AutoDock CrankPep: combining folding and docking to predict protein-peptide complexes

NCPepFold: Accurate Prediction of Noncanonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation

Artificial intelligence-based peptide structure prediction methods have revolutionized biomolecular science. However, restricting predictions to peptides composed solely of 20 natural amino acids significantly limits their practical application; as such, peptides often demonstrate poor stability under physiological conditions. Here, we present NCPepFold, a computational approach that can utilize a specific cyclic position matrix to directly predict the structure of cyclic peptides with noncanonical amino acids. By integrating multigranularity information at the residual and atomic level, along with fine-tuning techniques, NCPepFold significantly improves prediction accuracy, with the average peptide root-mean-square deviation (RMSD) for cyclic peptides being 1.640 Å. In summary, this is a novel deep learning model designed specifically for cyclic peptides with noncanonical amino acids, offering great potential for peptide drug design and advancing biomedical research.

Assessing the toxicological effects and mechanism of plasticizer exposure on inflammatory bowel disease based on network toxicology and molecular docking

Phthalates (PAEs) are one of the most commonly used plasticizers. Due to their good performance, they are widely used in daily production, such as food packaging, paints, adhesives, children's toys, lubricants and building materials. However, PAEs usually have weak interactions with polymers, which can easily cause environmental pollution in use. These plasticizers have been linked to various health conditions, including inflammatory disorders. They are less intensively studied in the occurrence of inflammation, especially inflammatory bowel disease (IBD), and the necessity to evaluate their pathogenic molecular toxicity is particularly urgent. In this study, network toxicology and molecular docking methods were used to study the toxicological mechanism of IBD induced by four common plasticizers (DBP, DEHP, DEP, DNOP). Potential related targets were predicted using the PharmMapper, SwissStargetPrediction, GeneCards, DisGeNET, OMIM and TTD databases, and 286 related targets were identified. Using Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, binding protein-Protein Interaction (PPI) networks and cytoHubba plug-ins, ten relevant signaling pathways (PI3K-Akt signaling pathway, lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, Proteoglycans in cancer, and so on.) and ten hub genes were identified. Four plasticizers (DBP, DEHP, DEP, DNOP) and the top 10 selected Hub gene targets (SRC, KRAS, PIK3CA, PIK3R1, JAK2, PTPN11, PIK3CD, HRAS, PIK3CG, EGFR) were analyzed by molecular docking. This study provides valuable insights into the molecular mechanisms of plasticizer-induced IBD and highlights the practicality of network toxicology in assessing the toxicity of emerging environmental pollutants. It enhances our understanding of the health risks posed by plasticizers and offers new strategies for mitigating their impact on inflammatory diseases.

Mitigating effects of hydroxysafflor yellow a on atherosclerotic inflammatory responses based on flavonoid macromolecule compound: Inhibition of Piezo1-YAP/JNK protein pathway

Atherosclerosis (AS) is an important cardiovascular disease caused by inflammation. The inhibitory effect of HysA on the Piezo1-YAP/JNK signaling pathway in a mouse model of AS was studied to clarify its anti-inflammatory effects. The study evaluated HysA's ability to bind Piezo1 through molecular docking analysis. C57BL/6 mice were used to establish AS model, and cell treatment and animal experiments were carried out. Inflammatory markers and lipid accumulation were evaluated using quantitative real-time PCR (qRT-PCR), Westernblot, enzyme-linked immunosorbent assay (ELISA), immunofluorescence, and oil red O staining techniques. Weight changes in mice were recorded to monitor disease progression. The molecular docking results show that HysA has a good binding affinity with Piezo1. In a mouse model of AS, treatment with HysA significantly reduced levels of inflammatory cytokines and inhibited the activation of YAP and JNK signaling pathways. The HysA treatment group showed lower oil red O staining levels, indicating that it effectively mitigated lipid deposition. Therefore, HysA significantly alleviates the inflammatory response of atherosclerosis by inhibiting the Piezo1-YAP/JNK signaling pathway, suggesting its potential application in the prevention and treatment of atherosclerosis.

Shared genetic association between inflammatory bowel disease and acute myeloid leukemia: insights from mendelian randomization and transcriptomic analyses

BACKGROUND

Observational studies suggest that a history of inflammatory bowel disease (IBD) is associated with the onset of acute myeloid leukemia (AML), often attributed to drug use. However, these findings are inconsistent. This study aimed to assess the causal relationship between IBD and AML, identify shared pathogenesis, and discover diagnostic and prognostic markers and potential therapeutic drugs.

METHODS

Two-sample Mendelian randomization (MR) was employed to analyze genetic associations between IBD [ulcerative colitis (UC) and Crohn's disease (CD)] and AML. Transcriptomic data from gene expression omnibus (GEO) identified differentially expressed genes (DEGs) in UC, AML, and controls. Weighted Gene Co-expression Network Analysis (WGCNA) and enrichment analyses [Gene Multiple Association Network Integration Algorithm (GeneMANIA), Kyoto Encyclopedia of Genes and Genomes (KEGG), Ractom pathway] and Gene Ontology (GO) explored shared genetic pathways. Receiver Operating Characteristic (ROC) curve and survival analyses screened diagnostic and prognostic markers. Cibersort and GSVA were employed to analyze the proportion of immune cells in UC and AML datasets, as well as to assess the association of specific genes with immune infiltration. The Drug Signatures Database (DSigDB) and Autodock molecular docking identified potential therapeutic small molecules.

RESULTS

MR analysis revealed a causal association between UC and the onset of AML. Differential expression and WGCNA analyses identified 23 co-driver genes regulated by Signal Transducer and Activator of Transcription 3 (STAT3) and Activating Transcription Factor 4 (AFT4), enriched in immune, inflammatory, and cell proliferation pathways. Tissue Inhibitor of Metalloproteinases 1 (TIMP1) and F2R-Like Trypsin Receptor 1 (F2RL1) were identified as practical diagnostic and prognostic markers for AML, with high TIMP1 and low F2RL1 expression promoting an immunosuppressive and inflammatory tumor microenvironment. Quercetin was identified as a promising candidate for UC-associated AML.

CONCLUSIONS

UC is a risk factor for AML pathogenesis. TIMP1 and F2RL1 are diagnostic and prognostic markers for UC-associated AML, potentially facilitating AML development through sustained inflammation and an immunosuppressive tumor microenvironment. Quercetin, a potential TIMP1 and F2RL1 inhibitor, may mitigate UC-AML transformation, providing insights into UC management, AML monitoring, and preventive therapy development.

Identification of novel TMEM16A blockers through integrated virtual screening, molecular dynamics, and experimental studies

The calcium-activated chloride channel TMEM16A is a promising drug target for treating hypertension, secretory diarrheas, and various cancers, including head and neck cancer. Despite its potential, no FDA-approved drugs have provided the structural basis for directly inhibiting TMEM16A. This study aims to identify a novel pore-blocker of TMEM16A by integrating virtual screening, molecular dynamics simulations, and in vitro studies. Using the calcium-bound structure of TMEM16A with and without the pore-blocker 1PBC, we performed virtual screening on nearly 90,000 compounds from the ChemDiv database. Approximately 67% of these compounds demonstrated better binding affinity than 1PBC. Among the top 20 compounds selected for short-circuit current assays using human lung adenocarcinoma cells (Calu-3), compounds N066-0059, N066-0060, and N066-0067 inhibited TMEM16A activity with IC50 values of 0.24 µM, 0.41 µM, and 0.48 µM, respectively, which was lower than that of a positive control Ani9 (9 µM). Due to its highest potency in electrophysiological assays, N066-0059 was subjected to mechanistic studies. Molecular dynamics simulations elucidated its binding stability and strength, showing superior performance to 1PBC over 500 ns with 3 replicates. This study advances TMEM16A-targeted drug development, offering new insights for anticancer therapies.

