UniProt: the universal protein knowledgebase in 2021

Structural and functional analyses of nematode-derived antimicrobial peptides support the occurrence of direct mechanisms of worm-microbiota interactions

The complex relationships between gastrointestinal (GI) nematodes and the host gut microbiota have been implicated in key aspects of helminth disease and infection outcomes. Nevertheless, the direct and indirect mechanisms governing these interactions are, thus far, largely unknown. In this proof-of-concept study, we demonstrate that the excretory-secretory products (ESPs) and extracellular vesicles (EVs) of key GI nematodes contain peptides that, when recombinantly expressed, exert antimicrobial activity in vitro against Bacillus subtilis. In particular, using time-lapse microfluidics microscopy, we demonstrate that exposure of B. subtilis to a recombinant saposin-domain containing peptide from the 'brown stomach worm', Teladorsagia circumcincta, and a metridin-like ShK toxin from the 'barber's pole worm', Haemonchus contortus, results in cell lysis and significantly reduced growth rates. Data from this study support the hypothesis that GI nematodes may modulate the composition of the vertebrate gut microbiota directly via the secretion of antimicrobial peptides, and pave the way for future investigations aimed at deciphering the impact of such changes on the pathophysiology of GI helminth infection and disease.

DeepNeuropePred: A robust and universal tool to predict cleavage sites from neuropeptide precursors by protein language model

Neuropeptides play critical roles in many biological processes such as growth, learning, memory, metabolism, and neuronal differentiation. A few approaches have been reported for predicting neuropeptides that are cleaved from precursor protein sequences. However, these models for cleavage site prediction of precursors were developed using a limited number of neuropeptide precursor datasets and simple precursors representation models. In addition, a universal method for predicting neuropeptide cleavage sites that can be applied to all species is still lacking. In this paper, we proposed a novel deep learning method called DeepNeuropePred, using a combination of pre-trained language model and Convolutional Neural Networks for feature extraction and predicting the neuropeptide cleavage sites from precursors. To demonstrate the model's effectiveness and robustness, we evaluated the performance of DeepNeuropePred and four models from the NeuroPred server in the independent dataset and our model achieved the highest AUC score (0.916), which are 6.9%, 7.8%, 8.8%, and 10.9% higher than Mammalian (0.857), insects (0.850), Mollusc (0.842) and Motif (0.826), respectively. For the convenience of researchers, we provide a web server (http://isyslab.info/NeuroPepV2/deepNeuropePred.jsp).

Visualizing metagenomic and metatranscriptomic data: A comprehensive review

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

PS-GO parametric protein search engine

With the explosive growth of protein-related data, we are confronted with a critical scientific inquiry: How can we effectively retrieve, compare, and profoundly comprehend these protein structures to maximize the utilization of such data resources? PS-GO, a parametric protein search engine, has been specifically designed and developed to maximize the utilization of the rapidly growing volume of protein-related data. This innovative tool addresses the critical need for effective retrieval, comparison, and deep understanding of protein structures. By integrating computational biology, bioinformatics, and data science, PS-GO is capable of managing large-scale data and accurately predicting and comparing protein structures and functions. The engine is built upon the concept of parametric protein design, a computer-aided method that adjusts and optimizes protein structures and sequences to achieve desired biological functions and structural stability. PS-GO utilizes key parameters such as amino acid sequence, side chain angle, and solvent accessibility, which have a significant influence on protein structure and function. Additionally, PS-GO leverages computable parameters, derived computationally, which are crucial for understanding and predicting protein behavior. The development of PS-GO underscores the potential of parametric protein design in a variety of applications, including enhancing enzyme activity, improving antibody affinity, and designing novel functional proteins. This advancement not only provides a robust theoretical foundation for the field of protein engineering and biotechnology but also offers practical guidelines for future progress in this domain.

Language models can identify enzymatic binding sites in protein sequences

Recent advances in language modeling have had a tremendous impact on how we handle sequential data in science. Language architectures have emerged as a hotbed of innovation and creativity in natural language processing over the last decade, and have since gained prominence in modeling proteins and chemical processes, elucidating structural relationships from textual/sequential data. Surprisingly, some of these relationships refer to three-dimensional structural features, raising important questions on the dimensionality of the information encoded within sequential data. Here, we demonstrate that the unsupervised use of a language model architecture to a language representation of bio-catalyzed chemical reactions can capture the signal at the base of the substrate-binding site atomic interactions. This allows us to identify the three-dimensional binding site position in unknown protein sequences. The language representation comprises a reaction-simplified molecular-input line-entry system (SMILES) for substrate and products, and amino acid sequence information for the enzyme. This approach can recover, with no supervision, 52.13% of the binding site when considering co-crystallized substrate-enzyme structures as ground truth, vastly outperforming other attention-based models.

Toxicological study on ibuprofen and selenium in freshwater mussel Lamellidens marginalis and exploring the microbial cytochrome through modelling and quantum mechanics approaches for its toxicity degradation in contaminated environment

Toxicological stress in aquatic organisms is caused by the discharge of hundreds of toxic pollutants and contaminants among which the current study concentrates on the toxic effect of non-steroidal anti-inflammatory drug ibuprofen (IBF) and the trace element selenium (Se). In this study, IBF and Se toxicity on freshwater mussel Lamellidens marginalis was studied for 14 days, and in silico predictions for their degradation were made using Molecular modelling and Quantum Mechanical approaches. The degrading propensity of cytochrome c oxidase proteins from Trametes verticillatus and Thauera selenatis (Turkey tail fungi and Gram-negative bacteria) is examined into atom level. The results of molecular modelling study indicate that ionic interactions occur in the T. selenatis-HEME bound complex by Se interacting directly with HEME, and in the T. versicolor-HEME bound complex by IBF bound to a nearby region of HEME. Experimental and theoretical findings suggest that, the toxicological effects of Se and IBF pollution can be reduced by bioremediation with special emphasis on T. versicolor, and T. selenatis, which can effectively interact with Se and IBF present in the environment and degrade them. Besides, this is the first time in freshwater mussel L. marginalis that ibuprofen and selenium toxicity have been studied utilizing both experimental and computational methodologies for their bioremediation study.

Decoding functional proteome information in model organisms using protein language models

Protein language models have been tested and proved to be reliable when used on curated datasets but have not yet been applied to full proteomes. Accordingly, we tested how two different machine learning-based methods performed when decoding functional information from the proteomes of selected model organisms. We found that protein language models are more precise and informative than deep learning methods for all the species tested and across the three gene ontologies studied, and that they better recover functional information from transcriptomic experiments. The results obtained indicate that these language models are likely to be suitable for large-scale annotation and downstream analyses, and we recommend a guide for their use.

Synthesis, insecticidal Activity, and molecular docking analysis of some benzo[h]quinoline derivatives against Culex pipiens L. Larvae

Some heterocycles bearing a benzo[h]quinoline moiety were synthesized through treating a 3-((2-chlorobenzo[h]quinolin-3-yl)methylene)-5-(p-tolyl)furan-2(3H)-one with four nitrogen nucleophiles comprising ammonium acetate, benzylamine, dodecan-1-amine, and 1,2-diaminoethane. Also, thiation reactions of furanone and pyrrolinone derivatives were investigated. The insecticidal activity of these compounds against mosquito larvae (Culex pipiens L.) was evaluated. All tested compounds exhibited significant larvicidal activity, surpassing that of the conventional insecticide chlorpyrifos. In silico docking analysis revealed that these compounds may act as acetyl cholinesterase (AChE) inhibitors, potentially explaining their larvicidal effect. Additionally, interactions with other neuroreceptors, such as nicotinic acetylcholine receptor and sodium channel voltage-gated alpha subunit were also predicted. The results obtained from this study reflected the potential of benzo[h]quinoline derivatives as promising candidates for developing more effective and sustainable mosquito control strategies. The ADME (absorption, distribution, metabolism, and excretion) analyses displayed their desirable drug-likeness and oral bioavailability properties.

