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Structure-based analysis of missense mutations impacting the catalytic and substrate binding sites of hRPE65

hRPE65 is a critical enzyme in the retinoid visual cycle and is implicated in retinal diseases caused by missense mutations that affect its function. However, many hRPE65 variants of uncertain significance (VUS) remain unclassified, hindering their clinical interpretation. This study aims to develop a molecular dynamics (MD)-based protocol to evaluate the pathogenicity of missense mutations located within the catalytic and substrate pockets of hRPE65. Using a full-length hRPE65 model complexed with all-trans-retinylpalmitate, we assessed 15 VUS for their structural and functional impacts. Our findings provide insights into the deleterious effects of these mutations, offering a framework for reclassifying VUS and identifying patients eligible for gene therapy. This approach may support clinicians in improving diagnostic precision and therapeutic decision-making for retinal diseases.

Screening of homing and tissue-penetrating peptides by microdialysis and in vivo phage display

In vivo phage display is a method used for identification of organ- or disease-specific vascular homing peptides for targeted delivery of pharmaceutics. It is agnostic as to the nature and identity of the target molecules. The current in vivo biopanning lacks inbuilt mechanisms to select for peptides capable of vascular homing that would also be capable of tissue penetration to reach therapeutically relevant cells in the tissue parenchyma. Here, we combined in vivo phage display with microdialysis-based parenchymal recovery and high-throughput sequencing to select for peptides that, besides vascular homing, facilitate extravasation and tissue penetration. We first demonstrated in skin wounds that the method can selectively separate known homing peptides from those with additional tissue-penetrating ability. Screening of a naïve peptide library identifies peptides that home and extravasate to extravascular granulation tissue in vascularized and diabetic wounds and cross blood-retina barrier in retinopathy. Our work suggests that in vivo phage display combined with microdialysis can be used for the discovery of vascular homing peptides capable of extravasation and tissue penetration.

Integrating network pharmacology and experimental validation to explore the potential mechanism by which resveratrol acts on osimertinib resistance in lung cancer

Globally, osimertinib resistance has been a long-term challenge. Resveratrol, a naturally occurring polyphenolic compound found in various plants, has the potential to modulate multidrug resistance mechanisms. However, the specific role of resveratrol in delaying osimertinib resistance in lung cancer is still unclear. The present study aimed to investigate the therapeutic effects and underlying mechanisms of resveratrol in delaying osimertinib resistance. Accordingly, the corresponding targets of resveratrol were screened through the Traditional Chinese Medicine Systems Pharmacology database. Similarly, the corresponding targets for osimertinib resistance were mined from the GeneCards database. A protein-protein interaction network was subsequently constructed to pinpoint key hub genes that resveratrol may target to delay resistance. Molecular docking analysis was then employed to assess the binding energy between the predicted key targets and resveratrol. Finally, in vitro experiments were performed to validate the results. Ultimately, 13 potential therapeutic targets of resveratrol related to delaying osimertinib resistance were identified. Kyoto Encyclopedia of Genes and Genomes analysis suggested that the effects of resveratrol may be associated with the apoptotic pathway. Molecular docking revealed that resveratrol has good binding affinities with MCL1 and BCL2L11. In vitro experiments confirmed that resveratrol inhibited the proliferation of osimertinib-resistant cells and upregulated the expression of BCL2L11. In conclusion, resveratrol may promote apoptosis by targeting BCL2L11 to delay osimertinib resistance.

A robust ensemble framework for anticancer peptide classification using multi-model voting approach

Anticancer peptides (ACPs) hold great potential for cancer therapeutics, yet accurately identifying them remains a challenging task due to the complexity of peptide sequences and their interactions with biological systems. In this study, we propose a novel machine learning-based framework for ACP classification, integrating multiple feature sets, including sequence composition, physicochemical properties, and embedding features derived from pre-trained language models. We evaluate the performance of various classifiers on benchmark datasets and compare our model against state-of-the-art methods. The results demonstrate that our model outperforms existing methods such as UniDL4BioPep, ACPred-Fuse, and iACP with an accuracy of 75.58%, an AUC of 0.8272, and an MCC of 0.5119. Our approach provides a more balanced sensitivity of 0.7384 and specificity of 0.773, ensuring robust identification of both ACPs and non-ACPs. These findings suggest that incorporating diverse feature sets can significantly enhance ACP classification, potentially facilitating the discovery of novel anticancer peptides for therapeutic applications.

Perillaldehyde targeting PARP1 to inhibit TRPM2-CaMKII/CaN signal transduction in diabetic cardiomyopathy

BACKGROUND

Diabetic cardiomyopathy (DC) is a serious complication of diabetes, characterized by myocardial fibrosis, hypertrophy, oxidative stress, and inflammation. Perillaldehyde (PAE), a natural monoterpene, has shown potential in mitigating cardiac damage.

PURPOSE

This study aims to elucidate the molecular mechanism of the protective effect of PAE on the DC and the interaction between DC pathogenesis.

METHODS

Network pharmacology and molecular docking were used to identify PARP1 as a core target for PAE in DC. Animal experiments involved intervening DC mice with PAE and assessing cardiac function, oxidative stress, and apoptosis. In vitro, high glucose-induced H9c2 cells were used to validate PAE's effects on cell viability and protein expression.

RESULTS

The results showed that PAE improved the general condition of DC mice, reduced cardiac injury and cardiac insufficiency, decreased myocardial mitochondrial damage, and reduced apoptosis. In addition, PAE upregulated the expression of Bcl-2, downregulated Bax protein expression, inhibited Caspase-3 activity, and inhibited the expression of PARP1, TRPM2, CaN, and CaMKII proteins in DC mice and high glucose-induced H9c2 cells.

CONCLUSION

Mechanically, this study clarified that PAE's inhibition of the PARP1-TRPM2-CaMKII/CaN pathway reduces calcium-activated mitochondrial damage, apoptosis, and oxidative stress in diabetic cardiomyopathy. This discovery provides an innovative therapeutic strategy for DC and an experimental foundation for PAE's drug development, with significant practical implications.

Supervised learning approaches for predicting Ebola-Human Protein-Protein interactions

The goal of this research work is to predict protein-protein interactions (PPIs) between the Ebola virus and the host who is at risk of infection. Since there are very limited databases available on the Ebola virus; we have prepared a comprehensive database of all the PPIs between the Ebola virus and human proteins (EbolaInt). Our work focuses on the finding of some new protein-protein interactions between humans and the Ebola virus using some state- of-the-arts machine learning techniques. However, it is basically a two-class problem with a positive interacting dataset and a negative non-interacting dataset. These datasets contain various sequence-based human protein features such as structure of amino acid and conjoint triad and domain-related features. In this research, we have briefly discussed and used some well-known supervised learning approaches to predict PPIs between human proteins and Ebola virus proteins, including K-nearest neighbours (KNN), random forest (RF), support vector machine (SVM), and deep feed-forward multi-layer perceptron (DMLP) etc. We have validated our prediction results using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Our goal with this prediction is to compare all other models' accuracy, precision, recall, and f1-score for predicting these PPIs. In the result section, DMLP is giving the highest accuracy along with the prediction of 2655 potential human target proteins.

CCDC134 controls TLR biogenesis through the ER chaperone Gp96

Toll-like receptors (TLRs) are central to initiate immune responses against invading pathogens. To ensure host defense while avoiding aberrant activation leading to pathogenic inflammation and autoimmune diseases, TLRs are tightly controlled by multilevel regulatory mechanisms. Through a loss-of-function genetic screen in a reporter cell line engineered to undergo cell death upon TLR7-induced IRF5 activation, we identified here CCDC134 as an essential factor for TLR responses. CCDC134 deficiency impaired endolysosomal TLR-induced NF-κB, MAPK, and IRF5 activation, as well as downstream production of proinflammatory cytokines and type I interferons. We further demonstrated that CCDC134 is an endoplasmic reticulum (ER)-resident interactor of Gp96 (HSP90B1/Grp94), an ER chaperone essential for folding and trafficking of plasma membrane and endolysosomal TLRs. CCDC134 controlled Gp96 stability as its loss led to Gp96 hyperglycosylation and ER-associated protein degradation (ERAD)-mediated clearance. Accordingly, CCDC134 deficiency impaired the folding, maturation, and trafficking of TLRs, resulting in blunted inflammatory responses upon stimulation. Altogether, this study reveals CCDC134 as a central regulator of the chaperone Gp96, thereby controlling TLR biogenesis and responses.