Prediction of Umami Peptides Based on a Large Language Model of Proteins

Umami peptides possess unique characteristics, making their study highly significant. To better understand umami peptides, this research systematically investigates them using protein language models. First, we collected IC50 and Kd data to construct a protein-peptide affinity model and combined it with protein-peptide docking techniques to explore the affinity relationships between umami peptides, non-umami peptides, and taste receptors. The results indicate that umami peptides exhibit stronger affinity to umami receptors compared to non-umami peptides but show no significant difference in affinity to bitter receptors. Subsequently, we systematically gathered 972 umami peptides and 608 non-umami peptides, developing the largest data set of umami peptides to date. Using protein language models combined with molecular docking and affinity prediction results, we constructed the most accurate umami peptide prediction model, achieving an accuracy of 82% and an area under the curve (AUC) of 0.87. Finally, we developed a user-friendly website for umami peptide analysis, UmamiMeta, accessible at https://hwwlab.com/Webserver/umamimeta, providing a convenient tool for the research and application of umami peptides.

Network Pharmacology and Experimental Validation of the Effects of Shenling Baizhu San, Quzhi Ruangan Fang and Gexia Zhuyu Tang on the Intestinal Flora of Rats with NAFLD

Objective

In this study, we investigated the effect of Shenling Baizhu San(SLBZS), Quzhi Ruangan Fang(QZRGF) and Gexia Zhuyu Tang(GXZYT) on the intestinal flora of NAFLD rats through network pharmacology and experimental validation.

Materials and Methods

Protein-protein interaction, Gene Ontology (GO), and molecular docking were performed. Male Sprague-Dawley (SD) rats were divided into 6 groups: Normal, Model, SLBZS (7.2g/kg), QZRGF (27.72g/kg), GXZYT (28.8 g/kg) and positive control (Fenofibrate, 18mg/kg); the NAFLD model was established by High-fat diet. After one week of acclimatisation feeding consecutively, continuous gavage was given for 8 W and 12 W. Serum, liver and faeces were collected and biochemical and pathological indices were determined. The diversity and abundance of intestinal flora were also analyzed using 16S rDNA amplified sequencing.

Results

A total of 132 active ingredients were obtained from the screening results of SLBZS. A total of 202 active ingredients were obtained from the screening results of GXZYT. The screening results of QZRGF obtained 34 active ingredients. Nine common hub genes were screened from the PPI network. GO functional analysis reported that these targets were mainly closely related to the response to bacterial molecules. The molecular docking results indicated that the 11 core constituents in three compound prescriptions has good binding ability with MAPK1, AKT1, CASP3, FOS, TP53, STAT3, MAPK3.

Conclusion

The Chinese herbal compounds SLBZS, QZRGF and GXZYT may exert lipid-lowering effects through multi-components, multi-targets and multi-methods for the treatment of NAFLD while improving the diversity and abundance of the intestinal flora of the rats, and the best effect was achieved with SLBZS.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

Discovery of New Pentapeptide Inhibitors Against Amyloid-β Aggregation Using Word2Vec and Molecular Simulation

Alzheimer's disease (AD) is characterized by the aggregation of amyloid-β (Aβ) peptides into toxic oligomers and fibrils. The efficacy of existing peptide inhibitors based on the central hydrophobic core (CHC) sequence of Aβ42 remains limited due to self-aggregation or poor inhibition. This study aimed to identify novel pentapeptide inhibitors with high similarity and low binding energy to the CHC region LVFFA using a new computational screening workflow based on Word2Vec and molecular simulation. The antimicrobial peptides and human brain protein sequences were used for training the Word2Vec model. After tuning the parameters of the Word2Vec model, 1017 pentapeptides with high similarity to LVFFA were identified. Molecular docking was employed to estimate the affinity of the pentapeptides for the target of Aβ14-42 pentamer, and 103 peptides with favorable docking scores were obtained. Finally, five pentapeptides with a low binding energy and high binding stability via molecular dynamics simulation were experimentally validated using thioflavin T assays. Surprisingly, one pentapeptide, i.e., PALIR, exhibited significant inhibition surpassing the positive control LPFFN. This study demonstrates an effective combinatorial strategy to discover new peptide inhibitors. With PALIR representing a promising lead candidate, further optimization of PALIR could aid in the development of improved therapies to prevent amyloid toxicity in AD.

The cyclin-dependent kinase inhibitor AT7519 is a human RORγt agonist

AT7519, which inhibits multiple cyclin-dependent kinases, has been extensively investigated in various types of cancer cells. Previous studies have demonstrated the ability of this molecule to suppress the expression of the nuclear receptor retinoic acid-related orphan receptor gamma (RORγ) and several genes involved in hepatocellular carcinoma progression. In this study, we identified a distinct agonistic effect of AT7519 on RORγt, an isoform expressed by various immune cells, including T helper 17 lymphocytes. These immune cells play pivotal roles in shaping the tumor microenvironment and promoting the anticancer response of the immune system. After exposure to AT7519 during differentiation, primary human CD4+ T cells presented increased expression of IL17A/F, IFNG and GZMB and decreased expression of PDCD1 and CTLA4. These findings elucidate a previously unrecognized facet of AT7519 activity and suggest the potential incorporation of this molecule into immune therapies to augment the effectiveness of diverse anticancer strategies involving anti-programmed cell death protein 1 (anti-PD-1) and anti-cytotoxic T-lymphocyte antigen 4 (anti-CTLA4) regimens.

TRPA1-Activated Peptides from Saiga Antelope Horn: Screening, Interaction Mechanism, and Bioactivity

Saiga antelope horn (SAH), a rare traditional Chinese medicine, exhibits activities of anti-feverish convulsions and anti-inflammation, whereas its underlying mechanism and specific pharmacological components are still unclear. In the present study, transient receptor potential ankyrin 1 (TRPA1), a major transient receptor potential cation channel was used as a target protein to identified TRPA1 high-affinity peptides (THPs) from SAH digests. Firstly, the SAH was digested under in vitro gastrointestinal conditions. With the method of affinity ultrafiltration and liquid chromatography-mass spectrometry (AUF-LC/MS), about 200 peptides that have a high-affinity interaction with the TRPA1 protein were screened from SAH digests. Subsequently, bioactivity databases and molecular docking were further exploited to identified three THPs, including RCWPDCR, FGFDGDF, and WFCEGSF. Furthermore, RIN-14B cells, characterized by the high expression of TRPA1 on cell surfaces, were used as the cell model to investigate the biological effect of THPs. Immunofluorescence and ELISA were conducted and showed that THPs can increase the intracellular Ca2+ concentration and serotonin (5-HT) secretion in RIN-14B cells by activating TRPA1, which is evidenced by impaired upregulation of intracellular Ca2+ levels and 5-HT secretion after pretreatment with the TRPA1 inhibitor (HC-030031). Moreover, an analysis of Western blots displayed that THPs up-regulated the expression levels of the 5-HT synthesis rate-limiting enzyme (TPH1) and 5-hydroxytryptophan decarboxylase (Ddc), while serotonin reuptake transporter (SERT) levels were down-regulated, suggesting that THPs enhance 5-HT secretion by regulating the 5-HT synthesis pathway. In summary, our findings demonstrate that THPs, which were identified from SAH digest via TRPA1-targeted affinity panning, exhibited the activation of the TRPA1 channel and enhanced 5-HT release in RIN-14B cells.

Effect of Rhei Radix Et Rhizome on treatment of polycystic ovary syndrome by regulating PI3K/AKT pathway and targeting EGFR/ALB in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Abnormal endocrine metabolism caused by polycystic ovary syndrome (PCOS) poses a serious risk to reproductive health in females. According to Traditional Chinese Medicine (TCM) theories, the leading causes of PCOS include turbid phlegm, blood stasis and stagnation of liver Qi. Rhei Radix Et Rhizome is widely used in TCM to attack stagnation, clear damp heat, relieve fire. Rhei Radix Et Rhizome is an important part of the TCM formulas for the treatment of PCOS, which has a long history of medicinal use. However, the specific effect and mechanisms of Rhei Radix Et Rhizome on PCOS have yet to be elucidated.

AIM OF THE STUDY

The object of this study aimed to investigate the effect and its pharmacological mechanism of Rhei Radix Et Rhizome on the treatment of polycystic ovary syndrome.

METHODS

PCOS was induced in female Sprague Dawley (SD) rats by administering letrozole (1 mg/kg, per orally, p.o.) for 21 days, then treated with Rhei Radix Et Rhizome at doses of 0.6 g/kg or 1.2 g/kg. Rats weight, blood glucose and estrus period are measured, and serum hormone include free testosterone (T), luteinizing hormone (LH), follicle-stimulating hormone (FSH) and ovarian lesions were observed to determine the effects of Rhei Radix Et Rhizome. Network pharmacology and molecular docking predicted the targets of Rhei Radix Et Rhizome on PCOS. Epidermal growth factor receptor (EGFR), albumin (ALB), PI3K and P-AKT/AKT protein expression levels in ovarian tissues were assessed by Western blot.