Exploring the potential molecular intersection of stroke and major depression disorder

Stroke and major depression disorder are common neurological diseases, and a large number of clinical studies have shown that there is a close relationship between the two diseases, but whether the two diseases are linked at the genetic level needs to be further explored. The purpose of this study was to explore the comorbidity mechanism of stroke and major depression by using bioinformatics technology and animal experiments. From the GEO database, we gathered transcriptome data of stroke and depression mice (GSE104036, GSE131712, GSE81672, and GSE146845) and identified comorbid gene set through edgR and WGCNA analyses. Further analysis revealed that these genes were enriched in pathways associated with cell death. Programmed cell death gene sets (PCDGs) are generated from genes related to apoptosis, necroptosis, pyroptosis and autophagy. The intersection of PCDGs and comorbid gene set resulted in two hub genes, Mlkl and Nlrp3. Single-cell sequencing analysis indicated that Mlkl and Nlrp3 are mainly influential on endothelial cells and microglia, suggesting that the impairment of these two cell types may be a factor in the relationship between stroke and major depression. This was experimentally confirmed by RT-PCR and immunofluorescence staining. Our research revealed that two specific genes, namely, Mlkl and Nlrp3, play crucial roles in the complex mechanism that links stroke and major depression. Additionally, we have predicted six possible therapeutic agents and the outcomes of docking simulations of target proteins and drug molecules.

Exploiting protein language models for the precise classification of ion channels and ion transporters

This study introduces TooT-PLM-ionCT, a comprehensive framework that consolidates three distinct systems, each meticulously tailored for one of the following tasks: distinguishing ion channels (ICs) from membrane proteins (MPs), segregating ion transporters (ITs) from MPs, and differentiating ICs from ITs. Drawing upon the strengths of six Protein Language Models (PLMs)-ProtBERT, ProtBERT-BFD, ESM-1b, ESM-2 (650M parameters), and ESM-2 (15B parameters), TooT-PLM-ionCT employs a combination of traditional classifiers and deep learning models for nuanced protein classification. Originally validated on an existing dataset by previous researchers, our systems demonstrated superior performance in identifying ITs from MPs and distinguishing ICs from ITs, with the IC-MP discrimination achieving state-of-the-art results. In light of recommendations for additional validation, we introduced a new dataset, significantly enhancing the robustness and generalization of our models across bioinformatics challenges. This new evaluation underscored the effectiveness of TooT-PLM-ionCT in adapting to novel data while maintaining high classification accuracy. Furthermore, this study explores critical factors affecting classification accuracy, such as dataset balancing, the impact of using frozen versus fine-tuned PLM representations, and the variance between half and full precision in floating-point computations. To facilitate broader application and accessibility, a web server (https://tootsuite.encs.concordia.ca/service/TooT-PLM-ionCT) has been developed, allowing users to evaluate unknown protein sequences through our specialized systems for IC-MP, IT-MP, and IC-IT classification tasks.

Graphical models for identifying pore-forming proteins

Pore-forming toxins (PFTs) are proteins that form lesions in biological membranes. Better understanding of the structure and function of these proteins will be beneficial in a number of biotechnological applications, including the development of new pest control methods in agriculture. When searching for new pore formers, existing sequence homology-based methods fail to discover truly novel proteins with low sequence identity to known proteins. Search methodologies based on protein structures would help us move beyond this limitation. As the number of known structures for PFTs is very limited, it's quite challenging to identify new proteins having similar structures using computational approaches like deep learning. In this article, we therefore propose a sample-efficient graphical model, where a protein structure graph is first constructed according to consensus secondary structures. A semi-Markov conditional random fields model is then developed to perform protein sequence segmentation. We demonstrate that our method is able to distinguish structurally similar proteins even in the absence of sequence similarity (pairwise sequence identity < 0.4)-a feat not achievable by traditional approaches like HMMs. To extract proteins of interest from a genome-wide protein database for further study, we also develop an efficient framework for UniRef50 with 43 million proteins.

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

Screening study on significant Chinese herb for anti-idiopathic pulmonary fibrosis by combining clinical experience prescriptions and molecular dynamics simulation technologies

Various techniques such as data mining, network pharmacology, molecular docking and molecular dynamics simulation were used in this study to screen and validate effective herbal medicines for the treatment of idiopathic pulmonary fibrosis (IPF) and to reveal their mechanisms of action at the molecular level. The use of this approach will provide new tools and ideas for future drug screening, especially for the application of herbal medicines in the treatment of complex diseases. Among them, the five identified core targets, including IL6, TP53, AKT1, VEGFA, and TNF, as well as a series of major active compounds, will be important references for future anti-IPF drug development. This information will accelerate the discovery and development of relevant drugs. Meanwhile, this study further confirmed the potential value of four Chinese herbal medicines, including Gancao, Danshen, Huangqin, and Sanqi, in the treatment of IPF. This will promote more clinical trials and practices to confirm and optimise the application of these herbs. Finally, this study is an important theoretical guide to enhance the advantages of Chinese herbal medicines in the prevention and treatment of major and difficult diseases, as well as to understand and utilise the potential efficacy of Chinese herbal medicines. This will further promote the scientific research and clinical application of herbal medicines and provide more possibilities for future disease treatmentCommunicated by Ramaswamy H. Sarma.

Novel variants in CSF1R associated with adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP)

The CSF1R gene, located on chromosome 5, encodes a 108 kDa protein and plays a critical role in regulating myeloid cell function. Mutations in CSF1R have been identified as a cause of a rare white matter disease called adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP, also known as CSF1R-related leukoencephalopathy), characterized by progressive neurological dysfunction. This study aimed to broaden the genetic basis of ALSP by identifying novel CSF1R variants in patients with characteristic clinical and imaging features of ALSP. Genetic analysis was performed through whole-exome sequencing or panel analysis for leukodystrophy genes. Variant annotation and classification were conducted using computational tools, and the identified variants were categorized following the recommendations of the American College of Medical Genetics and Genomics (ACMG). To assess the evolutionary conservation of the novel variants within the CSF1R protein, amino acid sequences were compared across different species. The study identified six previously unreported CSF1R variants (c.2384G>T, c.2133_2919del, c.1837G>A, c.2304C>A, c.2517G>T, c.2642C>T) in seven patients with ALSP, contributing to the expanding knowledge of the genetic diversity underlying this rare disease. The analysis revealed considerable genetic and clinical heterogeneity among these patients. The findings emphasize the need for a comprehensive understanding of the genetic basis of rare diseases like ALSP and underscored the importance of genetic testing, even in cases with no family history of the disease. The study's contribution to the growing spectrum of ALSP genetics and phenotypes enhances our knowledge of this condition, which can be crucial for both diagnosis and potential future treatments.

PTGS2 as target of compound Huangbai liquid in the nursing of pressure ulcer

OBJECTIVE

Pressure ulcer refers to ulceration and necrosis caused by local skin and cell tissues being compressed for a long time, continuous ischemia, hypoxia, and malnutrition. However, role of prostaglandin-endoperoxide synthase 2 (PTGS2) in the management of pressure ulcers in with compound Huangbai liquid is still unclear.

METHODS

Traditional Chinese medicine components and related targets of compound Huangbai liquid were collected through traditional Chinese medicine systems pharmacology (TCMSP) and Batman-traditional Chinese medicine database. Disease-related targets were obtained using the Gene Cards database. The protein-protein interaction (PPI) network was constructed using the Search tool for retrieval of interacting genes (STRING) and analyzed by Cytoscape to obtain the core components. To evaluate the clinical efficacy of the compound Huangbai liquid in the treatment of pressure ulcers, 40 patients with pressure ulcers were selected and divided into an observation group and a control group, with 20 individuals in each group. The observation group received treatment with compound Huangbai liquid.

