RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning

Integrated Network Pharmacology Approach to Evaluate Bioactive Phytochemicals of Acalypha indica and Their Mechanistic Actions to Suppress Target Genes of Tuberculosis

Mycobacterium tuberculosis is responsible for tuberculosis (TB) all over the world. Despite tremendous advancements in biomedical research, new treatment approaches, and preventive measures, TB incidence rates continue to ascend. The herbaceous plant Acalypha indica, also known as Indian Nettle, belongs to the Euphorbiaceae family and is known as one of the most important sources of medicines and pharmaceuticals for the medical therapy for a range of ailments. However, the precise molecular mechanism of its therapeutic action is still unknown. In this study, an integrated network pharmacology approach was employed to explore the potential mechanism of A. indica phytochemicals against TB. The active chemical components of A. indica were collected from two independent databases and published sources, whereas SwissTargetPrediction was used to identify the target genes of these phytochemicals. GeneCards and DisGeNET databases were employed to retrieve tuberculosis-related genes and variants. Following the evaluation of overlapped genes, gene enrichment analysis and PPI network analysis were performed using the DAVID and STRING databases, respectively. Later, to identify the potential target(s) for the disease, molecular docking was performed. A. indica revealed 9 active components with 259 potential therapeutic targets; TB attributed 694 intersecting genes from the two data sets; and both TB and A. indica overlapped 44 potential targets. The in-depth analysis based on the degree revealed that AKT1 and EGFR formed the foundation of the PPI network. Moreover, docking analysis followed by molecular dynamics simulations revealed that phytosterol and stigmasterol have higher binding affinities to AKT1 and EGFR to suppress tuberculosis. This study provides a convincing proof that A. indica can be exploited to target TB after experimental endorsement; further, it lays the framework for more experimental research on A. indica's anti-TB activity.

Exploring Citrus sinensis Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis

BACKGROUND

Structure-activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed through the Flare project, in which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds were obtained through the development of the 3D-QSAR model. For exploring the mechanism of action of active compounds against cocrystal inhibitors, molecular docking analysis was done through Molegro software (MVD) to identify lead compounds. Three new proteins, namely, 1T15, 3EU7, and 1T29, displayed the best Moldock scores. The quality of the docking study was assessed by a molecular dynamics simulation. Based on binding affinities to the receptor in the docking studies, three lead compounds (stigmasterol P8, epoxybergamottin P28, and nobiletin P29) were obtained, and they passed through absorption, distribution, metabolism, and excretion (ADME) studies via the SwissADME online service, which proved that P28 and P29 were the most active allosteric inhibitors with the lowest toxicity level against breast cancer. Then, density functional theory (DFT) studies were performed to measure the active compound's reactivity, hardness, and softness with the help of Gaussian 09 software.

CONCLUSIONS

This multistep screening of phytochemicals revealed high-reliability antagonists of breast cancer by 3D-QSAR using flare, docking analysis, and DFT studies. The present study helps in providing a proper guideline for the development of novel inhibitors of BRCA1 and BRCA2.

Chlamydiae as symbionts of photosynthetic dinoflagellates

Chlamydiae are ubiquitous intracellular bacteria and infect a wide diversity of eukaryotes, including mammals. However, chlamydiae have never been reported to infect photosynthetic organisms. Here, we describe a novel chlamydial genus and species, Candidatus Algichlamydia australiensis, capable of infecting the photosynthetic dinoflagellate Cladocopium sp. (originally isolated from a scleractinian coral). Algichlamydia australiensis was confirmed to be intracellular by fluorescence in situ hybridization and confocal laser scanning microscopy and temporally stable at the population level by monitoring its relative abundance across four weeks of host growth. Using a combination of short- and long-read sequencing, we recovered a high-quality (completeness 91.73% and contamination 0.27%) metagenome-assembled genome of A. australiensis. Phylogenetic analyses show that this chlamydial taxon represents a new genus and species within the Simkaniaceae family. Algichlamydia australiensis possesses all the hallmark genes for chlamydiae-host interactions, including a complete type III secretion system. In addition, a type IV secretion system is encoded on a plasmid and has previously been observed for only three other chlamydial species. Twenty orthologous groups of genes are unique to A. australiensis, one of which is structurally similar to a protein known from Cyanobacteria and Archaeplastida involved in thylakoid biogenesis and maintenance, hinting at potential chlamydiae interactions with the chloroplasts of Cladocopium cells. Our study shows that chlamydiae infect dinoflagellate symbionts of cnidarians, the first photosynthetic organism reported to harbor chlamydiae, thereby expanding the breadth of chlamydial hosts and providing a new contribution to the discussion around the role of chlamydiae in the establishment of the primary plastid.

E2EATP: Fast and High-Accuracy Protein-ATP Binding Residue Prediction via Protein Language Model Embedding

Identifying the ATP-binding sites of proteins is fundamentally important to uncover the mechanisms of protein functions and explore drug discovery. Many computational methods are proposed to predict ATP-binding sites. However, due to the limitation of the quality of feature representation, the prediction performance still has a big room for improvement. In this study, we propose an end-to-end deep learning model, E2EATP, to dig out more discriminative information from a protein sequence for improving the ATP-binding site prediction performance. Concretely, we employ a pretrained deep learning-based protein language model (ESM2) to automatically extract high-latent discriminative representations of protein sequences relevant for protein functions. Based on ESM2, we design a residual convolutional neural network to train a protein-ATP binding site prediction model. Furthermore, a weighted focal loss function is used to reduce the negative impact of imbalanced data on the model training stage. Experimental results on the two independent testing data sets demonstrate that E2EATP could achieve higher Matthew's correlation coefficient and AUC values than most existing state-of-the-art prediction methods. The speed (about 0.05 s per protein) of E2EATP is much faster than the other existing prediction methods. Detailed data analyses show that the major advantage of E2EATP lies at the utilization of the pretrained protein language model that extracts more discriminative information from the protein sequence only. The standalone package of E2EATP is freely available for academic at https://github.com/jun-csbio/e2eatp/.

MODOMICS: a database of RNA modifications and related information. 2023 update

The MODOMICS database was updated with recent data and now includes new data types related to RNA modifications. Changes to the database include an expanded modification catalog, encompassing both natural and synthetic residues identified in RNA structures. This addition aids in representing RNA sequences from the RCSB PDB database more effectively. To manage the increased number of modifications, adjustments to the nomenclature system were made. Updates in the RNA sequences section include the addition of new sequences and the reintroduction of sequence alignments for tRNAs and rRNAs. The protein section was updated and connected to structures from the RCSB PDB database and predictions by AlphaFold. MODOMICS now includes a data annotation system, with 'Evidence' and 'Estimated Reliability' features, offering clarity on data support and accuracy. This system is open to all MODOMICS entries, enhancing the accuracy of RNA modification data representation. MODOMICS is available at https://iimcb.genesilico.pl/modomics/.

DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options

The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

MESPEUS: a database of metal coordination groups in proteins

MESPEUS is a freely accessible database which uses carefully selected metal coordination groups found in metalloprotein structures taken from the Protein Data Bank. The database contains geometrical information of metal sites within proteins, including 40 metal types. In order to completely determine the metal coordination, the symmetry-related units of a given protein structure are taken into account and are generated using the appropriate space group symmetry operations. This permits a more complete description of the metal coordination geometry by including all coordinating atoms. The user-friendly web interface allows users to directly search for a metal site of interest using several useful options, including searching for metal elements, metal-donor distances, coordination number, donor residue group, and structural resolution. These searches can be carried out singly or in combination. The details of a metal site and the metal site(s) in the whole structure can be graphically displayed using the interactive web interface. MESPEUS is automatically updated monthly by synchronizing with the PDB database. An investigation for the Mg-ATP interaction is given to demonstrate how MESPEUS can be used to extract information about metal sites by selecting structure and coordination features. MESPEUS is available at http://mespeus.nchu.edu.tw/.

EMDB-the Electron Microscopy Data Bank

The Electron Microscopy Data Bank (EMDB) is the global public archive of three-dimensional electron microscopy (3DEM) maps of biological specimens derived from transmission electron microscopy experiments. As of 2021, EMDB is managed by the Worldwide Protein Data Bank consortium (wwPDB; wwpdb.org) as a wwPDB Core Archive, and the EMDB team is a core member of the consortium. Today, EMDB houses over 30 000 entries with maps containing macromolecules, complexes, viruses, organelles and cells. Herein, we provide an overview of the rapidly growing EMDB archive, including its current holdings, recent updates, and future plans.

TTD: Therapeutic Target Database describing target druggability information

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.

MultifacetedProtDB: a database of human proteins with multiple functions

MultifacetedProtDB is a database of multifunctional human proteins deriving information from other databases, including UniProt, GeneCards, Human Protein Atlas (HPA), Human Phenotype Ontology (HPO) and MONDO. It collects under the label 'multifaceted' multitasking proteins addressed in literature as pleiotropic, multidomain, promiscuous (in relation to enzymes catalysing multiple substrates) and moonlighting (with two or more molecular functions), and difficult to be retrieved with a direct search in existing non-specific databases. The study of multifunctional proteins is an expanding research area aiming to elucidate the complexities of biological processes, particularly in humans, where multifunctional proteins play roles in various processes, including signal transduction, metabolism, gene regulation and cellular communication, and are often involved in disease insurgence and progression. The webserver allows searching by gene, protein and any associated structural and functional information, like available structures from PDB, structural models and interactors, using multiple filters. Protein entries are supplemented with comprehensive annotations including EC number, GO terms (biological pathways, molecular functions, and cellular components), pathways from Reactome, subcellular localization from UniProt, tissue and cell type expression from HPA, and associated diseases following MONDO, Orphanet and OMIM classification. MultiFacetedProtDB is freely available as a web server at: https://multifacetedprotdb.biocomp.unibo.it/.

GproteinDb in 2024: new G protein-GPCR couplings, AlphaFold2-multimer models and interface interactions

G proteins are the major signal proteins of ∼800 receptors for medicines, hormones, neurotransmitters, tastants and odorants. GproteinDb offers integrated genomic, structural, and pharmacological data and tools for analysis, visualization and experiment design. Here, we present the first major update of GproteinDb greatly expanding its coupling data and structural templates, adding AlphaFold2 structure models of GPCR-G protein complexes and advancing the interactive analysis tools for their interfaces underlying coupling selectivity. We present insights on coupling agreement across datasets and parameters, including constitutive activity, agonist-induced activity and kinetics. GproteinDb is accessible at https://gproteindb.org.

UniTmp: unified resources for transmembrane proteins

The UNIfied database of TransMembrane Proteins (UniTmp) is a comprehensive and freely accessible resource of transmembrane protein structural information at different levels, from localization of protein segments, through the topology of the protein to the membrane-embedded 3D structure. We not only annotated tens of thousands of new structures and experiments, but we also developed a new system that can serve these resources in parallel. UniTmp is a unified platform that merges TOPDB (Topology Data Bank of Transmembrane Proteins), TOPDOM (database of conservatively located domains and motifs in proteins), PDBTM (Protein Data Bank of Transmembrane Proteins) and HTP (Human Transmembrane Proteome) databases and provides interoperability between the incorporated resources and an easy way to keep them regularly updated. The current update contains 9235 membrane-embedded structures, 9088 sequences with 536 035 topology-annotated segments and 8692 conservatively localized protein domains or motifs as well as 5466 annotated human transmembrane proteins. The UniTmp database can be accessed at https://www.unitmp.org.

ADCdb: the database of antibody-drug conjugates

Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model

Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.

Chlamydiae in corals: shared functional potential despite broad taxonomic diversity

Cnidarians, such as corals and sea anemones, associate with a wide range of bacteria that have essential functions, including nutrient cycling and the production of antimicrobial compounds. Within cnidarians, bacteria can colonize all microhabitats including the tissues. Among them are obligate intracellular bacteria of the phylum Chlamydiota (chlamydiae) whose impact on cnidarian hosts and holobionts, especially corals, remain unknown. Here, we conducted a meta-analysis of previously published 16S rRNA gene metabarcoding data from cnidarians (e.g. coral, jellyfish, and anemones), eight metagenome-assembled genomes (MAGs) of coral-associated chlamydiae, and one MAG of jellyfish-associated chlamydiae to decipher their diversity and functional potential. While the metabarcoding dataset showed an enormous diversity of cnidarian-associated chlamydiae, six out of nine MAGs were affiliated with the Simkaniaceae family. The other three MAGs were assigned to the Parasimkaniaceae, Rhabdochlamydiaceae, and Anoxychlamydiaceae, respectively. All MAGs lacked the genes necessary for an independent existence, lacking any nucleotide or vitamin and most amino acid biosynthesis pathways. Hallmark chlamydial genes, such as a type III secretion system, nucleotide transporters, and genes for host interaction, were encoded in all MAGs. Together these observations suggest an obligate intracellular lifestyle of coral-associated chlamydiae. No unique genes were found in coral-associated chlamydiae, suggesting a lack of host specificity. Additional studies are needed to understand how chlamydiae interact with their coral host, and other microbes in coral holobionts. This first study of the diversity and functional potential of coral-associated chlamydiae improves our understanding of both the coral microbiome and the chlamydial lifestyle and host range.

Network Pharmacology Study on Herb Pair Bletilla striata-Galla chinensis in the Treatment of Chronic Skin Ulcers

BACKGROUND

Herb pair Bletilla striata-Galla chinensis (BS-GC) is a classic combination of topical traditional Chinese medicine formulae in the treatment of chronic skin ulcers (CSUs).

OBJECTIVE

The aim of this study is to explore the effective active ingredients of BS-GC, as well as the core targets and signal transduction pathways of its action on CSUs.

