Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions.

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.

Drug discovery, which plays a vital role in maintaining human health, is a persistent challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery of novel candidate compounds. Computational tools in FBDD could help to identify potential drug leads in a cost-efficient and time-saving manner. The Auto Core Fragment in silico Screening (ACFIS) server is a well-established and effective online tool for FBDD. However, the accurate prediction of protein-fragment binding mode and affinity is still a major challenge for FBDD due to weak binding affinity. Here, we present an updated version (ACFIS 2.0), that incorporates a dynamic fragment growing strategy to consider protein flexibility. The major improvements of ACFIS 2.0 include (i) increased accuracy of hit compound identification (from 75.4% to 88.5% using the same test set), (ii) improved rationality of the protein-fragment binding mode, (iii) increased structural diversity due to expanded fragment libraries and (iv) inclusion of more comprehensive functionality for predicting molecular properties. Three successful cases of drug lead discovery using ACFIS 2.0 are described, including drugs leads to treat Parkinson's disease, cancer, and major depressive disorder. These cases demonstrate the utility of this web-based server. ACFIS 2.0 is freely available at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS2/.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations.

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics.

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals.

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

Molecular and functional analysis of a brown planthopper resistance protein with two nucleotide-binding site domains.

The brown planthopper (Nilaparvata lugens Stål, BPH) resistance gene BPH9 encodes an unusual coiled-coil (CC) nucleotide-binding leucine-rich repeat (LRR) protein with two nucleotide-binding site (NBS) domains. To understand how this CC-NBS-NBS-LRR (CNNL) protein regulates defense signaling and BPH resistance, we dissected each domain's functions. The CC domain of BPH9 self-associated and was sufficient to induce cell death. The region of 97-115 residues in the CC domain is crucial for self-association and activation. NBS2, which contains a complete set of NBS function motifs and inhibits CC domain activation, rather than NBS1, acts as a molecular switch to regulate the activity of BPH9. We demonstrated that the CC domain, the NBS domain, and the LRR domain of BPH9 associate with each other and themselves in planta. Further domain swapping experiments revealed that the CC domains of BPH9 and susceptible alleles were similarly competent to induce resistance and the hypersensitive response, while the LRR domain of BPH9 confers resistance specificity to BPH. These findings provide new insights into the regulatory mechanisms governing the activity of CNNL proteins.

Dihydromyricetin Imbues Antiadipogenic Effects on 3T3-L1 Cells via Direct Interactions with 78-kDa Glucose-Regulated Protein.

BACKGROUND: Obesity is among the most serious public health problems worldwide, with few safe pharmaceutical interventions. Natural products have become an important source of potential anti-obesity therapeutics. Dihydromyricetin (DHM) exerts antidiabetic effects. The biochemical target of DHM, however, has been unknown. It is crucial to identify the biochemical target of DHM for elucidating its physiological function and therapeutic value. OBJECTIVES: The objective of this study was to identify the biochemical target of DHM. METHODS: An abundant antiadipogenic flavanonol was extracted from the herbal plant Ampelopsis grossedentata through bioassay-guided fractionation and characterized with high-resolution LC-MS and 1H and 13C nuclear magnetic resonance. Antiadipogenic experiments were done with mouse 3T3-L1 preadipocytes. A biochemical target of the chemical of interest was identified with drug affinity responsive target stability assay. Direct interactions between the chemical of interest and the protein target in vitro were predicted with molecular docking and subsequently confirmed with surface plasmon resonance. Expression levels of peroxisome proliferator-activated receptor γ (PPARγ), which is associated with 78-kDa glucose-regulated protein (GRP78), were measured with real-time qPCR. RESULTS: DHM was isolated, purified, and structurally characterized. Cellular studies showed that DHM notably reduced intracellular oil droplet formation in 3T3-L1 cells with a median effective concentration of 294 µM (i.e., 94 µg/mL). DHM targeted the ATP binding site of GRP78, which is associated with adipogenesis. An equilibrium dissociation constant between DHM and GRP78 was 21.8 µM. In 3T3-L1 cells upon treatment with DHM at 50 µM (i.e., 16 µg/mL), the expression level of PPARγ was downregulated to 53.9% of the solvent vehicle control's level. CONCLUSIONS: DHM targets GRP78 in vitro. DHM is able to reduce lipid droplet formation in 3T3-L1 cells through a mode of action that is plausibly associated with direct interactions between GRP78 and DHM, which is a step forward in determining potential applications of DHM as an anti-obesity agent.