RESULTS

Sixty-five components and 480 targets of compound Huangbai liquid were obtained from TCMSP and Batman - traditional Chinese medicine databases. Two hundred seventy-three pressure ulcer-related targets were obtained. Seventy-two potential targets of compound Huangbai pigment in treatment of pressure ulcer were obtained, and 2 unrelated targets were deleted. There were 70 nodes and 1167 edges in PPI network. Gene ontology (GO) function is involved in biological processes such as reactive oxygen species metabolism and cellular response to chemical stress. Cellular components such as platelet α granules lumen and membrane rafts were involved. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results showed that compound Huangbai liquid in treatment of pressure ulcer. The clinical results indicate that the compound Huangbai liquid has a good therapeutic effect on pressure ulcers.

CONCLUSION

PTGS2 may be a target for treatment of pressure ulcers with compound Huangbai liquid, providing a new direction for its treatment.

DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms

DNA-binding proteins (DBPs) play critical roles in many biological processes, including gene expression, DNA replication, recombination and repair. Understanding the molecular mechanisms underlying these processes depends on the precise identification of DBPs. In recent times, several computational methods have been developed to identify DBPs. However, because of the generic nature of the models, these models are unable to identify species-specific DBPs with higher accuracy. Therefore, a species-specific computational model is needed to predict species-specific DBPs. In this paper, we introduce the computational DBPMod method, which makes use of a machine learning approach to identify species-specific DBPs. For prediction, both shallow learning algorithms and deep learning models were used, with shallow learning models achieving higher accuracy. Additionally, the evolutionary features outperformed sequence-derived features in terms of accuracy. Five model organisms, including Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Homo sapiens and Mus musculus, were used to assess the performance of DBPMod. Five-fold cross-validation and independent test set analyses were used to evaluate the prediction accuracy in terms of area under receiver operating characteristic curve (auROC) and area under precision-recall curve (auPRC), which was found to be ~89-92% and ~89-95%, respectively. The comparative results demonstrate that the DBPMod outperforms 12 current state-of-the-art computational approaches in identifying the DBPs for all five model organisms. We further developed the web server of DBPMod to make it easier for researchers to detect DBPs and is publicly available at https://iasri-sg.icar.gov.in/dbpmod/. DBPMod is expected to be an invaluable tool for discovering DBPs, supplementing the current experimental and computational methods.

Mixotrophic growth of a ubiquitous marine diatom

Diatoms are major players in the global carbon cycle, and their metabolism is affected by ocean conditions. Understanding the impact of changing inorganic nutrients in the oceans on diatoms is crucial, given the changes in global carbon dioxide levels. Here, we present a genome-scale metabolic model (iMK1961) for Cylindrotheca closterium, an in silico resource to understand uncharacterized metabolic functions in this ubiquitous diatom. iMK1961 represents the largest diatom metabolic model to date, comprising 1961 open reading frames and 6718 reactions. With iMK1961, we identified the metabolic response signature to cope with drastic changes in growth conditions. Comparing model predictions with Tara Oceans transcriptomics data unraveled C. closterium's metabolism in situ. Unexpectedly, the diatom only grows photoautotrophically in 21% of the sunlit ocean samples, while the majority of the samples indicate a mixotrophic (71%) or, in some cases, even a heterotrophic (8%) lifestyle in the light. Our findings highlight C. closterium's metabolic flexibility and its potential role in global carbon cycling.

Dissecting the role of the MS-ring protein FliF in Bacillus cereus flagella-related functions

The flagellar MS-ring, uniquely constituted by FliF, is essential for flagellar biogenesis and functionality in several bacteria. The aim of this study was to dissect the role of FliF in the Gram-positive and peritrichously flagellated Bacillus cereus. We demonstrate that fliF forms an operon with the upstream gene fliE. In silico analysis of B. cereus ATCC 14579 FliF identifies functional domains and amino acid residues that are essential for protein functioning. The analysis of a ΔfliF mutant of B. cereus, constructed in this study using an in frame markerless gene replacement method, reveals that the mutant is unexpectedly able to assemble flagella, although in reduced amounts compared to the parental strain. Nevertheless, motility is completely abolished by fliF deletion. FliF deprivation causes the production of submerged biofilms and affects the ability of B. cereus to adhere to gastrointestinal mucins. We additionally show that the fliF deletion does not compromise the secretion of the three components of hemolysin BL, a toxin secreted through the flagellar type III secretion system. Overall, our findings highlight the important role of B. cereus FliF in flagella-related functions, being the protein required for complete flagellation, motility, mucin adhesion, and pellicle biofilms.

A power-based sliding window approach to evaluate the clinical impact of rare genetic variants in the nucleotide sequence or the spatial position of the folded protein

Systematic determination of novel variant pathogenicity remains a major challenge, even when there is an established association between a gene and phenotype. Here we present Power Window (PW), a sliding window technique that identifies the impactful regions of a gene using population-scale clinico-genomic datasets. By sizing analysis windows on the number of variant carriers, rather than the number of variants or nucleotides, statistical power is held constant, enabling the localization of clinical phenotypes and removal of unassociated gene regions. The windows can be built by sliding across either the nucleotide sequence of the gene (through 1D space) or the positions of the amino acids in the folded protein (through 3D space). Using a training set of 350k exomes from the UK Biobank (UKB), we developed PW models for well-established gene-disease associations and tested their accuracy in two independent cohorts (117k UKB exomes and 65k exomes sequenced at Helix in the Healthy Nevada Project, myGenetics, or In Our DNA SC studies). The significant models retained a median of 49% of the qualifying variant carriers in each gene (range 2%-98%), with quantitative traits showing a median effect size improvement of 66% compared with aggregating variants across the entire gene, and binary traits' odds ratios improving by a median of 2.2-fold. PW showcases that electronic health record-based statistical analyses can accurately distinguish between novel coding variants in established genes that will have high phenotypic penetrance and those that will not, unlocking new potential for human genomics research, drug development, variant interpretation, and precision medicine.

HOTGpred: Enhancing human O-linked threonine glycosylation prediction using integrated pretrained protein language model-based features and multi-stage feature selection approach

O-linked glycosylation is a complex post-translational modification (PTM) in human proteins that plays a critical role in regulating various cellular metabolic and signaling pathways. In contrast to N-linked glycosylation, O-linked glycosylation lacks specific sequence features and maintains an unstable core structure. Identifying O-linked threonine glycosylation sites (OTGs) remains challenging, requiring extensive experimental tests. While bioinformatics tools have emerged for predicting OTGs, their reliance on limited conventional features and absence of well-defined feature selection strategies limit their effectiveness. To address these limitations, we introduced HOTGpred (Human O-linked Threonine Glycosylation predictor), employing a multi-stage feature selection process to identify the optimal feature set for accurately identifying OTGs. Initially, we assessed 25 different feature sets derived from various pretrained protein language model (PLM)-based embeddings and conventional feature descriptors using nine classifiers. Subsequently, we integrated the top five embeddings linearly and determined the most effective scoring function for ranking hybrid features, identifying the optimal feature set through a process of sequential forward search. Among the classifiers, the extreme gradient boosting (XGBT)-based model, using the optimal feature set (HOTGpred), achieved 92.03 % accuracy on the training dataset and 88.25 % on the balanced independent dataset. Notably, HOTGpred significantly outperformed the current state-of-the-art methods on both the balanced and imbalanced independent datasets, demonstrating its superior prediction capabilities. Additionally, SHapley Additive exPlanations (SHAP) and ablation analyses were conducted to identify the features contributing most significantly to HOTGpred. Finally, we developed an easy-to-navigate web server, accessible at https://balalab-skku.org/HOTGpred/, to support glycobiologists in their research on glycosylation structure and function.

