Abstract: To date, drugs released into the aquatic environment are a real problem, and among antibiotics, sulfamethoxazole is the one most widely found in wastewater; thus, the evaluation of its toxicity on marine organisms is very important. This study, for the first time, investigates the in vitro effects of 4 concentrations of sulfamethoxazole (0.05 mg/L, 0.5 mg/L, 5 mg/L, 50 mg/L) on the fertilization and development of the sea urchin Arbacia lixula. The gametes were exposed to drugs in three different stages: simultaneously with, prior to, and post-fertilization. The results show a significant reduction in the percentage of fertilized oocytes at the highest drug concentrations. Moreover, an increase in anomalies and delays in embryo development following the treatment with the drug was demonstrated. Therefore, the data suggest that this antibiotic can alter the development of marine organisms, making it urgent to act to reduce their release and to determine the concentration range with the greatest impact.

Abstract: Despite the significant progress made towards comprehending the deregulated signatures in lung cancer, these vary from study to study. We reanalyzed 25 studies from the Gene Expression Omnibus (GEO) to detect and annotate co-deregulated signatures in lung cancer and in single-gene or single-drug perturbation experiments. We aimed to decipher the networks that these co-deregulated genes (co-DEGs) form along with their upstream regulators. Differential expression and upstream regulators were computed using Characteristic Direction and Systems Biology tools, including GEO2Enrichr and X2K. Co-deregulated gene expression profiles were further validated across different molecular and immune subtypes in lung adenocarcinoma (TCGA-LUAD) and lung adenocarcinoma (TCGA-LUSC) datasets, as well as using immunohistochemistry data from the Human Protein Atlas, before being subjected to subsequent GO and KEGG enrichment analysis. The functional alterations of the co-upregulated genes in lung cancer were mostly related to immune response regulating the cell surface signaling pathway, in contrast to the co-downregulated genes, which were related to S-nitrosylation. Networks of hub proteins across the co-DEGs consisted of overlapping TFs (SOX2, MYC, KAT2A) and kinases (MAPK14, CSNK2A1 and CDKs). Furthermore, using Connectivity Map we highlighted putative repurposing drugs, including valproic acid, betonicine and astemizole. Similarly, we analyzed the co-DEG signatures in single-gene and single-drug perturbation experiments in lung cancer cell lines. In summary, we identified critical co-DEGs in lung cancer providing an innovative framework for their potential use in developing personalized therapeutic strategies.

Abstract: The prevalence of multi_drug therapies has been increasing in recent years, particularly among the elderly who are suffering from several diseases. However, unexpected Drug_Drug interaction (DDI) can cause adverse reactions or critical toxicity, which puts patients in danger. As the need for multi_drug treatment increases, it's becoming increasingly necessary to discover DDIs. Nevertheless, DDIs detection in an extensive number of drug pairs, both in-vitro and in-vivo, is costly and laborious. Therefore, DDI identification is one of the most concerns in drug-related researches. In this paper, we propose GNN-DDI, a deep learning-based method for predicting DDI-associated events in two stages. In the first stage, we collect the drugs information from different sources and then integrate them through the formation of an attributed heterogeneous network and generate a drug embedding vector based on different drug interaction types and drug attributes. In the second stage, we aggregate the representation vectors then predictions of the DDIs and their events are performed through a deep multi-model framework. Various evaluation results show that the proposed method can outperform state-of-the methods in the prediction of drug-drug interaction-associated events. The experimental results indicate that producing the drug's representations based on different drug interaction types and attributes is efficient and effective and can better show the intrinsic characteristics of a drug.

Abstract: Combinatorial CRISPR screening is useful for investigating synthetic lethality (SL) gene pairs. Here, we detail the steps for dual-gRNA library construction, with the introduction of two backbones, LentiGuide_DKO and LentiCRISPR_DKO. We describe steps for in vitro screening with 22Rv1-Cas9 and SaOS2-Cas9 cells followed by sequencing and data analysis. By introducing two backbones, we optimized the library construction process, facilitated standard pair-end sequencing, and provided options of screening on cells with or without modification of Cas9 expression.

Abstract: Discovery of potential drugs requires rapid and precise identification of drug targets. Although traditional experimental methodologies can accurately identify drug targets, they are time-consuming and inappropriate for high-throughput screening. Computational approaches based on machine learning (ML) algorithms can expedite the prediction of druggable proteins; however, the performance of the existing computational methods remains unsatisfactory. This study proposes a computational tool, SPIDER, to enhance the accurate prediction of druggable proteins. SPIDER employs various feature descriptors pertaining to several aspects, including physicochemical properties, compositional information, and composition-transition-distribution information, coupled with well-known ML algorithms to facilitate the construction of the final meta-predictor. The experimental results showed that SPIDER enabled more precise and robust prediction of druggable proteins than the baseline models and current existing methods in terms of the independent test dataset. An online web server was established and made freely available online.