METHODS

The ingredients of BS-GC were obtained from TCMSP and HERB databases. The targets of all active ingredients were retrieved from the SwissTargetPrediction database. The targets of CSUs were obtained from OMIM, GeneCards, Drugbank, and DisGeNET databases. A drug-disease target protein-protein interaction (PPI) network was constructed to select the most core targets, and an herb-ingredient-target network was built by utilizing Cytoscape 3.7.2. Furthermore, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes database (KEGG) analysis and verified the results of network pharmacology through molecular docking.

RESULTS

A total of 40 active ingredients from the herb pair BS-GC were initially screened, and a total of 528 targets were retrieved. Meanwhile, the total number of CSU targets was 1032. Then, the number of common targets between BS-GC and CSUs was 107. The 13 core targets of herb pair BS-GC with CSUs were filtered out according to the PPI network, including AKT1, TNF, EGFR, BCL2, HIF1A, MMP-9, etc. The 5 main core active ingredients were 1-(4-Hydroxybenzyl)-2-methoxy-9,10-dihydrophenanthrene-4,7-diol, 1-(4- Hydroxybenzyl)-4-methoxy-9,10-dihydrophenanthrene-2,7-diol, physcion, dihydromyricetin, and myricetin. The main biological processes were inflammation, oxidative stress, and immune response, involving the AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, NF-κB signaling pathway, and calcium signaling pathway. Molecular docking results showed good binding activity between the 5 main core active ingredients and 13 core targets.

CONCLUSION

This study predicted the core targets and signal transduction pathways in the treatment of CSUs to provide a reference for further molecular mechanism research.

PDB-BRE: A ligand-protein interaction binding residue extractor based on Protein Data Bank

Proteins typically exert their biological functions by interacting with other biomolecules or ligands. The study of ligand-protein interactions is crucial in elucidating the biological mechanisms of proteins. Most existing studies have focused on analyzing ligand-protein interactions, and they ignore the additional situational of inserted and modified residues. Besides, the resources often support only a single ligand type and cannot obtain satisfied results in analyzing novel complexes. Therefore, it is important to develop a general analytical tool to extract the binding residues of ligand-protein interactions in complexes fully. In this study, we propose a ligand-protein interaction binding residue extractor (PDB-BRE), which can be used to automatically extract interacting ligand or protein-binding residues from complex three-dimensional (3D) structures based on the RCSB Protein Data Bank (RCSB PDB). PDB-BRE offers a notable advantage in its comprehensive support for analyzing six distinct types of ligands, including proteins, peptides, DNA, RNA, mixed DNA and RNA entities, and non-polymeric entities. Moreover, it takes into account the consideration of inserted and modified residues within complexes. Compared to other state-of-the-art methods, PDB-BRE is more suitable for massively parallel batch analysis, and can be directly applied for downstream tasks, such as predicting binding residues of novel complexes. PDB-BRE is freely available at http://bliulab.net/PDB-BRE.

Network Pharmacology Combined with Molecular Docking Approach to Investigate the Mechanism of ChuShiWeiLing Decoction against Perianal Eczema

BACKGROUND

ChuShiWeiLing Decoction (CSWLD) is a famous classical Chinese prescription for the treatment of eczema with desirable effect in clinical practice. It has gradually exerted good curative effects on perianal eczema (PE) in recent years, but its specific mechanism is not elucidated yet.

OBJECTIVE

This research explores the underlying pharmacological mechanism of CSWLD in addressing PE through network pharmacology combined with molecular docking strategy.

METHODS

The key chemical compounds and potential target genes of CSWLD were screened by bioinformatics. The major targets of CSWLD were discovered using network modules. Functional annotation of Gene Ontology (GO) was undertaken, as well as pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Molecular docking of core protein-ligand interactions was modeled using AutoDock software. Pymol software was used to perform a molecular dynamics simulation for the ideal core protein-ligand that was discovered by molecular docking.

RESULTS

A total of 2,853 active compounds and 922 targets of CSWLD were collected. The target with a higher degree was identified through the PPI network, namely TNF, IL6, ALB, STAT3, EGFR, TLR4, CXCL8 and PTPRC. GO and KEGG analyses suggested that CSWLD treatment of PE mainly involves cellular activation, activation of leukocytes, and adhesion among leukocytes. The molecular docking results showed that wogonin, hederagenin and quercetin of CSWLD could bind to IL-6 and TNF, respectively.

CONCLUSION

Our results indicated that the bioactives, potential targets, and molecular mechanism of CSWLD against PE.

Web-Based Resources to Investigate Protease Function

In 2001, the release of the first draft of the human genome marked the beginning of the Big Data era for biological sciences. Since then, the complexity of datasets generated by laboratories worldwide has increased exponentially. Public repositories such as the Protein Data Bank, which has exceeded the 200000 entries in 2023, have been instrumental not only to collect, organize, and distill this enormous research output but also to promote further research enterprises. The achievements of artificial intelligence programs such as AlphaFold would not have been possible without the collective efforts of countless researchers who made their work publicly available. Here, I provide a practical, but far from exhaustive, list of resources useful to investigate protease function.

Thermococcus kodakarensis TK0353 is a novel AP lyase with a new fold

Hyperthermophilic organisms thrive in extreme environments prone to high levels of DNA damage. Growth at high temperature stimulates DNA base hydrolysis resulting in apurinic/apyrimidinic (AP) sites that destabilize the genome. Organisms across all domains have evolved enzymes to recognize and repair AP sites to maintain genome stability. The hyperthermophilic archaeon Thermococcus kodakarensis encodes several enzymes to repair AP site damage including the essential AP endonuclease TK endonuclease IV. Recently, using functional genomic screening, we discovered a new family of AP lyases typified by TK0353. Here, using biochemistry, structural analysis, and genetic deletion, we have characterized the TK0353 structure and function. TK0353 lacks glycosylase activity on a variety of damaged bases and is therefore either a monofunctional AP lyase or may be a glycosylase-lyase on a yet unidentified substrate. The crystal structure of TK0353 revealed a novel fold, which does not resemble other known DNA repair enzymes. The TK0353 gene is not essential for T. kodakarensis viability presumably because of redundant base excision repair enzymes involved in AP site processing. In summary, TK0353 is a novel AP lyase unique to hyperthermophiles that provides redundant repair activity necessary for genome maintenance.

Molecular mechanism of ChaiShi JieDu granule in treating dengue based on network pharmacology and molecular docking: A review

Dengue fever is a frequently occurring infectious disease caused by the Dengue virus, prevalent in tropical and subtropical regions. Chaishi Jiedu Granules (CSJD) is an empirical prescription of the Eighth Affiliated Hospital of Guangzhou Medical University in the treatment of dengue fever, which has been widely used in the treatment of dengue fever, and has shown good efficacy in improving the clinical symptoms of patients. This study aims to explore the molecular mechanism of CSJD in treating dengue fever using network pharmacology, molecular docking techniques, and virtual screening methods. The results showed that luteolin, quercetin and other compounds in CSJD could target important targets related to dengue virus, including STAT3, AKT1, TNF, IL-6, and other key genes, thus playing an antiviral role. Among them, luteolin and wogonin in CSJD also inhibited dengue virus replication and reduced inflammation, and showed good binding force with IL-6 and TNF. Therefore, this study provides an important reference for the development of CSJD as a potential drug for dengue fever treatment and a new perspective for research and development in this field.