Whole-genome sequencing of 13 Arctic plants and draft genomes of Oxyria digyna and Cochlearia groenlandica

To understand the genomic characteristics of Arctic plants, we generated 28-44 Gb of short-read sequencing data from 13 Arctic plants collected from the High Arctic Svalbard. We successfully estimated the genome sizes of eight species by using the k-mer-based method (180-894 Mb). Among these plants, the mountain sorrel (Oxyria digyna) and Greenland scurvy grass (Cochlearia groenlandica) had relatively small genome sizes and chromosome numbers. We obtained 45 × and 121 × high-fidelity long-read sequencing data. We assembled their reads into high-quality draft genomes (genome size: 561 and 250 Mb; contig N50 length: 36.9 and 14.8 Mb, respectively), and correspondingly annotated 43,105 and 29,675 genes using ~46 and ~85 million RNA sequencing reads. We identified 765,012 and 88,959 single-nucleotide variants, and 18,082 and 7,698 structural variants (variant size ≥ 50 bp). This study provided high-quality genome assemblies of O. digyna and C. groenlandica, which are valuable resources for the population and molecular genetic studies of these plants.

Study on the enantioselective behaviors, activity, toxicity and mechanism of novel SDHI fungicide benzovindiflupyr to reduce the environmental risks

Benzovindiflupyr (BEN) has emerged as one of the fastest-growing SDHI fungicides in recent years, but it is considered "very highly toxic" to aquatic fish, invertebrates and crustaceans (EC50 or LC50, 0.0035-0.056 mg/L, acute toxicity). The comprehensive study on bioactivity, toxicity, and degradation behaviors of BEN at the enantiomeric level would facilitate the development of a high-efficiency and low-risk application method. The bioactivities of 1S, 4R-(-)-BEN against five target pathogens (Alternaria alternata, Phoma multirostrata, Selerotium rolfsii, Magnaporthe oryzae, and Rhizoctonia solani) (EC50, 0.00562-0.329 mg/L, high-efficiency) were 6.7-1029 times higher than 1R, 4S-(+)-BEN, demonstrating significant enantioselectivity. For Danio rerio, 1S, 4R-(-)-BEN (LC50, 0.0360 mg/L, "very highly toxic") exhibited higher toxicity than 1 R, 4S-(+)-BEN, but the toxic interaction was concentration addition (TUrac, 0.94), indicating an enhanced toxicity in the presence of 1R, 4S-(+)-BEN. Molecular docking was employed to offer insights at the molecular level and elucidate the factors influencing enantioselectivity. The stronger binding affinity of 1S, 4R-(-)-BEN with SDH was in line with the quantitative experimental findings. The degradation of two BEN enantiomers in four different fruits followed the first-order degradation kinetics equation, and displayed enantioselectivity. The preferential degradation of 1R, 4S-(+)-BEN was found in pears and grapes, while varying enantioselectivity was found at different stages in tomatoes and watermelons. The residual concentrations of BEN in grapes were higher than the EU's MRL, which in the other three fruits were below the MRLs during the sampling. In conclusion, 1S, 4R-(-)-BEN proved to be the more effective monomer. Utilizing the pure monomer could not only reduce the dosage of racemate by about 44-59 %, but also mitigate the risk of introducing inefficient monomer into the environment (especially for fish).

Explore the active ingredients and potential mechanism of action on Actinidia arguta leaves against T2DM by integration of serum pharmacochemistry and network pharmacology

BACKGROUND

Actinidia arguta leaves (AAL) are traditionally consumed as a vegetable and as tea in folk China and Korea. Previous studies have reported the anti-diabetic effect of AAL, but its bioactive components and mechanism of action are still unclear.

AIM OF THE STUDY

This study aims to identify the hypoglycemic active components of AAL by combining serum pharmacochemistry and network pharmacology and to elucidate its possible mechanism of action.

METHODS

Firstly, the effective components in mice serum samples were characterized by UPLC-Q/TOF-MSE. Furthermore, based on these active ingredients, network pharmacology analysis was performed to establish an "H-C-T-P-D" interaction network and reveal possible biological mechanisms. Finally, the affinity between serum AAL components and the main proteins in the important pathways above was investigated through molecular docking analysis.

RESULTS

Serum pharmacochemistry analysis showed that 69 compounds in the serum samples were identified, including 23 prototypes and 46 metabolites. The metabolic reactions mainly included deglycosylation, dehydration, hydrogenation, methylation, acetylation, glucuronidation, and sulfation. Network pharmacology analysis showed that the key components quercetin, pinoresinol diglucoside, and 5-O-trans-p-coumaroyl quinic acid butyl ester mainly acted on the core targets PTGS2, HRAS, RELA, PRKCA, and BCL2 targets and through the PI3K-Akt signaling pathway, endocrine resistance, and MAPK signaling pathway to exert a hypoglycemic effect. Likewise, molecular docking results showed that the three potential active ingredients had good binding effects on the five key targets.

CONCLUSION

This study provides a basis for elucidating the pharmacodynamic substance basis of AA against T2DM and further exploring the mechanism of action.

Reduced Protein Stability of 11 Pathogenic Missense STXBP1/MUNC18-1 Variants and Improved Disease Prediction

BACKGROUND

Pathogenic variants in STXBP1/MUNC18-1 cause severe encephalopathies that are among the most common in genetic neurodevelopmental disorders. Different molecular disease mechanisms have been proposed, and pathogenicity prediction is limited. In this study, we aimed to define a generalized disease concept for STXBP1-related disorders and improve prediction.

METHODS

A cohort of 11 disease-associated and 5 neutral variants (detected in healthy individuals) were tested in 3 cell-free assays and in heterologous cells and primary neurons. Protein aggregation was tested using gel filtration and Triton X-100 insolubility. PRESR (predicting STXBP1-related disorder), a machine learning algorithm that uses both sequence- and 3-dimensional structure-based features, was developed to improve pathogenicity prediction using 231 known disease-associated variants and comparison to our experimental data.

RESULTS

Disease-associated variants, but none of the neutral variants, produced reduced protein levels. Cell-free assays demonstrated directly that disease-associated variants have reduced thermostability, with most variants denaturing around body temperature. In addition, most disease-associated variants impaired SNARE-mediated membrane fusion in a reconstituted assay. Aggregation/insolubility was observed for none of the variants in vitro or in neurons. PRESR outperformed existing tools substantially: Matthews correlation coefficient = 0.71 versus <0.55.

CONCLUSIONS

These data establish intrinsic protein instability as the generalizable, primary cause for STXBP1-related disorders and show that protein-specific ortholog and 3-dimensional information improve disease prediction. PRESR is a publicly available diagnostic tool.

Genomic characterization and virulence gene profiling of Erysipelothrix rhusiopathiae isolated from widespread muskox mortalities in the Canadian Arctic Archipelago

BACKGROUND

Muskoxen are important ecosystem components and provide food, economic opportunities, and cultural well-being for Indigenous communities in the Canadian Arctic. Between 2010 and 2021, Erysipelothrix rhusiopathiae was isolated from carcasses of muskoxen, caribou, a seal, and an Arctic fox during multiple large scale mortality events in the Canadian Arctic Archipelago. A single strain ('Arctic clone') of E. rhusiopathiae was associated with the mortalities on Banks, Victoria and Prince Patrick Islands, Northwest Territories and Nunavut, Canada (2010-2017). The objectives of this study were to (i) characterize the genomes of E. rhusiopathiae isolates obtained from more recent muskox mortalities in the Canadian Arctic in 2019 and 2021; (ii) identify and compare common virulence traits associated with the core genome and mobile genetic elements (i.e. pathogenicity islands and prophages) among Arctic clone versus other E. rhusiopathiae genomes; and iii) use pan-genome wide association studies (GWAS) to determine unique genetic contents of the Arctic clone that may encode virulence traits and that could be used for diagnostic purposes.