RESULTS

Rhei Radix Et Rhizome reduce abnormal weight and fasting blood glucose induced by letrozole (n = 5, p < 0.01), and improve the disturbed estrus cycle, reduce T, LH levels and LH/FSH ratio of PCOS rats (n = 4, p < 0.01). In addition, it alleviates the polycystic changes of ovaries in PCOS rats and reduces ovarian histopathological damage (n = 4, p < 0.01). Additionally, the core active components of Rhei Radix Et Rhizome for PCOS include Sennoside D_qt, Procyanidin B-5,3'-O-gallate, and Mutatochrome, which strongly bind to core therapeutic targets ALB and EGFR. Furthermore, the treatment reduces the increase of EGFR and ALB induced by letrozole (n = 4, p < 0.01). KEGG pathway enrichment analysis highlights endocrine resistance and prolactin signaling pathway, in both of which the PI3K/AKT pathway plays a crucial role. Our results show Rhei Radix Et Rhizome rescue the abnormal expression of PI3K/AKT pathway in PCOS rats (n = 4, p < 0.01). However, no significant dose-dependent relationship was observed in the tested dose range for the above experiments.

CONCLUSION

These findings suggest that Rhei Radix Et Rhizome can regulate the PI3K/AKT pathway and target EGFR and ALB to treat polycystic ovary syndrome in rats. This study provides a scientific basis for the use of Rhei Radix Et Rhizome in the treatment of PCOS and highlights its potential mechanism through modulation of the PI3K/AKT pathway.

Integrated analysis of bioinformatics, mendelian randomization, and experimental validation reveals novel diagnostic and therapeutic targets for osteoarthritis: progesterone as a potential therapeutic agent

BACKGROUND

Osteoarthritis (OA), characterized by progressive degeneration of cartilage and reactive proliferation of subchondral bone, stands as a prevalent condition in orthopedic clinics. However, the precise mechanisms underlying OA pathogenesis remain inadequately explored.

METHODS

In this study, Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) machine learning techniques were employed to identify hub genes. Based on these hub genes, an Artificial Neural Network (ANN) diagnostic model was constructed. The Drug Signatures Database (DSigDB) was utilized to screen small-molecule drugs targeting these hub genes, and molecular docking analyses and molecular dynamics simulations were employed to explore and validate the binding interactions between proteins and small-molecule drugs. Expression changes of the hub genes under inflammatory conditions were validated through in vitro experiments, including RT-qPCR and Western blotting, and the therapeutic effects of the identified small-molecule drug on chondrocytes under inflammatory conditions were further verified in vitro. Lastly, Mendelian randomization analysis was conducted to examine the causal association between progesterone levels and various OA phenotypes.

RESULTS

In this study, we identified three hub genes: interleukin 1 receptor-associated kinase 3 (IRAK3), integrin subunit beta-like 1 (ITGBL1), and Ras homolog family member U (RHOU). An Artificial Neural Network (ANN) diagnostic model constructed based on these hub genes demonstrated excellent performance in both training and validation phases. Screening with the Drug Signatures Database (DSigDB) identified progesterone as a small-molecule drug targeting these key proteins. Molecular docking analysis using AutoDock Vina revealed that progesterone exhibited binding energies of ≤ -7 kcal/mol with each of the key proteins, indicating strong binding affinity. Furthermore, molecular dynamics simulations validated the stability and strength of these interactions. RT-qPCR and Western blotting confirmed the downregulation of the hub genes in IL-1β-treated chondrocytes. Western blotting also demonstrated the potential therapeutic effects of progesterone on IL-1β-treated chondrocytes. Finally, Mendelian randomization analysis established a significant association between progesterone levels and multiple OA phenotypes.

CONCLUSION

In our study, IRAK3, ITGBL1, and RHOU were identified as potential novel diagnostic and therapeutic targets for OA. Progesterone was preliminarily validated as a small-molecule drug with potential effects on OA. Further research is crucial to elucidate the pathogenesis of OA and the specific therapeutic mechanisms involved.

Novel applications of metformin in the treatment of septic myocardial injury based on metabolomics and network pharmacology

BACKGROUND

While metformin has shown promise in treating septic myocardial injury (SMI), its underlying mechanisms and impact on metabolic disturbances remain poorly understood.

METHODS

This study employed an integrated approach of metabolomics and network pharmacology to identify key targets and pathways through which metformin may act against SMI. Findings were validated using a lipopolysaccharide (LPS)-induced mouse model.

RESULTS

Metformin was found to counter myocardial metabolic disruptions, indicated by the reversal of 49 metabolites primarily involved in purine metabolism, pantothenate and CoA biosynthesis, and histidine metabolism. In vivo, metformin significantly improved survival rates and cardiac function, reduced cardiomyocyte apoptosis, and inhibited inflammation and oxidative stress in LPS-induced mice. Integrated analyses identified 27 potential targets for metformin in SMI treatment. KEGG pathway analysis revealed significant enrichment in TNF, HIF-1, IL-17, and PI3K/AKT signaling pathways, while protein-protein interaction analysis pinpointed ten core targets, including IL6, IL1B, CCL2, CASP3, MMP9, HIF1A, IGF1, NOS3, MMP2, and LEP. Molecular docking and dynamics simulations demonstrated metformin's high affinity for these core targets. Further, RT-qPCR and Western blot analyses confirmed that metformin modulates core target expression to mitigate SMI. Notably, our data underscore the importance of PI3K/AKT and MMP2/MMP9 signaling pathways in SMI therapy.

CONCLUSION

This study elucidates the metabolic and molecular mechanisms of metformin in SMI treatment, supporting its potential repurposing for SMI.

Target-Specific De Novo Peptide Binder Design with DiffPepBuilder

Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein-peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three-dimensional and binding site information.

Mitochondria-targeted oligomeric α-synuclein induces TOM40 degradation and mitochondrial dysfunction in Parkinson's disease and parkinsonism-dementia of Guam

Mitochondrial dysfunction is a central aspect of Parkinson's disease (PD) pathology, yet the underlying mechanisms are not fully understood. This study investigates the link between α-Synuclein (α-Syn) pathology and the loss of translocase of the outer mitochondrial membrane 40 (TOM40), unraveling its implications for mitochondrial dysfunctions in neurons. We discovered that TOM40 protein depletion occurs in the brains of patients with Guam Parkinsonism-Dementia (Guam PD) and cultured neurons expressing α-Syn proteinopathy, notably, without corresponding changes in TOM40 mRNA levels. Cultured neurons expressing α-Syn mutants, with or without a mitochondria-targeting signal (MTS) underscores the role of α-Syn's mitochondrial localization in inducing TOM40 degradation. PDe-related etiological factors, such as 6-hydroxydopamine or ROS/metal ions stress, which promotes α-Syn oligomerization, exacerbate TOM40 depletion in PD patient-derived cells with SNCA gene triplication. Although α-Syn interacts with both TOM40 and TOM20 in the outer mitochondrial membrane, degradation is selective for TOM40, which occurs via the ubiquitin-proteasome system (UPS) pathway. Our comprehensive analyses using Seahorse technology, mitochondrial DNA sequencing, and damage assessments, demonstrate that mutant α-Syn-induced TOM40 loss results in mitochondrial dysfunction, characterized by reduced membrane potential, accumulation of mtDNA damage, deletion/insertion mutations, and altered oxygen consumption rates. Notably, ectopic supplementation of TOM40 or reducing pathological forms of α-Syn using ADP-ribosylation inhibitors ameliorate these mitochondrial defects, suggesting potential therapeutic avenues. In conclusion, our findings provide crucial mechanistic insights into how α-Syn accumulation leads to TOM40 degradation and mitochondrial dysfunction, offering insights for targeted interventions to alleviate mitochondrial defects in PD.