Mechanism of Huaiqihuang in treatment of diabetic kidney disease based on network pharmacology, molecular docking and in vitro experiment

BACKGROUND

This study aimed to investigate the active components, key targets, and potential molecular mechanisms Huaiqihuang (HQH) in the treatment of diabetic kidney disease (DKD) through network pharmacology, molecular docking, and in vitro experiments.

METHODS

The active components and potential targets of HQH were obtained from the TCMSP and HERB databases. The potential targets of DKD were obtained from the GeneCards, OMIM, DrugBank, and TTD databases. Protein interaction relationships were obtained from the STRING database, and a protein interaction network was constructed using Cytoscape software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed using the Metascape database. Molecular docking was performed using AutoDock software to verify the binding between key compounds and core target genes. In vitro experiments were conducted using human renal proximal tubular epithelial cells and various methods, such as CCK8, RT-PCR, immunofluorescence, and western blot, to evaluate the effects of HQH on inflammatory factors, key targets, and pathways.

RESULTS

A total of 48 active ingredients, 168 potential targets of HQH, and 1073 potential targets of DKD were obtained. A total of 118 potential targets, 438 biological processes, and 187 signal pathways were identified for the treatment of DKD. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that HQH may exert its therapeutic effects on DKD by regulating the expression of inflammatory factors through the nuclear factor kappa B (NF-κB) signaling pathway. The molecular docking results showed that β-sitosterol and baicalein had the highest binding affinity with key targets such as AKT1, IL6, TNF, PTGS2, IL1B, and CASP3, suggesting that they may be the most effective active ingredients of HQH in the treatment of DKD. In vitro experimental results demonstrated that HQH could enhance the viability of human renal proximal tubular epithelial cells inhibited by high glucose, decrease the levels of AKT1, TNF, IL6, PTGS2, IL1B, and CASP3, reduce the expression of NF-κB-P65 (P < .01), inhibit NF-κB-p65 nuclear translocation, and decrease chemokine expression (P < .01).

CONCLUSION

HQH may exert its therapeutic effects on DKD by inhibiting the NF-κB signaling pathway, reducing the level of pro-inflammatory cytokines, and alleviating the high glucose-induced injury of renal tubular epithelial cells.

Tolfenpyrad displays Francisella-targeted antibiotic activity that requires an oxidative stress response regulator for sensitivity

IMPORTANCE

Francisella species are highly pathogenic bacteria that pose a threat to global health security. These bacteria can be made resistant to antibiotics through facile methods, and we lack a safe and protective vaccine. Given their history of development as bioweapons, new treatment options must be developed to bolster public health preparedness. Here, we report that tolfenpyrad, a pesticide that is currently in use worldwide, effectively inhibits the growth of Francisella. This drug has an extensive history of use and a plethora of safety and toxicity data, making it a good candidate for development as an antibiotic. We identified mutations in Francisella novicida that confer resistance to tolfenpyrad and characterized a transcriptional regulator that is required for sensitivity to both tolfenpyrad and reactive oxygen species.

Open-ComBind: harnessing unlabeled data for improved binding pose prediction

Determination of the bound pose of a ligand is a critical first step in many in silico drug discovery tasks. Molecular docking is the main tool for the prediction of non-covalent binding of a protein and ligand system. Molecular docking pipelines often only utilize the information of one ligand binding to the protein despite the commonly held hypothesis that different ligands share binding interactions when bound to the same receptor. Here we describe Open-ComBind, an easy-to-use, open-source version of the ComBind molecular docking pipeline that leverages information from multiple ligands without known bound structures to enhance pose selection. We first create distributions of feature similarities between ligand pose pairs, comparing near-native poses with all sampled docked poses. These distributions capture the likelihood of observing similar features, such as hydrogen bonds or hydrophobic contacts, in different pose configurations. These similarity distributions are then combined with a per-ligand docking score to enhance overall pose selection by 5% and 4.5% for high-affinity and congeneric series helper ligands, respectively. Open-ComBind reduces the average RMSD of ligands in our benchmark dataset by 9.0%. We provide Open-ComBind as an easy-to-use command line and Python API to increase pose prediction performance at www.github.com/drewnutt/open_combind .

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins

Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.

Lavandula angustifolia mill. (Lamiaceae) ethanol extract and its main constituents as promising agents for the treatment of metabolic disorders: chemical profile, in vitro biological studies, and molecular docking

Lavandula angustifolia Mill. (lavender) is one of the most used medicinal plants. Herein, we chemically characterised and investigated the antioxidant properties and the capability to inhibit key enzymes for the treatment of type 2 diabetes (TD2) and obesity such as pancreatic lipase, α-glucosidase, and α-amylase of the ethanolic extract of two lavender samples (La1 and La2) from southern Italy. Both extracts significantly inhibited α-glucosidase, while La1 inhibited α-amylase and lipase more effectively than La2. To investigate whether these properties could be due to a direct interaction of the main constituents of the extracts with the targeted enzymes, molecular docking studies have been performed. As a result, the selected compounds were able to interact with the key residues of the binding site of the three proteins, thus supporting biological data. Current findings indicate the new potential of lavender ethanolic extract for the development of novel agents for T2D and obesity.

Eukaryotic CD-NTase, STING, and viperin proteins evolved via domain shuffling, horizontal transfer, and ancient inheritance from prokaryotes

Animals use a variety of cell-autonomous innate immune proteins to detect viral infections and prevent replication. Recent studies have discovered that a subset of mammalian antiviral proteins have homology to antiphage defense proteins in bacteria, implying that there are aspects of innate immunity that are shared across the Tree of Life. While the majority of these studies have focused on characterizing the diversity and biochemical functions of the bacterial proteins, the evolutionary relationships between animal and bacterial proteins are less clear. This ambiguity is partly due to the long evolutionary distances separating animal and bacterial proteins, which obscures their relationships. Here, we tackle this problem for 3 innate immune families (CD-NTases [including cGAS], STINGs, and viperins) by deeply sampling protein diversity across eukaryotes. We find that viperins and OAS family CD-NTases are ancient immune proteins, likely inherited since the earliest eukaryotes first arose. In contrast, we find other immune proteins that were acquired via at least 4 independent events of horizontal gene transfer (HGT) from bacteria. Two of these events allowed algae to acquire new bacterial viperins, while 2 more HGT events gave rise to distinct superfamilies of eukaryotic CD-NTases: the cGLR superfamily (containing cGAS) that has since diversified via a series of animal-specific duplications and a previously undefined eSMODS superfamily, which more closely resembles bacterial CD-NTases. Finally, we found that cGAS and STING proteins have substantially different histories, with STING protein domains undergoing convergent domain shuffling in bacteria and eukaryotes. Overall, our findings paint a picture of eukaryotic innate immunity as highly dynamic, where eukaryotes build upon their ancient antiviral repertoires through the reuse of protein domains and by repeatedly sampling a rich reservoir of bacterial antiphage genes.

