In Silico Target Prediction for Small Molecules

Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning

Estimating protein targets of compounds based on the similarity principle-similar molecules are likely to show comparable bioactivity-is a long-standing strategy in drug research. Having previously quantified this principle, we present here a large-scale evaluation of its predictive power for inferring macromolecular targets by reverse screening an unprecedented vast external test set of more than 300,000 active small molecules against another bioactivity set of more than 500,000 compounds. We show that machine-learning can predict the correct targets, with the highest probability among 2069 proteins, for more than 51% of the external molecules. The strong enrichment thus obtained demonstrates its usefulness in supporting phenotypic screens, polypharmacology, or repurposing. Moreover, we quantified the impact of the bioactivity knowledge available for proteins in terms of number and diversity of actives. Finally, we advise that developers of such approaches follow an application-oriented benchmarking strategy and use large, high-quality, non-overlapping datasets as provided here.

MasitinibL shows promise as a drug-like analog of masitinib that elicits comparable SARS-Cov-2 3CLpro inhibition with low kinase preference

SARS-CoV-2 infection has led to several million deaths worldwide and ravaged the economies of many countries. Hence, developing therapeutics against SARS-CoV-2 remains a core priority in the fight against COVID-19. Most of the drugs that have received emergency use authorization for treating SARS-CoV-2 infection exhibit a number of limitations, including side effects and questionable efficacy. This challenge is further compounded by reinfection after vaccination and the high likelihood of mutations, as well as the emergence of viral escape mutants that render SARS-CoV-2 spike glycoprotein-targeting vaccines ineffective. Employing de novo drug synthesis or repurposing to discover broad-spectrum antivirals that target highly conserved pathways within the viral machinery is a focus of current research. In a recent drug repurposing study, masitinib, a clinically safe drug against the human coronavirus OC43 (HCoV-OC43), was identified as an antiviral agent with effective inhibitory activity against the SARS-CoV-2 3CLpro. Masitinib is currently under clinical trial in combination with isoquercetin in hospitalized patients (NCT04622865). Nevertheless, masitinib has kinase-related side effects; hence, the development of masitinib analogs with lower anti-tyrosine kinase activity becomes necessary. In this study, in an attempt to address this limitation, we executed a comprehensive virtual workflow in silico to discover drug-like compounds matching selected pharmacophore features in the SARS-CoV-2 3CLpro-bound state of masitinib. We identified a novel lead compound, "masitinibL", a drug-like analog of masitinib that demonstrated strong inhibitory properties against the SARS-CoV-2 3CLpro. In addition, masitinibL further displayed low selectivity for tyrosine kinases, which strongly suggests that masitinibL is a highly promising therapeutic that is preferable to masitinib.

In silico prediction and biological assessment of novel angiogenesis modulators from traditional Chinese medicine

Uncontrolled angiogenesis is a common denominator underlying many deadly and debilitating diseases such as myocardial infarction, chronic wounds, cancer, and age-related macular degeneration. As the current range of FDA-approved angiogenesis-based medicines are far from meeting clinical demands, the vast reserve of natural products from traditional Chinese medicine (TCM) offers an alternative source for developing pro-angiogenic or anti-angiogenic modulators. Here, we investigated 100 traditional Chinese medicine-derived individual metabolites which had reported gene expression in MCF7 cell lines in the Gene Expression Omnibus (GSE85871). We extracted literature angiogenic activities for 51 individual metabolites, and subsequently analysed their predicted targets and differentially expressed genes to understand their mechanisms of action. The angiogenesis phenotype was used to generate decision trees for rationalising the poly-pharmacology of known angiogenesis modulators such as ferulic acid and curculigoside and validated by an in vitro endothelial tube formation assay and a zebrafish model of angiogenesis. Moreover, using an in silico model we prospectively examined the angiogenesis-modulating activities of the remaining 49 individual metabolites. In vitro, tetrahydropalmatine and 1 beta-hydroxyalantolactone stimulated, while cinobufotalin and isoalantolactone inhibited endothelial tube formation. In vivo, ginsenosides Rb3 and Rc, 1 beta-hydroxyalantolactone and surprisingly cinobufotalin, restored angiogenesis against PTK787-induced impairment in zebrafish. In the absence of PTK787, deoxycholic acid and ursodeoxycholic acid did not affect angiogenesis. Despite some limitations, these results suggest further refinements of in silico prediction combined with biological assessment will be a valuable platform for accelerating the research and development of natural products from traditional Chinese medicine and understanding their mechanisms of action, and also for other traditional medicines for the prevention and treatment of angiogenic diseases.