Mass Spectrometry-Based Proteomics in Clinical Diagnosis of Amyloidosis and Multiple Myeloma: A Review (2012-2024)

Proteomics is nowadays increasingly becoming part of the routine clinical practice of diagnostic laboratories, especially due to the advent of advanced mass spectrometry techniques. This review focuses on the application of proteomic analysis in the identification of pathological conditions in a hospital setting, with a particular focus on the analysis of protein biomarkers. In particular, the main purpose of the review is to highlight the challenges associated with the identification of specific disease-causing proteins, given their complex nature and the variety of posttranslational modifications (PTMs) they can undergo. PTMs, such as phosphorylation and glycosylation, play critical roles in protein function but can also lead to diseases if dysregulated. Proteomics plays an important role especially in various medical fields ranging from cardiology, internal medicine to hemato-oncology emphasizing the interdisciplinary nature of this field. Traditional methods such as electrophoretic or immunochemical methods have been mainstay in protein detection; however, these techniques are limited in terms of specificity and sensitivity. Examples include the diagnosis of multiple myeloma and the detection of its specific protein or amyloidosis, which relies heavily on these conventional methods, which sometimes lead to false positives or inadequate disease monitoring. Mass spectrometry in this respect emerges as a superior alternative, providing high sensitivity and specificity in the detection and quantification of specific protein sequences. This technique is particularly beneficial for monitoring minimal residual disease (MRD) in the diagnosis of multiple myeloma where traditional methods fall short. Furthermore mass spectrometry can provide precise typing of amyloid proteins, which is crucial for the appropriate treatment of amyloidosis. This review summarizes the opportunities for proteomic determination using mass spectrometry between 2012 and 2024, highlighting the transformative potential of mass spectrometry in clinical proteomics and encouraging its wider use in diagnostic laboratories.

Comprehensive analysis of the role of Caspases in glioma

Caspases (CASPs) are attractive targets for cancer therapy. Many prognostic models based on gene signatures include genes from the CASPs family in diffuse glioma. CASP3, CASP4 and CASP6 in glioma have been studied individually. However, specialized comprehensive analysis of the roles CASPs family in glioma is lacking. Therefore, this study utilized bioinformatics methods to investigate this issue. CASP1-10 expressions were significantly up-regulated in LGG and GBM and glioma, and varied significantly across different clinical subgroups of glioma and LGG and various cell types, and most of CASP1-10 showed significant differences in recurrence status of LGG. 10 signatures (CASP1-10) were associated with poor overall survival (OS) in glioma and LGG and GBM. However, pan-cancer survival analysis showed that CASP1-10 were associated with the prognosis of LGG, but not GBM. CASP1-10 were related to poor prognosis of glioma and LGG, except for CASP9, which was the opposite of a protective factor. CASP1-10 were independent prognostic factors for OS in glioma and LGG, except for CASP5, and also for recurrence-free survival (RFS) in LGG. Most of CASP1-10 were also independent prognostic factors for disease-specific survival (DSS) and progression-free interval (PFI) and had diagnostic value in glioma and LGG. Genetic alterations of CASP1-10 genes set were associated with poor prognosis in LGG. CASP1-10 were involved in immune infiltration and programmed cell death in glioma and LGG and GBM, and might promote the apoptosis of immune cells. Compared to GBM, CASP1-10 had a more significant impact on the prognosis, cancer-related pathways, and immune infiltration in LGG, indicating that CASP1-10 might play important roles in the recurrence and progression of LGG, and might be promising therapeutic targets for LGG. Therefore, it is speculated that natural caspase inhibitor p35 may be a promising drug for the treatment of glioma, especially for LGG.

Evaluation of the antibacterial and inhibitory activity of the NorA and TetK efflux pumps of Staphylococcus aureus by p-coumaric acid

The NorA and TetK efflux pumps mediate resistance to fluoroquinolone and tetracycline antibiotics by actively extruding these compounds and reducing their intracellular concentrations. Consequently, intense research has focused on inhibiting these efflux mechanisms using antimicrobial agents derived from natural or synthetic sources. This study used Fourier transform infrared spectroscopy (ATR-FTIR) to analyze the various functional groups present in p-coumaric acid. We also investigated the antibacterial activity of p-coumaric acid on strains of Staphylococcus aureus carrying the NorA and TetK efflux pumps, as well as the compound's ability to increase the fluorescence of ethidium bromide (EtBr) and Sytox Green. In addition, the interactions of this compound with NorA were analyzed using molecular docking, and its pharmacokinetic properties were evaluated using ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) modeling. The results revealed that p-coumaric acid exhibited no direct antibacterial activity against the tested Staphylococcus aureus strains. However, significant reductions in the minimum inhibitory concentrations (MICs) of norfloxacin and EtBr, both used as NorA substrates, were observed when combined with p-coumaric acid. It was also observed that p-coumaric acid increased the fluorescence emission of EtBr and Sytox Green in strains 1199 and 1199B, suggesting the inhibition of the efflux mechanism and enhanced membrane permeabilization in S. aureus. The in silico analysis demonstrated that p-coumaric acid exhibits a favorable binding energy with NorA, comparable to that of chlorpromazine. These results position p-coumaric acid as a promising antibiotic adjuvant and efflux inhibitor in strains harboring NorA.

Single-cell RNAseq reveals adverse metabolic transcriptional program in intrahepatic cholangiocarcinoma malignant cells

Intrahepatic cholangiocarcinoma (ICA) is a highly aggressive primary liver cancer, which originates from the epithelial cells of the bile ducts. The transcriptional profile of metabolic enzymes was investigated at both bulk and single-cell levels in tumor samples from distinct ICA cohorts. In a training cohort (TCGA consortium), 16 genes encoding for metabolic enzymes were found overexpressed in cases with poor survival. A computed metabolic gene expression score was significantly associated with worse ICA prognosis at the univariate level (overall survival [OS] log-rank p = 8.2e-4). After adjusting for Ishak fibrosis score and tumor staging, the metabolic expression remained an independent predictor of poor prognosis (multivariate OS log-rank p = 0.01). Seven genes encoding key enzymes (FH, MAT2B, PLOD2, PLOD1, PDE6D, ALDOC, and NT5DC3) were validated as markers of the proliferative subclass of ICA in the GSE32225 dataset, related to poor prognosis. The metabolic score was significantly different between the inflammatory and proliferative subclasses in the validation cohort (p < 2.2e-16). At the single-cell level, in the tumor microenvironment of 10 ICA patients, these seven enzymes were predominantly expressed by malignant cells. The single-cell metabolic score was thus higher in malignant cells. This study identifies a metabolic transcriptional program linked to poor prognosis in ICA, independent of fibrosis and tumor staging.

Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions

Alignments in bioinformatics refer to the arrangement of sequences to identify regions of similarity that can indicate functional, structural, or evolutionary relationships. They are crucial for bioinformaticians as they enable accurate predictions and analyses in various applications, including protein subcellular localization. The predictive model used in this article is based on a deep - convolutional architecture. We tested configurations of Deep N-to-1 convolutional neural networks of various depths and widths during experimentation for the evaluation of better-performing values across a diverse set of eight classes. For without alignment assessment, sequences are encoded using one-hot encoding, converting each character into a numerical representation, which is straightforward for non-numerical data and useful for machine learning models. For with alignments assessment, multiple sequence alignments (MSAs) are created using PSI-BLAST, capturing evolutionary information by calculating frequencies of residues and gaps. The average difference in peak performance between models with alignments and without alignments is approximately 15.82%. The average difference in the highest accuracy achieved with alignments compared with without alignments is approximately 15.16%. Thus, extensive experimentation indicates that higher alignment accuracy implies a more reliable model and improved prediction accuracy, which can be trusted to deliver consistent performance across different layers and classes of subcellular localization predictions. This research provides valuable insights into prediction accuracies with and without alignments, offering bioinformaticians an effective tool for better understanding while potentially reducing the need for extensive experimental validations. The source code and datasets are available at http://distilldeep.ucd.ie/SCL8/.