Human Schlafen 5 regulates reversible epithelial and mesenchymal transitions in breast cancer by suppression of ZEB1 transcription.

BACKGROUND: Human Schlafen 5 (SLFN5) has been reported to inhibit or promote cell invasion in tumours depending on their origin. However, its role in breast cancer (BRCA) is undetermined. METHODS: Differential expression analyses using The Cancer Genome Atlas (TCGA) data, clinical samples and cell lines were performed. Lentiviral knockdown and overexpression experiments were performed to detect changes in cell morphology, molecular markers and invasion. Chromatin immunoprecipitation-sequencing (ChIP-Seq) and luciferase reporter assays were performed to detect the SLFN5-binding motif. RESULTS: TCGA, clinical samples and cell lines showed that SLFN5 expression was negatively correlated with BRCA metastasis. SLFN5 knockdown induced epithelial-mesenchymal transition (EMT) and enhanced invasion in BRCA cell lines. However, overexpression triggered mesenchymal-epithelial transition (MET). SLFN5 inhibited the expression of ZEB1 but not ZEB2, SNAI1, SNAI2, TWIST1 or TWIST2. Knockdown and overexpression of ZEB1 indicated that it was a mediator of the SLFN5-governed phenotype and invasion changes. Moreover, SLFN5 inhibited ZEB1 transcription by directly binding to the SLFN5-binding motif on the ZEB1 promoter, but a SLFN5 C-terminal deletion mutant did not. CONCLUSION: SLFN5 regulates reversible epithelial and mesenchymal transitions, and inhibits BRCA metastasis by suppression of ZEB1 transcription, suggesting that SLFN5 could be a potential target for BRCA therapy.

The Coiled-Coil and Nucleotide Binding Domains of BROWN PLANTHOPPER RESISTANCE14 Function in Signaling and Resistance against Planthopper in Rice.

BROWN PLANTHOPPER RESISTANCE14 (BPH14), the first planthopper resistance gene isolated via map-based cloning in rice (Oryza sativa), encodes a coiled-coil, nucleotide binding site, leucine-rich repeat (CC-NB-LRR) protein. Several planthopper and aphid resistance genes encoding proteins with similar structures have recently been identified. Here, we analyzed the functions of the domains of BPH14 to identify molecular mechanisms underpinning BPH14-mediated planthopper resistance. The CC or NB domains alone or in combination (CC-NB [CN]) conferred a similar level of brown planthopper resistance to that of full-length (FL) BPH14. Both domains activated the salicylic acid signaling pathway and defense gene expression. In rice protoplasts and Nicotiana benthamiana leaves, these domains increased reactive oxygen species levels without triggering cell death. Additionally, the resistance domains and FL BPH14 protein formed homocomplexes that interacted with transcription factors WRKY46 and WRKY72. In rice protoplasts, the expression of FL BPH14 or its CC, NB, and CN domains increased the accumulation of WRKY46 and WRKY72 as well as WRKY46- and WRKY72-dependent transactivation activity. WRKY46 and WRKY72 bind to the promoters of the receptor-like cytoplasmic kinase gene RLCK281 and the callose synthase gene LOC_Os01g67364.1, whose transactivation activity is dependent on WRKY46 or WRKY72. These findings shed light on this important insect resistance mechanism.

Secretome Analysis and In Planta Expression of Salivary Proteins Identify Candidate Effectors from the Brown Planthopper Nilaparvata lugens.

