Abstract: The S100A1 protein in humans is a calcium-binding protein. Upon Ca(2+) binding to S100A1 EF-hand motifs, the conformation of S100A1 changes and promotes interactions with target proteins. RAGE consists of three domains: the cytoplasmic, transmembrane, and extracellular domains. The extracellular domain consists of C1, C2, and V domains. V domains are the primary receptors for the S100 protein. It was reported several years ago that S100A1 and RAGE V domains interact in a pathway involving S100A1-RAGE signaling, whereby S100A1 binds to the V domain, resulting in RAGE dimerization. The autophosphorylation of the cytoplasmic domain initiates a signaling cascade that regulates cell proliferation, cell growth, and tumor formation. In this study, we used pentamidine and a newly synthesized pentamidine analog (WLC-4059) to inhibit the S100A1-RAGE V interaction. (1)H-(15)N HSQC NMR titration was carried out to characterize the interaction between mS100A1 (mutant S100A1, C86S) and pentamidine analogs. We found that pentamidine analogs interact with S100A1 via (1)H-(15)N HSQC NMR spectroscopy. Based on the results, we utilized the HADDOCK program to generate structures of the mS100A1-WLC-4059 binary complex. Interestingly, the binary complex overlapped with the complex crystal structure of the mS100A1-RAGE-V domain, proving that WLC-4059 blocks interaction sites between S100A1 and RAGE-V. A WST-1 cell proliferation assay also supported these results. We conclude that pentamidine analogs could potentially enhance therapeutic approaches against cancers.

Abstract: The Kirsten rat sarcoma viral G12C (KRAS(G12C)) protein is one of the most common mutations in non-small-cell lung cancer (NSCLC). KRAS(G12C) inhibitors are promising for NSCLC treatment, but their weaker activity in resistant tumors is their drawback. This study aims to identify new KRAS(G12C) inhibitors from among the FDA-approved covalent drugs by taking advantage of artificial intelligence. The machine learning models were constructed using an extreme gradient boosting (XGBoost) algorithm. The models can predict KRAS(G12C) inhibitors well, with an accuracy score of validation = 0.85 and Q(2)(Ext) = 0.76. From 67 FDA-covalent drugs, afatinib, dacomitinib, acalabrutinib, neratinib, zanubrutinib, dutasteride, and finasteride were predicted to be active inhibitors. Afatinib obtained the highest predictive log-inhibitory concentration at 50% (pIC(50)) value against KRAS(G12C) protein close to the KRAS(G12C) inhibitors. Only afatinib, neratinib, and zanubrutinib covalently bond at the active site like the KRAS(G12C) inhibitors in the KRAS(G12C) protein (PDB ID: 6OIM). Moreover, afatinib, neratinib, and zanubrutinib exhibited a distance deviation between the KRAS(G2C) protein-ligand complex similar to the KRAS(G12C) inhibitors. Therefore, afatinib, neratinib, and zanubrutinib could be used as drug candidates against the KRAS(G12C) protein. This finding unfolds the benefit of artificial intelligence in drug repurposing against KRAS(G12C) protein.

Abstract: BACKGROUND: Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related burden and deaths, thus effective treatment strategies with lower side effects for NSCLC are urgently needed. To systematically analyze the mechanism of Bai He Gu Jin Tang (BHGJT) against NSCLC by network pharmacology and molecular docking. METHODS: The active compounds of BHGJT were obtained by searching the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine and Encyclopaedia of Traditional Chinese Medicine. Search tool for interactions of chemicals was used for acquiring the targets of BHGJT. The component-target network was mapped by Cytoscape. NSCLC-related genes were obtained by searching Genecards, DrugBank and Therapeutic Target Database. The protein-protein interaction network of intersection targets was established based on Search Tool for Recurring Instances of Neighboring Genes (STRING), and further, the therapeutic core targets were selected by topological parameters. The hub targets were transmitted to Database for Annotation, Visualization and Integrated Discovery for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, AutoDock Vina and MglTools were employed for molecular docking validation. RESULTS: Two hundred fifty-six compounds and 237 putative targets of BHGJT-related active compounds as well as 1721potential targets of NSCLC were retrieved. Network analysis showed that 8 active compounds of BHGJT including kaempferol, quercetin, luteolin, isorhamnetin, beta-sitosterol, stigmasterol, mairin and liquiritigenin as well as 15 hub targets such as AKR1B10 and AKR1C2 contribute to the treatment of BHGJT against NSCLC. GO functional enrichment analysis shows that BHGJT could regulate many biological processes, such as apoptotic process. Three modules of the endocrine related pathways including the inflammation, hypoxia related pathways as well as the other cancer related pathways based on Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis might explain the biological mechanisms of BHGJT in treating BHGJT. The results of molecular docking verified that AKR1B10 and AKR1C2 had the strongest binding activity with the 8 key compounds of NSCLC. CONCLUSION: Our study reveals the mechanism of BHGJT in treating NSCLC involving multiple components, multiple targets and multiple pathways. The present study laid an initial foundation for the subsequent research and clinical application of BHGJT and its active compounds against NSCLC.