PyPropel: a Python-based tool for efficiently processing and characterising protein data

BACKGROUND

The volume of protein sequence data has grown exponentially in recent years, driven by advancements in metagenomics. Despite this, a substantial proportion of these sequences remain poorly annotated, underscoring the need for robust bioinformatics tools to facilitate efficient characterisation and annotation for functional studies.

RESULTS

We present PyPropel, a Python-based computational tool developed to streamline the large-scale analysis of protein data, with a particular focus on applications in machine learning. PyPropel integrates sequence and structural data pre-processing, feature generation, and post-processing for model performance evaluation and visualisation, offering a comprehensive solution for handling complex protein datasets.

CONCLUSION

PyPropel provides added value over existing tools by offering a unified workflow that encompasses the full spectrum of protein research, from raw data pre-processing to functional annotation and model performance analysis, thereby supporting efficient protein function studies.

Proteomic insights into survival strategies of Escherichia coli in perchlorate-rich Martian brines

Brines, potentially formed by the deliquescence and freezing point depression of highly hygroscopic salts, such as perchlorates (ClO4-), may allow for the spatial and temporal stability of liquid water on present-day Mars. It is therefore of great interest to explore the microbial habitability of Martian brines, for which our current understanding is, however, still limited. Putative microbes growing in the perchlorate-rich Martian regolith may be harmed due to the induction of various stressors including osmotic, chaotropic, and oxidative stress. We adapted the model organism Escherichia coli to increasing sodium perchlorate concentrations and used a proteomic approach to characterize the adaptive phenotype. Separately, the microbe was adapted to elevated concentrations of sodium chloride and glycerol, which enabled us to distinguish perchlorate-specific adaptation mechanisms from those in response to osmotic, ion and water activity stress. We found that the perchlorate-specific stress response focused on pathways alleviating damage to nucleic acids, presumably caused by increased chaotropic and/or oxidative stress. The significant enrichments that have been found include DNA repair, RNA methylation and de novo inosine monophosphate (IMP) biosynthesis. Our study provides insights into the adaptive mechanisms necessary for microorganisms to survive under perchlorate stress, with implications for understanding the habitability of Martian brines.

Structure Characterization of Bacterial Microcompartment Shells via X-ray Scattering and Coordinate Modeling: Evidence for Adventitious Capture of Cytoplasmic Proteins

Bacterial microcompartments (BMCs) are self-assembling protein shell structures that are widely investigated across a broad range of biological and abiotic chemistry applications. A central challenge in BMC research is the targeted capture of enzymes during shell assembly. While crystallography and cryo-EM techniques have been successful in determining BMC shell structures, there has been only limited success in visualizing the location of BMC-captured enzyme cargo. Here, we demonstrate the opportunity to use small-angle X-ray scattering (SAXS) and pair distance distribution function (PDDF) measurements combined with quantitative comparison to coordinate structure models as an approach to characterize BMC shell structures in solution conditions directly relevant to biochemical function. Using this approach, we analyzed BMC shells from Haliangium ochraceum (HO) that were isolated following expression in E. coli. The analysis allowed the BMC shell structures and the extent of encapsulated enzyme cargo to be identified. Notably, the results demonstrate that HO-BMC shells adventitiously capture significant amounts of cytoplasmic cargo during assembly in E. coli. Our findings highlight the utility of SAXS/PDDF analysis for evaluating BMC architectures and enzyme encapsulation, offering valuable insights for designing BMC shells as platforms for biological and abiotic catalyst capture within confined environments.

On the way to diploidization and unexpected ploidy in the grass Sporobolus section Spartina mesopolyploids

Plant history is characterized by cyclical whole genome duplication and diploidization with important biological and ecological consequences. Here, we explore the genome history of two related iconic polyploid grasses (Sporobolus alterniflorus and S. maritimus), involved in a well-known example of neopolyploid speciation. We report particular genome dynamics where an ancestral Sporobolus genome (n = 2x = 20) duplicated 9.6-24.4 million years ago (MYA), which was followed by descending dysploidy resulting in a genome with an unexpected base chromosome number (n = 15). This diploidized genome duplicated again 2.1-6.2 MYA to form a tetraploid lineage (2n = 4x = 60), thus reshuffling the ploidy of these species previously thought hexaploids. We also elucidate the mechanism accompanying the speciation between S. maritimus (2n = 60) and S. alterniflorus (2n = 62), resulting from chromosome restructuring, and identify key adaptive genes in the corresponding regions. This represents critical findings to decipher molecular mechanisms underlying species expansion, adaptation to environmental challenge and invasiveness.

Toward deep learning sequence-structure co-generation for protein design

Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majority of today's models focus on generating either sequences or structures, emerging co-generation methods promise more accurate and controllable protein design, ideally achieved by modeling both modalities simultaneously. Here we review recent advances in deep generative models for protein design, with a particular focus on sequence-structure co-generation methods. We describe the key methodological and evaluation principles underlying these methods, highlight recent advances from the literature, and discuss opportunities for continued development of sequence-structure co-generation approaches.

Gene expression atlas of the Colorado potato beetle (Leptinotarsa decemlineata)

The Colorado potato beetle (CPB) is a major pest of potato crops, known for its remarkable ability to develop resistance to more than 50 pesticides. For decades, CPB has served as a model species for studying insecticide resistance, insect physiology, diapause, reproduction, and evolution. However, research progress on CPB has been hindered by the lack of comprehensive genomic and transcriptomic resources. Here, leveraging a recently established chromosome-level genome assembly, we constructed a gene expression atlas of CPB using transcriptomic data from 61 samples representing major organs and developmental stages. By integrating short- and long-read sequencing technologies, we enhanced the genome annotation and identified 6,623 additional genes that were previously undetected. Furthermore, we developed a web portal to facilitate the search and visualization of the gene expression atlas, providing an accessible resource for the research community. The CPB gene expression atlas offers valuable tools and comprehensive data that will accelerate future research in pest control and insect biology.

Do Porto D, Custódio FL, Trevizani R, Nicolás MF. PAPreC: A Pipeline for Antigenicity Prediction Comparison Methods across Bacteria

Antigenicity prediction plays a crucial role in vaccine development, antibody-based therapies, and diagnostic assays, as this predictive approach helps assess the potential of molecular structures to induce and recruit immune cells and drive antibody production. Several existing prediction methods, which target complete proteins and epitopes identified through reverse vaccinology, face limitations regarding input data constraints, feature extraction strategies, and insufficient flexibility for model evaluation and interpretation. This work presents PAPreC (Pipeline for Antigenicity Prediction Comparison), an open-source, versatile workflow (available at https://github.com/YasCoMa/paprec_nx_workflow) designed to address these challenges. PAPreC systematically examines three key factors: the selection of training data sets, feature extraction methods (including physicochemical descriptors and ESM-2 encoder-derived embeddings), and diverse classifiers. It provides automated model evaluation, interpretability through SHapley Additive exPlanations (SHAP) analysis, and applicability domain assessments, enabling researchers to identify optimal configurations for their specific data sets. Applying PAPreC to IEDB data as a reference, we demonstrate its effectiveness across the ESKAPE pathogen group. A case study involving Pseudomonas aeruginosa and Staphylococcus aureus shows that specific feature configurations are more suitable for different sequence types, and that ESM-2 embeddings enhance model performance. Moreover, our results indicate that separate models for Gram-positive and Gram-negative bacteria are not required. PAPreC offers a comprehensive, adaptable, and robust framework to streamline and improve antigenicity prediction for diverse bacterial data sets.