RESULTS

Phylogenetic analyses revealed that the newly sequenced E. rhusiopathiae isolates from Ellesmere Island, Nunavut (2021) also belong to the Arctic clone. Of 17 virulence genes analysed among 28 Arctic clone isolates, four genes - adhesin, rhusiopathiae surface protein-A (rspA), choline binding protein-B (cbpB) and CDP-glycerol glycerophosphotransferase (tagF) - had amino acid sequence variants unique to this clone when compared to 31 other E. rhusiopathiae genomes. These genes encode proteins that facilitate E. rhusiopathiae to attach to the host endothelial cells and form biofilms. GWAS analyses using Scoary found several unique genes to be overrepresented in the Arctic clone.

CONCLUSIONS

The Arctic clone of E. rhusiopathiae was associated with multiple muskox mortalities spanning over a decade and multiple Arctic islands with distances over 1000 km, highlighting the extent of its spatiotemporal spread. This clone possesses unique gene content, as well as amino acid variants in multiple virulence genes that are distinct from the other closely related E. rhusiopathiae isolates. This study establishes an essential foundation on which to investigate whether these differences are correlated with the apparent virulence of this specific clone through in vitro and in vivo studies.

Previously unknown regulatory role of extracellular RNA on bacterial directional migration

Bacterial directional migration plays a significant role in bacterial adaptation. However, the regulation of this process, particularly in young biofilms, remains unclear. Here, we demonstrated the critical role of extracellular RNA as part of the Universal Receptive System in bacterial directional migration using a multidisciplinary approach, including bacterial culture, biochemistry, and genetics. We found that the destruction or inactivation of extracellular RNA with RNase or RNA-specific antibodies in the presence of the chemoattractant triggered the formation of bacterial "runner cells» in what we call a "panic state" capable of directional migration. These cells quickly migrated even on the surface of 1.5% agar and formed evolved colonies that were transcriptionally and biochemically different from the ancestral cells. We have also shown that cell-free DNA from blood plasma can act as a potent bacterial chemoattractant. Our data revealed a previously unknown role of bacterial extracellular RNA in the regulation of bacterial migration and have shown that its destruction or inhibition triggered the directional migration of developing and mature biofilms towards the chemoattractant.

Evolutionary adaptation of an HP1-protein chromodomain integrates chromatin and DNA sequence signals

Members of the diverse heterochromatin protein 1 (HP1) family play crucial roles in heterochromatin formation and maintenance. Despite the similar affinities of their chromodomains for di- and tri-methylated histone H3 lysine 9 (H3K9me2/3), different HP1 proteins exhibit distinct chromatin-binding patterns, likely due to interactions with various specificity factors. Previously, we showed that the chromatin-binding pattern of the HP1 protein Rhino, a crucial factor of the Drosophila PIWI-interacting RNA (piRNA) pathway, is largely defined by a DNA sequence-specific C2H2 zinc finger protein named Kipferl (Baumgartner et al., 2022). Here, we elucidate the molecular basis of the interaction between Rhino and its guidance factor Kipferl. Through phylogenetic analyses, structure prediction, and in vivo genetics, we identify a single amino acid change within Rhino's chromodomain, G31D, that does not affect H3K9me2/3 binding but disrupts the interaction between Rhino and Kipferl. Flies carrying the rhinoG31D mutation phenocopy kipferl mutant flies, with Rhino redistributing from piRNA clusters to satellite repeats, causing pronounced changes in the ovarian piRNA profile of rhinoG31D flies. Thus, Rhino's chromodomain functions as a dual-specificity module, facilitating interactions with both a histone mark and a DNA-binding protein.

Descriptive molecular pharmacology of the δ opioid receptor (DOR): A computational study with structural approach

This work focuses on the δ receptor (DOR), a G protein-coupled receptor (GPCR) belonging to the opioid receptor group. DOR is expressed in numerous tissues, particularly within the nervous system. Our study explores computationally the receptor's interactions with various ligands, including opiates and opioid peptides. It elucidates how these interactions influence the δ receptor response, relevant in a wide range of health and pathological processes. Thus, our investigation aims to explore the significance of DOR as an incoming drug target for pain relief and neurodegenerative diseases and as a source for novel opioid non-narcotic analgesic alternatives. We analyze the receptor's structural properties and interactions using Molecular Dynamics (MD) simulations and Gaussian-accelerated MD across different functional states. To thoroughly assess the primary differences in the structural and conformational ensembles across our different simulated systems, we initiated our study with 1 μs of conventional Molecular Dynamics. The strategy was chosen to encompass the full activation cycle of GPCRs, as activation processes typically occur within this microsecond range. Following the cMD, we extended our study with an additional 100 ns of Gaussian accelerated Molecular Dynamics (GaMD) to enhance the sampling of conformational states. This simulation approach allowed us to capture a comprehensive range of dynamic interactions and conformational changes that are crucial for GPCR activation as influenced by different ligands. Our study includes comparing agonist and antagonist complexes to uncover the collective patterns of their functional states, regarding activation, blocking, and inactivation of DOR, starting from experimental data. In addition, we also explored interactions between agonist and antagonist molecules from opiate and opioid classifications to establish robust structure-activity relationships. These interactions have been systematically quantified using a Quantitative Structure-Activity Relationships (QSAR) model. This research significantly contributes to our understanding of this significant pharmacological target, which is emerging as an attractive subject for drug development.

Two newly established and mutually related subfamilies GH13_48 and GH13_49 of the α-amylase family GH13

Currently, the main α-amylase family GH13 has been divided into 47 subfamilies in CAZy, with new subfamilies regularly emerging. The present in silico study was performed to highlight the groups, represented by the maltogenic amylase from Thermotoga neapolitana and the α-amylase from Haloarcula japonica, which are worth of creating their own new GH13 subfamilies. This enlarges functional annotation and thus allows more precise prediction of the function of putative proteins. Interestingly, those two share certain sequence features, e.g. the highly conserved cysteine in the second conserved sequence region (CSR-II) directly preceding the catalytic nucleophile, or the well-preserved GQ character of the end of CSR-VII. On the other hand, the two groups bear also specific and highly conserved positions that distinguish them not only from each other but also from representatives of remaining GH13 subfamilies established so far. For the T. neapolitana maltogenic amylase group, it is the stretch of residues at the end of CSR-V highly conserved as L-[DN]. The H. japonica α-amylase group can be characterized by a highly conserved [WY]-[GA] sequence at the end of CSR-II. Other specific sequence features include an almost fully conserved aspartic acid located directly preceding the general acid/base in CSR-III or well-preserved glutamic acid in CSR-IV. The assumption that these two groups represent two mutually related, but simultaneously independent GH13 subfamilies has been supported by phylogenetic analysis as well as by comparison of tertiary structures. The main α-amylase family GH13 has thus been expanded by two novel subfamilies GH13_48 and GH13_49. KEY POINTS: • In silico analysis of two groups of family GH13 members with characterized representatives • Identification of certain common, but also some specific sequence features in seven CSRs • Creation of two novel subfamilies-GH13_48 and GH13_49 within the CAZy database.

