely accessible at http://cadd.zju.edu.cn/cidb/.

Structural Fluctuation in Homodimeric Aminoacyl-tRNA Synthetases Induces Half-of-the-Sites Activity

Enzymatic activity is regulated by various mechanisms to ensure biologically proper functions. Notable instances of such regulation in homodimeric enzymes include "all-of-the-sites activity" and "half-of-the-sites activity". The difference in these activities lies in whether one or both of the subunits are simultaneously active. Owing to its uniqueness, the mechanism of half-of-the-sites activity has been widely investigated. Consequently, structural asymmetry derived from cooperative motion is considered to induce half-of-the-sites activity. In contrast, recent investigations have suggested that subunit-intrinsic properties or structural fluctuation also induces structural asymmetry. Hence, the mechanism underlying half-of-the-sites activity has not been completely elucidated. Additionally, most previous studies have focused only on half-of-the-sites activity. Therefore, by comparing the structural and dynamical properties of two representative homodimers exhibiting all-of-the-sites and half-of-the-sites activities, respectively, we attempted to elucidate the mechanism of half-of-the-sites activity. Specifically, all-atom molecular dynamics simulations were applied to lysyl-tRNA synthetase and tyrosyl-tRNA synthetase. Our analysis revealed that structural fluctuation is sufficient to induce structural asymmetry in addition to the well-established factor of cooperative motion. Considering that structural fluctuation is a common characteristic of any enzyme, it could be a general factor in half-of-the-sites activity.

CG17192 is a Phospholipase That Regulates Signaling Lipids in the Drosophila Gut upon Infection

The chemoproteomics technique, activity-based protein profiling (ABPP), has proven to be an invaluable tool in assigning functions to enzymes. The serine hydrolase (SH) enzyme superfamily, in particular, has served as an excellent example in displaying the versatility of various ABPP platforms and has resulted in a comprehensive cataloging of the biochemical activities associated within this superfamily. Besides SHs, in mammals, several other enzyme classes have been thoroughly investigated using ABPP platforms. However, the utility of ABPP platforms in fly models remains underexplored. Realizing this knowledge gap, leveraging complementary ABPP platforms, we reported the full array of SH activities during various developmental stages and adult tissues in the fruit fly (Drosophila melanogaster). Following up on this study, using ABPP, we mapped SH activities in adult fruit flies in an infection model and found that a gut-resident lipase CG17192 showed increased activity during infection. To assign a biological function to this uncharacterized lipase, we performed an untargeted lipidomics analysis and found that phosphatidylinositols were significantly elevated when CG17192 was depleted in the adult fruit fly gut. Next, we overexpressed this lipase in insect cells, and using biochemical assays, we show that CG17192 is a secreted enzyme that has phospholipase C (PLC) type activity, with phosphatidylinositol being a preferred substrate. Finally, we show during infection that heightened CG17192 regulates phosphatidylinositol levels and, by doing so, likely modulates signaling pathways in the adult fruit fly gut that might be involved in the resolution of this pathophysiological condition.

Mapping the sclerostin-LRP4 binding interface identifies critical interaction hotspots in loops 1 and 3 of sclerostin

The interaction of sclerostin (Scl) with the low-density lipoprotein receptor-related protein 4 (LRP4) leads to a marked reduction in bone formation by inhibiting the Wnt/β-catenin pathway. To characterize the Scl-LRP4 binding interface, we sorted a combinatorial library of Scl variants and isolated variants with reduced affinity to LRP4. We identified Scl single-mutation variants enriched during the sorting process and verified their reduction in affinity toward LRP4-a reduction that was not a result of changes in the variants' secondary structure or stability. We found that Scl positions K75 (loop 1) and V136 (loop 3) are critical hotspots for binding to LRP4. Our findings establish the foundation for targeting these hotspots for developing novel therapeutic strategies to promote bone formation.