Heterozygous variants disrupting the interaction of ERF with activated ERK1/2 cause microcephaly, developmental delay, and skeletal anomalies

Heterozygous deleterious null alleles and specific missense variants in the DNA-binding domain of the ETS2 repressor factor (ERF) cause craniosynostosis, while the recurrent p.(Tyr89Cys) missense variant is associated with Chitayat syndrome. Exome and whole transcriptome sequencing revealed the ERF de novo in-frame indel c.911_913del selectively removing the serine of the FSF motif, which interacts with the extracellular signal-regulated kinases (ERKs), in a 10-year-old girl with microcephaly, multiple congenital joint dislocations, generalized joint hypermobility, and Pierre-Robin sequence. Three additional cases with developmental delay variably associated with microcephaly, Pierre-Robin sequence and minor skeletal anomalies were detected carrying heterozygous de novo non-truncating alleles (two with c.911_913del and one with the missense c.907 T > A change) in the same FSF motif. Protein affinity maps, co-immunoprecipitation experiments and subcellular distribution showed that both the variants impair the interaction between ERF and activated ERK1/2 and increase ERF nuclear localization, affecting ERF repressor activity that may lead to developmental defects. Our work expands the phenotypic spectrum of ERF-related disorders to a pleiotropic condition with microcephaly, developmental delay and skeletal anomalies, that we termed MIDES syndrome, and adds to the understanding of the relevance of the ERF-ERK interaction in human development and disease.

Identification, screening, and comprehensive evaluation of novel thrombin inhibitory peptides from the hirudo produced using pepsin

Purpose

The inhibition of thrombin has proven to be an efficacious therapeutic approach for managing cardiovascular disease (CVD), with widespread implementation in clinical settings. Oral ingestion of peptides and protein drugs is influenced by gastrointestinal digestive enzymes. We aimed to evaluate the thrombin inhibitory properties of hirudo hydrolysates (HHS) produced by pepsin and propose a comprehensive approach to screen and evaluate thrombin inhibitors.

Methods

We evaluated the in vitro inhibitory properties of the hirudo extract, both before and after hydrolysis with pepsin, toward thrombin. We screened for the most potent thrombin inhibitory peptide (TIP) using nano liquid chromatography-tandem mass spectrometry (Nano LC-MS/MS) coupled with in silico analysis. Next, we employed the thrombin inhibition activity IC50 to investigate the interaction between TIP and thrombin, and conducted in vitro evaluations of its anticoagulant effects (APTT, TT, PT), as well as its ability to inhibit platelet aggregation. Furthermore, we utilized UV-Vis spectroscopy to explore structural changes in thrombin upon binding with TIP and employed molecular dynamics simulations to delve deeper into the potential atomic-level interaction modes between thrombin and TIP.

Results

The retention rate of thrombin inhibition for HHS was found to be between 60% and 75%. A total of 90 peptides from the HHS were identified using LC-MS/MS combined with de novo sequencing. Asn-Asp-Leu-Trp-Asp-Gln-Gly-Leu-Val-Ser-Gln-Asp-Leu (NDLWDQGLVSQDL, P1) was identified as the most potent thrombin inhibitory peptide after in silico screening (molecular docking and ADMET). Then, the in vitro study revealed that P1 had a high inhibitory effect on thrombin (IC50: 2,425.5 ± 109.7 μM). P1 exhibited a dose-dependent prolongation of the thrombin time (TT) and a reduction in platelet aggregation rate. Both UV-Vis spectroscopy and molecular dynamics simulations demonstrated that P1 binds effectively to thrombin.

Conclusion

Overall, the results suggested that HHS provides new insights for searching and evaluating potential antithrombotic compounds. The obtained P1 can be structurally optimized for in-depth evaluation in animal and cellular experiments.

Structure of a Cyclic Peptide as an Inhibitor of Mycobacterium tuberculosis Transcription: NMR and Molecular Dynamics Simulations

BACKGROUND AND OBJECTIVES

A novel antitubercular cyclic peptide, Cyclo(1,6)-Ac-CLYHFC-NH2, was designed to bind at the rifampicin (RIF) binding site on the RNA polymerase (RNAP) of Mycobacterium tuberculosis (MTB). This peptide inhibits RNA elongation in the MTB transcription initiation assay in the nanomolar range, which can halt the MTB transcription initiation complex, similar to RIF. Therefore, determining the solution conformation of this peptide is useful in improving the peptide's binding affinity to the RNAP.

METHODS

Here, the solution structure of Cyclo(1,6)-Ac-CLYHFC-NH2 was determined by two-dimensional (2D) NMR experiments and NMR-restrained molecular dynamic (MD) simulations.

RESULTS

All protons of Cyclo(1,6)-Ac-CLYHFC-NH2 were assigned using TOCSY and NOE NMR spectroscopy. The NOE cross-peak intensities were used to calculate interproton distances within the peptide. The JNH-HCα coupling constants were used to determine the possible Phi angles within the peptide. The interproton distances and calculated Phi angles from NMR were used in NMR-restrained MD simulations. The NOE spectra showed NH-to-NH cross-peaks at Leu2-to-Tyr3 and Tyr3-to-His4, indicating a βI-turn formation at the Cys1-Leu2-Tyr3-His4 sequence.

CONCLUSIONS

The NMR-restrained MD simulations showed several low-energy conformations that were congruent with the NMR data. Finally, the conformation of this peptide will be used to design derivatives that can better inhibit RNAP activity.

PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model

The protein-peptide interaction plays a pivotal role in fields such as drug development, yet remains underexplored experimentally and challenging to model computationally. Herein, we introduce PepCA, a sequence-based approach for predicting peptide-binding sites on proteins. A primary obstacle in predicting peptide-protein interactions is the difficulty in acquiring precise protein structures, coupled with the uncertainty of polypeptide configurations. To address this, we first encode protein sequences using the Evolutionary Scale Modeling 2 (ESM-2) pre-trained model to extract latent structural information. Additionally, we have developed a multi-input coattention mechanism to concurrently update the encoding of both peptide and protein residues. PepCA integrates this module within an encoder-decoder structure. This model's high precision in identifying binding sites significantly advances the field of computational biology, offering vital insights for peptide drug development and protein science.

Different molecular recognition by three domains of the full-length GRB2 to SOS1 proline-rich motifs and EGFR phosphorylated sites

The adaptor protein human GRB2 plays crucial roles in mediating signal transduction from cell membrane receptors to RAS and its downstream proteins by recruiting SOS1. Recent studies have revealed that GRB2 also serves as a scaffold for liquid-liquid phase separation (LLPS) with SOS1 and transmembrane receptors, which is thought to regulate the magnitude of cell signalling pathways. In this study, we employed solution NMR spectroscopy to investigate the interactions of the full-length GRB2 with proline-rich motifs (PRMs) derived from ten potential GRB2-binding sites in SOS1, as well as a peptide from a phosphorylation site of EGFR. Our findings indicate that the binding affinity of the two SH3 domains of GRB2 for PRMs differs by a factor of ten to twenty, with the N-terminal SH3 domain (NSH3) exhibiting a markedly higher affinity. The interactions of PRMs with the SH3 domains affected not only the regions surrounding the PRM binding sites on the SH3 domains but also the linker area connecting the three domains and parts of the SH2 domain. Analysis of the interaction between the phosphorylated EGFR binding site and the SH2 domain revealed chemical shift perturbations in regions distal from the known binding site of SH2. Moreover, we observed that the inter-domain interactions of the two SH3 domains with the SH2 domain of GRB2 are asymmetric. These findings suggest that the local binding of PRMs and phosphorylated EGFR to GRB2 impacts the overall structure of the GRB2 molecule, including domain orientation and dimerisation, which may contribute to LLPS formation.

Evaluation of the activity of cardiac glycosides on RORγ and RORγT nuclear receptors

Cardiac glycosides, derived from plants and animals, have been recognized since ancient times. These substances hinder the function of the sodium-potassium pump within eukaryotic cells. Many reports have shown that these compounds influence the activity of nuclear receptors. Thus, we assessed the effects of various cardiac glycosides at nontoxic concentrations on RORγ and RORγT. RORγT is a crucial protein involved in the differentiation of Th17 lymphocytes. Sixteen analyzed cardiac glycosides exhibited varying toxicities in HepG2 cells, all of which demonstrated agonistic effects on RORγ, as confirmed in the RORγ-HepG2 reporter cell line. The overexpression of both the RORγ and RORγT isoforms intensified the effects of these compounds. Additionally, these glycosides induced the expression of G6PC, a gene regulated by RORγ, in HepG2 cells. Subsequently, the effects of two endogenous cardiac glycosides (marinobufagenin and ouabain) and the three most potent glycosides (bufalin, oleandrin, and telecinobufagenin) were evaluated in Th17 primary lymphocytes. All of these compounds increased the expression of the IL17A, IL17F, IFNG, and CXCL10 genes, but they exhibited varying effects on GZMB and CCL20 expression. Molecular docking analysis revealed the robust binding affinity of cardiac glycosides for the ligand binding domain of the RORγ/RORγT receptors. Thus, we demonstrated that at nontoxic concentrations, cardiac glycosides have agonistic effects on RORγ/RORγT nuclear receptors, augmenting their activity. This potential can be harnessed to modulate the phenotype of IL17-expressing cells (e.g., Th17 or Tc17 lymphocytes) in adoptive therapy for combating various types of cancer.

