Target identification and mechanism of action in chemical biology and drug discovery

NFX1-123: A potential therapeutic target in cervical cancer

NFX1-123 is a splice variant isoform of the NFX1 gene. It is highly expressed in cervical cancers caused by HPV, and NFX1-123 is a protein partner with the HPV oncoprotein E6. Together, NFX1-123 and E6 affect cellular growth, longevity, and differentiation. The expression status of NFX1-123 in cancers beyond cervical and head and neck cancers, and its potential as therapeutic target, have not been investigated. TSVdb of TCGA was used to quantify NFX1-123 expression in 24 cancers compared with normal tissues. The NFX1-123 protein structure was predicted and then submitted to retrieve suitable drug molecules. The top four compounds, found to bind in silico to NFX1-123, were tested experimentally to determine their effects on NFX1-123-related cellular growth, survival, and migration. 46% of cancers (11 of 24 had significant differences in NFX1-123 expression, with nine having had greater NFX1-123 expression, when compared with adjacent normal tissues. Bioinformatics and proteomic predictive analysis modeled the three-dimensional structure of NFX1-123, and drug libraries were screened for high-binding affinity compounds using this modeled structure. Seventeen drugs with binding energies ranging from -1.3 to -10 Kcal/mol were identified. The top four compounds were used to treat HPV- and HPV+ cervical cancer cell lines, three of which (Ropitoin, R428 and Ketoconazole) reduced NFX1-123 protein levels, inhibited cellular growth, survival, and migration, and enhanced the cytotoxicity of Cisplatin. These findings highlight cancers expressing high levels of NFX1-123, and drugs that target it, may reduce cellular growth, survival, and migration, making NFX1-123 a potential novel therapeutic target.

Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models

Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-driven cancer pharmacology. However, owing to the highly correlated and high-dimensional nature of transcriptomic data, naively applying current explainable machine-learning strategies to large transcriptomic datasets leads to suboptimal outcomes. Here by using feature attribution methods, we show that the quality of the explanations can be increased by leveraging ensembles of explainable machine-learning models. We applied the approach to a dataset of 133 combinations of 46 anticancer drugs tested in ex vivo tumour samples from 285 patients with acute myeloid leukaemia and uncovered a haematopoietic-differentiation signature underlying drug combinations with therapeutic synergy. Ensembles of machine-learning models trained to predict drug combination synergies on the basis of gene-expression data may improve the feature attribution quality of complex machine-learning models.

Next Generation Gold Drugs and Probes: Chemistry and Biomedical Applications

The gold drugs, gold sodium thiomalate (Myocrisin), aurothioglucose (Solganal), and the orally administered auranofin (Ridaura), are utilized in modern medicine for the treatment of inflammatory arthritis including rheumatoid and juvenile arthritis; however, new gold agents have been slow to enter the clinic. Repurposing of auranofin in different disease indications such as cancer, parasitic, and microbial infections in the clinic has provided impetus for the development of new gold complexes for biomedical applications based on unique mechanistic insights differentiated from auranofin. Various chemical methods for the preparation of physiologically stable gold complexes and associated mechanisms have been explored in biomedicine such as therapeutics or chemical probes. In this Review, we discuss the chemistry of next generation gold drugs, which encompasses oxidation states, geometry, ligands, coordination, and organometallic compounds for infectious diseases, cancer, inflammation, and as tools for chemical biology via gold-protein interactions. We will focus on the development of gold agents in biomedicine within the past decade. The Review provides readers with an accessible overview of the utility, development, and mechanism of action of gold-based small molecules to establish context and basis for the thriving resurgence of gold in medicine.

CETSA and thermal proteome profiling strategies for target identification and drug discovery of natural products

BACKGROUND

Monitoring target engagement at various stages of drug development is essential for natural product (NP)-based drug discovery and development. The cellular thermal shift assay (CETSA) developed in 2013 is a novel, broadly applicable, label-free biophysical assay based on the principle of ligand-induced thermal stabilization of target proteins, which enables direct assessment of drug-target engagement in physiologically relevant contexts, including intact cells, cell lysates and tissues. This review aims to provide an overview of the work principles of CETSA and its derivative strategies and their recent progress in protein target validation, target identification and drug lead discovery of NPs.

METHODS

A literature-based survey was conducted using the Web of Science and PubMed databases. The required information was reviewed and discussed to highlight the important role of CETSA-derived strategies in NP studies.

RESULTS

After nearly ten years of upgrading and evolution, CETSA has been mainly developed into three formats: classic Western blotting (WB)-CETSA for target validation, thermal proteome profiling (TPP, also known as MS-CETSA) for unbiased proteome-wide target identification, and high-throughput (HT)-CETSA for drug hit discovery and lead optimization. Importantly, the application possibilities of a variety of TPP approaches for the target discovery of bioactive NPs are highlighted and discussed, including TPP-temperature range (TPP-TR), TPP-compound concentration range (TPP-CCR), two-dimensional TPP (2D-TPP), cell surface-TPP (CS-TPP), simplified TPP (STPP), thermal stability shift-based fluorescence difference in 2D gel electrophoresis (TS-FITGE) and precipitate supported TPP (PSTPP). In addition, the key advantages, limitations and future outlook of CETSA strategies for NP studies are discussed.

CONCLUSION

The accumulation of CETSA-based data can significantly accelerate the elucidation of the mechanism of action and drug lead discovery of NPs, and provide strong evidence for NP treatment against certain diseases. The CETSA strategy will certainly bring a great return far beyond the initial investment and open up more possibilities for future NP-based drug research and development.

The critical role of mode of action studies in kinetoplastid drug discovery

Understanding the target and mode of action of compounds identified by phenotypic screening can greatly facilitate the process of drug discovery and development. Here, we outline the tools currently available for target identification against the neglected tropical diseases, human African trypanosomiasis, visceral leishmaniasis and Chagas' disease. We provide examples how these tools can be used to identify and triage undesirable mechanisms, to identify potential toxic liabilities in patients and to manage a balanced portfolio of target-based campaigns. We review the primary targets of drugs that are currently in clinical development that were initially identified via phenotypic screening, and whose modes of action affect protein turnover, RNA trans-splicing or signalling in these protozoan parasites.