System biology analysis to develop diagnostic biomarkers, monitoring pathological indexes, and novel therapeutic approaches for immune targeting based on maggot bioactive compounds and polyphenolic cocktails in mice with gastric cancer

Early diagnosis and prognosis are prerequisites for mitigating mortality in gastric cancer (GaCa). Identifying some causative or sensitive elements (coding RNA (cRNA)-non-cRNAs (ncRNAs)) can be very helpful in the early diagnosis of GaCa. Notably, despite significant development in the GaCa treatment, the outcome of patients does not remain satisfactory due to limitations such as multi-drug resistance and tumor relapse. Therefore, more attention has been drawn to complementary therapies and the use of supplements. In this regard, Polyphenol natural compounds (PNC) and maggot larvae (MaLa) alone or in combination were administered along with chemotherapy (paclitaxel) to N-methyl-N-nitrosourea (MNU)- induced murine tumor model. In addition, in order to identify potential diagnostic or prognostic biomarkers, transcriptomics analysis was performed through a bioinformatics approach. Then transcription profile of ncRNAs with their target hub genes was assessed through qPCR Real-Time, Western blot, and ELISA. According to the bioinformatics results, 17 hub genes (e.g., IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, and IFN-γ) were explored that contribute towards inflammation and oxidative stress and ultimately GaCa development. Upstream of the mentioned hub genes, regulatory factors (lncRNA XIST and NEAT1) were also identified and introduced as prognosis and diagnosis biomarkers for GaCa. Our results showed that PNC alone and in combination with MaLa was able to reduce the size and number of tumors, which is related to the reduction of genes expression levels (including IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, IFN-γ, NEAT1, and XIST). In conclusion, PNC and MaLa have the potential to be considered as complementary and improving chemotherapy due to their effective compounds. Also, the introduced hub gene and lncRNA in addition to diagnostic and prognostic biomarkers can be used as druggable proteins for novel therapeutic targeting of GaCa.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints

Ribonucleic acid (RNA) molecules serve as master regulators of cells by encoding their biological function in the ribonucleotide sequence, particularly their ability to interact with other molecules. To understand how RNA molecules perform their biological tasks and to design new sequences with specific functions, it is of great benefit to be able to computationally predict how RNA folds and interacts in the cellular environment. Our workflow for computational modeling of the 3D structures of RNA and its interactions with other molecules uses a set of methods developed in our laboratory, including MeSSPredRNA for predicting canonical and non-canonical base pairs, PARNASSUS for detecting remote homology based on comparisons of sequences and secondary structures, ModeRNA for comparative modeling, the SimRNA family of programs for modeling RNA 3D structure and its complexes with other molecules, and QRNAS for model refinement. In this study, we present the results of testing this workflow in predicting RNA 3D structures in the CASP15 experiment. The overall high score of the computational models predicted by our group demonstrates the robustness of our workflow and its individual components in terms of predicting RNA 3D structures of acceptable quality that are close to the target structures. However, the variance in prediction quality is still quite high, and the results are still too far from the level of protein 3D structure predictions. This exercise led us to consider several improvements, especially to better predict and enforce stacking interactions and non-canonical base pairs.

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

Simplified Synthesis of Renieramycin T Derivatives to Target Cancer Stem Cells via β-Catenin Proteasomal Degradation in Human Lung Cancer

Cancer stem cells (CSCs) found within cancer tissue play a pivotal role in its resistance to therapy and its potential to metastasize, contributing to elevated mortality rates among patients. Significant strides in understanding the molecular foundations of CSCs have led to preclinical investigations and clinical trials focused on CSC regulator β-catenin signaling targeted interventions in malignancies. As part of the ongoing advancements in marine-organism-derived compound development, it was observed that among the six analogs of Renieramycin T (RT), a potential lead alkaloid from the blue sponge Xestospongia sp., the compound DH_32, displayed the most robust anti-cancer activity in lung cancer A549, H23, and H292 cells. In various lung cancer cell lines, DH_32 exhibited the highest efficacy, with IC50 values of 4.06 ± 0.24 μM, 2.07 ± 0.11 μM, and 1.46 ± 0.06 μM in A549, H23, and H292 cells, respectively. In contrast, parental RT compounds had IC50 values of 5.76 ± 0.23 μM, 2.93 ± 0.07 μM, and 1.52 ± 0.05 μM in the same order. Furthermore, at a dosage of 25 nM, DH_32 showed a stronger ability to inhibit colony formation compared to the lead compound, RT. DH_32 was capable of inducing apoptosis in lung cancer cells, as demonstrated by increased PARP cleavage and reduced levels of the proapoptotic protein Bcl2. Our discovery confirms that DH_32 treatment of lung cancer cells led to a reduced level of CD133, which is associated with the suppression of stem-cell-related transcription factors like OCT4. Moreover, DH_32 significantly suppressed the ability of tumor spheroids to form compared to the original RT compound. Additionally, DH_32 inhibited CSCs by promoting the degradation of β-catenin through ubiquitin-proteasomal pathways. In computational molecular docking, a high-affinity interaction was observed between DH_32 (grid score = -35.559 kcal/mol) and β-catenin, indicating a stronger binding interaction compared to the reference compound R9Q (grid score = -29.044 kcal/mol). In summary, DH_32, a newly developed derivative of the right-half analog of RT, effectively inhibited the initiation of lung cancer spheroids and the self-renewal of lung cancer cells through the upstream process of β-catenin ubiquitin-proteasomal degradation.

In silico description of the adsorption of cell signaling pathway proteins ovalbumin, glutathione, LC3, TLR4, ASC PYCARD, PI3K and NF-Kβ on 7.0 nm gold nanoparticles: obtaining their Lennard-Jones-like potentials through docking and molecular mechanics

The impact of vaccination on the world's population is difficult to calculate. For developing different types of vaccines, adjuvants are substances added to vaccines to increase the magnitude and durability of the immune response and the effectiveness of the vaccine. This work explores the potential use of spherical gold nanoparticles (AuNPs) as adjuvants. Thus, we employed docking techniques and molecular mechanics to describe how a AuNP 7.0 nm in diameter interacts with cell signaling pathway proteins. Initially, we used X-ray crystallization data of the proteins ovalbumin, glutathione, LC3, TLR4, ASC PYCARD, PI3K, and NF-Kβ to study the adsorption with an AuNP through molecular docking. Therefore, interaction energies were obtained for the AuNP complexes and individual proteins, as well as the AuNP and OVA complex (AuNP@OVA) with each cellular protein, respectively. Results showed that AuNPs had the highest affinity for OVA individually, followed by glutathione, ASC PYCARD domain, LC3, PI3K, NF-Kβ, and TLR4. Furthermore, when evaluating the AuNP@OVA complex, glutathione showed a greater affinity with more potent interaction energy when compared to the other studied systems.