Abstract: BACKGROUND: Identifying drug-target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching scope of potential drugs and speed up the process of drug discovery. There are a variety of non-negativity matrix factorization based methods to predict DTIs, but the convergence of the algorithms used in the matrix factorization are often overlooked and the results can be further improved. RESULTS: In order to predict DTIs more accurately and quickly, we propose an alternating direction algorithm to solve graph regularized non-negative matrix factorization with prior knowledge consistency constraint (ADA-GRMFC). Based on known DTIs, drug chemical structures and target sequences, ADA-GRMFC at first constructs a DTI matrix, a drug similarity matrix and a target similarity matrix. Then DTI prediction is modeled as the non-negative factorization of the DTI matrix with graph dual regularization terms and a prior knowledge consistency constraint. The graph dual regularization terms are used to integrate the information from the drug similarity matrix and the target similarity matrix, and the prior knowledge consistency constraint is used to ensure the matrix decomposition result should be consistent with the prior knowledge of known DTIs. Finally, an alternating direction algorithm is used to solve the matrix factorization. Furthermore, we prove that the algorithm can converge to a stationary point. Extensive experimental results of 10-fold cross-validation show that ADA-GRMFC has better performance than other state-of-the-art methods. In the case study, ADA-GRMFC is also used to predict the targets interacting with the drug olanzapine, and all of the 10 highest-scoring targets have been accurately predicted. In predicting drug interactions with target estrogen receptors alpha, 17 of the 20 highest-scoring drugs have been validated.

Abstract: Syphilis, a sexually transmitted infection, is a deadly disease caused by Treponema pallidum. It is a Gram-negative spirochete that can infect nearly every organ of the human body. It can be transmitted both sexually and perinatally. Since syphilis is the second most fatal sexually transmitted disease after AIDS, an efficient vaccine candidate is needed to establish long-term protection against infections by T. pallidum. This study used reverse-vaccinology-based immunoinformatic pathway subtractive proteomics to find the best antigenic proteins for multi-epitope vaccine production. Six essential virulent and antigenic proteins were identified, including the membrane lipoprotein TpN32 (UniProt ID: O07950), DNA translocase FtsK (UniProt ID: O83964), Protein Soj homolog (UniProt ID: O83296), site-determining protein (UniProt ID: F7IVD2), ABC transporter, ATP-binding protein (UniProt ID: O83930), and Sugar ABC superfamily ATP-binding cassette transporter, ABC protein (UniProt ID: O83782). We found that the multiepitope subunit vaccine consisting of 4 CTL, 4 HTL, and 11 B-cell epitopes mixed with the adjuvant TLR-2 agonist ESAT6 has potent antigenic characteristics and does not induce an allergic response. Before being docked at Toll-like receptors 2 and 4, the developed vaccine was modeled, improved, and validated. Docking studies revealed significant binding interactions, whereas molecular dynamics simulations demonstrated its stability. Furthermore, the immune system simulation indicated significant and long-lasting immunological responses. The vaccine was then reverse-transcribed into a DNA sequence and cloned into the pET28a (+) vector to validate translational activity as well as the microbial production process. The vaccine developed in this study requires further scientific consensus before it can be used against T. pallidum to confirm its safety and efficacy.