A multilevel screening pipeline in zebrafish identifies therapeutic drugs for GAN

Giant axonal neuropathy (GAN) is a fatal neurodegenerative disorder for which there is currently no treatment. Affecting the nervous system, GAN starts in infancy with motor deficits that rapidly evolve toward total loss of ambulation. Using the gan zebrafish model that reproduces the loss of motility as seen in patients, we conducted the first pharmacological screening for the GAN pathology. Here, we established a multilevel pipeline to identify small molecules restoring both the physiological and the cellular deficits in GAN. We combined behavioral, in silico, and high-content imaging analyses to refine our Hits to five drugs restoring locomotion, axonal outgrowth, and stabilizing neuromuscular junctions in the gan zebrafish. The postsynaptic nature of the drug's cellular targets provides direct evidence for the pivotal role the neuromuscular junction holds in the restoration of motility. Our results identify the first drug candidates that can now be integrated in a repositioning approach to fasten therapy for the GAN disease. Moreover, we anticipate both our methodological development and the identified hits to be of benefit to other neuromuscular diseases.

High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery

Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial activity testing has also proven to be challenging. In addition to exploring new sources of natural products and synthetic biology, omics technology helped to study the biosynthetic machinery of existing natural sources enabling the construction of unnatural synthesizers of bioactive molecules and the identification of molecular targets of antibacterial agents. On the other hand, newer and smarter strategies have been continuously pursued to screen synthetic molecule libraries for new antibiotics and new druggable targets. Biomimetic conditions are explored to mimic the real infection model to better study the ligand-target interaction to enable the designing of more effective antibacterial drugs. This narrative review describes various traditional and contemporaneous approaches of high-throughput screening of natural products and synthetic molecule libraries for antibacterial drug discovery. It further discusses critical factors for HTS assay design, makes a general recommendation, and discusses possible alternatives to traditional HTS of natural products and synthetic molecule libraries for antibacterial drug discovery.

Targeting the PEDV 3CL protease for identification of small molecule inhibitors: an insight from virtual screening, ADMET prediction, molecular dynamics, free energy landscape, and binding energy calculations

BACKGROUND

The porcine epidemic diarrhea virus (PEDV) represents a major health issue for piglets worldwide and does significant damage to the pork industry. Thus, new therapeutic approaches are urgently needed to manage PEDV infections. Due to the current lack of a reliable remedy, this present study aims to identify novel compounds that inhibit the 3CL protease of the virus involved in replication and pathogenesis.

RESULTS

To identify potent antiviral compounds against the 3CL protease, a virtual screening of natural compounds (n = 97,999) was conducted. The top 10 compounds were selected based on the lowest binding energy and the protein-ligand interaction analyzed. Further, the top five compounds that demonstrated a strong binding affinity were subjected to drug-likeness analysis using the ADMET prediction, which was followed by molecular dynamics simulations (500 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method. Based on these parameters, four putative lead (ZINC38167083, ZINC09517223, ZINC04339983, and ZINC09517238) compounds were identified that represent potentially effective inhibitors of the 3CL protease.

CONCLUSION

Therefore, these can be utilized for the development of novel antiviral drugs against PEDV. However, this requires further validation through in vitro and in vivo studies.

Molecular basis and mechanism of action of Albizia julibrissin in depression treatment and clinical application of its formulae

Albizzia julibrissin is empirically used as an antidepressant in clinical practice. Preclinical studies have indicated that its total extracts or bioactive constituents exerted antidepressant-like responses in animal models, providing the molecular basis to reveal its underlying mechanism of action. While attempts have been made to understand the antidepressant effect of A. julibrissin, many fundamental questions regarding its mechanism of action remain to be addressed at the molecular and systems levels. In this review, we conclusively discussed the mechanism of action of A. julibrissin and A. julibrissin formulae by reviewing recent preclinical and clinical studies conducted by using depressive animal models and depressive patients. Several representative bioactive constituents and formulae were highlighted as examples, and their mechanisms of action were discussed. In addition, some representative A. julibrissin formulae that have been shown to be compatible with conventional antidepressants in clinical practice were also reviewed. Furthermore, we discussed the future research directions to reveal the underlying mechanism of A. julibrissin at the molecular and systems levels in depression treatment. The integrated study using both the molecular and systematic approaches is required not only for improving our understanding of its molecular basis and mechanisms of action, but also for providing a way to discover novel agents or approaches for the effective and systematic treatment of depression.

Proteome-Wide Fragment-Based Ligand and Target Discovery

Chemical probes are invaluable tools to investigate biological processes and can serve as lead molecules for the development of new therapies. However, despite their utility, only a fraction of human proteins have selective chemical probes, and more generally, our knowledge of the "chemically-tractable" proteome is limited, leaving many potential therapeutic targets unexploited. To help address these challenges, powerful chemical proteomic approaches have recently been developed to globally survey the ability of proteins to bind small molecules (i. e., ligandability) directly in native systems. In this review, we discuss the utility of such approaches, with a focus on the integration of chemoproteomic methods with fragment-based ligand discovery (FBLD), to facilitate the broad mapping of the ligandable proteome while also providing starting points for progression into lead chemical probes.

