Protein-protein Interaction Modules Research Articles

Molecular glues for protein-protein interactions: Progressing toward a new dream.

The modulation of protein-protein interactions with small molecules is one of the most rapidly developing areas in drug discovery. In this review, we discuss advances over the past decade (2014-2023) focusing on molecular glues (MGs)-monovalent small molecules that induce proximity, either by stabilizing native interactions or by inducing neomorphic interactions. We include both serendipitous and rational discoveries and describe the different approaches that were used to identify them. We classify the compounds in three main categories: degradative MGs, non-degradative MGs or PPI stabilizers, and MGs that induce self-association. Diverse, illustrative examples with structural data are described in detail, emphasizing the elements of molecular recognition and cooperative binding at the interface that are fundamental for a MG mechanism of action.

Design of inhibitor peptide sequences based on the interfacial knowledge of the protein G-IgG crystallographic complex and their binding studies with IgG.

Protein-protein interactions (PPI) have emerged as valuable targets in medicinal chemistry due to their key roles in important biological processes. The modulation of PPI by small peptides offers an excellent opportunity to develop drugs against human diseases. Here, we exploited the knowledge of the binding interface of the IgG-protein G complex (PDB:1FCC) for designing peptides that can inhibit these complexes. Herein, we have designed several closely related peptides, and the comparison of results from experiments and computational studies indicated that all the peptides bind close to the expected binding site on IgG and the complexes are stable. A minimal sequence consisting of 11 amino acids (P5) with binding constants in the range of 100nM was identified. We propose that the main affinity differences across the series of peptides arose from the presence of polar amino acid residues. Further, the molecular dynamic studies helped to understand the dynamic properties of complexes in terms of flexibility of residues and structural stability at the interface. The ability of P5 to compete with the protein G in recognizing IgG can help in the detection and purification of antibodies. Further, it can serve as a versatile tool for a better understanding of protein-protein interactions.

The effect of dapagliflozin on anemia in elderly patients with heart failure by bioinformatics analysis.

Anemia associated with heart failure is frequent and can exacerbate the symptoms of heart failure. Dapagliflozin is the first SGLT-2 inhibitor with significant cardiovascular protection. However, the effect of dapagliflozin on anemia in elderly patients with heart failure is unknown. We aimed to study the effect of dapagliflozin on anemia in elderly patients with heart failure by bioinformatics analysis. The target genes were determined, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The protein-protein interaction (PPI) network and modules were constructed. The dapagliflozin-targets network in anemia and heart failure was constructed. Molecular docking experiments between dapagliflozin and its key target AKT1 were performed. We found 1 dapagliflozin related target gene and 2 disease related genes. Totally, 134 target genes of dapagliflozin on anemia in elderly patients with heart failure were determined. The pathways may involve lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, hepatitis B, insulin signaling pathway, fluid shear stress and atherosclerosis, neurotrophin signaling pathway, insulin resistance, toxoplasmosis, colorectal cancer, and EGFR tyrosine kinase inhibitor resistance. The hub genes in network were AKT1, TP53, GAPDH, TNF, CASP3, EGFR, and MAPK3. The structure of dapagliflozin and AKT1 molecular docking was exhibited. The hub genes in network were AKT1, TP53, GAPDH, TNF, CASP3, EGFR, and MAPK3. The structure of dapagliflozin and AKT1 molecular docking was exhibited.

The C-terminal protein interaction domain of the chromatin reader Yaf9 is critical for pathogenesis of Candida albicans.