A Data Deposition Platform for Sharing Nuclear Magnetic Resonance Data

Nuclear magnetic resonance (NMR) data are rarely deposited in open databases, leading to loss of critical scientific knowledge. Existing data reporting methods (images, tables, lists of values) contain less information than raw data and are poorly standardized. Together, these issues limit FAIR (findable, accessible, interoperable, reusable) access to these data, which in turn creates barriers for compound dereplication and the development of new data-driven discovery tools. Existing NMR databases either are not designed for natural products data or employ complex deposition interfaces that disincentivize deposition. Journals, including the Journal of Natural Products (JNP), are now requiring data submission as part of the publication process, creating the need for a streamlined, user-friendly mechanism to deposit and distribute NMR data.

Structural and pKa Estimation of the Amphipathic HR1 in SARS-CoV-2: Insights from Constant pH MD, Linear vs. Nonlinear Normal Mode Analysis

A comprehensive understanding of molecular interactions and functions is imperative for unraveling the intricacies of viral protein behavior and conformational dynamics during cellular entry. Focusing on the SARS-CoV-2 spike protein (SARS-CoV-2 sp), a Principal Component Analysis (PCA) on a subset comprising 131 A-chain structures in presence of various inhibitors was conducted. Our analyses unveiled a compelling correlation between PCA modes and Anisotropic Network Model (ANM) modes, underscoring the reliability and functional significance of low-frequency modes in adapting to diverse inhibitor binding scenarios. The role of HR1 in viral processing, both linear Normal Mode Analysis (NMA) and Nonlinear NMA were implemented. Linear NMA exhibited substantial inter-structure variability, as evident from a higher Root Mean Square Deviation (RMSD) range (7.30 Å), nonlinear NMA show stability throughout the simulations (RMSD 4.85 Å). Frequency analysis further emphasized that the energy requirements for conformational changes in nonlinear modes are notably lower compared to their linear counterparts. Using simulations of molecular dynamics at constant pH (cpH-MD), we successfully predicted the pKa order of the interconnected residues within the HR1 mutations at lower pH values, suggesting a transition to a post-fusion structure. The pKa determination study illustrates the profound effects of pH variations on protein structure. Key results include pKa values of 9.5179 for lys-921 in the D936H mutant, 9.50 for the D950N mutant, and a slightly higher value of 10.49 for the D936Y variant. To further understand the behavior and physicochemical characteristics of the protein in a biologically relevant setting, we also examine hydrophobic regions in the prefused states of the HR1 protein mutants D950N, D936Y, and D936H in our study. This analysis was conducted to ascertain the hydrophobic moment of the protein within a lipid environment, shedding light on its behavior and physicochemical properties in a biologically relevant context.

An efficient one-pot synthesis and docking studies of bioactive new antiproliferative dispiro[oxindole/acenaphthylenone‒benzofuranone] pyrrolidine scaffolds

A new and efficient method has been developed to synthesize dispiro[oxindole/acenaphthylenone-benzofuranone]pyrrolidine compounds. This is done by triggering the 1,3-dipolar cycloaddition reaction of azomethine ylides by reacting isatin/acenaphthoquinone with L-picolinic acid/L-proline/sarcosine/L-thioproline/tetrahydroisoquinolines, in a highly regioselective manner in an ionic liquid [DBU][Ac] with 4'-chloro-auron[2-(4-chlorobenzylidene)benzofuran-3(2H)-one]. Single-crystal X-ray diffraction data support the proposed structures of the new compounds. The heterocycles derived from amino acids such as L-picolinic acid, L-proline, and L-thioproline showed significant inhibitory effects against six human solid tumors, including lung, breast, cervix, colon, and others. These new structures were also tested in the active sites of the MDM2 receptor to further study their antiproliferative effects.

Synthesis, computational and experimental pharmacological studies for (thio)ether-triazine 5-HT6R ligands with noticeable action on AChE/BChE and chalcogen-dependent intrinsic activity in search for new class of drugs against Alzheimer's disease

Alzheimer's disease is becoming a growing problem increasing at a tremendous rate. Serotonin 5-HT6 receptors appear to be a particularly attractive target from a therapeutic perspective, due to their involvement not only in cognitive processes, but also in depression and psychosis. In this work, we present the synthesis and broad biological characterization of a new series of 18 compounds with a unique 1,3,5-triazine backbone, as potent 5-HT6 receptor ligands. The main aim of this research is to compare the biological activity of the newly synthesized sulfur derivatives with their oxygen analogues and their N-demethylated O- and S-metabolites obtained for the first time. Most of the new triazines displayed high affinity (Ki < 200 nM) and selectivity towards 5-HT6R, with respect to 5-HT2AR, 5-HT7R, and D2R, in the radioligand binding assays. For selected, active compounds crystallographic studies, functional bioassays, and ADME-Tox profile in vitro were performed. The exciting novelty is that the sulfur derivatives exhibit an agonistic mode of action contrary to all other compounds obtained to date in this chemical class herein and previously reported. Advanced computational studies indicated that this intriguing functional shift might be caused by presence of chalcogen bonds formed only by the sulfur atom. In addition, the N-demethylated derivatives have emerged highly potent antioxidants and, moreover, show a significant improvement in metabolic stability compared to the parent structures. The cholinesterase study present micromolar inhibitory AChE and BChE activity for both 5-HT6 agonist 19 and potent antagonist 5. Finally, the behavioral experiments of compound 19 demonstrated its antidepressant-like properties and slight ability to improve cognitive deficits, without inducing memory impairments by itself. Described pharmacological properties of both compounds (5 and 19) allow to give a design clue for the development of multitarget compounds with 5-HT6 (both agonist and antagonist)/AChE and/or BChE mechanism in the group of 1,3,5-triazine derivatives.