Pipeline for Characterizing Alternative Mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity

The cells of colorectal cancer (CRC) in their microenvironment experience constant stress, leading to dysregulated activity in the tumor niche. As a result, cancer cells acquire alternative pathways in response to the changing microenvironment, posing significant challenges for the design of effective cancer treatment strategies. While computational studies on high-throughput omics data have advanced our understanding of CRC subtypes, characterizing the heterogeneity of this disease remains remarkably complex. Here, we present a novel computational Pipeline for Characterizing Alternative Mechanisms (PCAM) based on biclustering to gain a more detailed understanding of cancer heterogeneity. Our application of PCAM to large-scale CRC transcriptomics datasets suggests that PCAM can generate a wealth of information leading to new biological understanding and predictive markers of alternative mechanisms. Our key findings include: 1) A comprehensive collection of alternative pathways in CRC, associated with biological and clinical factors. 2) Full annotation of detected alternative mechanisms, including their enrichment in known pathways and associations with various clinical outcomes. 3) A mechanistic relationship between known clinical subtypes and outcomes on a consensus map, visualized by the presence of alternative mechanisms. 4) Several potential novel alternative drug resistance mechanisms for Oxaliplatin, 5-Fluorouracil, and FOLFOX, some of which were validated on independent datasets. We believe that gaining a deeper understanding of alternative mechanisms is a critical step towards characterizing the heterogeneity of CRC. The hypotheses generated by PCAM, along with the comprehensive collection of biologically and clinically associated alternative pathways in CRC, could provide valuable insights into the underlying mechanisms driving cancer progression and drug resistance, which could aid in the development of more effective cancer therapies and guide experimental design towards more targeted and personalized treatment strategies. The computational pipeline of PCAM is available in GitHub (https://github.com/changwn/BC-CRC).

Base-Exchange Enabling the Visualization of SARM1 Activities in Sciatic Nerve-Injured Mice

Enzymes are important in homeostasis in living organisms. Since abnormal enzyme activities are highly associated with many human diseases, detection of in vivo activities of a specific enzyme is important to study the pathology of the related diseases. In this work, we have designed and synthesized a series of new small-molecule-activatable fluorescent probes for the imaging of Sterile Alpha and TIR Motif-containing 1 (SARM1) activities based on its transglycosidase activities (base-exchange reactions of NAD⁺). Probe 1a was found to undergo base-exchange reactions with NAD⁺ in the presence of activated SARM1 but not CD38 nor NADase and formed a highly emissive product AD-1a [about a 100-fold fluorescence enhancement in 20 min with a 150 nm (5665 cm^-1) Stokes shift and a 100 nm (3812 cm^-1) red shift]. This probe exhibited a higher reactivity and sensitivity than those commonly used for SARM1 imaging. The utilities of 1a have also been demonstrated in live-cell imaging and detection of in vivo activities of SARM1 in a sciatic nerve injury mouse model.

Chemical Biology Approaches Confirm MCT4 as the Therapeutic Target of a Cellular Optimized Hit

Lactic acid transport is a key process maintaining glycolytic flux in tumors. Inhibition of this process will result in glycolytic shutdown, impacting on cell growth and survival and thus has been pursued as a therapeutic approach for cancers. Using a cell-based screen in a MCT4-dependent cell line, we identified and optimized compounds for their ability to inhibit the efflux of intracellular lactic acid with good physical and pharmacokinetic properties. To deconvolute the mechanism of lactic acid efflux inhibition, we have developed three assays to measure cellular target engagement. Specifically, we synthesized a biologically active photoaffinity probe (IC₅₀ < 10 nM), and using this probe, we demonstrated selective engagement of MCT4 of our parent molecule through a combination of confocal microscopy and in-cell chemoproteomics. As an orthogonal assay, the cellular thermal shift assay (CETSA) confirmed binding to MCT4 in the cellular system. Comparisons of lactic acid efflux potencies in cells with differential expression of MCT family members further confirmed that the optimized compounds inhibit the efflux of lactic acid through the inhibition of MCT4. Taken together, these data demonstrate the power of orthogonal chemical biology methods to determine cellular target engagement, particularly for proteins not readily amenable to traditional biophysical methods.

Computational Prediction of the Phenotypic Effect of Flavonoids on Adiponectin Biosynthesis

In silico machine learning applications for phenotype-based screening have primarily been limited due to the lack of machine-readable data related to disease phenotypes. Adiponectin, a nuclear receptor (NR)-regulated adipocytokine, is relatively downregulated in human metabolic diseases. Here, we present a machine-learning model to predict the adiponectin-secretion-promoting activity of flavonoid-associated phytochemicals (FAPs). We modeled a structure-activity relationship between the chemical similarity of FAPs and their bioactivities using a random forest-based classifier, which provided the NR activity of each FAP as a probability. To link the classifier-predicted NR activity to the phenotype, we next designed a single-cell transcriptomics-based multiple linear regression model to generate the relative adiponectin score (RAS) of FAPs. In experimental validation, estimated RAS values of FAPs isolated from Scutellaria baicalensis exhibited a significant correlation with their adiponectin-secretion-promoting activity. The combined cheminformatics and bioinformatics approach enables the computational reconstruction of phenotype-based screening systems.

Activity-based CRISPR scanning uncovers allostery in DNA methylation maintenance machinery

Allostery enables dynamic control of protein function. A paradigmatic example is the tightly orchestrated process of DNA methylation maintenance. Despite the fundamental importance of allosteric sites, their identification remains highly challenging. Here, we perform CRISPR scanning on the essential maintenance methylation machinery-DNMT1 and its partner UHRF1-with the activity-based inhibitor decitabine to uncover allosteric mechanisms regulating DNMT1. In contrast to non-covalent DNMT1 inhibition, activity-based selection implicates numerous regions outside the catalytic domain in DNMT1 function. Through computational analyses, we identify putative mutational hotspots in DNMT1 distal from the active site that encompass mutations spanning a multi-domain autoinhibitory interface and the uncharacterized BAH2 domain. We biochemically characterize these mutations as gain-of-function, exhibiting increased DNMT1 activity. Extrapolating our analysis to UHRF1, we discern putative gain-of-function mutations in multiple domains, including key residues across the autoinhibitory TTD-PBR interface. Collectively, our study highlights the utility of activity-based CRISPR scanning for nominating candidate allosteric sites, and more broadly, introduces new analytical tools that further refine the CRISPR scanning framework.