Fungal infections cause a large health burden but are treated by only a handful of antifungal drug classes. Chromatin factors have emerged as possible targets for new antifungals. These targets include the reader proteins, which interact with posttranslationally modified histones to influence DNA transcription and repair. The YEATS domain is one such reader recognizing both crotonylated and acetylated histones. Here, we performed a detailed structure/function analysis of the Candida albicans YEATS domain reader Yaf9, a subunit of the NuA4 histone acetyltransferase and the SWR1 chromatin remodeling complex. We have previously demonstrated that the homozygous deletion mutant yaf9Δ/Δ displays growth defects and is avirulent in mice. Here we show that a YEATS domain mutant expected to inactivate Yaf9's chromatin binding does not display strong phenotypes in vitro, nor during infection of immune cells or in a mouse systemic infection model, with only a minor virulence reduction in vivo. In contrast to the YEATS domain mutation, deletion of the C-terminal domain of Yaf9, a protein-protein interaction module necessary for its interactions with SWR1 and NuA4, phenocopies the null mutant. This shows that the C-terminal domain is essential for Yaf9 roles in vitro and in vivo, including C. albicans virulence. Our study informs on the strategies for therapeutic targeting of Yaf9, showing that approaches taken for the mammalian YEATS domains by disrupting their chromatin binding might not be effective in C. albicans, and provides a foundation for studying YEATS proteins in human fungal pathogens.IMPORTANCEThe scarcity of available antifungal drugs and rising resistance demand the development of therapies with new modes of action. In this context, chromatin regulation may be a target for novel antifungal therapeutics. To realize this potential, we must better understand the roles of chromatin regulators in fungal pathogens. Toward this goal, here, we studied the YEATS domain chromatin reader Yaf9 in Candida albicans. Yaf9 uses the YEATS domain for chromatin binding and a C-terminal domain to interact with chromatin remodeling complexes. By constructing mutants in these domains and characterizing their phenotypes, our data indicate that the Yaf9 YEATS domain might not be a suitable therapeutic drug target. Instead, the Yaf9 C-terminal domain is critical for C. albicans virulence. Collectively, our study informs how a class of chromatin regulators performs their cellular and pathogenesis roles in C. albicans and reveals strategies to inhibit them.

Exploration of the Key Genes Involved in Non-alcoholic Fatty Liver Disease and Possible MicroRNA Therapeutic Targets

BackgroundMicroRNAs (miRNAs) are promising therapeutic agents for nonalcoholic fatty liver disease (NAFLD). This study aimed to identify key genes/proteins involved in NAFLD pathogenesis and progression and to evaluate miRNAs influencing their expression. MethodsGene expression profiles from datasets GSE151158, GSE163211, GSE135251, GSE167523, GSE46300 and online databases were analyzed to identify significant NAFLD-related genes. Then, protein-protein interaction networks and module analysis identified hub genes/proteins, which were validated using real-time PCR in oleic acid-treated HepG2 cells. Functional enrichment analysis evaluated signaling pathways and biological processes. Gene-miRNA interaction networks identified miRNAs targeting critical NAFLD genes. ResultsThe most critical overexpressed hub genes/proteins included: TNF, VEGFA, TLR4, CYP2E1, ACE, SCD, FASN, SREBF2 and TGFB1 based on PPI network analysis, of which TNF, TLR4, SCD, FASN, SREBF2 and TGFB1 were up-regulated in oleic acid-treated HepG2 cells. Functional enrichment analysis for biological processes highlighted programmed necrotic cell death, lipid metabolic process response to reactive oxygen species, and inflammation. In Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the highest adjusted P-value signaling pathways encompassed AGE-RAGE in diabetic complications, TNF and HIF-1 signaling pathways. In gene-miRNA network analysis, miR-16 and miR-124 were highlighted as the miRNAs exerting the most influence on important NAFLD-related genes. ConclusionIn silico analyses identified NAFLD therapeutic targets and miRNA candidates to guide further experimental investigation.

TRIP13 Enhances Radioresistance of Lung Adenocarcinoma Cells through the Homologous Recombination Pathway