Biochemical analyses of cystatin-C dimers and cathepsin-B reveals a trypsin-driven feedback mechanism in acute pancreatitis

Acute pancreatitis (AP) is characterised by self-digestion of the pancreas by its own proteases. This pathophysiological initiating event in AP occurs inside pancreatic acinar cells where intrapancreatic trypsinogen becomes prematurely activated by cathepsin B (CTSB), and induces the digestive protease cascade, while cathepsin L (CTSL) degrades trypsin and trypsinogen and therefore prevents the development of AP. These proteases are located in the secretory compartment of acinar cells together with cystatin C (CST3), an endogenous inhibitor of CTSB and CTSL. The results are based on detailed biochemical analysis, site-directed mutagenesis and molecular dynamics simulations in combination with an experimental disease model of AP using CST3 deficient mice. This identifies that CST3 is a critical regulator of CTSB and CTSL activity during AP. CST3 deficient mice show a higher intracellular CTSB activity resulting in elevated trypsinogen activation accompanied by an increased disease severity. This reveals that CST3 can be cleaved by trypsin disabling the inhibition of CTSB, but not of CTSL. Furthermore, dimerised CST3 enhances the CTSB activity by binding to an allosteric pocket specific to the CTSB structure. CST3 shifts from an inhibitor to an activator of CTSB and therefore fuels the intrapancreatic protease cascade during the onset of AP.

CAZyme3D: A Database of 3D Structures for Carbohydrate-active Enzymes

CAZymes (Carbohydrate Active EnZymes) degrade, synthesize, and modify all complex carbohydrates on Earth. CAZymes are extremely important to research in human health, nutrition, gut microbiome, bioenergy, plant disease, and global carbon recycling. Current CAZyme annotation tools are all based on sequence similarity. A more powerful approach is to detect protein structural similarity between query proteins and known CAZymes indicative of distant homology. Here, we developed CAZyme3D (https://pro.unl.edu/CAZyme3D/) to fill the research gap that no dedicated 3D structure databases are currently available for CAZymes. CAZyme3D contains a total of 870,740 AlphaFold predicted 3D structures (named Whole dataset). A subset of CAZymes 3D structures from 188,574 nonredundant sequences (named ID50 dataset) were subject to structural similarity-based clustering analyses. Such clustering allowed us to organize all CAZyme structures using a hierarchical classification, which includes existing levels defined by the CAZy database (class, clan, family, subfamily) and newly defined levels (subclasses, structural cluster [SC] groups, and SCs). The inter-family structural clustering successfully grouped CAZy families and clans with the same structural folds in the same subclasses. The intra-family structural clustering classified structurally similar CAZymes into SCs, which were further classified into SC groups. SCs and SC groups differed from sequence similarity-based CAZy subfamilies. With CAZyme structures as the search database, we created job submission pages, where users can submit query protein sequences or PDB structures for a structural similarity search. CAZyme3D will be a useful new tool to assist the discovery of novel CAZymes by providing a comprehensive database of CAZyme 3D structures.

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine

Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-consuming, and frequent failures. AI-driven approaches streamline key phases, including target identification, lead optimization, de novo drug design, and drug repurposing. Frameworks such as deep neural networks (DNNs), convolutional neural networks (CNNs), and deep reinforcement learning (DRL) models have shown promise in identifying drug targets, optimizing delivery systems, and accelerating drug repurposing. Generative adversarial networks (GANs) and variational autoencoders (VAEs) aid de novo drug design by creating novel drug-like compounds with desired properties. Case studies, such as DDR1 kinase inhibitors designed using generative models and CDK20 inhibitors developed via structure-based methods, highlight AI's ability to produce highly specific therapeutics. Models like SNF-CVAE and DeepDR further advance drug repurposing by uncovering new therapeutic applications for existing drugs. Advanced ML algorithms enhance precision in predicting drug efficacy, toxicity, and ADME-Tox properties, reducing development costs and improving drug-target interactions. AI also supports polypharmacology by optimizing multi-target drug interactions and enhances combination therapy through predictions of drug synergies and antagonisms. In nanomedicine, AI models like CURATE.AI and the Hartung algorithm optimize personalized treatments by predicting toxicological risks and real-time dosing adjustments with high accuracy. Despite its potential, challenges like data quality, model interpretability, and ethical concerns must be addressed. High-quality datasets, transparent models, and unbiased algorithms are essential for reliable AI applications. As AI continues to evolve, it is poised to revolutionize drug discovery and personalized medicine, advancing therapeutic development and patient care.

Proximity proteomics provides a new resource for exploring the function of Afadin and the complexity of cell-cell adherens junctions

The network of proteins at the interface between cell-cell adherens junctions and the actomyosin cytoskeleton provides robust yet dynamic connections that facilitate cell shape change and motility. While this was initially thought to be a simple linear connection via classic cadherins and their associated catenins, we now have come to appreciate that many more proteins are involved, providing robustness and mechanosensitivity. Defining the full set of proteins in this network remains a key objective in our field. Proximity proteomics provides a means to define these networks. Mammalian Afadin and its Drosophila homolog Canoe are key parts of this protein network, facilitating diverse cell shape changes during gastrulation and other events of embryonic morphogenesis. Here we report results of several proximity proteomics screens, defining proteins in the neighborhood of both the N- and C-termini of mammalian Afadin in the premier epithelial model, MDCK cells. We compare our results with previous screens done in other cell types, and with proximity proteomics efforts with other junctional proteins. These reveal the value of multiple screens in defining the full network of neighbors and offer interesting insights into the overlap in protein composition between different epithelial cell junctions.

Anthelmintic Potential and In Silico Studies of Ricinoleic Acid from the Seed Oil of Ricinus communis L

The prevalence of human intestinal helminth parasitic infections is extensive, with over half of the global population estimated to suffer from these infections. Traditionally, various plant species, including Ricinus communis L. (Euphorbiaceae), are used to treat helminth infections. In this study, ricinoleic acid was isolated from the base hydrolysate of the petroleum ether seed extract of R. communis using column chromatography and transformed into ricinoleic acid methyl ester through esterification. The extract, ricinoleic acid and its methyl ester were evaluated for their anthelmintic activities against the model organism Caenorhabditis elegans. The results revealed that at a concentration of 1 mg/mL, ricinoleic acid and its methyl ester killed 97.40% and 97.83% of C. elegans worms, respectively. Molecular docking studies of ricinoleic acid on succinate dehydrogenase (SDH), glucose-6-phosphate 1-dehydrogenase (G6PD), and tubulin beta-2 chain (TBB2C) revealed that ricinoleic acid has a more favorable interaction with succinate dehydrogenase (-5.408 kcal/mol) compared to glucose-6-phosphate 1-dehydrogenase (-3.758 kcal/mol) and tubulin beta-2 chain (-1.444 kcal/mol). Furthermore, Absorption, Distribution, Metabolism, and Excretion (ADME) analyses unveiled that ricinoleic acid adheres to Lipinski's rule of five, positioning it as a potential compound to treat helminths. The current study demonstrated that R. communis seed oil possesses genuine anthelmintic activity against C. elegans, which is likely due to ricinoleic acid.

A combined in silico approach to design peptide ligands with increased receptor-subtype selectivity

G-protein coupled receptors are major drug targets that change their conformation upon binding of ligands to their extracellular binding pocket to transduce the signal to intracellular G-proteins or arrestins. In drug screening campaigns, computational methods are frequently used to predict binding affinities for chemical compounds in silico before experimental testing. Some of these methods take into consideration the inherent flexibility of the ligand and to some extent also of the receptor. Due to high structural flexibility, peptide ligands are exceptionally difficult to handle and approaches to effectively sample in silico receptor-peptide ligand interactions are limited. Here we describe a pipeline starting from microseconds molecular dynamics simulations of receptor and receptor ligand complexes to find reasonable starting conformations and derive constraints for subsequent flexible docking of peptide ligands, using Rosetta's FlexPepDock approach. We applied this approach to predict binding affinities for dynorphin and its variants to members of the opioid receptor family. Using an ensemble of docking poses, Rosetta's fixbb protein design method explored the sequence space at defined positions, to enhance binding affinities, aiming to increase subtype selectivity towards κ-opioid receptor while decreasing it towards μ-opioid receptor. The results of our computations were validated experimentally in a related study (Zangrandi et al., 2024[1]). Four out of six proposed variants lead to a significant increase in subtype selectivity in favor of κ-opioid receptor, highlighting the potential of our approach to design subtype selective peptide variants. The established workflow may also apply for other receptor types activated by peptide ligands.