Discovery of antimicrobial peptides in the global microbiome with machine learning

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

Immunoinformatics and structural aided approach to develop multi-epitope based subunit vaccine against Mycobacterium tuberculosis

Tuberculosis is a highly contagious disease caused by Mycobacterium tuberculosis (Mtb), which is one of the prominent reasons for the death of millions worldwide. The bacterium has a substantially higher mortality rate than other bacterial diseases, and the rapid rise of drug-resistant strains only makes the situation more concerning. Currently, the only licensed vaccine BCG (Bacillus Calmette-Guérin) is ineffective in preventing adult pulmonary tuberculosis prophylaxis and latent tuberculosis re-activation. Therefore, there is a pressing need to find novel and safe vaccines that provide robust immune defense and have various applications. Vaccines that combine epitopes from multiple candidate proteins have been shown to boost immunity against Mtb infection. This study applies an immunoinformatic strategy to generate an adequate multi-epitope immunization against Mtb employing five antigenic proteins. Potential B-cell, cytotoxic T lymphocyte, and helper T lymphocyte epitopes were speculated from the intended proteins and coupled with 50 s ribosomal L7/L12 adjuvant, and the vaccine was constructed. The vaccine's physicochemical profile demonstrates antigenic, soluble, and non-allergic. In the meantime, docking, molecular dynamics simulations, and essential dynamics analysis revealed that the multi-epitope vaccine structure interacted strongly with Toll-like receptors (TLR2 and TLR3). MM-PBSA analysis was performed to ascertain the system's intermolecular binding free energies accurately. The immune simulation was applied to the vaccine to forecast its immunogenic profile. Finally, in silico cloning was used to validate the vaccine's efficacy. The immunoinformatics analysis suggests the multi-epitope vaccine could induce specific immune responses, making it a potential candidate against Mtb. However, validation through the in-vivo study of the developed vaccine is essential to assess its efficacy and immunogenicity profile, which will assure active protection against Mtb.

Evidence of mitochondria origin of SARS-CoV-2 double-membrane vesicles: a review

Coronavirus Disease-19 (COVID-19) pandemic is caused by SARS-CoV-2 that has infected more than 600 million people and killed more than 6 million people worldwide. This infection affects mainly certain groups of people that have high susceptibility to present severe COVID-19 due to comorbidities. Moreover, the long-COVID-19 comprises a series of symptoms that may remain in some patients for months after infection that further compromises their health. Thus, since this pandemic is profoundly affecting health, economy, and social life of societies, a deeper understanding of viral replication cycle could help to envisage novel therapeutic alternatives that limit or stop COVID-19. Several findings have unexpectedly discovered that mitochondria play a critical role in SARS-CoV-2 cell infection. Indeed, it has been suggested that this organelle could be the origin of its replication niches, the double membrane vesicles (DMV). In this regard, mitochondria derived vesicles (MDV), involved in mitochondria quality control, discovered almost 15 years ago, comprise a subpopulation characterized by a double membrane. MDV shedding is induced by mitochondrial stress, and it has a fast assembly dynamic, reason that perhaps has precluded their identification in electron microscopy or tomography studies. These and other features of MDV together with recent SARS-CoV-2 protein interactome and other findings link SARS-CoV-2 to mitochondria and support that these vesicles are the precursors of SARS-CoV-2 induced DMV. In this work, the morphological, biochemical, molecular, and cellular evidence that supports this hypothesis is reviewed and integrated into the current model of SARS-CoV-2 cell infection. In this scheme, some relevant questions are raised as pending topics for research that would help in the near future to test this hypothesis. The intention of this work is to provide a novel framework that could open new possibilities to tackle SARS-CoV-2 pandemic through mitochondria and DMV targeted therapies.

Tumor Regression Following Engineered Polyomavirus-Specific T Cell Therapy in Immune Checkpoint Inhibitor-Refractory Merkel Cell Carcinoma

Although immune check-point inhibitors (CPIs) revolutionized treatment of Merkel cell carcinoma (MCC), patients with CPI-refractory MCC lack effective therapy. More than 80% of MCC express T-antigens encoded by Merkel cell polyomavirus, which is an ideal target for T-cell receptor (TCR)-based immunotherapy. However, MCC often repress HLA expression, requiring additional strategies to reverse the downregulation for allowing T cells to recognize their targets. We identified TCRMCC1 that recognizes a T-antigen epitope restricted to human leukocyte antigen (HLA)-A*02:01. Seven CPI-refractory metastatic MCC patients received CD4 and CD8 T cells transduced with TCRMCC1 (TTCR-MCC1) preceded either by lymphodepleting chemotherapy or an HLA-upregulating regimen (single-fraction radiation therapy (SFRT) or systemic interferon gamma (IFNγ)) with concurrent avelumab. Two patients who received preceding SFRT and IFNγ respectively experienced tumor regression. One experienced regression of 13/14 subcutaneous lesions with 1 'escape' lesion and the other had delayed tumor regression in all lesions after initial progression. Although TTCR-MCC1 cells with an activated phenotype infiltrated tumors including the 'escape' lesion, all progressing lesions transcriptionally lacked HLA expression. While SFRT/IFNγ did not immediately upregulate tumor HLA expression, a secondary endogenous antigen-specific T cell infiltrate was detected in one of the regressing tumors and associated with HLA upregulation, indicating in situ immune responses have the potential to reverse HLA downregulation. Indeed, supplying a strong co-stimulatory signal via a CD200R-CD28 switch receptor allows TTCR-MCC1 cells to control HLA-downregulated MCC cells in a xenograft mouse model, upregulating HLA expression. Our results demonstrate the potential of TCR gene therapy for metastatic MCC and propose a next strategy for overcoming epigenetic downregulation of HLA in MCC.

Low-input PacBio sequencing generates high-quality individual fly genomes and characterizes mutational processes

Long-read sequencing, exemplified by PacBio, revolutionizes genomics, overcoming challenges like repetitive sequences. However, the high DNA requirement ( > 1 µg) is prohibitive for small organisms. We develop a low-input (100 ng), low-cost, and amplification-free library-generation method for PacBio sequencing (LILAP) using Tn5-based tagmentation and DNA circularization within one tube. We test LILAP with two Drosophila melanogaster individuals, and generate near-complete genomes, surpassing preexisting single-fly genomes. By analyzing variations in these two genomes, we characterize mutational processes: complex transpositions (transposon insertions together with extra duplications and/or deletions) prefer regions characterized by non-B DNA structures, and gene conversion of transposons occurs on both DNA and RNA levels. Concurrently, we generate two complete assemblies for the endosymbiotic bacterium Wolbachia in these flies and similarly detect transposon conversion. Thus, LILAP promises a broad PacBio sequencing adoption for not only mutational studies of flies and their symbionts but also explorations of other small organisms or precious samples.

Pred-O3, a web server to predict molecules, olfactory receptors and odor relationships

The sense of smell is a biological process involving volatile molecules that interact with proteins called olfactory receptors to transmit a nervous message that allows the recognition of a perceived odor. However, the relationships between odorant molecules, olfactory receptors and odors (O3) are far from being well understood due to the combinatorial olfactory codes and large family of olfactory receptors. This is the reason why, based on 5802 odorant molecules and their annotations to 863 olfactory receptors (human) and 7029 odors and flavors annotations, a web server called Pred-O3 has been designed to provide insights into olfaction. Predictive models based on Artificial Intelligence have been developed allowing to suggest olfactory receptors and odors associated with a new molecule. In addition, based on the encoding of the odorant molecule's structure, physicochemical features related to odors and/or olfactory receptors are proposed. Finally, based on the structural models of the 98 olfactory receptors a systematic docking protocol can be applied and suggest if a molecule can bind or not to an olfactory receptor. Therefore, Pred-O3 is well suited to aid in the design of new odorant molecules and assist in fragrance research and sensory neuroscience. Pred-O3 is accessible at ' https://odor.rpbs.univ-paris-diderot.fr/'.