Morphological profiling by means of the Cell Painting assay enables identification of tubulin-targeting compounds

In phenotypic compound discovery, conclusive identification of cellular targets and mode of action are often impaired by off-target binding. In particular, microtubules are frequently targeted in cellular assays. However, in vitro tubulin binding assays do not correctly reflect the cellular context, and conclusive high-throughput phenotypic assays monitoring tubulin binding are scarce, such that tubulin binding is rarely identified. We report that morphological profiling using the Cell Painting assay (CPA) can efficiently detect tubulin modulators in compound collections with a high throughput, including annotated reference compounds and unannotated compound classes with unrelated chemotypes and scaffolds. Small-molecule tubulin binders share similar CPA fingerprints, which enables prediction and experimental validation of microtubule-binding activity. Our findings suggest that CPA or a related morphological profiling approach will be an invaluable addition to small-molecule discovery programs in chemical biology and medicinal chemistry, enabling early identification of one of the most frequently observed off-target activities.

Learning from Nature: From a Marine Natural Product to Synthetic Cyclooxygenase-1 Inhibitors by Automated De Novo Design

The repertoire of natural products offers tremendous opportunities for chemical biology and drug discovery. Natural product-inspired synthetic molecules represent an ecologically and economically sustainable alternative to the direct utilization of natural products. De novo design with machine intelligence bridges the gap between the worlds of bioactive natural products and synthetic molecules. On employing the compound Marinopyrrole A from marine Streptomyces as a design template, the algorithm constructs innovative small molecules that can be synthesized in three steps, following the computationally suggested synthesis route. Computational activity prediction reveals cyclooxygenase (COX) as a putative target of both Marinopyrrole A and the de novo designs. The molecular designs are experimentally confirmed as selective COX-1 inhibitors with nanomolar potency. X-ray structure analysis reveals the binding of the most selective compound to COX-1. This molecular design approach provides a blueprint for natural product-inspired hit and lead identification for drug discovery with machine intelligence.

How Aconiti Radix Cocta can Treat Gouty Arthritis Based on Systematic Pharmacology and UPLC-QTOF-MS/MS

Background: Gouty arthritis (GA) is a common metabolic disease caused by a long-term disorder of purine metabolism and increased serum levels of uric acid. The processed product of dried root of Aconitum carmichaeli Debeaux (Aconiti Radix cocta, ARC) is used often in traditional Chinese medicine (TCM) to treat GA, but its specific active components and mechanism of action are not clear.

Methods: First, we used ultra-performance liquid chromatography-quadrupole/time-of-flight tandem mass spectrometry to identify the chemical spectrum of ARC. Based on this result, we explored the active components of ARC in GA treatment and their potential targets and pathways. Simultaneously, we used computer simulations, in vitro cell experiments and animal experiments to verify the prediction results of systems pharmacology. In vitro, we used aurantiamide acetate (AA) to treat monosodium urate (MSU)-stimulated THP-1 cells and demonstrated the reliability of the prediction by western blotting and real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR). ELISAs kit were used to measure changes in levels of proinflammatory factors in rats with GA induced by MSU to demonstrate the efficacy of ARC in GA treatment.