Lysine acetylation in cyanobacteria: emerging mechanisms and functions

Cyanobacteria are ancient and abundant photosynthetic prokaryotes that play crucial roles in global carbon and nitrogen cycles. They exist in a variety of environments and have been used extensively as model organisms for studies of photosynthesis and environmental adaptation. Lysine acetylation (Kac), a widespread and evolutionarily conserved protein posttranslational modification, is reversibly catalyzed by lysine acetyltransferases (KAT) and lysine deacetylases (KDACs). Over the past decade, a growing number of acetylated proteins have been identified in cyanobacteria, and Kac is increasingly recognized as having essential roles in many cellular processes, such as photosynthesis, energy metabolism, and stress responses. Recently, cGNAT2 and CddA were identified as KAT and KDAC in the model cyanobacterium Synechococcus sp. PCC 7002, respectively. The identified Kac regulatory enzymes provide novel insight into the mechanisms that globally regulate photosynthesis in cyanobacteria and potentially other photosynthetic organisms. This review summarizes recent progress in our understanding of the functions and mechanisms of lysine acetylation in Cyanobacteria. The challenges and future perspectives in this field are also discussed.

Drosophila Topoisomerase 3β binds to mRNAs in vivo, contributes to their localization and stability, and counteracts premature aging

Topoisomerase 3β (Top3β) works not only on DNA but also on RNA. We isolated and identified the naturally cross-linked RNA targets of Drosophila Top3β from an early embryonic stage that contains almost exclusively maternal mRNAs. Favorite targets were long RNAs, particularly with long 3'UTRs, and RNAs that become localized in large cells. Top3β lacking only the hydroxyl group that makes the covalent bond to the RNA, did not allow normal expression and localization of Top3β mRNA targets or their protein products, demonstrating the importance of the enzymatic activity of Top3 β for optimized gene expression. Top3β is not essential for development to the adult stage but to maintain the morphology of the adult neuromuscular junction and to prevent premature loss of coordinated movement and aging. Alterations in human Top3β have been associated with several neurological diseases and cancers. The homologs of genes and (pre)mRNAs mis-expressed in these conditions show the same characteristics identified in the Drosophila Top3β targets, suggesting that Drosophila could model human Top3β. An in vivo test of this model showed that the enzymatic activity of Top3β reduces the neurodegeneration caused by the cytotoxic human (G4C2)49 RNA. Top3β supports normal gene expression, particularly of long and complex transcripts that must be transported and translationally controlled. These RNAs encode large cytoskeletal, cortical, and membrane proteins that are particularly important in large and long cells like motoneurons. Their reduced expression in the mutant seems to stress the cells, increasing the chances of developing neurodegenerative diseases.

Normalized Protein-Ligand Distance Likelihood Score for End-to-End Blind Docking and Virtual Screening

Molecular Docking is a critical task in structure-based virtual screening. Recent advancements have showcased the efficacy of diffusion-based generative models for blind docking tasks. However, these models do not inherently estimate protein-ligand binding strength thus cannot be directly applied to virtual screening tasks. Protein-ligand scoring functions serve as fast and approximate computational methods to evaluate the binding strength between the protein and ligand. In this work, we introduce normalized mixture density network (NMDN) score, a deep learning (DL)-based scoring function learning the probability density distribution of distances between protein residues and ligand atoms. The NMDN score addresses limitations observed in existing DL scoring functions and performs robustly in both pose selection and virtual screening tasks. Additionally, we incorporate an interaction module to predict the experimental binding affinity score to fully utilize the learned protein and ligand representations. Finally, we present an end-to-end blind docking and virtual screening protocol named DiffDock-NMDN. For each protein-ligand pair, we employ DiffDock to sample multiple poses, followed by utilizing the NMDN score to select the optimal binding pose, and estimating the binding affinity using scoring functions. Our protocol achieves an average enrichment factor of 4.96 on the LIT-PCBA data set, proving effective in real-world drug discovery scenarios where binder information is limited. This work not only presents a robust DL-based scoring function with superior pose selection and virtual screening capabilities but also offers a blind docking protocol and benchmarks to guide future scoring function development.

Candida albicans: a comprehensive view of the proteome

We describe a new release of the Candida albicans PeptideAtlas proteomics spectral resource (build 2024-03), providing a sequence coverage of 79.5% at the canonical protein level, matched mass spectrometry spectra, and experimental evidence identifying 3382 and 536 phosphorylated serine and threonine sites with false localization rates of 1% and 5.3%, respectively. We provide a tutorial on how to use the PeptideAtlas and associated tools to access this information. The C. albicans PeptideAtlas summary web page provides "Build overview", "PTM coverage", "Experiment contribution", and "Dataset contribution" information. The protein and peptide information can also be accessed via the Candida Genome Database via hyperlinks on each protein page. This allows users to peruse identified peptides, protein coverage, post-translational modifications (PTMs), and experiments identifying each protein. Given the value of understanding the PTM landscape in the sequence of each protein, a more detailed explanation of how to interpret and analyse PTM results is provided in the PeptideAtlas of this important pathogen. Candida albicans PeptideAtlas web page: https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/buildDetails?atlas_build_id=578.

SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction

BACKGROUND

A massive amount of protein sequences have been obtained, but their functions remain challenging to discern. In recent research on protein function prediction, Protein-Protein Interaction (PPI) Networks have played a crucial role. Uncovering potential function relationships between distant proteins within PPI networks is essential for improving the accuracy of protein function prediction. Most current studies attempt to capture these distant relationships by stacking graph network layers, but performance gains diminish as the number of layers increases.

RESULTS

To further explore the potential functional relationships between multi-hop proteins in PPI networks, this paper proposes SEGT-GO, a Graph Transformer method based on PPI multi-hop neighborhood Serialization and Explainable artificial intelligence for large-scale multispecies protein function prediction. The multi-hop neighborhood serialization maps multi-hop information in the PPI Network into serialized feature embeddings, enabling the Graph Transformer to learn deeper functional features within the PPI Network. Based on game theory, the SHAP eXplainable Artificial Intelligence (XAI) framework optimizes model input and filters out feature noise, enhancing model performance.

CONCLUSIONS

Compared to the advanced network method DeepGraphGO, SEGT-GO achieves more competitive results in standard large-scale datasets and superior results on small ones, validating its ability to extract functional information from deep proteins. Furthermore, SEGT-GO achieves superior results in cross-species learning and prediction of the functions of unseen proteins, further proving the method's strong generalization.

The genome sequence of a snakefly, Xanthostigma xanthostigma (Schummel, 1832)

We present a genome assembly from an individual male snakefly, Xanthostigma xanthostigma (Arthropoda; Insecta; Raphidioptera; Raphidiidae). The genome sequence has a total length of 623.30 megabases. Most of the assembly (99.74%) is scaffolded into 13 chromosomal pseudomolecules, including the X sex chromosome. The mitochondrial genome has also been assembled and is 17.75 kilobases in length. Gene annotation of this assembly on Ensembl identified 13,251 protein-coding genes.

Propelling sustainable energy: Multi-omics analysis of pennycress FATTY ACID ELONGATION1 knockout for biofuel production

The aviation industry's growing interest in renewable jet fuel has encouraged the exploration of alternative oilseed crops. Replacing traditional fossil fuels with a sustainable, domestically sourced crop can substantially reduce carbon emissions, thus mitigating global climate instability. Pennycress (Thlaspi arvense L.) is an emerging oilseed intermediate crop that can be grown during the offseason between maize (Zea mays) and soybean (Glycine max) to produce renewable biofuel. Pennycress is being domesticated through breeding and mutagenesis, providing opportunities for trait enhancement. Here, we employed metabolic engineering strategies to improve seed oil composition and bolster the plant's economic competitiveness. FATTY ACID ELONGATION1 (FAE1) was targeted using CRISPR-Cas 9 gene editing to eliminate very long chain fatty acids (VLCFAs) from pennycress seed oil, thereby enhancing its cold flow properties. Through an integrated multiomics approach, we investigated the impact of eliminating VLCFAs in developing and mature plant embryos. Our findings revealed improved cold-germination efficiency in fae1, with seedling emergence occurring up to 3 d earlier at 10 °C. However, these alterations led to a tradeoff between storage oil content and composition. Additionally, these shifts in lipid biosynthesis were accompanied by broad metabolic changes, such as the accumulation of glucose and ADP-glucose quantities consistent with increased starch production. Furthermore, shifts to shorter FA chains triggered the upregulation of heat shock proteins, underscoring the importance of VLCFAs in stress signaling pathways. Overall, this research provides crucial insights for optimizing pennycress seed oil while preserving essential traits for biofuel applications.