Peptidomimetics design and characterization: Bridging experimental and computer-based approaches

Peptidomimetics, designed to mimic peptide biological activity with more drug-like properties, are increasingly pivotal in medicinal chemistry. They offer enhanced systemic delivery, cell penetration, target specificity, and protection against peptidases when compared to their native peptide counterparts. Already utilized in treating diverse diseases like neurodegenerative disorders, cancer and infectious diseases, their future in medicine seems bright, with many peptidomimetics in clinical trials or development stages. Peptidomimetics are well-suited for addressing disturbed protein-protein interactions (PPIs), which often underlie various pathologies. Structural biology and computational methods like molecular dynamics simulations facilitate rational design, whereas machine learning algorithms accelerate protein structure prediction, enabling efficient drug development. Experimental validation via various spectroscopic, biophysical, and biochemical assays confirms computational predictions and guides further optimization. Peptidomimetics, with their tailored constrained structures, represent a frontier in drug design focused on targeting PPIs. In this overview, we present a comprehensive landscape of peptidomimetics, encompassing perspectives on involvement in pathologies, chemical strategies, and methodologies for their characterization, spanning in silico, in vitro and in cell approaches. With increasing interest from pharmaceutical sectors, peptidomimetics hold promise for revolutionizing therapeutic approaches, marking a new era of precision drug discovery.

3D Porous Polymer Scaffold-Conjugated KGF-Mimetic Peptide Promotes Functional Skin Regeneration in Chronic Diabetic Wounds

The re-epithelialization process gets severely dysregulated in chronic nonhealing diabetic foot ulcers/wounds. Keratinocyte growth factor (KGF or FGF-7) is the major modulator of the re-epithelialization process, which regulates the physiological phenotypes of cutaneous keratinocytes. The existing therapeutic strategies of growth factor administration have several limitations. To overcome these, we have designed a KGF-mimetic peptide (KGFp, 13mer) based on the receptor interaction sites in murine KGF. KGFp enhanced migration and transdifferentiation of mouse bone marrow-derived MSCs toward keratinocyte-like cells (KLCs). A significant increase in the expression of skin-specific markers Bnc1 (28.5-fold), Ck5 (14.6-fold), Ck14 (26.1-fold), Ck10 (187.7-fold), and epithelial markers EpCam (23.3-fold) and Cdh1 (64.2-fold) was associated with the activation of ERK1/2 and STAT3 molecular signaling in the KLCs. Further, to enhance the stability of KGFp in the wound microenvironment, it was conjugated to biocompatible 3D porous polymer scaffolds without compromising its active binding sites followed by chemical characterization using Fourier transform infrared spectroscopy, field-emission scanning electron microscopy, dynamic mechanical analysis, and thermogravimetry. In vitro evaluation of the KGFp-conjugated 3D polymer scaffolds revealed its potential for transdifferentiation of MSCs into KLCs. Transplantation of allogeneic MSCGFP using KGFp-conjugated 3D polymer scaffolds in chronic nonhealing type 2 diabetic wounds (db/db transgenic, 50-52 weeks old male mice) significantly enhanced re-epithelialization-mediated wound closure rate (79.3%) as compared to the control groups (Untransplanted -22.4%, MSCGFP-3D polymer scaffold -38.5%). Thus, KGFp-conjugated 3D porous polymer scaffolds drive the fate of the MSCs toward keratinocytes that may serve as potential stem cell delivery platform technology for tissue engineering and transplantation.

Synthesis, characterization, and mechanistic insights into the enhanced anti-inflammatory activity of baicalin butyl ester via the PI3K-AKT pathway

Objective

To investigate the anti-inflammatory activity and mechanism of Baicalin derivative (Baicalin butyl ester, BE).

Methods

BE was synthesized and identified using UV-Vis spectroscopy, FT-IR spectroscopy, mass spectrometry (MS) and high-performance liquid chromatography (HPLC) methods. Its anti-inflammatory potential was explored by an in vitro inflammation model. Network pharmacology was employed to predict the anti-inflammatory targets of BE, construct protein-protein interaction (PPI) networks, and analysis topological features and KEGG pathway enrichment. Additionally, molecular docking was conducted to evaluate the binding affinity between BE and its core targets. qRT-PCR analysis was conducted to validate the network pharmacology results. The organizational efficiency was further evaluated through octanol-water partition coefficient and transmembrane activity analysis.

Results

UV-Vis, FT-IR, MS, and HPLC analyses confirmed the successfully synthesis of BE with a high purity of 93.75%. In vitro anti-inflammatory research showed that BE could more effectively suppress the expression of NO, COX-2, IL-6, IL-1β, and iNOS. Network pharmacology and in vitro experiments validated that BE's anti-inflammatory effects was mediated through the suppression of SRC, HSP90AA1, PIK3CA, JAK2, AKT1, and NF-κB via PI3K-AKT pathway. Molecular docking results revealed that the binding affinities of BA to the core targets were lower than those of BE. The Log p-value of BE (1.7) was markedly higher than that of BA (-0.5). Furthermore, BE accumulated in cells at a level approximately 200 times greater than BA.

Conclusion

BE exhibits stronger anti-inflammatory activity relative to BA, possibly attributed to its better lipid solubility and cellular penetration capabilities. The anti-inflammatory mechanism of BE may be mediated through the PI3K-AKT pathway.

Exploring the therapeutic effect of core components in Xuanshen Yishen mixture on hypertension through network pharmacology

OBJECTIVE

This study aims to elucidate the mechanism of action and impact of the "Xuanshen Yishen Mixture" (XYM) on hypertension.

METHODS

Active components were identified and potential targets were predicted using the Traditional Chinese Medicine Systems Pharmacology database. Hypertension-related targets were collected from GeneCards, DRUGBANK, OMIM, TTD, and PharmaGKB databases. Intersections of disease and drug targets were visualized using the R package "VennDiagram". A protein-protein interaction network was established via the STRING database. GO function enrichment and KEGG pathway analyses were conducted using "clusterProfiler", while "Cytoscape" was used to construct a "drug-component-target" network. Additionally, data from 60 patients with essential hypertension from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine were retrospectively analyzed. Patients were divided into a control group (n = 30) and an XYM group (n = 30) based on treatment regimen.

RESULTS

Sixty active ingredients and 98 related targets were identified from Uncaria, Radix Scrophulariae, and Epimedium in hypertension treatment. Key active components such as quercetin, kaempferol, yohimbine, and beta-sitosterol were pinpointed, with PTGS2, PTGS1, AR, DPP4, and F2 as crucial targets. KEGG pathway analysis highlighted significant pathways including IL-17 signaling, TNF signaling, Relaxin signaling, and HIF-1 signaling. Clinical data indicated that XYM's therapeutic effects are comparable to those of valsartan, which significantly reduced diastolic and systolic blood pressure and demonstrated good biosafety.

CONCLUSIONS

Uncaria, Radix Scrophulariae, and Epimedium effectively mitigate hypertension through multiple components, targets, and pathways. Additionally, DPP4, IL-17, and TNF-α are identified as potential therapeutic targets for traditional Chinese medicine preparations in hypertension treatment. This study provides a foundation for further investigation into XYM's mechanisms in hypertension management.

Exploration of Berberine Against Ulcerative Colitis via TLR4/NF-κB/HIF-1α Pathway by Bioinformatics and Experimental Validation

Purpose

This study aimed to delineate the molecular processes underlying the therapeutic effects of berberine on UC by employing network pharmacology tactics, molecular docking, and dynamic simulations supported by empirical validations both in vivo and in vitro.