The brown planthopper (BPH), Nilaparvata lugens (Stål), is a phloem sap-feeding insect. During feeding on rice plants, BPH secretes salivary proteins with potential effector functions, which may play a critical role in the plant-insect interactions. However, a limited number of BPH effector proteins have been identified to date. Here, we sequenced the salivary gland transcriptomes of five BPH populations and subsequently established a N. lugens secretome consisting of 1,140 protein-encoding genes. Secretome analysis revealed the presence of both conserved and rapidly evolving salivary proteins. A screen for potential effectors that elicit responses in the plant was performed via the transient expression analysis of 64 BPH salivary proteins in Nicotiana benthamiana leaves and rice protoplasts. The salivary proteins Nl12, Nl16, Nl28, and Nl43 induced cell death, whereas Nl40 induced chlorosis and Nl32 induced a dwarf phenotype in N. benthamiana, indicating effector properties of these proteins. Ectopic expression of the six salivary proteins in N. benthamiana upregulated expression of defense-related genes and callose deposition. Tissue expression analysis showed a higher expression level of the six candidate effectors in salivary glands than in other tissues. Subcellular localization and analysis of the domain required for cell death showed a diverse structure of the six effectors. Nl28, Nl40, and Nl43 are N. lugens specific; in contrast, Nl12, Nl16, and Nl32 are conserved among insects. The Nl40 family has numerous isoforms produced by alternative splicing, exemplifying rapid evolution and expansion of effector proteins in the BPH. Our results suggest a potential large effector repertoire in BPH and a higher level of effector conservation exist in BPH compared with that in plant pathogens.

A Mucin-Like Protein of Planthopper Is Required for Feeding and Induces Immunity Response in Plants.

The brown planthopper, Nilaparvata lugens, is a pest that threatens rice (Oryza sativa) production worldwide. While feeding on rice plants, planthoppers secrete saliva, which plays crucial roles in nutrient ingestion and modulating plant defense responses, although the specific functions of salivary proteins remain largely unknown. We identified an N. lugens-secreted mucin-like protein (NlMLP) by transcriptome and proteome analyses and characterized its function, both in brown planthopper and in plants. NlMLP is highly expressed in salivary glands and is secreted into rice during feeding. Inhibition of NlMLP expression in planthoppers disturbs the formation of salivary sheaths, thereby reducing their performance. In plants, NlMLP induces cell death, the expression of defense-related genes, and callose deposition. These defense responses are related to Ca2+ mobilization and the MEK2 MAP kinase and jasmonic acid signaling pathways. The active region of NlMLP that elicits plant responses is located in its carboxyl terminus. Our work provides a detailed characterization of a salivary protein from a piercing-sucking insect other than aphids. Our finding that the protein functions in plant immune responses offers new insights into the mechanism underlying interactions between plants and herbivorous insects.

Allelic diversity in an NLR gene BPH9 enables rice to combat planthopper variation.

Brown planthopper (BPH), Nilaparvata lugens Stål, is one of the most devastating insect pests of rice (Oryza sativa L.). Currently, 30 BPH-resistance genes have been genetically defined, most of which are clustered on specific chromosome regions. Here, we describe molecular cloning and characterization of a BPH-resistance gene, BPH9, mapped on the long arm of rice chromosome 12 (12L). BPH9 encodes a rare type of nucleotide-binding and leucine-rich repeat (NLR)-containing protein that localizes to the endomembrane system and causes a cell death phenotype. BPH9 activates salicylic acid- and jasmonic acid-signaling pathways in rice plants and confers both antixenosis and antibiosis to BPH. We further demonstrated that the eight BPH-resistance genes that are clustered on chromosome 12L, including the widely used BPH1, are allelic with each other. To honor the priority in the literature, we thus designated this locus as BPH1/9 These eight genes can be classified into four allelotypes, BPH1/9-1, -2, -7, and -9 These allelotypes confer varying levels of resistance to different biotypes of BPH. The coding region of BPH1/9 shows a high level of diversity in rice germplasm. Homologous fragments of the nucleotide-binding (NB) and leucine-rich repeat (LRR) domains exist, which might have served as a repository for generating allele diversity. Our findings reveal a rice plant strategy for modifying the genetic information to gain the upper hand in the struggle against insect herbivores. Further exploration of natural allelic variation and artificial shuffling within this gene may allow breeding to be tailored to control emerging biotypes of BPH.