The genome sequence of the Stable Fly, Stomoxys calcitrans (Linnaeus, 1758)

We present a genome assembly from an individual specimen of Stomoxys calcitrans (Stable Fly; Arthropoda; Insecta; Diptera; Muscidae). The genome sequence has a total length of 1,070.90 megabases. Most of the assembly (98.96%) is scaffolded into 5 chromosomal pseudomolecules.The mitochondrial genome has also been assembled and is 17.6 kilobases in length. Gene annotation of this assembly on Ensembl identified 15,757 protein-coding genes.

Clinical study, network pharmacology, and molecular docking of Kunxian capsule in treating idiopathic membranous nephropathy

Objective

A new Tripterygium wilfordii preparation called Kunxian capsule (KX) has been approved in China. However, it is still unknown whether KX is safe and effective for idiopathic membranous nephropathy (IMN) and its therapeutic mechanism of action is unclear.

Methods

We conducted a retrospective study of 39 patients with IMN who received KX to investigate its efficacy and side effects of KX in treating IMN. We also used network pharmacology and molecular docking methods to explore the potential mechanism of action of KX in IMN.

Results

In patients with IMN receiving KX treatment, 24 h urine protein was markedly decreased, whereas serum albumin levels increased. The overall clinical response rate was 79.49% after 6 months of treatment, and there were no significant adverse events. Quercetin, luteolin and kaempferol were the main bioactive ingredients of KX in treating IMN. AKT1, IL6, and TNF were core targets. The main potential mechanism of KX in treating IMN were pathways involved in cancer, the AGE-RAGE signaling pathway in diabetic complications, lipid and atherosclerosis. Molecular docking results showed that the binding force between the active ingredient and core target was relatively stable.

Conclusion

KX is a safe and effective treatment option for IMN and can effectively improve serum albumin and 24 h urine protein levels in patients with IMN. This study preliminarily reveals the possible mechanism of KX in the treatment of IMN and provides a theoretical basis for future clinical research.

Genetic Code Expansion for Controlled Surfactin Production in a High Cell-Density Bacillus subtilis Strain

BACKGROUND

In biotechnology, B. subtilis is established for heterologous protein production. In addition, the species provides a variety of bioactive metabolites, including the non-ribosomally produced surfactin lipopeptide. However, to control the formation of the target product-forming enzyme, different expression systems could be introduced, including the principle of genetic code expansion by the incorporation of externally supplied non-canonical amino acids.

METHODS

Integration of an amber stop codon into the srfA operon and additional chromosomal integration of an aminoacyl-tRNA synthetase/tRNA mutant pair from Methanococcus jannaschii enabled site-directed incorporation of the non-canonical amino acid O-methyl-L-tyrosine (OMeY). In different fed-batch bioreactor approaches, OMeY-associated surfactin production was quantified by high-performance thin-layer chromatography (HPTLC). Physiological adaptations of the B. subtilis production strain were analyzed by mass spectrometric proteomics.

RESULTS

Using a surfactin-forming B. subtilis production strain, which enables high cell density fermentation processes, the principle of genetic code expansion was introduced. Accordingly, the biosynthesis of the surfactin-forming non-ribosomal peptide synthetase (NRPS) was linked to the addition of the non-canonical amino acid OMeY. In OMeY-associated fed-batch bioreactor fermentation processes, a maximum surfactin titre of 10.8 g/L was achieved. In addition, the effect of surfactin induction was investigated by mass spectrometric proteome analyses. Among other things, adaptations in the B. subtilis motility towards a more sessile state and increased abundances of surfactin precursor-producing enzymes were detected.

CONCLUSIONS

The principle of genetic code expansion enabled a precise control of the surfactin bioproduction as a representative of bioactive secondary metabolites in B. subtilis. This allowed the establishment of inducer-associated regulation at the post-transcriptional level with simultaneous use of the native promoter system. In this way, inductor-dependent control of the production of the target metabolite-forming enzyme could be achieved.

Accurate Prediction of Protein Complex Stoichiometry by Integrating AlphaFold3 and Template Information

Protein structure prediction methods require stoichiometry information (i.e., subunit counts) to predict the quaternary structure of protein complexes. However, this information is often unavailable, making stoichiometry prediction crucial for complexes with unknown stoichiometry. Despite its importance, few computational methods address this challenge. In this study, we present an approach that integrates AlphaFold3 structure predictions with homologous template data to predict stoichiometry. The method generates candidate stoichiometries, builds structural models for them using AlphaFold3, ranks them based on AlphaFold3 scores, and further refine predictions with template-based information when available. In the 16th community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP16), our method achieved 71.4% top-1 accuracy and 92.9% top-3 accuracy, outperforming other predictors in terms of the overall performance. This demonstrates the complementary strengths of AlphaFold3- and template-based predictions and highlights its applicability for uncharacterized protein complexes lacking stoichiometry data.

Novel 1,2,4-triazole-derived Schiff base derivatives: Design, synthesis, and multi-enzyme targeting potential for therapeutic applications

This study synthesized a series of Schiff base derivatives featuring a 1,2,4-triazole framework and characterized through FT-IR, 1H NMR, 13C NMR, 19F NMR, MS, and elemental analysis. Subsequently, the inhibitory activities of these compounds were systematically evaluated in vitro against human carbonic anhydrase (hCA) isozymes I and II, acetylcholinesterase (AChE), and butyrylcholinesterase (BChE). The results revealed that compounds 5a and 5c were particularly effective against cholinesterase enzymes, demonstrating their potential for neuroprotective applications. Meanwhile, compounds 5f and 5g exhibited remarkable inhibition of hCA I and II isozymes, suggesting their promise as selective inhibitors for therapeutic areas. Furthermore, molecular docking analyses revealed strong and specific interactions between the active compounds and enzyme binding sites, further supported by molecular dynamics simulations. Additionally, ADMET profiling of all compounds indicated favourable pharmacokinetic properties. The ADMET results suggest that these compounds hold significant potential for clinical applications in central nervous system and various disorders. These findings strongly suggest that the synthesized compounds are promising candidates for addressing unmet therapeutic needs in neurodegenerative and metabolic disorders, with potential applications in multi-enzyme targeting therapies.

An Integrative Multitiered Computational Analysis for Better Understanding the Structure and Function of 85 Miniproteins

Background

Miniproteins, defined as polypeptides containing fewer than 50 amino acids, have recently elicited significant interest due to an emerging understanding of their diverse roles in fundamental biological processes. In addition, miniprotein dysregulation underlies human diseases and is a significant focus for biotechnology and drug development. Notably, the human genome project revealed the existence of many novel miniproteins, most of which remain uncharacterized. This study reports an approach for analyzing and scoring previously uncharacterized miniproteins by integrating knowledge from classic sequence-based bioinformatics, computational biophysics, and system biology annotations. Our results demonstrate that these approaches provide novel information on the structure-function relationship of these molecules with a particular focus on their biomedical relevance.

Methods

We identified 85 human miniproteins using a simple multi-tier approach. First, we performed a sequence-based analysis of these proteins using several algorithms to identify regions of structural and functional importance. Protein-protein interactions and gene ontology annotations were used to analyze miniprotein function. Then, we predicted miniprotein three-dimensional structures using AI-based methods and peptide modeling to determine their relative yields for these understudied polymers. Subsequently, we used several computational biophysics methods and structure-based calculations to annotate and evaluate results from both algorithms.