Radiation therapy is one of the most common treatments for non-small cell lung cancer (NSCLC). However, the insensitivity of some tumor cells to radiation is one of the major reasons for the poor efficacy of radiotherapy and the poor prognosis of patients, and exploring the underlying mechanisms behind radioresistance is the key to solving this clinical challenge. This study aimed to identify the molecules associated with radioresistance in lung adenocarcinoma (LUAD), identified thyroid hormone receptor interactor 13 (TRIP13) as the main target initially, and explored whether TRIP13 is related to radioresistance in LUAD and the specific mechanism, with the aim of providing theoretical basis and potential targets for the combination therapy of LUAD patients receiving radiotherapy in the clinic. Three datasets, GSE18842, GSE19188 and GSE33532, were selected from the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (|log FC|>1.5, P<0.05) in each of the three datasets using the R 4.1.3 software, and then Venn diagram was used to find out the differentially expressed genes common to the three datasets. The screened differential genes were then subjected to protein-protein interaction (PPI) analysis and module analysis with the help of STRING online tool and Cytoscape software, and survival prognosis analysis was performed for each gene with the help of Kaplan-Meier Plotter database, and the TRIP13 gene was identified as the main molecule for subsequent studies. Subsequently, the human LUAD cell line H292 was irradiated with multiple X-rays using a sub-lethal dose irradiation method to construct a radioresistant cell line, H292DR. The radioresistance of H292DR cells was verified using cell counting kit-8 (CCK-8) assay and clone formation assay. The expression levels of TRIP13 in H292 and H292DR cells were measured by Western blot. Small interfering RNA (siRNA) was used to silence the expression of TRIP13 in H292DR cells and Western blot assay was performed. The clone formation ability and migration ability of H292DR cells were observed after TRIP13 silencing, followed by the detection of changes in the expression levels of proteins closely related to homologous recombination, such as ataxia telangiectasia mutated (ATM) protein. Screening of multiple GEO datasets, validation of external datasets and survival analysis revealed that TRIP13 was highly expressed in LUAD and was associated with poor prognosis in LUAD patients who had received radiation therapy. And the results of gene set enrichment analysis (GSEA) of TRIP13 suggested that TRIP13 might be closely associated with LUAD radioresistance by promoting homologous recombination repair after radiation therapy. Experimentally, TRIP13 expression was found to be upregulated in H292DR, and silencing of TRIP13 was able to increase the sensitivity of H292DR cells to radiation. TRIP13 is associated with poor prognosis in LUAD patients treated with radiation, possibly by promoting a homologous recombination repair pathway to mediate resistance of LUAD cells to radiation.

TAC-tics for Leveraging Proximity Biology in Drug Discovery.

Chemically induced proximity (CIP) refers to co-opting naturally occurring biological pathways using synthetic molecules to recruit neosubstrates that are not normally encountered or to enhance the affinity of naturally occurring interactions. Leveraging proximity biology through CIPs has become a rapidly evolving field and has garnered considerable interest in basic research and drug discovery. PROteolysis TArgeting Chimera (PROTAC) is a well-established CIP modality that induces the proximity between a target protein and an E3 ubiquitin ligase, causing target protein degradation via the ubiquitin-proteasome system. Inspired by PROTACs, several other induced proximity modalities have emerged to modulate both proteins and RNA over recent years. In this review, we summarize the critical advances and opportunities in the field, focusing on protein degraders, RNA degraders and non-degrader modalities such as post-translational modification (PTM) and protein-protein interaction (PPI) modulators. We envision that these emerging proximity-based drug modalities will be valuable resources for both biological research and therapeutic discovery in the future.

Mechanism of Compound Kushen Injection in the Treatment of Acute Myeloid Leukemia from the Analysis Perspectives

Background: Chemotherapy resistance often occurs in the conventional treatment with AML and results in poor cure rates. CKI was found to have a good therapeutic effect when it was combined with other chemotherapy drugs in the clinical treatment of AML. However, the underlying mechanism is unclear. Therefore, this study aims to preliminarily describe the pharmacological activity and mechanism of CKI through comprehensive network pharmacology methods. Objective: This study aimed to explore the possible mechanism of Compound Kushen Injection (CKI) in the treatment of acute myeloid leukemia (AML) by using network pharmacology, molecular docking, and molecular dynamics techniques. Methods: Active compounds of CKI were identified based on the Traditional Chinese Medicine Systems Pharmacy (TCMSP) database, and the related targets of the active compounds were predicted using Swiss Target Prediction; AML-related targets from Gene Cards and Online Mendelian Inheritance in Man (OMIM) were collected. Protein-protein interaction (PPI) network was constructed, and its mechanism was predicted through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. The protein-protein interaction (PPI) network construction, module partitioning, and hub node screening were visualized by using the Cytoscape software and its plugins. These module partitionings were also verified by using molecular docking and molecular dynamics. Results: Fifty-six active ingredients corresponding to 223 potential targets were identified. Biological function analysis showed that 731, 70, and 137 GO entries were associated with biological processes, cellular components, and molecular functions, respectively. A total of 163 KEGG pathways were identified. Network analysis showed that the key anti-AML targets of CKI are MAPK3, EGFR, SRC, PIK3CA, and PIK3R1 targets, which are involved in the PI3K/Akt and Ras/MAPK signaling pathways or related crosstalk pathways. Conclusion: Our results suggested that the key anti-AML targets of CKI, such as MAPK3, EGFR, SRC, PIK3CA and PIK3R1, are involved in the PI3K/Akt and Ras/MAPK signaling pathways or related crosstalk pathways. Concentrating on the dynamic and complex crosstalk regulation between PI3K/Akt and Ras/MAPK signal pathways and related signal pathways may be a new direction in anti-AML therapy in the future.

Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network and 18F-FDG PET/CT Radiomics.

The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401-0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.

Development of actin dimerization inducers inspired by actin-depolymerizing macrolides.

Several natural cytotoxic C2-symmetric bis-lactones, such as swinholide A and rhizopodin, sequester actin dimer from the actin network and potently inhibit actin dynamics. To develop new protein-protein interaction (PPI) modulators, we synthesized structurally simplified actin-binding side-chain dimers of antitumor macrolide aplyronine A. By fixing the two side-chains closer than those of rhizopodin, the C4 linker analog depolymerized filamentous actin more potently than natural aplyronines. Cross-link experiments revealed that actin dimer was formed by treatment with the C4 linker analog. Molecular dynamics simulations showed that this analog significantly changed the interaction and spatial arrangement of the two actins compared to those in rhizopodin to provide a highly distorted and twisted orientation in the complex. Our study may promote the development of PPI-based anticancer and other drug leads related to cytoskeletal dynamics.

Red-light modulated ortho-chloro azobenzene photoswitch for peptide stapling via aromatic substitution.

The application of peptide stapling using photoswitchable linkers has gained notable interest for potential therapeutic applications. However, many existing methodologies of photoswitching still rely on the use of tissue-damaging and weakly skin-penetrating UV light. Herein, we describe the development of a tetra-ortho-chloro azobenzene linker that was successfully used for cysteine-selective peptide stapling via SNAr. This linker facilitates precise photocontrol of peptide structure via trans to cis isomerisation under red light irradiation. As a proof-of-concept, we applied the developed peptide stapling platform to a modified PMI peptide, targeting the inhibition of MDM2/p53 protein-protein interaction (PPI). Biophysical characterisation of the photoswitchable peptide by competitive fluorescence polarisation showed a significant difference in affinity between the trans and cis isomer for the p53-interacting domain of the human MDM2. Remarkably, the cis isomer displayed a >240-fold higher potency. To the best of our knowledge, this is the highest reported difference in binding affinity between isoforms of a photoswitchable therapeutic peptide. Overall, our findings demonstrate the potential of this novel photoswitchable peptide stapling system for tuneable, selective modulation of PPIs via visible-light isomerisation with deeply-tissue penetrating red light.

A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators with Functional Group Information and Hypergraph Structure.

Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-specific. Recently, deep learning models based on graph neural networks have made remarkable progress in molecular representation learning. However, many graph-based approaches ignore molecular hierarchical structure modeling guided by domain knowledge. In chemistry, the functional groups of a molecule determine its interaction with specific targets. Therefore, we propose a hierarchical graph neural network framework (called HiGPPIM) for predicting PPIMs by integrating atom-level and functional group-level features of molecules. HiGPPIM constructs atom-level and functional group-level graphs based on chemical knowledge and learns graph representations using graph attention networks. Furthermore, a hypergraph attention network is designed in HiGPPIM to aggregate and transform two-level graph information. We evaluate the performance of HiGPPIM on eight PPI families and two prediction tasks, namely PPIM identification and potency prediction. Experimental results demonstrate that HiGPPIM achieves state-of-the-art performance on both tasks and that using functional group information to guide PPIM prediction is effective. The source code and datasets are freely available at https://github.com/1zzt/HiGPPIM.