Abstract: Ulcerative colitis (UC) is a chronic nonspecific intestinal inflammatory disease, which belongs to a subtype of inflammatory bowel disease, but still lacks effective drug treatment. Bletilla striata (B. striata) is one of the most valuable traditional Chinese medicines (TCMs) in China, can stop bleeding, can promote wound healing, and can regulate immunity. Based on data mining, B. striata was found to be a common TCM for the treatment of UC, but the exact therapeutic mechanism is not yet known. This study aims to explore the potential mechanisms of B. striata in the treatment of UC using network pharmacology, molecular docking techniques, and in vivo experimental research. We extracted the active ingredients and targets of B. striata from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and analysis platform. We retrieved and screened the corresponding UC-related target genes in multiple databases. Subsequently, we constructed an herb-ingredient-target-disease-network, generated a protein-protein interaction network, performed Gene Ontology enrichment analysis, and performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to identify potential treatment mechanisms. After screening for key active ingredients and target genes, we performed molecular docking using AutoDock Vina software to select the best binding target for molecular docking and validate the binding activity. The UC model was established in mice, and the results of network pharmacology and molecular docking were verified by in vivo experiments. In all, 5 compounds were obtained from the TCMSP database, and 74 UC-related pathogenic genes were obtained from GeneCards, DisGeNET, OMIM, TTD, and DrugBank. After KEGG enrichment analysis, pathways in cancer, the phosphatidylinositol 3-kinase (PI3K)/AKT signalling pathway, and metabolic pathways were identified as the top three signalling pathways associated with UC treatment. The results of molecular docking showed that the active components of B. striata have good binding activities to the pivotal targets epidermal growth factor receptor (EGFR) and PIK3CA. In a dextran sulphate sodium-induced colitis model, we found that B. striata can alleviate the symptoms of UC, decrease the secretion of the inflammatory cytokines interleukin-6 and tumour necrosis factor-alpha, and downregulate the expression levels of EGFR, PIK3CA, and p-AKT. In conclusion, the treatment of UC with B. striata may alleviate the inflammatory response of the colon, and B. striata mainly inhibits the EGFR/PI3K/AKT signalling pathways.

Abstract: BACKGROUND: Drug-drug interactions (DDIs) occur when two or more drugs are taken simultaneously or successively. Early detection of adverse drug interactions can be essential in preventing medical errors and reducing healthcare costs. Many computational methods already predict interactions between small molecule drugs (SMDs). As the number of biotechnology drugs (BioDs) increases, so makes the threat of interactions between SMDs and BioDs. However, few computational methods are available to predict their interactions. RESULTS: Considering the structural specificity and relational complexity of SMDs and BioDs, a novel multi-modal representation learning method called Multi-SBI is proposed to predict their interactions. First, multi-modal features are used to adequately represent the heterogeneous structure and complex relationships of SMDs and BioDs. Second, an undersampling method based on Positive-unlabeled learning (PU-sampling) is introduced to obtain negative samples with high confidence from the unlabeled data set. Finally, both learned representations of SMD and BioD are fed into DNN classifiers to predict their interaction events. In addition, we also conduct a retrospective analysis. CONCLUSIONS: Our proposed multi-modal representation learning method can extract drug features more comprehensively in heterogeneous drugs. In addition, PU-sampling can effectively reduce the noise in the sampling procedure. Our proposed method significantly outperforms other state-of-the-art drug interaction prediction methods. In a retrospective analysis of DrugBank 5.1.0, 14 out of the 20 predictions with the highest confidence were validated in the latest version of DrugBank 5.1.8, demonstrating that Multi-SBI is a valuable tool for predicting new drug interactions through effectively extracting and learning heterogeneous drug features.

Abstract: Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC(50) values ranging from 0.04 to 6 muM), three OATP1B1 inhibitors (2.69 to 10 muM), and five OATP1B3 inhibitors (1.53 to 10 muM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC(50) values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC(50) = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

Abstract: The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Abstract: The continual rise in sulfadoxine (SDX) resistance affects the therapeutic efficacy of sulfadoxine-pyrimethamine; therefore, careful monitoring will help guide its prolonged usage. Mutations in Plasmodium falciparum dihydropteroate synthase (Pfdhps) are being surveilled, based on their link with SDX resistance. However, there is a lack of continuous analyses and data on the potential effect of molecular markers on the Pfdhps structure and function. This study explored single-nucleotide polymorphisms (SNPs) in Pfdhps that were isolated in Africa and other countries, highlighting the regional distribution and its link with structure. In total, 6336 genomic sequences from 13 countries were subjected to SNPs, haplotypes, and structure-based analyses. The SNP analysis revealed that the key SDX resistance marker, A437G, was nearing fixation in all countries, peaking in Malawi. The mutation A613S was rare except in isolates from the Democratic Republic of Congo and Malawi. Molecular docking revealed a general loss of interactions when comparing mutant proteins to the wild-type protein. During MD simulations, SDX was released from the active site in mutants A581G and A613S before the end of run-time, whereas an unstable binding of SDX to mutant A613S and haplotype A437A/A581G/A613S was observed. Conformational changes in mutant A581G and the haplotypes A581G/A613S, A437G/A581G, and A437G/A581G/A613S were seen. The radius of gyration revealed an unfolding behavior for the A613S, K540E/A581G, and A437G/A581G systems. Overall, tracking such mutations by the continuous analysis of Pfdhps SNPs is encouraged. SNPs on the Pfdhps structure may cause protein-drug function loss, which could affect the applicability of SDX in preventing malaria in pregnant women and children.