Results

We find several relations between predicted structure and functional properties to assign these proteins to several groups with similar properties. Sequence-based analysis leads us to identify motifs and residues that link structure-to-function for most of these proteins. We suggest novel miniprotein functions, such as thymosin beta proteins regulating the shelterin complex through TERF1 and POT1 interactions, FAM86JP and FAM66E participating in endocytic processes, and BAGE1 influencing chromatin remodeling through interaction with nuclear proteins. Further, known functions of miniproteins, such as STRIT1, STMP1, and SLN, were supported. Finally, structure-based scoring led us to build 3D models that provided complementary information to ontologies. We identify that structural propensity is not strictly dependent on polymer length. In fact, in this dataset, peptide-based algorithms may have advantages over AI-based algorithms for certain groups of miniproteins.

Conclusion

This analytic approach and resulting identification and annotation of miniproteins adds much to what is currently known about miniproteins. Our determination of novel properties of miniproteins bears significant mechanistic and biomedical relevance. We propose novel functions of miniproteins, which expands our understanding of their potential roles in cellular processes. And, we practically identify which sequence and structure-based tools provide the most information, aiding future studies of miniproteins.

Prokaryotic cellulase gene clusters derived from 2,305 metagenomes

Cellulose is a carbon source widespread in nature. However, it is a difficult task for any organism to get carbon atoms from the cellulose as it has a highly complex structure. Only a few taxonomic groups are known to decompose cellulose. They do it by producing cellulases, the various enzymes which break beta-glycosidic bonds in the cellulose. Cellulases were identified in 1,735 metagenomes from 225 bioprojects. The set of 12,837 metagenome-derived cellulases encompass three catalytic functions: exoglucanases (CBH, 1,042), endoglucanases (EG, 5,685), and beta-glucosidases (βG, 6,110). All three enzymatic functions are thought to be necessary for driving cellulase to a cascade of reactions that can make cellulose available as glucose. These metagenome-derived cellulases were clustered into protein families for each EC category individually, resulting in a total of 136 clusters, with the majority observed for EG (97 clusters), followed by βG (19 clusters) and CBH (19 clusters). These clusters provided a useful cellulase dataset for future research on cellulase utilization.

Massive Gene Loss in the Fungus Sporothrix epigloea Accompanied a Shift to Life in a Glucuronoxylomannan-based Gel Matrix

Fungi are well-known for their ability to both produce and catabolize complex carbohydrates to acquire carbon, often in the most extreme of environments. Glucuronoxylomannan (GXM)-based gel matrices are widely produced by fungi in nature and though they are of key interest in medicine and pharmaceuticals, their biodegradation is poorly understood. Though some organisms, including other fungi, are adapted to life in and on GXM-like matrices in nature, they are almost entirely unstudied, and it is unknown if they are involved in matrix degradation. Sporothrix epigloea is an ascomycete fungus that completes its life cycle entirely in the short-lived secreted polysaccharide matrix of a white jelly fungus, Tremella fuciformis. To gain insight into how S. epigloea adapted to life in this unusual microhabitat, we compared the predicted protein composition of S. epigloea to that of 21 other Sporothrix species. We found that the genome of S. epigloea is smaller than that of any other sampled Sporothrix, with widespread functional gene loss, including those coding for serine proteases and biotin synthesis. In addition, many predicted CAZymes degrading both plant and fungal cell wall components were lost while a lytic polysaccharide monooxygenase with no previously established activity or substrate specificity, appears to have been gained. Phenotype assays suggest narrow use of mannans and other oligosaccharides as carbon sources. Taken together, the results suggest a streamlined machinery, including potential carbon sourcing from GXM building blocks, facilitates the hyperspecialized ecology of S. epigloea in the GXM-like milieu.

Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction

Identifying and characterizing virulence proteins secreted by Gram-negative bacteria are fundamental for deciphering microbial pathogenicity as well as aiding the development of therapeutic strategies. Effector predictors utilizing pre-trained protein language models (PLMs) have shown sound performance by leveraging extensive evolutionary and sequential protein features. However, the accuracy and sensitivity of effector prediction remain challenging. Here, we introduce a model named Contrastive-learning of Language Embedding and Biological Features (CLEF) leveraging contrastive learning to integrate PLM representations with supplementary biological features. Biologically information is captured in learned contextualized embeddings to yield meaningful representations. With cross-modality biological features, CLEF outperforms state-of-the-art (SOTA) models in predicting type III, type IV, and type VI secreted effectors (T3SEs/T4SEs/T6SEs) in enteric pathogens. All experimentally verified effectors in Enterohemorrhagic Escherichia coli and 41 of 43 experimentally verified T3SEs of Salmonella Typhimurium are recognized. Moreover, 12 predicted T3SEs and 11 predicted T6SEs are validated by extensive experiments in Edwardsiella piscicida. Furthermore, integrating omics data via CLEF framework enhances protein representations to illustrate effector-effector interactions and determine in vivo colonization-essential genes. Collectively, CLEF provides a blueprint to bridge the gap between in silico PLM's capacity and experimental biological information to fulfill complicated tasks.

Unraveling the anti-breast cancer activity of Cimicifugae rhizoma using biological network pathways and molecular dynamics simulation

Cimicifugae is a commonly used treatment for breast cancer, but the specific molecular mechanisms underlying its effectiveness remain unclear. In this research, we employ a combination of network pharmacology, molecular docking, and molecular dynamics simulations to uncover the most potent phytochemical within Cimicifugae rhizoma in order to delve into its interaction with the target protein in breast cancer treatment. We identified 18 active compounds and 89 associated targets, primarily associated to various biological processes such as lipid metabolism, the signaling pathway in diabetes, viral infections, and cancer-related pathways. Molecular docking analysis revealed that the two most active compounds, Formononetin and Cimigenol, exhibit strong binding to the target protein AKT1. Through molecular dynamics simulations, we found that the Cimigenol-AKT1 complex exhibits greater structural stability and lower interaction energy compared to the stigmasterol-AKT1 complex. Our study demonstrates that Cimicifugae rhizoma exerts its effects in breast cancer treatment through a multi-component, multi-target synergistic approach. Furthermore, we propose that Cimigenol, targeting AKT-1, represents the most effective compound, offering valuable insights into the molecular mechanisms underpinning its role in breast cancer therapy.

Updates implemented in version 4 of the GlyCosmos Glycoscience Portal

Glycosylation, characterized by its complexity and diversity, is a common system across all domains of life. The glycosylation of proteins or lipids imparts them with structural and functional roles, ranging from development to infectious or Mendelian disease. The high-throughput-based omics data has revealed that glycans are involved in important cellular processes. Comprehensive knowledge of glycosylation has contributed not only to the fundamental concepts in glycoscience but also to its applications, including the development of molecular markers for diagnosis and therapeutic tools for treating diseases. The GlyCosmos Glycoscience Portal (GlyCosmos) has undergone significant updates to better support the scientific community in studying glycosylation-related phenomena. Key enhancements include the integration of expanded datasets linking glycans to other omics fields, improved tools for glycan structure prediction and analysis, and upgraded visualization capabilities to streamline data interpretation. A strengthened focus on data standardization has also been introduced, fostering interoperability between glycoscience resources and external databases. Since its release in 2019, the portal has seen a fivefold increase in user engagement, reflecting its growing relevance. These recent advancements aim to provide researchers with a more comprehensive and user-friendly platform, enabling deeper insights into glycan roles in cellular processes and disease mechanisms. GlyCosmos will continue to evolve, prioritizing community needs and advancing the integration of glycoscience with broader biological and biomedical research.