DeepTM: A deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences

Thermally stable proteins find extensive applications in industrial production, pharmaceutical development, and serve as a highly evolved starting point in protein engineering. The thermal stability of proteins is commonly characterized by their melting temperature (Tm). However, due to the limited availability of experimentally determined Tm data and the insufficient accuracy of existing computational methods in predicting Tm, there is an urgent need for a computational approach to accurately forecast the Tm values of thermophilic proteins. Here, we present a deep learning-based model, called DeepTM, which exclusively utilizes protein sequences as input and accurately predicts the Tm values of target thermophilic proteins on a dataset consisting of 7790 thermophilic protein entries. On a test set of 1550 samples, DeepTM demonstrates excellent performance with a coefficient of determination (R2) of 0.75, Pearson correlation coefficient (P) of 0.87, and root mean square error (RMSE) of 6.24 ℃. We further analyzed the sequence features that determine the thermal stability of thermophilic proteins and found that dipeptide frequency, optimal growth temperature (OGT) of the host organisms, and the evolutionary information of the protein significantly affect its melting temperature. We compared the performance of DeepTM with recently reported methods, ProTstab2 and DeepSTABp, in predicting the Tm values on two blind test datasets. One dataset comprised 22 PET plastic-degrading enzymes, while the other included 29 thermally stable proteins of broader classification. In the PET plastic-degrading enzyme dataset, DeepTM achieved RMSE of 8.25 ℃. Compared to ProTstab2 (20.05 ℃) and DeepSTABp (20.97 ℃), DeepTM demonstrated a reduction in RMSE of 58.85% and 60.66%, respectively. In the dataset of thermally stable proteins, DeepTM (RMSE=7.66 ℃) demonstrated a 51.73% reduction in RMSE compared to ProTstab2 (RMSE=15.87 ℃). DeepTM, with the sole requirement of protein sequence information, accurately predicts the melting temperature and achieves a fully end-to-end prediction process, thus providing enhanced convenience and expediency for further protein engineering.

GPCNDTA: Prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores

Drug-target affinity prediction is a challenging task in drug discovery. The latest computational models have limitations in mining edge information in molecule graphs, accessing to knowledge in pharmacophores, integrating multimodal data of the same biomolecule and realizing effective interactions between two different biomolecules. To solve these problems, we proposed a method called Graph features and Pharmacophores augmented Cross-attention Networks based Drug-Target binding Affinity prediction (GPCNDTA). First, we utilized the GNN module, the linear projection unit and self-attention layer to correspondingly extract features of drugs and proteins. Second, we devised intramolecular and intermolecular cross-attention to respectively fuse and interact features of drugs and proteins. Finally, the linear projection unit was applied to gain final features of drugs and proteins, and the Multi-Layer Perceptron was employed to predict drug-target binding affinity. Three major innovations of GPCNDTA are as follows: (i) developing the residual CensNet and the residual EW-GCN to correspondingly extract features of drug and protein graphs, (ii) regarding pharmacophores as a new type of priors to heighten drug-target affinity prediction performance, and (iii) devising intramolecular and intermolecular cross-attention, in which the intramolecular cross-attention realizes the effective fusion of different modal data related to the same biomolecule, and the intermolecular cross-attention fulfills the information interaction between two different biomolecules in attention space. The test results on five benchmark datasets imply that GPCNDTA achieves the best performance compared with state-of-the-art computational models. Besides, relying on ablation experiments, we proved effectiveness of GNN modules, pharmacophores and two cross-attention strategies in improving the prediction accuracy, stability and reliability of GPCNDA. In case studies, we applied GPCNDTA to predict binding affinities between 3C-like proteinase and 185 drugs, and observed that most binding affinities predicted by GPCNDTA are close to corresponding experimental measurements.

TT3D: Leveraging precomputed protein 3D sequence models to predict protein-protein interactions

MOTIVATION

High-quality computational structural models are now precomputed and available for nearly every protein in UniProt. However, the best way to leverage these models to predict which pairs of proteins interact in a high-throughput manner is not immediately clear. The recent Foldseek method of van Kempen et al. encodes the structural information of distances and angles along the protein backbone into a linear string of the same length as the protein string, using tokens from a 21-letter discretized structural alphabet (3Di).

RESULTS

We show that using both the amino acid sequence and the 3Di sequence generated by Foldseek as inputs to our recent deep-learning method, Topsy-Turvy, substantially improves the performance of predicting protein-protein interactions cross-species. Thus TT3D (Topsy-Turvy 3D) presents a way to reuse all the computational effort going into producing high-quality structural models from sequence, while being sufficiently lightweight so that high-quality binary protein-protein interaction predictions across all protein pairs can be made genome-wide.

AVAILABILITY AND IMPLEMENTATION

TT3D is available at https://github.com/samsledje/D-SCRIPT. An archived version of the code at time of submission can be found at https://zenodo.org/records/10037674.

Kalium 3.0 is a comprehensive depository of natural, artificial, and labeled polypeptides acting on potassium channels

Here, we introduce the third release of Kalium database (http://kaliumdb.org/), a manually curated comprehensive depository that accumulates data on polypeptide ligands of potassium channels. The major goal of this amplitudinous update is to summarize findings for natural polypeptide ligands of K+ channels, as well as data for the artificial derivatives of these substances obtained over the decades of exploration. We manually analyzed more than 700 original manuscripts and systematized the information on mutagenesis, production of radio- and fluorescently labeled derivatives, and the molecular pharmacology of K+ channel ligands. As a result, data on more than 1200 substances were processed and added enriching the database content fivefold. We also included the electrophysiological data obtained on the understudied and neglected K+ channels including the heteromeric and concatenated channels. We associated target channels in Kalium with corresponding entries in the official database of the International Union of Basic and Clinical Pharmacology. Kalium was supplemented with an adaptive Statistics page, where users are able to obtain actual data output. Several other improvements were introduced, such as a color code to distinguish the range of ligand activity concentrations and advanced tools for filtration and sorting. Kalium is a fully open-access database, crosslinked to other databases of interest. It can be utilized as a convenient resource containing ample up-to-date information about polypeptide ligands of K+ channels.

PyPCN: protein contact networks in PyMOL

MOTIVATION

Protein contact networks (PCNs) represent the 3D structure of a protein using network formalism. Inter-residue contacts are described as binary adjacency matrices, which are derived from the graph representation of residues (as α-carbons, β-carbons or centroids) and Euclidean distances according to defined thresholds. Functional characterization algorithms are computed on binary adjacency matrices to unveil allosteric, dynamic, and interaction mechanisms in proteins. Such strategies are usually applied in a combinatorial manner, although rarely in seamless and user-friendly implementations.

RESULTS

PyPCN is a plugin for PyMOL wrapping more than twenty PCN algorithms and metrics in an easy-to-use graphical user interface, to support PCN analysis. The plugin accepts 3D structures from the Protein Data Bank, user-provided PDBs, or precomputed adjacency matrices. The results are directly mapped to 3D protein structures and organized into interactive diagrams for their visualization. A dedicated graphical user interface combined with PyMOL visual support makes analysis more intuitive and easier, extending the applicability of PCNs.

AVAILABILITY AND IMPLEMENTATION

https://github.com/pcnproject/PyPCN.

Investigating the mechanism of action of Yanghe Pingchuan Granule in the treatment of bronchial asthma based on bioinformatics and experimental validation

Background

Yanghe Pingchuan Granule (YPG) is a patented Chinese medicine developed independently by the Anhui Provincial Hospital of Traditional Chinese Medicine. For many years, it has been used for the treatment of asthma with remarkable clinical effects. However, the composition of YPG is complex, and its potential active ingredients and mechanism of action for the treatment of asthma are unknown.