MULTI-OMICS INTEGRATION IDENTIFIES GENES INFLUENCING TRAITS ASSOCIATED WITH CARDIOVASCULAR RISKS

Abstract The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that protect LLFS participants against age-related cardiovascular risks, we developed a freely available multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from Rare-Variant Analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 51 significant genes after Bonferroni correction (P ≤ 2.8×10-7). CETP, NLRC5, SLC45A3, and TOMM40 lie within 50 Kb of a known trait-associated variant (previously associated genes). Analysis of protein-protein interaction (PPI) networks identified another 63 genes (passing genes) that (1) have CMA p-value ≤ 5×10-3, (2) lie in a PPI module (highly connected subnetwork) enriched for genes with low P-values, and (3) are annotated with a biological process that is enriched among module genes, ten of which were previously associated with the same traits. Permutation analysis showed that passing genes have a false positive rate of 1 in 14876 and are more likely to be previously known than non-passing genes with similar p-values. CMA improved on the 3 input analyses by producing the largest number of modules enriched for genes with low P-values and highly enriched for genes participating in shared biological processes. Overall, module analysis identified highly plausible candidate causal genes whose P-values after CMA alone were merely suggestive.

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions.

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.

Exploration of the biomarkers of comorbidity of psoriasis with inflammatory bowel disease and their association with immune infiltration.

There was evidence that significant bidirectional associations between psoriasis and inflammatory bowel diseases (IBDs), which influences management strategy of the patients, so the investigation on the mechanisms by which these two diseases co-occur is important. The Gene Expression Omnibus (GEO) database was used to download gene expression profiles of psoriasis and IBD. The differentially expressed genes (DEGs) between disease and health control groups for each data set were calculated, and Venn diagram was used to obtain for intersection. We performed Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the intersection, followed by developing a protein-protein interaction network and module construction, and identified hub genes by cytoHubba. Thereafter, least absolute shrinkage and selection operator algorithms was used to identify the co-biomarkers of psoriasis and IBD from the top 50 hub genes. The biomarkers were used to construct a screening model, the discriminatory capacity of which was verified by receiver operating characteristic (ROC) curves. CIBERSORT algorithm was utilized to estimate the compositional patterns of immune cell infiltration in biomarkers of psoriasis and IBD. Spearman rank correlation analysis was used to further evaluate the correlation between the identified biomarkers and immune cells. A total of 271 shared DEGs were screened. The GO and KEGG enrichment analysis indicated that the shared DEGs were mainly enriched in response to lipopolysaccharide, secretory granule lumen, cytokine activity, and interleukin (IL)-17 signaling pathway. Fifty genes such as IL1B, IL6, were identified as hub genes, based on which, FOS, IFI44, MMP9, MNDA, PTGS2, S100A9, and STAT1 were identified as biomarkers of psoriasis. CCL20, CD274, CTGF, CXCL1, CXCL10, CXCL2, CXCL9, FCGR3B, FOS, GBP1, GZMB, IFI27, IFI6, IL1RN, ISG15, ISG20, LCN2, LILRB2, MMP12, MMP7, S100A8, TLR8, and TNFSF13B were identified as biomarkers of IBD. FOS was the common biomarker of psoriasis and IBD. Screening models were validated in the validation data set (Psoriasis: area under the curve (AUC)=1.000, IBD: AUC=0.870). Immunocyte infiltration analysis showed the macrophages cells, mast cells resting, and T cells CD4 memory activated have the common characteristics in psoriasis and IBD. FOS may play a key role in the occurrence and development of psoriasis complicated with IBD and macrophages cells may be an entrance for treating this comorbidity.

Diversity of short linear interaction motifs in SARS-CoV-2 nucleocapsid protein.

Short linear motifs (SLiMs) are 3-10 amino acid long binding motifs in intrinsically disordered protein regions (IDRs) that serve as ubiquitous protein-protein interaction modules in eukaryotic cells. Through molecular mimicry, viruses hijack these sequence motifs to control host cellular processes. It is thought that the small size of SLiMs and the high mutation frequencies of viral IDRs allow rapid host adaptation. However, a salient characteristic of RNA viruses, due to high replication errors, is their obligate existence as mutant swarms. Taking advantage of the uniquely large genomic database of SARS-CoV-2, here, we analyze the role of sequence diversity in the presentation of SLiMs, focusing on the highly abundant, multi-functional nucleocapsid protein. We find that motif mimicry is a highly dynamic process that produces an abundance of motifs transiently present in subsets of mutant species. This diversity allows the virus to efficiently explore eukaryotic motifs and evolve the host-virus interface.

ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66589 potential allosteric sites, covering>80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leadsand (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic explorationand drug discovery.

The N-terminus of Spt16 anchors FACT to MCM2-7 for parental histone recycling.

Parental histone recycling is vital for maintaining chromatin-based epigenetic information during replication, yet its underlying mechanisms remain unclear. Here, we uncover an unexpected role of histone chaperone FACT and its N-terminus of the Spt16 subunit during parental histone recycling and transfer in budding yeast. Depletion of Spt16 and mutations at its middle domain that impair histone binding compromise parental histone recycling on both the leading and lagging strands of DNA replication forks. Intriguingly, deletion of the Spt16-N domain impairs parental histone recycling, with a more pronounced defect observed on the lagging strand. Mechanistically, the Spt16-N domain interacts with the replicative helicase MCM2-7 and facilitates the formation of a ternary complex involving FACT, histone H3/H4and Mcm2 histone binding domain, critical for the recycling and transfer of parental histones to lagging strands. Lack of the Spt16-N domain weakens the FACT-MCM interaction and reduces parental histone recycling. We propose that the Spt16-N domain acts as a protein-protein interaction module, enabling FACT to function as a shuttle chaperone in collaboration with Mcm2 and potentially other replisome components for efficient local parental histone recycling and inheritance.

Identification of novel potential genes in testicular germ cell tumors: A transcriptome analysis.

Testicular germ cell tumors (TGCTs), containing pure seminoma and non-seminoma, occupy the most majority of testicular cancers in adolescents and young men, which has increased dramatically in recent decades. Therefore, it is important to find crucial genes for improving diagnosis and prognosis in TGCTs. However, the diagnostic and prognostic markers of TGCTs are limited. In this study, our main objective is to explore novel potential genes that can be used as diagnostic and prognostic biomarkers in TGCTs. Our study detected 732 differentially expressed genes (DEGs) using three microarray expression profiling datasets from Gene Expression Omnibus (GEO). Multiple analysis was performed to identify the roles of DEGs, including pathway and functional enrichment analysis, protein-protein interaction (PPI) network analysis, module analysis, and survival analysis. In total, 322 upregulated genes and 406 downregulated genes were identified as DEGs The functional and pathway enrichment analysis shows that DEGs were highly enriched in multiple biological attributes such as T cell activation, reproduction in multicellular organism, sperm flagellum, antigen processing and presentation Then, seven potential crucial genes were identified via PPI network analysis, module analysis, and survival analysis. Furthermore, 7 potential crucial genes had shown to play a key role in regulating immune cell infiltration level in patients with TGCTs. We identified seven potential crucial genes (LAPTM5, NCF2, PECAM1, CD14, COL4A2, ANPEP and RGS1), which may be molecular markers in improving the way of diagnosis and prognosis in TGCTs.

Bioinformatics analysis of potential common pathogenic mechanisms for systemic lupus erythematosus and acute myocardial infarction.

Systemic lupus erythematosus (SLE) patients have a higher risk of acute myocardial infarction (AMI) compared to the general population. However, the underlying common mechanism of this association is not fully understood. This study aims to investigate the molecular mechanism of this complication. Gene expression profiles of SLE (GSE50772) and AMI (GSE66360) were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) in SLE and AMI were identified, and functional annotation, protein-protein interaction (PPI) network analysis, module construction, and hub gene identification were performed. Additionally, transcription factor (TF)-gene regulatory network and TF-miRNA regulatory network were constructed for the hub genes. 70 common DEGs (7 downregulated genes and 63 upregulated genes) were identified and were mostly enriched in signaling pathways such as the IL-17 signaling pathway, TNF signaling pathway, lipid metabolism, and atherosclerosis. Using cytoHubba, 12 significant hub genes were identified, including IL1B, TNF, FOS, CXCL8, JUN, PTGS2, FN1, EGR1, CXCL1, DUSP1, MMP9, and ZFP36. This study reveals a common pathogenesis of SLE and AMI and provides new perspectives for further mechanism research. The identified common pathways and hub genes may have important clinical implications for the prevention and treatment of AMI in SLE patients.