Structural prediction of chimeric immunogen candidates to elicit targeted antibodies against betacoronaviruses

Betacoronaviruses pose an ongoing pandemic threat. Antigenic evolution of the SARS-CoV-2 virus has shown that much of the spontaneous antibody response is narrowly focused rather than broadly neutralizing against even SARS-CoV-2 variants, let alone future threats. One way to overcome this is by focusing the antibody response against better-conserved regions of the viral spike protein. This has been demonstrated empirically in prior work, but we posit that systematic design tools will further potentiate antigenic focusing approaches. Here, we present a design approach to predict stable chimeras between SARS-CoV-2 and other coronaviruses, creating synthetic spike proteins that display a desired conserved region, in this case S2, and vary other regions. We leverage AlphaFold to predict chimeric structures and create a new metric for scoring chimera stability based on AlphaFold outputs. We evaluated 114 candidate spike chimeras using this approach. Top chimeras were further evaluated using molecular dynamics simulation as an intermediate validation technique, showing good stability compared to low-scoring controls. Experimental testing of five predicted-stable and two predicted-unstable chimeras confirmed 5/7 predictions, with one intermediate result. This demonstrates the feasibility of the underlying approach, which can be used to design custom immunogens to focus the immune response against a desired viral glycoprotein epitope.

Genomic landscaping of receptor tyrosine kinase ALK with highly frequent rearrangements in cancers

Anaplastic lymphoma kinase (ALK) fusion tyrosine kinases (TKs) are commonly found in various cancers and are considered as promising targets for therapy due to their intricate biological processes. However, the reasons for the higher frequency of ALKs fusion compared to other TKs are not well elucidated. Physicochemical properties, secondary and tertiary structures, and phylogenetic trees, along with protein sequence alignments of receptor tyrosine kinases (RTKs) and ALK fused partner genes, were examined using the resources provided by the National Center for Biotechnology Information (NCBI) and the Catalogue of Somatic Mutations in Cancer (COSMIC). Sequence alignments were performed to identify common sequences between partner genes and search for common breakpoints within the COSMIC database. ALK is a large, unstable, acidic protein with similarly conservation among RTKs. ALK fusion partners are mostly acidic, unstable proteins, mostly consisting of α-helices and random coil. However, EML4 and NPM1 are the most frequently occurring partner genes and have their own unique structural characteristics. By functional domain analysis, we found that the functions of the first half of the ALK partner gene (the part fused to ALK) are mostly focused on signaling. ALK is identified as a large hydrophilic protein，exhibits a higher proportion of random coils. Compared to other RTKs, ALK has fewer structural domains (PTKC_ALK_LTK domain). Pairwise comparison with fusion partner genes revealed a conserved sequence predicted to have structural stability and act as a common binding site for nucleases. Exon 20 of ALK is a fusion frequent site according to COSMIC database analysis. The structural instability of ALK and partner genes, coupled with the inherent variability of breakpoint sequences, leads to the formation of potent kinase-activated oncogenes, which play a critical role in tumorigenesis. While the occurrence of ALK fusions with partner genes is random, specific combinations lead to the generation of oncogenes.

Comparative assessment of different anti-CD147/Basigin 2 antibodies as a potential therapeutic anticancer target by molecular modeling and dynamic simulation

Cluster of differentiation 147 (CD147) is an attractive target for anticancer therapy since it is pivotal in developing and progressing several of malignant tumors in the context of its high expression levels. Although anti-CD147 antibodies by different laboratories are designed for the Ig-like domains of CD147, there is a demand to provide priority among these anti-CD147 antibodies for developing of therapeutic anti-CD147 antibody before experimental validations. This study uses molecular docking and dynamic simulation techniques to compare the binding modes and affinities of nine antibody models against the Ig-like domains of CD147. After obtaining the model antibodies by homology modeling via Robetta, we predicted the CDRs of nine antibodies and the epitopes of CD147 to reach more accurate results for antigen affinity in molecular docking. Next, from HADDOCK 2.4., we meticulously handpicked the most superior model clusters (Z-Score: - 2.5 to - 1.2) and identified that meplazumab had higher affinities according to the success rate as the percentage of a scoring scale. We achieved stable simulations of CD147-antibody interaction. Our outcomes hold hypothetical importance for further experimental cancer research on the design and development of the relevant model antibodies.

Structural and molecular investigation of the impact of S30L and D88N substitutions in G9R protein on coupling with E4R from Monkeypox virus (MPXV)

Understanding the pathogenesis mechanism of the Monkeypox virus (MPXV) is essential to guide therapeutic development against the Monkeypox virus. In the current study, we investigated the impact of the only two reported substitutions, S30L, D88N, and S30L-D88N on the G9R of the replication complex in 2022 with E4R using structural modeling, simulation, and free energy calculation methods. From the molecular docking and dissociation constant (KD) results, it was observed that the binding affinity did not increase in the mutants, but the interaction paradigm was altered by these substitutions. Molecular simulation data revealed that these mutations are responsible for destabilization, changes in protein packing, and internal residue fluctuations, which can cause functional variance. Additionally, hydrogen bonding analysis revealed that the estimated number of hydrogen bonds are almost equal among the wild-type G9R and each mutant. The total binding free energy for the wild-type G9R with E4R was -85.00 kcal/mol while for the mutants the TBE was -42.75 kcal/mol, -43.68 kcal/mol, and -48.65 kcal/mol respectively. This shows that there is no direct impact of these two reported mutations on the binding with E4R, or it may affect the whole replication complex or any other mechanism involved in pathogenesis. To explore these variations further, we conducted PCA and FEL analyses. Based on our findings, we speculate that within the context of interaction with E4R, the mutations in the G9R protein might be benign, potentially leading to functional diversity associated with other proteins.Communicated by Ramaswamy H. Sarma.

AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations

Protein structure holds immense potential for pathogenicity prediction, albeit structure-based predictors are limited compared to the sequence-based counterparts due to the "structure knowledge gap" between large number of available protein sequences and relatively limited number of structures. Leveraging the highly accurate protein structures predicted by AlphaFold2 (AF2), we introduce AFFIPred, an ensemble machine learning classifier that combines sequence and AF2-based structural characteristics to predict missense variant pathogenicity. Based on the assessments on unseen datasets, AFFIPred reached a comparable level of performance with the state-of-the-art predictors such as AlphaMissense. We also showed that the recruitment of AF2 structures that are full-length and represent the unbound states ensures more precise SASA calculations compared to the recruitment of experimental structures. In line with the completeness of the AF2 structures, their use provide a more comprehensive view of the structural characteristics of the missense variation datasets by capturing all variants. AFFIPred maintains high-level accuracy without the limitations of PDB-based classifiers. AFFIPred has predicted over 210 million variations of the human proteome, which are accessible at https://affipred.timucinlab.com/.

Binding of Selected Ligands to Human Protein Disulfide Isomerase and Microsomal Triglyceride Transfer Protein Complex and the Associated Conformational Changes: A Computational Molecular Modelling Study

Human protein disulfide isomerase (PDI) is a multifunctional protein, and also serves as the β subunit of the human microsomal triglyceride transfer protein (MTP) complex, a lipid transfer machinery. Dysfunction of the MTP complex is associated with certain disease conditions such as abetalipoproteinemia and cardiovascular diseases. It is known that the functions of PDI or the MTP complex can be regulated by the binding of a small-molecule ligand to either of these two proteins. In the present study, the conformational changes of the MTP complex upon the binding of three selected small-molecule ligands (17β-estradiol, lomitapide and a phospholipid) are investigated based on the available biochemical and structural information by using the protein-ligand docking method and molecular dynamics (MD) simulation. The ligand-binding sites, the binding poses and binding strengths, the key binding site residues, and the ligand binding-induced conformational changes in the MTP complex are analyzed based on the MD trajectories. The open-to-closed or closed-to-open transitions of PDI is found to occur in both reduced and oxidized states of PDI and also independent of the presence or absence of small-molecule ligands. It is predicted that lomitapide and 1,2-diacyl-sn-glycero-3-phosphocholine (a phospholipid) can bind inside the lipid-binding pocket in the MTP complex with high affinities, whereas 17β-estradiol interacts with the lipid-binding pocket in addition to its binding to the interface region of the MTP complex. Additionally, lomitapide can bind to the b' domain of PDI as reported earlier for E2. Key residues for the ligand-binding interactions are identified in this study. It will be of interest to further explore whether the binding of small molecules can facilitate the conformational transitions of PDI in the future. The molecular and structural insights gained from the present work are of value for understanding some of the important biological functions of PDI and the MTP complex.