Stereodivergent Desymmetrization of Phenols En Route to Modular Access to Densely Functionalized Quinazoline and Oxazine Scaffolds

The de novo assembly of stereochemically and skeletally diverse scaffolds is a powerful tool for the discovery of novel chemotypes. Hence, the development of modular, step- and atom-economic synthetic methods to access stereochemically and skeletally diverse compound collection is particularly important. Herein, we show a metal-free, stereodivergent build/couple/pair strategy that allows access to a unique collection of benzo[5,6][1,4]oxazino[4,3-a]quinazoline, quinolino[1,2-a]quinazoline and benzo[b]benzo [4,5]imidazo[1,2-d][1,4]oxazine scaffolds with complete diastereocontrol and wide distribution of molecular architectures. This metal-free process proceeds via desymmetrization of phenol derivatives. The cascade unites Mannich with aza-Michael addition reactions, providing expeditious entries to diverse classes of molecular shapes in a single operation.

System-wide health risk prediction for 4-methyl-2,4-bis(4-hydroxyphenyl)pent-1-ene(MBP), a major active metabolite of environmental pollutant and food contaminant - Bisphenol A

Human exposure to plastic contaminated foods and environmental micro/nano plastic derived chemicals necessitates system-wide health risk assessment. Hence, current study intend to explore the mode of action (MoA) based adverse outcome pathways of 4-methyl-2,4-bis(4-hydroxyphenyl)pent-1-ene (MBP), the major active metabolite of bisphenol A (BPA). The computational study employed broad range of target prediction, systems biology tools and molecular docking protocols. Further, validation of MBP targets was done using protein-ligand fluorescence quenching assay, endothelial cell culture and chicken embryo vascular angiogenesis models. Interestingly, the current results illustrate that various physiological signaling pathways (MAPK and VEGF related angiogenesis signaling) and disease progression pathways (hypertension, cancer and endocrine disorders) were enriched as potential targets of MBP. Further, docking studies highlights the possible binding mechanism of MBP with important targets including endothelial nitric oxide synthase (eNOS) and serum albumin (BSA). In addition, the validation studies on MBP-BSA interaction (fluorescence quenching), eNOS derived nitric oxide (NOx) generation in endothelial cells and chicken embryo angiogenesis support the system-wide impacts of MBP with highlights on cardiovascular pathogenesis. Thus, the current observation provides novel insights into the system wide impacts of MBP for the futuristic health risk assessment of plastic derived chemicals.

Nanotechnology Involved in Treating Urinary Tract Infections: An Overview

Considered as the most frequent contaminations that do not require hospitalization, urinary tract infections (UTIs) are largely known to cause significant personal burdens on patients. Although UTIs overall are highly preventable health issues, the recourse to antibiotics as drug treatments for these infections is a worryingly spread approach that should be addressed and gradually overcome in a contemporary, modernized healthcare system. With a virtually alarming global rise of antibiotic resistance overall, nanotechnologies may prove to be the much-needed 'lifebuoy' that will eventually suppress this prejudicial phenomenon. This review aims to present the most promising, currently known nano-solutions, with glimpses on clinical and epidemiological aspects of the UTIs, prospective diagnostic instruments, and non-antibiotic treatments, all of these engulfed in a comprehensive overview.

DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms

Understanding the mechanisms of candidate drugs play an important role in drug discovery. The activating/inhibiting mechanisms between drugs and targets are major types of mechanisms of drugs. Owing to the complexity of drug-target (DT) mechanisms and data scarcity, modelling this problem based on deep learning methods to accurately predict DT activating/inhibiting mechanisms remains a considerable challenge. Here, by considering network pharmacology, we propose a multi-view deep learning model, DrugAI, which combines four modules, i.e. a graph neural network for drugs, a convolutional neural network for targets, a network embedding module for drugs and targets and a deep neural network for predicting activating/inhibiting mechanisms between drugs and targets. Computational experiments show that DrugAI performs better than state-of-the-art methods and has good robustness and generalization. To demonstrate the reliability of the predictive results of DrugAI, bioassay experiments are conducted to validate two drugs (notopterol and alpha-asarone) predicted to activate TRPV1. Moreover, external validation bears out 61 pairs of mechanism relationships between natural products and their targets predicted by DrugAI based on independent literatures and PubChem bioassays. DrugAI, for the first time, provides a powerful multi-view deep learning framework for robust prediction of DT activating/inhibiting mechanisms.

Deep learning in image-based phenotypic drug discovery

Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal 'hit' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD).

How to Design Peptides

Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.

Roles of anoikis in colorectal cancer therapy and the assessment of anoikis-regulatory molecules as therapeutic targets

Colorectal cancer (CRC) is a deadly malignancy and therapeutic approaches for CRC are evolving every day. Anoikis is a key mechanism for programmed cell death of cancer cells that undergo anchorage-independent growth at a different matrix than the one which is expected. Yet, anoikis is a less studied mechanism of cell death in comparison to other mechanisms such as apoptosis. Relating to this, resistance to anoikis among cancer cells remains critical for improved metastasis and survival in a new environment evading anoikis. Since CRC cells have the ability to metastasize from proximal sites to secondary organs such as liver and promote cancer in those distant sites, a clear knowledge of the mechanisms essential for anchorage-independent growth and subsequent metastasis is necessary to counteract CRC progression and spread. Therefore, the identification of novel drug candidates and studying the roles of anoikis in assisting CRC therapy using such drugs can prevent anchorage-independent cancer cell growth. Additionally, the identification of novel biomarkers or therapeutic targets seems essential for implementing superior therapy, impeding relapse among malignant cells and improving the survival rate of clinical patients. As there are no reviews published on this topic till date, anoikis as a mechanism of cell death and its therapeutic roles in CRC are discussed in this review. In addition, several molecules were identified as therapeutic targets for CRC.