3D-QSAR (CoMFA, CoMSIA), molecular docking and molecular dynamics simulations study of 6-aryl-5-cyano-pyrimidine derivatives to explore the structure requirements of LSD1 inhibitors.

Recently, Histone Lysine Specific Demethylase 1 (LSD1) was regarded as a promising anticancer target for the novel drug discovery. And several small molecules as LSD1 inhibitors in different structures have been reported. In this work, we carried out a molecular modeling study on the 6-aryl-5-cyano-pyrimidine fragment LSD1 inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. The results show that the best CoMFA model has q2=0.802, r2ncv=0.979, and the best CoMSIA model has q2=0.799, r2ncv=0.982. The electrostatic, hydrophobic and H-bond donor fields play important roles in the models. Molecular docking studies predict the binding mode and the interactions between the ligand and the receptor protein. Molecular dynamics simulations results reveal that the complex of the ligand and the receptor protein are stable at 300K. All the results can provide us more useful information for our further drug design.

Genes associated with thermosensitive genic male sterility in rice identified by comparative expression profiling.

BACKGROUND: Thermosensitive genic male sterile (TGMS) lines and photoperiod-sensitive genic male sterile (PGMS) lines have been successfully used in hybridization to improve rice yields. However, the molecular mechanisms underlying male sterility transitions in most PGMS/TGMS rice lines are unclear. In the recently developed TGMS-Co27 line, the male sterility is based on co-suppression of a UDP-glucose pyrophosphorylase gene (Ugp1), but further study is needed to fully elucidate the molecular mechanisms involved. RESULTS: Microarray-based transcriptome profiling of TGMS-Co27 and wild-type Hejiang 19 (H1493) plants grown at high and low temperatures revealed that 15462 probe sets representing 8303 genes were differentially expressed in the two lines, under the two conditions, or both. Environmental factors strongly affected global gene expression. Some genes important for pollen development were strongly repressed in TGMS-Co27 at high temperature. More significantly, series-cluster analysis of differentially expressed genes (DEGs) between TGMS-Co27 plants grown under the two conditions showed that low temperature induced the expression of a gene cluster. This cluster was found to be essential for sterility transition. It includes many meiosis stage-related genes that are probably important for thermosensitive male sterility in TGMS-Co27, inter alia: Arg/Ser-rich domain (RS)-containing zinc finger proteins, polypyrimidine tract-binding proteins (PTBs), DEAD/DEAH box RNA helicases, ZOS (C2H2 zinc finger proteins of Oryza sativa), at least one polyadenylate-binding protein and some other RNA recognition motif (RRM) domain-containing proteins involved in post-transcriptional processes, eukaryotic initiation factor 5B (eIF5B), ribosomal proteins (L37, L1p/L10e, L27 and L24), aminoacyl-tRNA synthetases (ARSs), eukaryotic elongation factor Tu (eEF-Tu) and a peptide chain release factor protein involved in translation. The differential expression of 12 DEGs that are important for pollen development, low temperature responses or TGMS was validated by quantitative RT-PCR (qRT-PCR). CONCLUSIONS: Temperature strongly affects global gene expression and may be the common regulator of fertility in PGMS/TGMS rice lines. The identified expression changes reflect perturbations in the transcriptomic regulation of pollen development networks in TGMS-Co27. Findings from this and previous studies indicate that sets of genes involved in post-transcriptional and translation processes are involved in thermosensitive male sterility transitions in TGMS-Co27.

Temporal Network Embedding Enhanced With Long-Range Dynamics and Self-Supervised Learning.

Temporal network embedding (TNE) has promoted the research of knowledge discovery and reasoning on networks. It aims to embed vertices of temporal networks into a low-dimensional vector space while preserving network structures and temporal properties. However, most existing methods have limitations in capturing dynamics over long distances, which makes it difficult to explore multihop topological associations among vertices. To tackle this challenge, we propose LongTNE, which learns the long-range dynamics of vertices to endow TNE with the ability to capture high-order proximity (HP) of networks. In LongTNE, we employ graph self-supervised learning (Graph SSL) to optimize the establishment probability of deep links in each network snapshot. We also present an accumulated forward update (AFU) module to fathom global temporal evolution among multiple network snapshots. The empirical results on six temporal networks demonstrate that, in addition to achieving state-of-the-art performance on network mining tasks, LongTNE can be handily extended to existing TNE methods.

GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases.

Gene set knowledge discovery is essential for advancing human functional genomics. Recent studies have shown promising performance by harnessing the power of Large Language Models (LLMs) on this task. Nonetheless, their results are subject to several limitations common in LLMs such as hallucinations. In response, we present GeneAgent, a first-of-its-kind language agent featuring self-verification capability. It autonomously interacts with various biological databases and leverages relevant domain knowledge to improve accuracy and reduce hallucination occurrences. Benchmarking on 1,106 gene sets from different sources, GeneAgent consistently outperforms standard GPT-4 by a significant margin. Moreover, a detailed manual review confirms the effectiveness of the self-verification module in minimizing hallucinations and generating more reliable analytical narratives. To demonstrate its practical utility, we apply GeneAgent to seven novel gene sets derived from mouse B2905 melanoma cell lines, with expert evaluations showing that GeneAgent offers novel insights into gene functions and subsequently expedites knowledge discovery.

Discrepant soil microbial community and C cycling function responses to conventional and biodegradable microplastics.

Biodegradable microplastics (MPs) are promising alternatives to conventional MPs and are of high global concern. However, their discrepant effects on soil microorganisms and functions are poorly understood. In this study, polyethylene (PE) and polylactic acid (PLA) MPs were selected to investigate the different effects on soil microbiome and C-cycling genes using high-throughput sequencing and real-time quantitative PCR, as well as the morphology and functional group changes of MPs, using scanning electron microscopy and Fourier transform infrared spectroscopy, and the driving factors were identified. The results showed that distinct taxa with potential for MP degradation and nitrogen cycling were enriched in soils with PLA and PE, respectively. PLA, smaller size (150-180 µm), and 5% (w/w) of MPs enhanced the network complexity compared with PE, larger size (250-300 µm), and 1% (w/w) of MPs, respectively. PLA increased ß-glucosidase by up to 2.53 times, while PE (150-180 µm) reduced by 38.26-44.01% and PE (250-300 µm) increased by 19.00-22.51% at 30 days. Amylase was increased by up to 5.83 times by PLA (150-180 µm) but reduced by 40.26-62.96% by PLA (250-300 µm) and 16.11-43.92% by PE. The genes cbbL, cbhI, abfA, and Lac were enhanced by 37.16%- 1.99 times, 46.35%- 26.46 times, 8.41%- 69.04%, and 90.81%- 5.85 times by PLA except for PLA1B/5B at 30 days. These effects were associated with soil pH, NO3--N, and MP biodegradability. These findings systematically provide an understanding of the impact of biodegradable MPs on the potential for global climate change.

PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge.

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases, and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

Mutation characteristics and molecular evolution of ovarian metastasis from gastric cancer and potential biomarkers for paclitaxel treatment.

Ovarian metastasis is one of the major causes of treatment failure in patients with gastric cancer (GC). However, the genomic characteristics of ovarian metastasis in GC remain poorly understood. In this study, we enroll 74 GC patients with ovarian metastasis, with 64 having matched primary and metastatic samples. Here, we show a characterization of the mutation landscape of this disease, alongside an investigation into the molecular heterogeneity and pathway mutation enrichments between synchronous and metachronous metastasis. We classify patients into distinct clonal evolution patterns based on the distribution of mutations in paired samples. Notably, the parallel evolution group exhibits the most favorable prognosis. Additionally, by analyzing the differential response to chemotherapy, we identify potential biomarkers, including SALL4, CCDC105, and CLDN18, for predicting the efficacy of paclitaxel treatment. Furthermore, we validate that CLDN18 fusion mutations improve tumor response to paclitaxel treatment in GC with ovarian metastasis in vitro and vivo.