Robustness of mitochondrial biogenesis and respiration explain aerobic glycolysis

A long-standing observation is that in fast-growing cells, respiration rate declines with increasing growth rate and is compensated by an increase in fermentation, despite respiration being more efficient than fermentation. This apparent preference for fermentation even in the presence of oxygen is known as aerobic glycolysis, and occurs in bacteria, yeast, and cancer cells. Considerable work has focused on understanding the potential benefits that might justify this seemingly wasteful metabolic strategy, but its mechanistic basis remains unclear. Here we show that aerobic glycolysis results from the saturation of mitochondrial respiration and the decoupling of mitochondrial biogenesis from the production of other cellular components. Respiration rate is insensitive to acute perturbations of cellular energetic demands or nutrient supplies, and is explained simply by the amount of mitochondria per cell. Mitochondria accumulate at a nearly constant rate across different growth conditions, resulting in mitochondrial amount being largely determined by cell division time. In contrast, glucose uptake rate is not saturated, and is accurately predicted by the abundances and affinities of glucose transporters. Combining these models of glucose uptake and respiration provides a quantitative, mechanistic explanation for aerobic glycolysis. The robustness of specific respiration rate and mitochondrial biogenesis, paired with the flexibility of other bioenergetic and biosynthetic fluxes, may play a broad role in shaping eukaryotic cell metabolism.

PypKa server: online pKa predictions and biomolecular structure preparation with precomputed data from PDB and AlphaFold DB

When preparing biomolecular structures for molecular dynamics simulations, pKa calculations are required to provide at least a representative protonation state at a given pH value. Neglecting this step and adopting the reference protonation states of the amino acid residues in water, often leads to wrong electrostatics and nonphysical simulations. Fortunately, several methods have been developed to prepare structures considering the protonation preference of residues in their specific environments (pKa values), and some are even available for online usage. In this work, we present the PypKa server, which allows users to run physics-based, as well as ML-accelerated methods suitable for larger systems, to obtain pKa values, isoelectric points, titration curves, and structures with representative pH-dependent protonation states compatible with commonly used force fields (AMBER, CHARMM, GROMOS). The user may upload a custom structure or submit an identifier code from PBD or UniProtKB. The results for over 200k structures taken from the Protein Data Bank and the AlphaFold DB have been precomputed, and their data can be retrieved without extra calculations. All this information can also be obtained from an application programming interface (API) facilitating its usage and integration into existing pipelines as well as other web services. The web server is available at pypka.org.

Nicotinamide N-methyltransferase inhibition mimics and boosts exercise-mediated improvements in muscle function in aged mice

Human hallmarks of sarcopenia include muscle weakness and a blunted response to exercise. Nicotinamide N-methyltransferase inhibitors (NNMTis) increase strength and promote the regenerative capacity of aged muscle, thus offering a promising treatment for sarcopenia. Since human hallmarks of sarcopenia are recapitulated in aged (24-month-old) mice, we treated mice from 22 to 24 months of age with NNMTi, intensive exercise, or a combination of both, and compared skeletal muscle adaptations, including grip strength, longitudinal running capacity, plantarflexor peak torque, fatigue, and muscle mass, fiber type, cross-sectional area, and intramyocellular lipid (IMCL) content. Exhaustive proteome and metabolome analyses were completed to identify the molecular mechanisms underlying the measured changes in skeletal muscle pathophysiology. Remarkably, NNMTi-treated aged sedentary mice showed ~ 40% greater grip strength than sedentary controls, while aged exercised mice only showed a 20% increase relative to controls. Importantly, the grip strength improvements resulting from NNMTi treatment and exercise were additive, with NNMTi-treated exercised mice developing a 60% increase in grip strength relative to sedentary controls. NNMTi treatment also promoted quantifiable improvements in IMCL content and, in combination with exercise, significantly increased gastrocnemius fiber CSA. Detailed skeletal muscle proteome and metabolome analyses revealed unique molecular mechanisms associated with NNMTi treatment and distinct molecular mechanisms and cellular processes arising from a combination of NNMTi and exercise relative to those given a single intervention. These studies suggest that NNMTi-based drugs, either alone or combined with exercise, will be beneficial in treating sarcopenia and a wide range of age-related myopathies.

PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.

Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins

Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein "unknownme". This large knowledge gap prevents the biological community from fully leveraging the plethora of genomic data that is now available. Machine-learning approaches are showing some promise in propagating functional knowledge from experimentally characterized proteins to the correct set of isofunctional orthologs. However, they largely fail to predict enzymatic functions unseen in the training set, as shown by dissecting the predictions made for 450 enzymes of unknown function from the model bacteria Escherichia coli using the DeepECTransformer platform. Lessons from these failures can help the community develop machine-learning methods that assist domain experts in making testable functional predictions for more members of the uncharacterized proteome.

Discovery of a novel symbiotic lineage associated with a hematophagous leech from the genus Haementeria

Similarly to other strict blood feeders, leeches from the Haementeria genus (Hirudinida: Glossiphoniidae) have established a symbiotic association with bacteria harbored intracellularly in esophageal bacteriomes. Previous genome sequence analyses of these endosymbionts revealed co-divergence with their hosts, a strong genome reduction, and a simplified metabolism largely dedicated to the production of B vitamins, which are nutrients lacking from a blood diet. 'Candidatus Providencia siddallii' has been identified as the obligate nutritional endosymbiont of a monophyletic clade of Mexican and South American Haementeria spp. However, the Haementeria genus includes a sister clade of congeners from Central and South America, where the presence or absence of the aforementioned symbiont taxon remains unknown. In this work, we report on a novel bacterial endosymbiont found in a representative from this Haementeria clade. We found that this symbiont lineage has evolved from within the Pluralibacter genus, known mainly from clinical but also environmental strains. Similarly to Ca. Providencia siddallii, the Haementeria-associated Pluralibacter symbiont displays clear signs of genome reduction, accompanied by an A+T-biased sequence composition. Genomic analysis of its metabolic potential revealed a retention of pathways related to B vitamin biosynthesis, supporting its role as a nutritional endosymbiont. Finally, comparative genomics of both Haementeria symbiont lineages suggests that an ancient Providencia symbiont was likely replaced by the novel Pluralibacter one, thus constituting the first reported case of nutritional symbiont replacement in a leech without morphological changes in the bacteriome.

IMPORTANCE

Obligate symbiotic associations with a nutritional base have likely evolved more than once in strict blood-feeding leeches. Unlike those symbioses found in hematophagous arthropods, the nature, identity, and evolutionary history of these remains poorly studied. In this work, we further explored obligate nutritional associations between Haementeria leeches and their microbial symbionts, which led to the unexpected discovery of a novel symbiosis with a member of the Pluralibacter genus. When compared to Providencia siddallii, an obligate nutritional symbiont of other Haementeria leeches, this novel bacterial symbiont shows convergent retention of the metabolic pathways involved in B vitamin biosynthesis. Moreover, the genomic characteristics of this Pluralibacter symbiont suggest a more recent association than that of Pr. siddallii and Haementeria. We conclude that the once-thought stable associations between blood-feeding Glossiphoniidae and their symbionts (i.e., one bacteriome structure, one symbiont lineage) can break down, mirroring symbiont turnover observed in various arthropod lineages.

Copper transporter protein (MctB) as a therapeutic target to elicit antimycobacterial activity against tuberculosis

Tuberculosis (TB) is a prehistoric infection and major etiologic agent of TB, Mycobacterium tuberculosis, which is considered to have advanced from an early progenitor species found in Eastern Africa. By the 1800s, there were approximately 800 to 1000 fatality case reports per 100,000 people in Europe and North America. This research suggests an In-silico study to identify potential inhibitory compounds for the target Mycobacterial copper transport protein (Mctb). ADME-based virtual screening, molecular docking, and molecular dynamics simulations were conducted to find promising compounds to modulate the function of the target protein. Four chemical compounds, namely Anti-MCT1, Anti-MCT2, Anti-MCT3 and Anti-MCT4 out of 1500 small molecules from the Diverse-lib of MTiOpenScreen were observed to completely satisfy Lipinski rule of five and Veber's rule. Further, significantly steady interactions with the MctB target protein were observed. Docking experiments have presented 9 compounds with less than -9.0 kcal/mol free binding energies and further MD simulation eventually gave 4 compounds having potential interactions and affinity with target protein and favorable binding energy ranging from -9.2 to -9.3 kcal/mol. We may propose these compounds as an effective candidate to reduce the growth of M. tuberculosis and may also assist present a novel therapeutic approach for Tuberculosis. In vivo and In vitro validation would be needed to proceed further in this direction.Communicated by Ramaswamy H. Sarma.