Ebselen suitably interacts with the potential SARS-CoV-2 targets: an in-silico approach

Ebselen (SPI-1005) is an active selenoorganic compound that can be found potential inhibitory activity against different types of viral infections such as zika virus, influenza A virus, HCV, and HIV-1; and also be found to exhibit promising antiviral activity against SARS-CoV-2 in cell-based assays but its particular target action against specific non-structural and structural proteins of SARS-CoV-2 is unclear to date. The purpose of this study is to evaluate the anti-SARS-CoV-2 efficacy of Ebselen along with the determination of the specific target among the 12 most common target proteins of SARS-CoV-2. AutoDock Vina in PyRx platform was used for docking analysis against the 12 selected SARS-CoV-2 encoded drug targets. ADME profiling was examined by using SwissADME online server. The stability of binding mode in the target active sites was evaluated using molecular dynamics (MD) simulation studies through NAMD and Desmond package software application. In this docking study, we recognized that Ebselen possesses the highest affinity to N protein (C domain) (PDB ID: 6YUN) and PLpro (PDB ID: 6WUU) among the selected SARS-CoV-2 targets showing -7.4 kcal/mol binding energy. The stability of Ebselen-6YUN and Ebselen-6WUU was determined by a 100 ns trajectory of all-atom molecular dynamics simulation. Structural conformation of these two complexes displayed stable root mean square deviation (RMSD), while root mean square fluctuations (RMSF) were also found to be consistent. This molecular docking study may propose the efficiency of Ebselen against SARS-CoV-2 to a significant extent which makes it a candidature of COVID-19 treatment.

Discovery of Pan-peroxisome Proliferator-Activated Receptor Modulators from an Endolichenic Fungus, Daldinia childiae

Adiponectin-synthesis-promoting compounds possess therapeutic potential to treat diverse metabolic diseases, including obesity and diabetes. Phenotypic screening to find adiponectin-synthesis-promoting compounds was performed using the adipogenesis model of human bone marrow mesenchymal stem cells. The extract of the endolichenic fungus Daldinia childiae 047215 significantly promoted adiponectin production. Bioactivity-guided isolation led to 13 active polyketides (1-13), which include naphthol monomers, dimers, and trimers. To the best of our knowledge, trimers of naphthol (1-4) have not been previously isolated as either natural or synthetic products. The novel naphthol trimer 3,1',3',3″-ternaphthalene-5,5',5″-trimethoxy-4,4',4″-triol (2) and a dimer, nodulisporin A (12), exhibited concentration-dependent adiponectin-synthesis-promoting activity (EC₅₀ 30.8 and 15.2 μM, respectively). Compounds 2 and 12 bound to all three peroxisome proliferator-activated receptor (PPAR) subtypes, PPARα, PPARγ, and PPARδ. In addition, compound 2 transactivated retinoid X receptor α, whereas 12 did not. Naphthol oligomers 2 and 12 represent novel pan-PPAR modulators and are potential pharmacophores for designing new therapeutic agents against hypoadiponectinemia-associated metabolic diseases.

Radotinib attenuates TGFβ -mediated pulmonary fibrosis in vitro and in vivo: exploring the potential of drug repurposing

BACKGROUND

Tyrosine kinase (TK) plays a crucial role in the pathogenesis of idiopathic pulmonary fibrosis. Here, we aimed to investigate whether radotinib (Rb) could inhibit pulmonary fibrosis by inhibiting TK in vitro and in vivo.

METHODS

The antifibrotic effects of Rb in transforming growth factor-β (TGF-β)1-stimulated A549 cells were determined using real-time polymerase chain reaction, western blotting, and immunocytochemistry assays. Rb inhibition of bleomycin-induced lung fibrosis in Sprague Dawley (SD) rats was determined by histopathological and​ immunohistochemical analyses. Rb-interfering metabolites were analyzed using LC-MS/MS.

RESULTS

Rb concentrations of up to 1000 nM did not affect the viability of A549 cells, but Rb (30 nM) significantly reduced expression of TGF-β1 (10 ng/mL)-induced ECM factors, such as Snail, Twist, and F-actin. Rb also regulated TGF-β1-overexpressed signal cascades, such as fibronectin and α-smooth muscle actin. Furthermore, Rb attenuated the phosphorylation of Smad2 and phosphorylation of kinases, such as, extracellular signal-regulated kinase, and protein kinase B. In the inhibitory test against bleomycin (5 mg/kg)-induced lung fibrosis, the Rb (30 mg/kg/daily)-treated group showed a half-pulmonary fibrosis region compared to the positive control group. In addition, Rb significantly reduced collagen type I and fibronectin expression in the bleomycin-induced fibrotic region of SD rats. Further, the identified metabolite pantothenic acid was not altered by Rb.

CONCLUSION

Taken together, these results indicate that Rb inhibits TGF-β1-induced pulmonary fibrosis both in vitro and in vivo. These findings suggest that Rb may be an effective treatment for pulmonary fibrosis-related disorders and idiopathic pulmonary fibrosis.

High-throughput functional annotation of natural products by integrated activity profiling

Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the gene expression platform Functional Signature Ontology, and pairs these data with untargeted metabolomics analysis for de novo bioactive compound discovery. The predictive value of the integrative approach was assessed using a library of target-annotated small molecules as benchmarks. Using Kolmogorov-Smirnov (KS) tests to compare in-class to out-of-class similarity, we found that SNF retains the ability to identify significant in-class similarity across a diverse set of target classes, and could find target classes not detectable in either platform alone. This confirmed that integration of expression-based and image-based phenotypes can accurately report on MOA. Furthermore, we integrated untargeted metabolomics of complex natural product fractions with the SNF network to map biological signatures to specific metabolites. Three examples are presented where SNF coupled with metabolomics was used to directly functionally characterize natural products and accelerate identification of bioactive metabolites, including the discovery of the azoxy-containing biaryl compounds parkamycins A and B. Our results support SNF integration of multiple phenotypic screening approaches along with untargeted metabolomics as a powerful approach for advancing natural products drug discovery.