Quantum chemical calculation dataset for representative protein folds by the fragment molecular orbital method

The function of a biomacromolecule is not only determined by its three-dimensional structure but also by its electronic state. Quantum chemical calculations are promising non-empirical methods available for determining the electronic state of a given structure. In this study, we used the fragment molecular orbital (FMO) method, which applies to biopolymers such as proteins, to provide physicochemical property values on representative structures in the SCOP2 database of protein families, a subset of the Protein Data Bank. Our dataset was constructed by over 5,000 protein structures, including over 200 million inter-fragment interaction energies (IFIEs) and their energy components obtained by pair interaction energy decomposition analysis (PIEDA) using FMO-MP2/6-31 G*. Moreover, three basis sets, 6-31 G*, 6-31 G**, and cc-pVDZ, were used for the FMO calculations of each structure, making it possible to compare the energies obtained with different basis functions for the same fragment pair. The total data size is approximately 6.7 GB. Our dataset will be useful for functional analyses and machine learning based on the physicochemical property values of proteins.

Severe anemia in preterm infants associated with increased bacterial virulence potential and metabolic disequilibrium

BACKGROUND

Anemia in preterm infants is associated with gut dysbiosis and necrotizing enterocolitis. Our study aimed to identify the bacterial functions and metabolites that can explain the underlying mechanisms of anemia associated disease conditions.

METHODS

We conducted a case control study in preterm infants with cases having a hematocrit ≤ 25%. The control infants were matched by birth gestational age and weight. Fecal samples were collected before, at the onset, and after the onset of anemia in cases and with matched postnatal age in controls for metagenomics and metabolomics analyzes.

RESULTS

18 anemic and 20 control infants with fecal samples collected at 17 days, 5 weeks, and 7 weeks postnatal age were included. Virulence factor potential, decrease in beta diversity evolution, and larger changes in metabolome were associated with severe anemia. Metabolite abundances of N-acetylneuraminate and butyrobetaine were associated with virulence factor potential. Anemic group had decreased prostaglandin and lactic acid levels.

CONCLUSION

Fecal omics data showed that severe anemia is associated with a pro-inflammatory gut microbiota with more virulent and less commensal anaerobic bacterial activities. Future studies can examine the link between anemia-associated dysbiosis and clinical outcomes and predict an infant-specific hematocrit threshold that negatively affects clinical outcomes.

IMPACT

Severe anemia in preterm infants contributes to a pro-inflammatory gut with greater bacterial virulence and less commensal bacterial activities. The multiomics approach using non-invasive fecal biospecimens identified functional and metabolic changes in the gut microbiota and these mechanistic changes are plausible explanations for anemia-associated disease conditions in preterm infants. Our findings identified biological changes of the gut environment in severely anemic preterm infants that can offer guidance for clinical management.

Nanoscale Topography Dictates Residue Hydropathy in Proteins

Proteins exhibit diverse structures, including pockets, cavities, channels, and bumps, which are crucial in determining their functions. This diversity in topography also introduces significant chemical heterogeneity, with polar and charged domains often juxtaposed with nonpolar domains in proximity. Consequently, accurately assessing the hydropathy of amino acid residues within the intricate nanoscale topology of proteins is essential. This study presents quantitative hydropathy data for 277,877 amino acid residues, computed using the Protocol for Assigning a Residue's Character on a Hydropathy (PARCH) scale. Leveraging this data set comprising 1000 structurally diverse proteins sourced from the Protein Data Bank, we examined residues situated in various nanoscale environments and analyzed hydropathy in relation to protein topography. Our findings indicate that the hydropathy of a residue is intricately linked to both its individual characteristics and the geometric features of its neighboring residues in response to water. Changes in the number and chemical identity of the neighbors, as well as the nanoscale topography surrounding a residue, are mirrored in its hydropathy profile. Our calculations reveal the intricate interplay of hydrophilic, hydroneutral, and hydrophobic residues distributed across the surface and core of proteins. Notably, we observe that protein surfaces can be ten times more hydrophilic than their cores.