Patients and Methods

We systematically screened potential targets and relevant pathways affected by berberine for UC treatment from comprehensive databases, including GeneCards, DisGeNET, and GEO. Molecular docking and simulation protocols were used to assess the interaction stability between berberine and its principal targets. The predictions were validated using both a DSS-induced UC mouse model and a lipopolysaccharide (LPS)-stimulated NCM460 cellular inflammation model.

Results

Network pharmacology analysis revealed the regulatory effect of the TLR4/NF-κB/HIF-1α pathway in the ameliorative action of berberine in UC. Docking and simulation studies predicted the high-affinity interactions of berberine with pivotal targets: TLR4, NF-κB, HIF-1α, and the HIF inhibitor KC7F2. Moreover, in vivo analyses demonstrated that berberine attenuates clinical severity, as reflected by decreased disease activity index (DAI) scores, reduced weight loss, and mitigated intestinal inflammation in DSS-challenged mice. These outcomes include suppression of the proinflammatory cytokines IL-6 and TNF-α and downregulation of TLR4/NF-κB/HIF-1α mRNA and protein levels. Correspondingly, in vitro findings indicate that berberine decreases cellular inflammatory injury and suppresses TLR4/NF-κB/HIF-1α signaling, with notable effectiveness similar to that of the HIF-1α inhibitor KC7F2.

Conclusion

Through network pharmacology analysis and experimental substantiation, this study confirmed that berberine enhances UC treatment outcomes by inhibiting the TLR4/NF-κB/HIF-1α axis, thereby mitigating inflammatory reactions and improving colonic pathology.

Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations

The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

Multi-epitopevaccines, from design to expression; an in silico approach

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.

Thyroid Hormone Receptor Agonistic and Antagonistic Activity of Newly Synthesized Dihydroxylated Polybrominated Diphenyl Ethers: An In Vitro and In Silico Coactivator Recruitment Study

Dihydroxylated polybrominated diphenyl ethers (DiOH-PBDEs) could be the metabolites of PBDEs of some organisms or the natural products of certain marine bacteria and algae. OH-PBDEs may demonstrate binding affinity to thyroid hormone receptors (TRs) and can disrupt the functioning of the systems modulated by TRs. However, the thyroid hormone disruption mechanism of diOH-PBDEs remains elusive due to the absence of diOH-PBDEs standards. This investigation explores the potential disruptive effects of OH/diOH-PBDEs on thyroid hormones via competitive binding and coactivator recruitment with TRα and TRβ. At levels of 5000 nM and 25,000 nM, 6-OH-BDE-47 demonstrated significant recruitment of steroid receptor coactivator (SRC), whereas none of the diOH-PBDEs exhibited SRC recruitment within the range of 0.32-25,000 nM. AutoDock CrankPep (ADCP) simulations suggest that the conformation of SRC and TR-ligand complexes, particularly their interaction with Helix 12, rather than binding affinity, plays a pivotal role in ligand agonistic activity. 6,6'-diOH-BDE-47 displayed antagonistic activity towards both TRα and TRβ, while the antagonism of 3,5-diOH-BDE-100 for TRα and TRβ was concentration-dependent. 3,5-diOH-BDE-17 and 3,5-diOH-BDE-51 exhibited no discernible agonistic or antagonistic activities. Molecular docking analysis revealed that the binding energy of 3,3',5-triiodo-L-thyronine (T3) surpassed that of OH/diOH-PBDEs. 3,5-diOH-BDE-100 exhibited the highest binding energy, whereas 6,6'-diOH-BDE-47 displayed the lowest. These findings suggest that the structural determinants influencing the agonistic and antagonistic activities of halogen phenols may be more intricate than previously proposed, involving factors beyond high-brominated PBDEs or hydroxyl group and bromine substitutions. It is likely that the agonistic or antagonistic propensities of OH/diOH-PBDEs are instigated by protein conformational changes rather than considerations of binding energy.

HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.

Potential pharmacological mechanisms of tanshinone IIA in the treatment of human neuroblastoma based on network pharmacological and molecular docking Technology

Tanshinone IIA (Tan-IIA) is the main bioactive component of Chinese herbal medicine salvia miltiorrhiza (Danshen). Sodium sulfonate of Tan-IIA is widely used in the treatment of cardiovascular and cerebrovascular diseases. Tan-IIA also has inhibitory effects on tumor cells such as gastric cancer, but its therapeutic effect and mechanism on human neuroblastoma have not been evaluated, so its pharmacological mechanism is systematically evaluated by the combined method of network pharmacology and molecular docking. PharmMapper and SwissTargetPrediction predicted 331 potential Tan-IIA-related targets, and 1,152 potential neuroblastoma-related targets were obtained from GeneCards, DisGeNET, DrugBank, OMIM and Therapeutic Target databases (TTD), 107 common targets for Tan-IIA and neuroblastoma. Through gene ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomesa (KEGG) pathway enrichment, protein-protein interaction (PPI) network and cytoHubba plug-in, 10 related signal pathways (Pathways in cancer, PI3K-Akt signaling pathway, Prostate cancer, etc.) and 10 hub genes were identified. The results of molecular docking showed that Tan-IIA could interact with 10 targets: GRB2, SRC, EGFR, PTPN1, ESR1, IGF1, MAPK1, PIK3R1, AKT1 and IGF1R. This study analyzed the related pathways and targets of Tan-IIA in the treatment of human neuroblastoma, as well as the potential anticancer and anti-tumor targets and related signaling pathways of Tan-IIA, which provides a reference for us to find and explore effective drugs for the treatment of human neuroblastoma.

Mitochondria-Targeted Oligomeric α-Synuclein Induces TOM40 Degradation and Mitochondrial Dysfunction in Parkinson's Disease and Parkinsonism-Dementia of Guam

Mitochondrial dysfunction is a central aspect of Parkinson's disease (PD) pathology, yet the underlying mechanisms are not fully understood. This study investigates the link between α-Synuclein (α-Syn) pathology and the loss of translocase of the outer mitochondrial membrane 40 (TOM40), unraveling its implications for mitochondrial dysfunctions in neurons. We discovered that TOM40 protein depletion occurs in the brains of patients with Guam Parkinsonism Dementia (Guam PD) and cultured neurons expressing α-Syn proteinopathy, notably, without corresponding changes in TOM40 mRNA levels. Cultured neurons expressing α-Syn mutants, with or without a mitochondria-targeting signal (MTS) underscore the role of α-Syn's mitochondrial localization in inducing TOM40 degradation. Parkinson's Disease related etiological factors, such as 6-hydroxy dopamine or ROS/metal ions stress, which promote α-Syn oligomerization, exacerbate TOM40 depletion in PD patient-derived cells with SNCA gene triplication. Although α-Syn interacts with both TOM40 and TOM20 in the outer mitochondrial membrane, degradation is selective for TOM40, which occurs via the ubiquitin-proteasome system (UPS) pathway. Our comprehensive analyses using Seahorse technology, mitochondrial DNA sequencing, and damage assessments, demonstrate that mutant α-Syn-induced TOM40 loss results in mitochondrial dysfunction, characterized by reduced membrane potential, accumulation of mtDNA damage, deletion/insertion mutations, and altered oxygen consumption rates. Notably, ectopic supplementation of TOM40 or reducing pathological forms of α-Syn using ADP-ribosylation inhibitors ameliorate these mitochondrial defects, suggesting potential therapeutic avenues. In conclusion, our findings provide crucial mechanistic insights into how α-Syn accumulation leads to TOM40 degradation and mitochondrial dysfunction, offering insights for targeted interventions to alleviate mitochondrial defects in PD.