CRISPR-Suppressor Scanning for Systematic Discovery of Drug-Resistance Mutations

CRISPR-Cas9 genome editing technologies have enabled complex genetic manipulations in situ, including large-scale, pooled screening approaches to probe and uncover mechanistic insights across various biological processes. The RNA-programmable nature of CRISPR-Cas9 greatly empowers tiling mutagenesis approaches to elucidate molecular details of protein function, in particular the interrogation of mechanisms of resistance to small molecules, an approach termed CRISPR-suppressor scanning. In a typical CRISPR-suppressor scanning experiment, a pooled library of single-guide RNAs is designed to target across the coding sequence(s) of one or more genes, enabling the Cas9 nuclease to systematically mutate the targeted proteins and generate large numbers of diverse protein variants in situ. This cellular pool of protein variants is then challenged with drug treatment to identify mutations conferring a fitness advantage. Drug-resistance mutations identified with this approach can not only elucidate drug mechanism of action but also reveal deeper mechanistic insights into protein structure-function relationships. In this article, we outline the framework for a standard CRISPR-suppressor scanning experiment. Specifically, we provide instructions for the design and construction of a pooled sgRNA library, execution of a CRISPR-suppressor scanning screen, and basic computational analysis of the resulting data. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Design and generation of a pooled sgRNA library Support Protocol 1: sgRNA library design using command-line CRISPOR Support Protocol 2: Production and titering of pooled sgRNA library lentivirus Basic Protocol 2: Execution and analysis of a CRISPR-suppressor scanning experiment.

Efforts in Bioprospecting Research: A Survey of Novel Anticancer Phytochemicals Reported in the Last Decade

Bioprospecting natural products to find prominent agents for medical application is an area of scientific endeavor that has produced many clinically used bioactive compounds, including anticancer agents. These compounds come from plants, microorganisms, and marine life. They are so-called secondary metabolites that are important for a species to survive in the hostile environment of its respective ecosystem. The kingdom of Plantae has been an important source of traditional medicine in the past and is also enormously used today as an exquisite reservoir for detecting novel bioactive compounds that are potent against hard-to-treat maladies such as cancer. Cancer therapies, especially chemotherapies, are fraught with many factors that are difficult to manage, such as drug resistance, adverse side effects, less selectivity, complexity, etc. Here, we report the results of an exploration of the databases of PubMed, Science Direct, and Google Scholar for bioactive anticancer phytochemicals published between 2010 and 2020. Our report is restricted to new compounds with strong-to-moderate bioactivity potential for which mass spectroscopic structural data are available. Each of the phytochemicals reported in this review was assigned to chemical classes with peculiar anticancer properties. In our survey, we found anticancer phytochemicals that are reported to have selective toxicity against cancer cells, to sensitize MDR cancer cells, and to have multitarget effects in several signaling pathways. Surprisingly, many of these compounds have limited follow-up studies. Detailed investigations into the synthesis of more functional derivatives, chemical genetics, and the clinical relevance of these compounds are required to achieve safer chemotherapy.

The Regulatory Network of Gastric Cancer Pathogenesis and Its Potential Therapeutic Active Ingredients of Traditional Chinese Medicine Based on Bioinformatics, Molecular Docking, and Molecular Dynamics Simulation

Objective. This study aims to investigate the functional gene network in gastric carcinogenesis by using bioinformatics; besides, the diagnostic utility of key genes and potential active ingredients of traditional Chinese medicine (TCM) for treatment in gastric cancer have been explored. Methods. The Cancer Genome Atlas and Gene Expression Omnibus databases have been applied to analyze the differentially expressed genes (DEGs) between gastric cancer and normal gastric tissues. Then, the DEGs underwent Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the Metascape database. The STRING database and the Cytoscape software were utilized for the protein-protein interaction network of DEGs and hub genes screening. Furthermore, survival and expression analyses of hub genes were conducted using Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases. By using the Comparative Toxicogenomics Database, the hub genes interconnected with active ingredients of TCM were analyzed to provide potential information for the treatment of gastric cancer. After the molecular docking of the active ingredients of TCM to specific hub gene receptor proteins, the molecular dynamics simulation GROMACS was applied to validate the conformation of the strongest binding ability in the molecular docking. Results. A total of 291 significant DEGs were found, from which 12 hub genes were screened out. Among these hub genes, the expressions of five hub genes including COL1A1, COL5A2, MMP12, SERPINE1, and VCAN were significantly correlated with the overall survival. Furthermore, four potential therapeutic active ingredients of TCM were acquired, including quercetin, resveratrol, emodin, and schizandrin B. In addition, the molecular docking results exhibited that the active ingredients of TCM formed stable binding with the hub gene targets. SERPINE1 (3UT3)-Emodin and COL1A1 (7DV6)-Quercetin were subjected to molecular dynamics simulations as conformations of continuing research significance, and both were found to be stably bound as a result of the interaction of van der Waals potentials, electrostatic, and hydrogen bonding. Conclusion. Our findings may provide novel insights and references for the screening of biomarkers, the prognostic evaluation, and the identification of potential active ingredients of TCM for gastric cancer treatment.

Processes in DNA damage response from a whole-cell multi-omics perspective

Technological advances have made it feasible to collect multi-condition multi-omic time courses of cellular response to perturbation, but the complexity of these datasets impedes discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we report a whole-cell mechanistic analysis of HL-60 cellular response to bendamustine. We integrate both enrichment and network analysis to show the progression of DNA damage and programmed cell death over time in molecular, pathway, and process-level detail using an interactive analysis framework for multi-omics data. Our framework, Mechanism of Action Generator Involving Network analysis (MAGINE), automates network construction and enrichment analysis across multiple samples and platforms, which can be integrated into our annotated gene-set network to combine the strengths of networks and ontology-driven analysis. Taken together, our work demonstrates how multi-omics integration can be used to explore signaling processes at various resolutions and demonstrates multi-pathway involvement beyond the canonical bendamustine mechanism.

Morphology and gene expression profiling provide complementary information for mapping cell state

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

Genome-wide CRISPR Screen Reveal Targets of Chiral Gold(I) Anticancer Compound in Mammalian Cells

Metal-based drugs, such as cisplatin and auranofin, are used for the treatment of cancer and rheumatoid arthritis, respectively. Auranofin and other gold-derived compounds have been shown to possess anticancer, anti-inflammatory, antimicrobial, and antiparasitic activity in preclinical and clinical trials. Unlike platinum agents which are known to target DNA, the target of gold is not well elucidated. To better understand the targets and effects of gold agents in mammalian cells, we used a targeted CRISPR (ToxCRISPR) screen in K562 cancer cells to identify genes that modulate cellular sensitivity to gold. We synthesized a novel chiral gold(I) compound, JHK-21, with potent anticancer activity. Among the most sensitizing hits were proteins involved in mitochondrial carriers, mitochondrial metabolism, and oxidative phosphorylation. Further analysis revealed that JHK-21 induced inner mitochondria membrane dysfunction and modulated ATP-binding cassette subfamily member C (ABCC1) function in a manner distinct from auranofin. Characterizing the therapeutic effects and toxicities of metallodrugs in mammalian cells is of growing interest to guide future drug discovery, and cellular and preclinical/clinical studies.