Abstract: MOTIVATION: While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. RESULTS: We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response. AVAILABILITY AND IMPLEMENTATION: DrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: Protein-protein interactions (PPIs) and protein-metabolite interactions play a key role in many biochemical processes, yet they are often viewed as being independent. However, the fact that small molecule drugs have been successful in inhibiting PPIs suggests a deeper relationship between protein pockets that bind small molecules and PPIs. We demonstrate that 2/3 of PPI interfaces, including antibody-epitope interfaces, contain at least one significant small molecule ligand binding pocket. In a representative library of 50 distinct protein-protein interactions involving hundreds of mutations, >75% of hot spot residues overlap with small molecule ligand binding pockets. Hence, ligand binding pockets play an essential role in PPIs. In representative cases, evolutionary unrelated monomers that are involved in different multimeric interactions yet share the same pocket are predicted to bind the same metabolites/drugs; these results are confirmed by examples in the PDB. Thus, the binding of a metabolite can shift the equilibrium between monomers and multimers. This implicit coupling of PPI equilibria, termed "metabolic entanglement", was successfully employed to suggest novel functional relationships among protein multimers that do not directly interact. Thus, the current work provides an approach to unify metabolomics and protein interactomics.

Abstract: This work explores the study of a synthesized nickel complex as a possible inhibitor against the main protease (Mpro) of the recent emerging coronavirus disease (COVID-19). Overall, the template reaction of 3-acetyl-2-hydroxy-6-methyl-4H-pyran-4-one with nickel(II) chloride hexahydrate in N,N-dimethylformamide (DMF) medium leads to the formation of neutral nickel complex. This resulting complex is formulated as [Ni(DHA)2(DMF)2] on the basis of FT-IR, UV-Vis., single-crystal X-ray diffraction analysis, magnetic susceptibility and CV measurements as well as DFT quantum chemical calculations. Its single crystal suggests was found to be surrounded by the both pairs of molecules of DHA and DMF through six oxygen atoms with octahedral coordination sphere. The obtained magnetic susceptibilities are positive and agree with its paramagnetic state. In addition to the experimental investigations, optimized geometry, spectroscopic and electronic properties were also performed using DFT calculation with B3LYP/6-31G(d,p) level of theory. The nonlinear optical (NLO) properties of this complex are again examined. Some suitable quantum descriptors (EHOMO, ELUMO, Energy gap, Global hardness), Milliken atomic charge, Electrophilic potion and Molecular Electrostatic Potential) have been elegantly described. Molecular docking results demonstrated that the docked nickel complex displayed remarkable binding energy with Mpro. Besides, important molecular properties and ADME pharmacokinetic profiles of possible Mpro inhibitors were assessed by in silico prediction.

Abstract: Allosteric mechanisms are pervasive in nature, but human-designed allosteric perturbagens are rare. The history of KRAS(G12C) inhibitor development suggests that covalent chemistry may be a key to expanding the armamentarium of allosteric inhibitors. In that effort, irreversible targeting of a cysteine converted a non-deal allosteric binding pocket and low affinity ligands into a tractable drugging strategy. Here we examine the feasibility of expanding this approach to other allosteric pockets of RAS and kinase family members, given that both protein families are regulators of vital cellular processes that are often dysregulated in cancer and other human diseases. Moreover, these heavily studied families are the subject of numerous drug development campaigns that have resulted, sometimes serendipitously, in the discovery of allosteric inhibitors. We consequently conducted a comprehensive search for cysteines, a commonly targeted amino acid for covalent drugs, using AlphaFold-generated structures of those families. This new analysis presents potential opportunities for allosteric targeting of validated and understudied drug targets, with an emphasis on cancer therapy.

Abstract: The emergence of drug-resistant mycobacteria, including Mycobacterium tuberculosis (Mtb) and non-tuberculous mycobacteria (NTM), poses an increasing global threat that urgently demands the development of new potent anti-mycobacterial drugs. One of the approaches toward the identification of new drugs is fragment-based drug discovery (FBDD), which is the most ingenious among other drug discovery models, such as structure-based drug design (SBDD) and high-throughput screening. Specialized techniques, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and many others, are part of the drug discovery approach to combat the Mtb and NTM global menaces. Moreover, the primary drawbacks of traditional methods, such as the limited measurement of biomolecular toxicity and uncertain bioavailability evaluation, are successfully overcome by the FBDD approach. The current review focuses on the recognition of fragment-based drug discovery as a popular approach using virtual, computational, and biophysical methods to identify potent fragment molecules. FBDD focuses on designing optimal inhibitors against potential therapeutic targets of NTM and Mtb (PurC, ArgB, MmpL3, and TrmD). Additionally, we have elaborated on the challenges associated with the FBDD approach in the identification and development of novel compounds. Insights into the applications and overcoming the challenges of FBDD approaches will aid in the identification of potential therapeutic compounds to treat drug-sensitive and drug-resistant NTMs and Mtb infections.