Peptide-Conjugated MRI Probe Targeted to Netrin-1, a Novel Metastatic Breast Cancer Biomarker

Despite significant progress in cancer imaging and treatment over the years, early diagnosis and metastasis detection remain a challenge. Molecular magnetic resonance imaging (MRI), with its high resolution, can be well adapted to fulfill this need, requiring the design of contrast agents which target specific tumor biomarkers. Netrin-1 is an extracellular protein overexpressed in metastatic breast cancer and implicated in tumor progression and the appearance of metastasis. This study focuses on the design and preclinical evaluation of a novel Netrin-1-specific peptide-based MRI probe, GdDOTA-KKTHDAVR (Gd-K), to visualize metastatic breast cancer. The targeting peptide sequence was identified based on the X-ray structure of the complex between Netrin-1 and its transmembrane receptor DCC. Molecular docking simulations support the probe design. In vitro studies evidenced submicromolar affinity of Gd-K for Netrin-1 (KD = 0.29 μM) and good MRI efficacy (proton relaxivity, r1 = 4.75 mM-1 s-1 at 9.4 T, 37 °C). In vivo MRI studies in a murine model of triple-negative metastatic breast cancer revealed successful tumor visualization at earlier stages of tumor development (smaller tumor volume). Excellent signal enhancement, 120% at 2 min and 70% up to 35 min post injection, was achieved (0.2 mmol/kg injected dose), representing a reasonable imaging time window and a superior contrast enhancement in the tumor as compared to Dotarem injection.

Studies on the selectivity of the SARS-CoV-2 papain-like protease reveal the importance of the P2' proline of the viral polyprotein

The SARS-CoV-2 papain-like protease (PLpro) is an antiviral drug target that catalyzes the hydrolysis of the viral polyproteins pp1a/1ab, so releasing the non-structural proteins (nsps) 1-3 that are essential for the coronavirus lifecycle. The LXGG↓X motif in pp1a/1ab is crucial for recognition and cleavage by PLpro. We describe molecular dynamics, docking, and quantum mechanics/molecular mechanics (QM/MM) calculations to investigate how oligopeptide substrates derived from the viral polyprotein bind to PLpro. The results reveal how the substrate sequence affects the efficiency of PLpro-catalyzed hydrolysis. In particular, a proline at the P2' position promotes catalysis, as validated by residue substitutions and mass spectrometry-based analyses. Analysis of PLpro catalyzed hydrolysis of LXGG motif-containing oligopeptides derived from human proteins suggests that factors beyond the LXGG motif and the presence of a proline residue at P2' contribute to catalytic efficiency, possibly reflecting the promiscuity of PLpro. The results will help in identifying PLpro substrates and guiding inhibitor design.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Screening of novel bovine-elastin-derived peptides with elastase inhibition and photoprotective potential: a combined in silico and in vitro study

BACKGROUND

The demand for food-based anti-photoaging products is surging because of the rising recognition of health and beauty, as well as enhanced comprehension of the detrimental impact of ultraviolet (UV) radiation. This study aimed to investigate the potential of bioactive peptides derived from bovine elastin, specifically focusing on identifying novel elastase inhibitory peptides and assessing their photoprotective properties using bioinformatics techniques.

RESULTS

A total of 48 bioactive peptides were screened in bovine elastin hydrolysate (EH) utilizing Peptide Ranker analysis. Three novel elastase inhibitory peptides, GAGQPFPI, FFPGAG and FPGIG (in descending order of activity), exhibited potent inhibitory effects on elastase in vitro, surpassing the inhibitory effect of EH by a factor of 1-2 and reaching significantly lower concentrations (8-15 times lower) than EH. The cumulative inhibitory effect of GAGQPFPI, FFPGAG, and FPGIG reached 91.5%. Further analysis revealed that FFPGAG and FPGIG exhibited mixed inhibition, whereas GAGQPFPI displayed non-competitive inhibition. Molecular simulations showed that these peptides interacted effectively with the elastase active site through hydrogen bonding and hydrophobic interactions. Furthermore, GAGQPFPI, FFPGAG, and FPGIG demonstrated high stability in gastrointestinal digestion, demonstrated transcellular permeability across Caco-2 cell monolayers, and exhibited remarkable photoprotective properties against UVB-irradiated HaCaT cells.

CONCLUSION

GAGQPFPI showed the most promising potential as a functional food with photoprotective effects against UVB damage and inhibitory properties against elastase. © 2023 Society of Chemical Industry.

Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

A dual-population multi-objective evolutionary algorithm driven by generative adversarial networks for benchmarking and protein-peptide docking

Multi-objective optimization problems (MOPs) are characterized as optimization problems in which multiple conflicting objective functions are optimized simultaneously. To solve MOPs, some algorithms used machine learning models to drive the evolutionary algorithms, leading to the design of a variety of model-based evolutionary algorithms. However, model collapse occurs during the generation of candidate solutions, which results in local optima and poor diversity in model-based evolutionary algorithms. To address this problem, we propose a dual-population multi-objective evolutionary algorithm driven by Wasserstein generative adversarial network with gradient penalty (DGMOEA), where the dual-populations coordinate and cooperate to generate high-quality solutions, thus improving the performance of the evolutionary algorithm. We compare the proposed algorithm with the 7 state-of-the-art algorithms on 20 multi-objective benchmark functions. Experimental results indicate that DGMOEA achieves significant results in solving MOPs, where the metrics IGD and HV outperform the other comparative algorithms on 15 and 18 out of 20 benchmarks, respectively. Our algorithm is evaluated on the LEADS-PEP dataset containing 53 protein-peptide complexes, and the experimental results on solving the protein-peptide docking problem indicated that DGMOEA can effectively reduce the RMSD between the generated and the original peptide's 3D poses and achieve more competitive results.

A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.

Mass spectrometric assays monitoring the deubiquitinase activity of the SARS-CoV-2 papain-like protease inform on the basis of substrate selectivity and have utility for substrate identification

The SARS-CoV-2 papain-like protease (PLpro) and main protease (Mpro) are nucleophilic cysteine enzymes that catalyze hydrolysis of the viral polyproteins pp1a/1ab. By contrast with Mpro, PLpro is also a deubiquitinase (DUB) that accepts post-translationally modified human proteins as substrates. Here we report studies on the DUB activity of PLpro using synthetic Nε-lysine-branched oligopeptides as substrates that mimic post-translational protein modifications by ubiquitin (Ub) or Ub-like modifiers (UBLs), such as interferon stimulated gene 15 (ISG15). Mass spectrometry (MS)-based assays confirm the DUB activity of isolated recombinant PLpro. They reveal that the sequence of both the peptide fragment derived from the post-translationally modified protein and that derived from the UBL affects PLpro catalysis; the nature of substrate binding in the S sites appears to be more important for catalytic efficiency than binding in the S' sites. Importantly, the results reflect the reported cellular substrate selectivity of PLpro, i.e. human proteins conjugated to ISG15 are better substrates than those conjugated to Ub or other UBLs. The combined experimental and modelling results imply that PLpro catalysis is affected not only by the identity of the substrate residues binding in the S and S' sites, but also by the substrate fold and the conformational dynamics of the blocking loop 2 of the PLpro:substrate complex. Nε-Lysine-branched oligopeptides thus have potential to help the identification of PLpro substrates. More generally, the results imply that MS-based assays with Nε-lysine-branched oligopeptides have potential to monitor catalysis by human DUBs and hence to inform on their substrate preferences.

The divergent ER-mitochondria encounter structures (ERMES) are conserved in parabasalids but lost in several anaerobic lineages with hydrogenosomes

BACKGROUND

The endoplasmic reticulum (ER)-mitochondria membrane contact sites (MCS) are extensively studied in aerobic eukaryotes; however, little is known about MCS in anaerobes with reduced forms of mitochondria named hydrogenosomes. In several eukaryotic lineages, the direct physical tether between ER and the outer mitochondrial membrane is formed by ER-mitochondria encounter structure (ERMES). The complex consists of four core proteins (Mmm1, Mmm2, Mdm12, and Mdm10) which are involved in phospholipid trafficking. Here we investigated ERMES distribution in organisms bearing hydrogenosomes and employed Trichomonas vaginalis as a model to estimate ERMES cellular localization, structure, and function.

RESULTS

Homology searches revealed that Parabasalia-Anaeramoebae, anaerobic jakobids, and anaerobic fungi are lineages with hydrogenosomes that retain ERMES, while ERMES components were gradually lost in Fornicata, and are absent in Preaxostyla and Archamoebae. In T. vaginalis and other parabasalids, three ERMES components were found with the expansion of Mmm1. Immunofluorescence microscopy confirmed that Mmm1 localized in ER, while Mdm12 and Mmm2 were partially localized in hydrogenosomes. Pull-down assays and mass spectrometry of the ERMES components identified a parabasalid-specific Porin2 as a substitute for the Mdm10. ERMES modeling predicted a formation of a continuous hydrophobic tunnel of TvMmm1-TvMdm12-TvMmm2 that is anchored via Porin2 to the hydrogenosomal outer membrane. Phospholipid-ERMES docking and Mdm12-phospholipid dot-blot indicated that ERMES is involved in the transport of phosphatidylinositol phosphates. The absence of enzymes involved in hydrogenosomal phospholipid metabolism implies that ERMES is not involved in the exchange of substrates between ER and hydrogenosomes but in the unidirectional import of phospholipids into hydrogenosomal membranes.