Open access databases available for the pesticide lead discovery

Pesticide research is a multi-disciplinary collaborative study, and big data analysis based on integrating information from databases benefits decision-making in pesticide research. In the last 40 years, dozens of pesticide-related databases have been built up to describe their biological activities, toxicity, modes of action, and environmental risks, etc. However, these data are scattered and overlapping in different databases in multiple inconsistent formats, which is not convenient for information analysis and comparison. In this study, the content of 26 open access databases related to pesticide research was illustrated according to the information provided for the ligand-based drug design (LBDD) and receptor-based (or structure-based drug design, SBDD), and was summarized into three categories:1) the correspondence between the chemical structures and functional properties (biological activity, resistance, toxicity, environmental adaptation); 2) action mode study (target identification, target structures, and biological pathways); 3) computational servers for pesticide design. To our knowledge, this is the first review about the open access databases for pesticide research. The data classification could facilitate the information accessibility for pesticide research, and speed up the decision-making process in pesticide discovery.

Druggable Metabolic Vulnerabilities Are Exposed and Masked during Progression to Castration Resistant Prostate Cancer

There is an urgent need for exploring new actionable targets other than androgen receptor to improve outcome from lethal castration-resistant prostate cancer. Tumor metabolism has reemerged as a hallmark of cancer that drives and supports oncogenesis. In this regard, it is important to understand the relationship between distinctive metabolic features, androgen receptor signaling, genetic drivers in prostate cancer, and the tumor microenvironment (symbiotic and competitive metabolic interactions) to identify metabolic vulnerabilities. We explore the links between metabolism and gene regulation, and thus the unique metabolic signatures that define the malignant phenotypes at given stages of prostate tumor progression. We also provide an overview of current metabolism-based pharmacological strategies to be developed or repurposed for metabolism-based therapeutics for castration-resistant prostate cancer.

Cellular Target Deconvolution of Small Molecules Using a Selection-Based Genetic Screening Platform

Small-molecule drug target identification is an essential and often rate-limiting step in phenotypic drug discovery and remains a major challenge. Here, we report a novel platform for target identification of activators of signaling pathways by leveraging the power of a clustered regularly interspaced short palindromic repeats (CRISPR) knockout library. This platform links the expression of a suicide gene to the small-molecule-activated signaling pathway to create a selection system. With this system, loss-of-function screening using a CRISPR single-guide (sg) RNA library positively enriches cells in which the target has been knocked out. The identities of the drug targets and other essential genes required for the activity of small molecules of interest are then uncovered by sequencing. We tested this platform on BDW568, a newly discovered type-I interferon signaling activator, and identified stimulator of interferon genes (STING) as its target and carboxylesterase 1 (CES1) to be a key metabolizing enzyme required to activate BDW568 for target engagement. The platform we present here can be a general method applicable for target identification for a wide range of small molecules that activate different signaling pathways.

Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro

Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.

Heterogeneous network propagation with forward similarity integration to enhance drug-target association prediction

Identification of drug-target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug-drug and nine target-target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug-disease associations, and the cosine scores of drug-drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.

Dihydroartemisinin suppresses renal fibrosis in mice by inhibiting DNA-methyltransferase 1 and increasing Klotho

Renal fibrosis is an unavoidable end result of all forms of progressive chronic kidney diseases (CKD). Discovery of efficacious drugs against renal fibrosis is in crucial need. In a preliminary study we found that a derivative of artemisinin, dihydroartemisinin (DHA), exerted strong renoprotection, and reversed renal fibrosis in adenine-induced CKD mouse model. In this study we investigated the anti-fibrotic mechanisms of DHA, particularly its specific target in renal cells. Renal fibrosis was induced in mice by unilateral ureteral obstruction (UUO) or oral administration of adenine (80 mg · kg-1), the mice received DHA (30 mg · kg-1 · d-1, i.g.) for 14 or 21 days, respectively. We showed that DHA administration markedly attenuated the inflammation and fibrotic responses in the kidneys and significantly improved the renal function in both the renal fibrosis mouse models. In adenine-treated mice, DHA was more effective than 5-azacytidine against renal fibrosis. The anti-fibrotic effects of DHA were also observed in TGF-β1-treated HK-2 cells. In order to determine the target protein of DHA, we conducted pull-down technology coupled with shotgun proteomics using a small-molecule probe based on the structure of DHA (biotin-DHA). As a results, DNA methyltransferase 1 (DNMT1) was identified as the anti-fibrotic target of DHA in 3 different types of renal cell lines (HK-2, HEK293 and 3T3). We demonstrated that DHA directly bound to Asn 1529 and Thr 1528 of DNMT1 with a Kd value of 8.18 μM. In primary mouse renal tubular cells, we showed that DHA (10 μM) promoted DNMT1 degradation via the ubiquitin-proteasome pathway. DHA-reduced DNMT1 expression effectively reversed Klotho promoter hypermethylation, which led to the reversal of Klotho protein loss in the kidney of UUO mice. This subsequently resulted in inhibition of the Wnt/β-catenin and TGF-β/Smad signaling pathways and consequently conferred renoprotection in the animals. Knockdown of Klotho abolished the renoprotective effect of DHA in UUO mice. Our study reveals a novel pharmacological activity for DHA, i.e., renoprotection. DHA exhibits this effect by targeting DNMT1 to reverse Klotho repression. This study provides an evidence for the possible clinical application of DHA in the treatment of renal fibrosis.