Materials and methods

In this study, we investigated the potential mechanism of action of YPG in the treatment of asthma through a combination of bioinformatics and in vivo experimental validation. We searched for active compounds in YPG and asthma targets from multiple databases and obtained common targets. Subsequently, a protein-protein interaction (PPI) network for compound disease was constructed using the protein interaction database for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, hematoxylin and eosin (H&E) staining, Masson staining, enzyme-linked immunosorbent assay (ELISA) analysis, immunofluorescence (IF) experiments, and Western blot (WB) experiments were performed to verify the possible mechanism of action of YPG for asthma treatment.

Results

We obtained 72 active ingredients and 318 drug target genes that overlap with asthma. Serine/threonine-protein kinase (AKT1), tumor protein p53 (TP53), tumor necrosis factor (TNF), interleukin (IL)-6, IL-1β, vascular endothelial growth factor-A (VEGFA), prostaglandin-endoperoxide synthase 2 (PTGS2), caspase-3 (CASP3), mitogen-activated protein kinase 3 (MAPK3) and epidermal growth factor receptor (EGFR) were the most relevant genes in the PPI network. KEGG analysis showed a high number of genes enriched for the nuclear factor kappa-B (NF-κB) signaling pathway. Animal experiments confirmed that YPG reduced inflammatory cell infiltration and down-regulated the expression of ovalbumin-induced inflammatory factors. Furthermore, YPG treatment decreased the protein expression of NFĸB1, nuclear factor kappa B kinase subunit beta (IKBKB), vascular endothelial growth factor (VEGF), and vascular endothelial growth factor receptor 2 (VEGFR2) in lung tissue.

Conclusion

YPG has a positive effect on asthma by interfering with multiple targets. Furthermore, YPG may significantly inhibit the follicle-induced inflammatory response through the NF-ĸB signaling pathway.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Ultrasound-Induced Gold Nanoparticle United with Acoustic Reprogramming of Macrophages for Enhanced Cancer Therapy

Sonodynamic therapy (SDT) has considerable potential in cancer treatment and exhibits high tissue penetration with minimal damage to healthy tissues. The efficiency of SDT is constrained by the complex immunological environment and tumor treatment resistance. Herein, a specific acoustic-actuated tumor-targeted nanomachine is proposed to generate mechanical damage to lysosomes for cancer SDT. The hybrid nanomachine was assembled with gold nanoparticles (GNPs) as the core and encapsulated with macrophage exosomes modified by AS1411 aptamers (GNP@EXO-APs) to optimize the pharmacokinetics and tumor aggregation. GNP@EXO-APs could be specifically transferred to the lysosomes of tumor cells. After induction with ultrasound, GNP@EXO-APs generated strong mechanical stress to produce lysosomal-dependent cell death in cancer cells. Notably, tumor-associated macrophages were reprogrammed in the ultrasound environment to an antitumor phenotype. Enhanced mechanical destruction via GNP@EXO-APs and immunotherapy of cancer cells were verified both in vitro and in vivo under SDT. This study provides a new direction for inside-out killing effects on tumor cells for cancer treatment.

CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks

We present a novel and interpretable approach for predicting small-molecule binding affinities using context explanation networks (CENs). Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to predict the contributions of pre-calculated terms to the overall binding affinity. We show that CENsible can effectively distinguish active vs. inactive compounds for many systems. Its primary benefit over related machine-learning scoring functions, however, is that it retains interpretability, allowing researchers to identify the contribution of each pre-calculated term to the final affinity prediction, with implications for subsequent lead optimization.

Artificial intelligence and the analysis of cryo-EM data provide structural insight into the molecular mechanisms underlying LN-lamininopathies

Laminins (Lm) are major components of basement membranes (BM), which polymerize to form a planar lattice on cell surface. Genetic alternations of Lm affect their oligomerization patterns and lead to failures in BM assembly manifesting in a group of human disorders collectively defined as Lm N-terminal domain lamininopathies (LN-lamininopathies). We have employed a recently determined cryo-EM structure of the Lm polymer node, the basic repeating unit of the Lm lattice, along with structure prediction and modeling to systematically analyze structures of twenty-three pathogenic Lm polymer nodes implicated in human disease. Our analysis provides the detailed mechanistic explanation how Lm mutations lead to failures in Lm polymerization underlining LN-lamininopathies. We propose the new categorization scheme of LN-lamininopathies based on the insight gained from the structural analysis. Our results can help to facilitate rational drug design aiming in the treatment of Lm deficiencies.

De novo prediction of explicit water molecule positions by a novel algorithm within the protein design software MUMBO

By mediating interatomic interactions, water molecules play a major role in protein-protein, protein-DNA and protein-ligand interfaces, significantly affecting affinity and specificity. This notwithstanding, explicit water molecules are usually not considered in protein design software because of high computational costs. To challenge this situation, we analyzed the binding characteristics of 60,000 waters from high resolution crystal structures and used the observed parameters to implement the prediction of water molecules in the protein design and side chain-packing software MUMBO. To reduce the complexity of the problem, we incorporated water molecules through the solvation of rotamer pairs instead of relying on solvated rotamer libraries. Our validation demonstrates the potential of our algorithm by achieving recovery rates of 67% for bridging water molecules and up to 86% for fully coordinated waters. The efficacy of our algorithm is highlighted further by the prediction of 3 different proteinligand complexes. Here, 91% of water-mediated interactions between protein and ligand are correctly predicted. These results suggest that the new algorithm could prove highly beneficial for structure-based protein design, particularly for the optimization of ligand-binding pockets or protein-protein interfaces.

Design of SC PEP with enhanced stability against pepsin digestion and increased activity by machine learning and structural parameters modeling

Prolyl endopeptidases from Sphingomonas capsulata (SC PEP) has attracted much attention as promising oral therapy candidate for celiac sprue, however, its low stability in the gastric environment leads to unsatisfactory clinical results. Therefore, improving its stability against pepsin digestion at low pH is crucial for clinical applications, but challenging. In this study, machine learning and physical parameter model were combined to design SC PEP mutants. After iterations, 20 mutants had higher hydrolysis activity in stomach environment, which was up to 14.1-fold compared with wild-type SC PEP. Mutant M24 involving stable and active mutations and pegylated M24 (M24-PEG) had higher activity of hydrolyzing immunogen in bread than wild-type SC PEP in vitro and in vivo, and residual immunogens in simulated gastric environment were only 1/8 and 1/10 of that in the wild-type SC PEP group. The total residual immunogens in the gastrointestinal tract of mice in the M24 and M24-PEG groups were <20 ppm, reaching the standard of non-toxic food. Our results indicate that the combination of M24 (or M24-PEG) with EP-B2 may be a promising candidate for celiac disease, and the strategies developed in this study provide a paradigm for the design of SC PEP stability mutants.

A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.