Higher-order structure and proteoforms of co-occurring C4b-binding protein assemblies in human serum

The complement is a conserved cascade that plays a central role in the innate immune system. To maintain a delicate equilibrium preventing excessive complement activation, complement inhibitors are essential. One of the major fluid-phase complement inhibitors is C4b-binding protein (C4BP). Human C4BP is a macromolecular glycoprotein composed of two distinct subunits, C4BPα and C4BPβ. These associate with vitamin K-dependent protein S (ProS) forming an ensemble of co-occurring higher-order structures. Here, we characterize these C4BP assemblies. We resolve and quantify isoforms of purified human serum C4BP using distinct single-particle detection techniques: charge detection mass spectrometry, and mass photometry accompanied by high-speed atomic force microscopy. Combining cross-linking mass spectrometry, glycoproteomics, and structural modeling, we report comprehensive glycoproteoform profiles and full-length structural models of the endogenous C4BP assemblies, expanding knowledge of this key complement inhibitor's structure and composition. Finally, we reveal that an increased C4BPα to C4BPβ ratio coincides with elevated C-reactive protein levels in patient plasma samples. This observation highlights C4BP isoform variation and affirms a distinct role of co-occurring C4BP assemblies upon acute phase inflammation.

High-throughput and proteome-wide discovery of endogenous biomolecular condensates

Phase separation inside mammalian cells regulates the formation of the biomolecular condensates that are related to gene expression, signalling, development and disease. However, a large population of endogenous condensates and their candidate phase-separating proteins have yet to be discovered in a quantitative and high-throughput manner. Here we demonstrate that endogenously expressed biomolecular condensates can be identified across a cell's proteome by sorting proteins across varying oligomeric states. We employ volumetric compression to modulate the concentrations of intracellular proteins and the degree of crowdedness, which are physical regulators of cellular biomolecular condensates. The changes in degree of the partition of proteins into condensates or phase separation led to varying oligomeric states of the proteins, which can be detected by coupling density gradient ultracentrifugation and quantitative mass spectrometry. In total, we identified 1,518 endogenous condensate proteins, of which 538 have not been reported before. Furthermore, we demonstrate that our strategy can identify condensate proteins that respond to specific biological processes.

Development, validation and analysis of a human profurin 3D model using comparative modeling and molecular dynamics simulations

The emergence of new viruses can lead to the outbreak of pandemics as occurred at the end of 2019 with the coronavirus disease (or COVID-19). The fastest way to effectively control viral infections is to develop broad-spectrum antivirals that can fight at least an entire class of viruses. Profurin, the furin precursor propeptide, is responsible for the autoactivation step which is crucial for the maturation of several viral substrates. This role makes the study of furin and profurin interactions interesting for the development of new potential broad-spectrum antivirals for the treatment against several human viral diseases. Since there is no 3D model of profurin published in the literature or deposited in a database, this work reports the development, validation and analysis of a profurin 3D model using comparative modeling and molecular dynamics. The model is available in ModelArchive at https://www.modelarchive.org/doi/10.5452/ma-ct8l7. The usage of this model will make possible further studies of molecular docking and MD simulations of the profurin-furin system, in the design of new potential broad-spectrum antivirals for the treatment against several human viral diseases.Communicated by Ramaswamy H. Sarma.

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

Genome-resolved metagenomics: a game changer for microbiome medicine

Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.

Explore the underlying oral efficacy of α-, β-, γ-Cyclodextrin against the ulcerative colitis using in vitro and in vivo studies assisted by network pharmacology

The incidence of ulcerative colitis (UC) is rising worldwide. As a refractory and recurrent disease, UC could seriously affect the patients' quality of life. However, current clinical medical treatments for UC are accompanied by various side effects, especially for long-term applications. Here, the underlying efficacy of cyclodextrins (CDs) was studied. As common excipients, CDs endow proven safety for long-term applications. Results of predictive methods derived from network pharmacology prompted the potential anti-inflammatory effects of CDs by oral administration. RAW264.7 cell experiments verified that CDs could inhibit the excessive secretion of TNF-α (β-CD > α-CD ≈ γ-CD), IL-6, and NO (α-CD > β-CD ≈ γ-CD) as predicted. In mice with DSS-induced acute UC, oral administration of CDs could effectively mitigate the pathological damage of colon tissue and reduce the level of inflammatory mediators. Moreover, 16S rRNA sequencing displayed that gut microbes disturbed by DSS were significantly regulated by CDs. Conclusively, the study showed the therapeutic application prospects of CDs in UC treatment and indicated the feasibility and advantages of developing 'new' therapeutic activities of 'old' ingredients.Communicated by Ramaswamy H. Sarma.

Hotspot Wizard-informed engineering of a hyperthermophilic β-glucosidase for enhanced enzyme activity at low temperatures

Hyperthermophilic enzymes serve as an important source of industrial enzymes due to their high thermostability. Unfortunately, most hyperthermophilic enzymes suffer from reduced activity at low temperatures (e.g., ambient temperature), limiting their applicability. In addition, evolving hyperthermophilic enzymes to increase low temperature activity without compromising other desired properties is generally difficult. In the current study, a variant of β-glucosidase from Pyrococcus furiosus (PfBGL) was engineered to enhance enzyme activity at low temperatures through the construction of a saturation mutagenesis library guided by the HotSpot Wizard analysis, followed by its screening for activity and thermostability. From this library construction and screening, one PfBGL mutant, PfBGL-A4 containing Q214S/A264S/F344I mutations, showed an over twofold increase in β-glucosidase activity at 25 and 50°C compared to the wild type, without compromising high-temperature activity, thermostability and substrate specificity. Our experimental and computational characterizations suggest that the findings with PfBGL-A4 may be due to the elevation of local conformational flexibility around the active site, while slightly compacting the global protein structure. This study showcases the potential of HotSpot Wizard-informed engineering of hyperthermophilic enzymes and underscores the interplays among temperature, enzyme activity, and conformational flexibility in these enzymes.

A multiomics approach reveals evidence for phenylbutyrate as a potential treatment for combined D,L-2- hydroxyglutaric aciduria

PURPOSE

To identify therapies for combined D, L-2-hydroxyglutaric aciduria (C-2HGA), a rare genetic disorder caused by recessive variants in the SLC25A1 gene.

METHODS

Patients C-2HGA were identified and diagnosed by whole exome sequencing and biochemical genetic testing. Patient derived fibroblasts were then treated with phenylbutyrate and the functional effects assessed by metabolomics and RNA-sequencing.

RESULTS

In this study, we demonstrated that C-2HGA patient derived fibroblasts exhibited impaired cellular bioenergetics. Moreover, Fibroblasts form one patient exhibited worsened cellular bioenergetics when supplemented with citrate. We hypothesized that treating patient cells with phenylbutyrate (PB), an FDA approved pharmaceutical drug that conjugates glutamine for renal excretion, would reduce mitochondrial 2-ketoglutarate, thereby leading to improved cellular bioenergetics. Metabolomic and RNA-seq analyses of PB-treated fibroblasts demonstrated a significant decrease in intracellular 2-ketoglutarate, 2-hydroxyglutarate, and in levels of mRNA coding for citrate synthase and isocitrate dehydrogenase. Consistent with the known action of PB, an increased level of phenylacetylglutamine in patient cells was consistent with the drug acting as 2-ketoglutarate sink.

CONCLUSION

Our pre-clinical studies suggest that citrate supplementation has the possibility exacerbating energy metabolism in this condition. However, improvement in cellular bioenergetics suggests phenylbutyrate might have interventional utility for this rare disease.