Abstract: OBJECTIVE: Rapid transmission and reproduction of RNA viruses prepare conducive conditions to have a high rate of mutations in their genetic sequence. The viral mutations make adapt the severe acute respiratory syndrome coronavirus 2 in the host environment and help the evolution of the virus then also caused a high mortality rate by the virus that threatens worldwide health. Mutations and adaptation help the virus to escape confrontations that are done against it. METHODS: In the present study, we analyzed 6,510,947 sequences of non-structural protein 1 as one of the conserved regions of the virus to find out frequent mutations and substitute amino acids in comparison with the wild type. NSP1 mutations rate divided into continents were different. RESULTS: Based on this continental categorization, E87D in global vision and also in Europe notably increased. The E87D mutation has signed up to January 2022 as the first frequent mutation observed. The remarkable mutations, H110Y and R24C have the second and third frequencies, respectively. CONCLUSION: According to the important role of non-structural protein 1 on the host mRNA translation, developing drug design against the protein could be so hopeful to find more effective ways the control and then treatment of the global pandemic coronavirus disease 2019.

Abstract: Microphthalmia transcription factor (MiT) family translocation renal cell carcinoma (tRCC) is a rare type of kidney cancer, which is not well characterized. Here we show the comprehensive proteogenomic analysis of tRCC tumors and normal adjacent tissues to elucidate the molecular landscape of this disease. Our study reveals that defective DNA repair plays an important role in tRCC carcinogenesis and progression. Metabolic processes are markedly dysregulated at both the mRNA and protein levels. Proteomic and phosphoproteome data identify mTOR signaling pathway as a potential therapeutic target. Moreover, molecular subtyping and immune infiltration analysis characterize the inter-tumoral heterogeneity of tRCC. Multi-omic integration reveals the dysregulation of cellular processes affected by genomic alterations, including oxidative phosphorylation, autophagy, transcription factor activity, and proteasome function. This study represents a comprehensive proteogenomic analysis of tRCC, providing valuable insights into its biological mechanisms, disease diagnosis, and prognostication.

Abstract: BACKGROUND: Associations of drugs with diseases provide important information for expediting drug development. Due to the number of known drug-disease associations is still insufficient, and considering that inferring associations between them through traditional in vitro experiments is time-consuming and costly. Therefore, more accurate and reliable computational methods urgent need to be developed to predict potential associations of drugs with diseases. METHODS: In this study, we present the model called weighted graph regularized collaborative non-negative matrix factorization for drug-disease association prediction (WNMFDDA). More specifically, we first calculated the drug similarity and disease similarity based on the chemical structures of drugs and medical description information of diseases, respectively. Then, to extend the model to work for new drugs and diseases, weighted [Formula: see text] nearest neighbor was used as a preprocessing step to reconstruct the interaction score profiles of drugs with diseases. Finally, a graph regularized non-negative matrix factorization model was used to identify potential associations between drug and disease. RESULTS: During the cross-validation process, WNMFDDA achieved the AUC values of 0.939 and 0.952 on Fdataset and Cdataset under ten-fold cross validation, respectively, which outperforms other competing prediction methods. Moreover, case studies for several drugs and diseases were carried out to further verify the predictive performance of WNMFDDA. As a result, 13(Doxorubicin), 13(Amiodarone), 12(Obesity) and 12(Asthma) of the top 15 corresponding candidate diseases or drugs were confirmed by existing databases. CONCLUSIONS: The experimental results adequately demonstrated that WNMFDDA is a very effective method for drug-disease association prediction. We believe that WNMFDDA is helpful for relevant biomedical researchers in follow-up studies.

Abstract: Ligand GA is introduced in this work and approaches the problem of finding small molecules inhibiting protein functions by using the protein site to find close to optimal or optimal small molecule binders. Genetic algorithms (GA) are an effective means for approximating or solving computationally hard mathematics problems with large search spaces such as this one. The algorithm is designed to include constraints on the generated molecules from ADME restriction, localization in a binding site, specified hydrogen bond requirements, toxicity prevention from multiple proteins, sub-structure restrictions, and database inclusion. This algorithm and work is in the context of computational modeling, ligand design and docking to protein sites.

Abstract: To analyze the molecular mechanism of Qinghao-Biejia (QH-BJ) drug pair in the treatment of systemic lupus erythematosus (SLE) based on the method of network pharmacology and molecular docking technology. The components and related targets of QH-BJ drug pair, as well as SLE-related targets, were obtained. Intersection targets of QH-BJ drug pair and SLE were screened to construct the protein-protein interaction network, conduct gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and establish the component-target-pathway network. The core active components and core targets of QH-BJ drug pair for the treatment of SLE were selected, and molecular docking was carried out between the ligand components and the receptor target proteins. The core active components of QH-BJ drug pair for the treatment of SLE are luteolin, quercetin, and kaempferol; the core targets are PTGS2, HSP90AA1, RELA, MAPK1, MAPK14, AKT1, JUN, TNF, TP53. The ligand components can spontaneously bind to the receptor target proteins. Besides, QH-BJ drug pair is likely to act on PI3K/Akt signal pathway, interleukin-17 signal pathway, and TNF signal pathway in the treatment of SLE. The study indicates that QH-BJ drug pair might play a role in the treatment of SLE through multi-components, multi-targets, and multi-pathways.