Light-Controlled Traceless Protein Labeling via Decaging Thio-o-naphthoquinone Methide Chemistry

We report the molecular design of a novel multifunctional reagent and its application for light-controlled selective protein labeling. This molecule integrates functions of protein-ligand recognition, bioconjugation, ligand cleavage, and photoactivation by merging the photochemistries of 2-nitrophenylpropyloxycarbonyl and 3-hydroxymethyl-2-naphthol with an affinity ligand and fluorescein. Highly electrophilic o-naphthoquinone methide was photochemically released and underwent proximity-driven selective labeling with the protein of interest (e.g., carbonic anhydrases), which retains its native function after labeling.

Whole proteome mapping of compound-protein interactions

Off-target binding is one of the primary causes of toxic side effects of drugs in clinical development, resulting in failures of clinical trials. While off-target drug binding is a known phenomenon, experimental identification of the undesired protein binders can be prohibitively expensive due to the large pool of possible biological targets. Here, we propose a new strategy combining chemical similarity principle and deep learning to enable proteome-wide mapping of compound-protein interactions. We have developed a pipeline to identify the targets of bioactive molecules by matching them with chemically similar annotated "bait" compounds and ranking them with deep learning. We have constructed a user-friendly web server for drug-target identification based on chemical similarity (DRIFT) to perform searches across annotated bioactive compound datasets, thus enabling high-throughput, multi-ligand target identification, as well as chemical fragmentation of target-binding moieties.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

On-DNA Synthesis of Functionalized 4H-Pyran Scaffolds for Focused DNA-Encoded Chemical Libraries

The functionalized 4H-pyran scaffold has aroused synthetic attention because it is widely found in many interesting pharmacologically relevant compounds. We here disclose its incorporation into DNA-encoded chemical libraries, combining this scaffold with the merits of scaffold architecture in drug design. Under the optimized DNA-compatible conditions, functionalized 4H-pyrans were efficiently formed with a broad substrate scope. Among the 4H-pyrans formed, the axial structure features rotational restriction, and the spirocyclic structure provides rigidity and three-dimensionality. These efforts open the door for the construction of DNA-encoded chemical libraries with more consideration for this structural architecture.

IIFDTI: predicting drug-target interactions through interactive and independent features based on attention mechanism

MOTIVATION

Identifying drug-target interactions is a crucial step for drug discovery and design. Traditional biochemical experiments are credible to accurately validate drug-target interactions. However, they are also extremely laborious, time-consuming and expensive. With the collection of more validated biomedical data and the advancement of computing technology, the computational methods based on chemogenomics gradually attract more attention, which guide the experimental verifications.

RESULTS

In this study, we propose an end-to-end deep learning-based method named IIFDTI to predict drug-target interactions (DTIs) based on independent features of drug-target pairs and interactive features of their substructures. First, the interactive features of substructures between drugs and targets are extracted by the bidirectional encoder-decoder architecture. The independent features of drugs and targets are extracted by the graph neural networks and convolutional neural networks, respectively. Then, all extracted features are fused and inputted into fully connected dense layers in downstream tasks for predicting DTIs. IIFDTI takes into account the independent features of drugs/targets and simulates the interactive features of the substructures from the biological perspective. Multiple experiments show that IIFDTI outperforms the state-of-the-art methods in terms of the area under the receiver operating characteristics curve (AUC), the area under the precision-recall curve (AUPR), precision, and recall on benchmark datasets. In addition, the mapped visualizations of attention weights indicate that IIFDTI has learned the biological knowledge insights, and two case studies illustrate the capabilities of IIFDTI in practical applications.

AVAILABILITY AND IMPLEMENTATION

The data and codes underlying this article are available in Github at https://github.com/czjczj/IIFDTI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data

Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. It is of great significance to identify drug-induced target-organ toxicity, especially the detailed pathological findings, which are crucial for toxicity assessment, in the early stage of drug development process. A large variety of studies have devoted to identify drug toxicity. However, most of them are limited to single organ or only binary toxicity. Here we proposed a novel multi-label learning model named Att-RethinkNet, for predicting drug-induced pathological findings targeted on liver and kidney based on toxicogenomics data. The Att-RethinkNet is equipped with a memory structure and can effectively use the label association information. Besides, attention mechanism is embedded to focus on the important features and obtain better feature presentation. Our Att-RethinkNet is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized. And more importantly, it predicts multiple pathological findings at the same time, instead of predicting each pathology separately as the previous model did. To demonstrate the effectiveness of the proposed model, we compared the proposed method with a series of state-of-the-arts methods. Our model shows competitive performance and can predict potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way. The implementation of the proposed method is available at https://github.com/RanSuLab/Drug-Toxicity-Prediction-MultiLabel.

Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein-ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning-based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true-positive rate to false-positive rate. This work indicates that advances in modeling protein-ligand interactions, particularly using machine learning-based approaches, are needed to better harness AlphaFold2 for drug discovery.

Can plant-derived anti-HIV compounds be used in COVID-19 cases?

People living with HIV are more exposed to the adverse health effects of the worldwide COVID-19 pandemic. The pandemic's health and social repercussions may promote drug abuse and inadequate HIV management among this demographic. The coronavirus pandemic of 2019 (COVID-19) has caused unprecedented disruption worldwide in people's lives and health care. When the COVID-19 epidemic was identified, people with HIV faced significant obstacles and hurdles to achieving optimal care results. The viral spike protein (S-Protein) and the cognate host cell receptor angiotensin-converting enzyme 2 (ACE2) are both realistic and appropriate intervention targets. Calanolides A, Holy Basil, Kuwanon-L, and Patentiflorin have anti-HIV effects. Our computational biology study investigated that these compounds all had interaction binding scores related to S protein of coronavirus of -9.0 kcal /mol, -7.1 kcal /mol, -9.1 kcal /mol, and -10.3 kcal/mol/mol, respectively. A combination of plant-derived anti-HIV compounds like protease inhibitors and nucleoside analogs, which are commonly used to treat HIV infection, might be explored in clinical trials for the treatment of COVID-19.