CONCLUSIONS

Our investigation demonstrated that ERMES mediates ER-hydrogenosome interactions in parabasalid T. vaginalis, while the complex was lost in several other lineages with hydrogenosomes.

Assessing Genetic Algorithm-Based Docking Protocols for Prediction of Heparin Oligosaccharide Binding Geometries onto Proteins

Although molecular docking has evolved dramatically over the years, its application to glycosaminoglycans (GAGs) has remained challenging because of their intrinsic flexibility, highly anionic character and rather ill-defined site of binding on proteins. GAGs have been treated as either fully "rigid" or fully "flexible" in molecular docking. We reasoned that an intermediate semi-rigid docking (SRD) protocol may be better for the recapitulation of native heparin/heparan sulfate (Hp/HS) topologies. Herein, we study 18 Hp/HS-protein co-complexes containing chains from disaccharide to decasaccharide using genetic algorithm-based docking with rigid, semi-rigid, and flexible docking protocols. Our work reveals that rigid and semi-rigid protocols recapitulate native poses for longer chains (5→10 mers) significantly better than the flexible protocol, while 2→4-mer poses are better predicted using the semi-rigid approach. More importantly, the semi-rigid docking protocol is likely to perform better when no crystal structure information is available. We also present a new parameter for parsing selective versus non-selective GAG-protein systems, which relies on two computational parameters including consistency of binding (i.e., RMSD) and docking score (i.e., GOLD Score). The new semi-rigid protocol in combination with the new computational parameter is expected to be particularly useful in high-throughput screening of GAG sequences for identifying promising druggable targets as well as drug-like Hp/HS sequences.

Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function

When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.

Finding Novel AMPs Secreted from the Human Microbiome as Potent Antibacterial and Antibiofilm Agents and Studying Their Synergistic Activity with Ag NCs

Due to the enhanced resistance of bacteria to antibiotics, researchers always try to find effective alternatives to treat drug-resistant bacterial infections. In this context, we have explored antimicrobial peptides (AMPs), which are a broad class of small peptide molecules, and investigated their efficacy as potent antibacterial and antibiofilm agents. AMPs can cause cell death either through disruption of the cell membrane or by inhibiting vital intracellular functions, by binding to RNA, DNA, or intracellular components upon transversion through the cell membrane. We attempted to find potent intracellular cationic AMPs that can demonstrate antibacterial activity through interaction with DNA. As a source of AMPs, we have utilized those that are secreted from the human microbiome with the anticipation that these will be non-toxic in nature. Out of the total 1087 AMPs, 27 were screened on the basis of amino acid length and efficacy to cross the cell membrane barrier. From the list of 27 peptides, 4 candidates were selected through the docking score of these peptides with the DNA binding domain of H2A proteins. Further, the molecular dynamics simulation analysis demonstrated that 2 AMPs, i.e., peptides 7 and 25, are having considerable membrane permeation and DNA binding ability. Further, the in vitro analysis indicated that both peptides 7 and 25 could exhibit potent antibacterial and antibiofilm activities. In order to further enhance the antibiofilm potency, the above AMPs were used as supplements to silver nanoclusters (Ag NCs) to get synergistic activity. The synergistic activity of Ag NCs was found to be significantly increased with both the above AMPs.

Discovery and pharmacophoric characterization of chemokine network inhibitors using phage-display, saturation mutagenesis and computational modelling

CC and CXC-chemokines are the primary drivers of chemotaxis in inflammation, but chemokine network redundancy thwarts pharmacological intervention. Tick evasins promiscuously bind CC and CXC-chemokines, overcoming redundancy. Here we show that short peptides that promiscuously bind both chemokine classes can be identified from evasins by phage-display screening performed with multiple chemokines in parallel. We identify two conserved motifs within these peptides and show using saturation-mutagenesis phage-display and chemotaxis studies of an exemplar peptide that an anionic patch in the first motif and hydrophobic, aromatic and cysteine residues in the second are functionally necessary. AlphaFold2-Multimer modelling suggests that the peptide occludes distinct receptor-binding regions in CC and in CXC-chemokines, with the first and second motifs contributing ionic and hydrophobic interactions respectively. Our results indicate that peptides with broad-spectrum anti-chemokine activity and therapeutic potential may be identified from evasins, and the pharmacophore characterised by phage display, saturation mutagenesis and computational modelling.

Interactions of the N- and C-Terminal SH3 Domains of Drosophila Drk with the Proline-Rich Peptides from Sos and Dos

Drk, a homologue of human GRB2 in Drosophila, receives signals from outside the cells through the interaction of its SH2 domain with the phospho-tyrosine residues in the intracellular regions of receptor tyrosine kinases (RTKs) such as Sevenless, and transduces the signals downstream through the association of its N- and C-terminal SH3 domains (Drk-NSH3 and Drk-CSH3, respectively) with proline-rich motifs (PRMs) in Son of Sevenless (Sos) or Daughter of Sevenless (Dos). Isolated Drk-NSH3 exhibits a conformational equilibrium between the folded and unfolded states, while Drk-CSH3 adopts only a folded confirmation. Drk interacts with PRMs of the PxxPxR motif in Sos and the PxxxRxxKP motif in Dos. Our previous study has shown that Drk-CSH3 can bind to Sos, but the interaction between Drk-NSH3 and Dos has not been investigated. To assess the affinities of both SH3 domains towards Sos and Dos, we conducted NMR titration experiments using peptides derived from Sos and Dos. Sos-S1 binds to Drk-NSH3 with the highest affinity, strongly suggesting that the Drk-Sos multivalent interaction is initiated by the binding of Sos-S1 and NSH3. Our results also revealed that the two Sos-derived PRMs clearly favour NSH3 for binding, whereas the two Dos-derived PRMs show almost similar affinity for NSH3 and CSH3. We have also performed docking simulations based on the chemical shift perturbations caused by the addition of Sos- and Dos-derived peptides. Finally, we discussed the various modes in the interactions of Drk with Sos/Dos.

Identification of bioactive compounds and potential mechanisms of scutellariae radix-coptidis rhizoma in the treatment of atherosclerosis by integrating network pharmacology and experimental validation

OBJECTIVE

This study aims at investigating the potential targets and functional mechanisms of Scutellariae Radix-Coptidis Rhizoma (QLYD) against atherosclerosis (AS) through network pharmacology, molecular docking, bioinformatic analysis and experimental validation.

METHODS

The compositions of QLYD were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and literature, where the main active components of QLYD and corresponding targets were identified. The potential therapeutic targets of AS were excavated using the OMIM database, DrugBank database, DisGeNET database, CTD database and GEO datasets. The protein-protein interaction (PPI) network of common targets was constructed and visualized by Cytoscape 3.7.2 software. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed to analyze the function of core targets in the PPI network. Molecular docking was carried out using AutoDockTools, AutoDock Vina, and PyMOL software to verify the correlation between the main components of QLYD and the core targets. Mouse AS model was established and the results of network pharmacology were verified by in vivo experiments.

RESULTS

Totally 49 active components and 225 corresponding targets of QLYD were obtained, where 68 common targets were identified by intersecting with AS-related targets. Five hub genes including IL6, VEGFA, AKT1, TNF, and IL1B were screened from the PPI network. GO functional analysis reported that these targets had associations mainly with cellular response to oxidative stress, regulation of inflammatory response, epithelial cell apoptotic process, and blood coagulation. KEGG pathway analysis demonstrated that these targets were correlated to AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, IL-17 signaling pathway, MAPK signaling pathway, and NF-kappa B signaling pathway. Results of molecular docking indicated good binding affinity of QLYD to FOS, AKT1, and TNF. Animal experiments showed that QLYD could inhibit inflammation, improve blood lipid levels and reduce plaque area in AS mice to prevent and treat AS.

CONCLUSION

QLYD may exert anti-inflammatory and anti-oxidative stress effects through multi-component, multi-target and multi-pathway to treat AS.