Abstract: Polycystic ovary syndrome (PCOS) is a common reproductive metabolic disorder, normally accompanied by insulin resistance (IR). The specific pathogenesis of this disease remains unclear. To identify the underlying pathogenesis of PCOS with IR and explore the potential efficacy and mechanism of Jiawei Huanglian-Wendan decoction (JHWD) by a network pharmacology approach. The effective components and the potential drug and disease-related targets are retrieved. Drug-disease overlapped targets are being obtained by Venny analysis. The construction of protein-protein interaction network relied on Search Tool for the Retrieval of Interacting Genes/Proteins database (STRING), after uploading drug-disease overlapped targets. The drug-component-target-disease interaction network map was displayed , after importing their data into Cytoscape 3.7.2 software. Bioinformatics analyses are being performed by Metascape and Kyoto Encyclopedia of Genes and Genomes databases, respectively. Further, molecular docking analysis was carried out using AutoDock software. Finally, the influence of JHWD is verified by means of traditional Chinese medicine syndrome score, the rate of resumption of normal menstrual cycles and regular ovulation, the blood lipid levels, the blood glucose and insulin levels, and the inflammatory cytokines in PCOS with IR patients. Four primary interaction networks of JHWD are constructed. The enrichment analysis of PCOS-IR-related targets demonstrated that the top enriched pathways in the development of PCOS with IR are pathways in cancer, metabolic, phosphoinositide-3-kinase-protein kinase B signaling, lipid and atherosclerosis, and mitogen-activated protein kinase signaling pathways. Molecular docking analysis revealed strong binding interactions of the key targets with the active components. Further confirmations showed that the active components of JHWD exhibited significant clinical efficacy in improving the clinical syndromes, menstrual cyclicity and ovulatory function, and significantly reducing the blood lipid levels, blood glucose and insulin levels, and inflammatory cytokines in PCOS with IR patients. The combination of the network pharmacological analysis and clinical validation stated that the active compounds in JHWD could regulate glycolipid metabolism, reduce IR, and exert anti-inflammatory effects in the treatment of PCOS with IR, promoting Chinese classical formulations.

Abstract: Undruggability of RAS proteins has necessitated alternative strategies for the development of effective inhibitors. In this respect, phosphorylation has recently come into prominence as this reversible post-translational modification attenuates sensitivity of RAS towards RAF. As such, in this study, we set out to unveil the impact of phosphorylation on dynamics of HRAS(WT) and aim to invoke similar behavior in HRAS(G12D) mutant by means of small therapeutic molecules. To this end, we performed molecular dynamics (MD) simulations using phosphorylated HRAS and showed that phosphorylation of Y32 distorted Switch I, hence the RAS/RAF interface. Consequently, we targeted Switch I in HRAS(G12D) by means of approved therapeutic molecules and showed that the ligands enabled detachment of Switch I from the nucleotide-binding pocket. Moreover, we demonstrated that displacement of Switch I from the nucleotide-binding pocket was energetically more favorable in the presence of the ligand. Importantly, we verified computational findings in vitro where HRAS(G12D)/RAF interaction was prevented by the ligand in HEK293T cells that expressed HRAS(G12D) mutant protein. Therefore, these findings suggest that targeting Switch I, hence making Y32 accessible might open up new avenues in future drug discovery strategies that target mutant RAS proteins.

Abstract: BACKGROUND: Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug-disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs. METHODS: In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug-drug similarities and disease-disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease-protein associations and drug-protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug-disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations. RESULTS: To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.

Abstract: Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter- or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next-generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi-omics strategies. We also describe intratumour dissection through clonal inference and single-cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice.

Abstract: The collection of increasing amounts of data in health care has become relevant for pain therapy and research. This poses problems for analyses with classical approaches, which is why artificial intelligence (AI) and machine learning (ML) methods are being included into pain research. The current literature on AI and ML in the context of pain research was automatically searched and manually curated. Common machine learning methods and pain settings covered were evaluated. Further focus was on the origin of the publication and technical details, such as the included sample sizes of the studies analyzed with ML. Machine learning was identified in 475 publications from 18 countries, with 79% of the studies published since 2019. Most addressed pain conditions included low back pain, musculoskeletal disorders, osteoarthritis, neuropathic pain, and inflammatory pain. Most used ML algorithms included random forests and support vector machines; however, deep learning was used when medical images were involved in the diagnosis of painful conditions. Cohort sizes ranged from 11 to 2,164,872, with a mode at n = 100; however, deep learning required larger data sets often only available from medical images. Artificial intelligence and ML, in particular, are increasingly being applied to pain-related data. This report presents application examples and highlights advantages and limitations, such as the ability to process complex data, sometimes, but not always, at the cost of big data requirements or black-box decisions.