Results: Forty-three chemical constituents in ARC were identified. ARC could regulate 65 targets through 29 active components, and then treat GA, which involved 1427 Gene Ontology (GO) terms and 146 signaling pathways. Signaling pathways such as proteoglycans in cancer, C-type lectin receptor signaling pathway, and TNF signaling pathway may have an important role in GA treatment with ARC. In silico results showed that the active components songoramine and ignavine had high binding to mitogen-activated protein kinase p38 alpha (MAPK14) and matrix metallopeptidase (MMP)9, indicating that ARC treatment of GA was through multiple components and multiple targets. In vitro experiments showed that AA in ARC could effectively reduce expression of MAPK14, MMP9, and cyclooxygenase2 (PTGS2) in THP-1 cells stimulated by MSU, whereas it could significantly inhibit the mRNA expression of Caspase-1, spleen tyrosine kinase (SYK), and PTGS2. Animal experiments showed that a ARC aqueous extract could significantly reduce expression of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and intereleukin (IL)-18 in the serum of GA rats stimulated by MSU. Hence, ARC may inhibit inflammation by regulating the proteoglycans in cancer-associated signaling pathways.

Conclusion: ARC treatment of GA may have the following mechanisms, ARC can reduce MSU crystal-induced joint swelling, reduce synovial tissue damage, and reduce the expression of inflammatory factors in serum. AA in ARC may inhibit inflammation by regulating the protein expression of MAPK14, MMP9, and PTGS2 and the mRNA expression of caspase-1, SYK, and PTGS2.

Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions

The understanding of the mechanism-of-action (MoA) of compounds and the prediction of potential drug targets play an important role in small-molecule drug discovery. The aim of this work was to compare chemical and cell morphology information for bioactivity prediction. The comparison was performed using bioactivity data from the ExCAPE database, image data (in the form of CellProfiler features) from the Cell Painting data set (the largest publicly available data set of cell images with ∼30,000 compound perturbations), and extended connectivity fingerprints (ECFPs) using the multitask Bayesian matrix factorization (BMF) approach Macau. We found that the BMF Macau and random forest (RF) performance were overall similar when ECFPs were used as compound descriptors. However, BMF Macau outperformed RF in 159 out of 224 targets (71%) when image data were used as compound information. Using BMF Macau, 100 (corresponding to about 45%) and 90 (about 40%) of the 224 targets were predicted with high predictive performance (AUC > 0.8) with ECFP data and image data as side information, respectively. There were targets better predicted by image data as side information, such as β-catenin, and others better predicted by fingerprint-based side information, such as proteins belonging to the G-protein-Coupled Receptor 1 family, which could be rationalized from the underlying data distributions in each descriptor domain. In conclusion, both cell morphology changes and chemical structure information contain information about compound bioactivity, which is also partially complementary, and can hence contribute to in silico MoA analysis.

Morphological profiling of small molecules

Profiling approaches such as gene expression or proteome profiling generate small-molecule bioactivity profiles that describe a perturbed cellular state in a rather unbiased manner and have become indispensable tools in the evaluation of bioactive small molecules. Automated imaging and image analysis can record morphological alterations that are induced by small molecules through the detection of hundreds of morphological features in high-throughput experiments. Thus, morphological profiling has gained growing attention in academia and the pharmaceutical industry as it enables detection of bioactivity in compound collections in a broader biological context in the early stages of compound development and the drug-discovery process. Profiling may be used successfully to predict mode of action or targets of unexplored compounds and to uncover unanticipated activity for already characterized small molecules. Here, we review the reported approaches to morphological profiling and the kind of bioactivity that can be detected so far and, thus, predicted.

Acylpolyamine Mygalin as a TLR4 Antagonist Based on Molecular Docking and In Vitro Analyses

Toll-like receptors (TLRs) are transmembrane proteins that are key regulators of innate and adaptive immune responses, particularly TLR4, and they have been identified as potential drug targets for the treatment of disease. Several low-molecular-weight compounds are being considered as new drug targets for various applications, including as immune modulators. Mygalin, a 417 Da synthetic bis-acylpolyamine, is an analog of spermidine that has microbicidal activity. In this study, we investigated the effect of mygalin on the innate immune response based on a virtual screening (VS) and molecular docking analysis. Bone marrow-derived macrophages and the cell lines J774A.1 and RAW 264.7 stimulated with lipopolysaccharide (LPS) were used to confirm the data obtained in silico. Virtual screening and molecular docking suggested that mygalin binds to TLR4 via the protein myeloid differentiation factor 2 (MD-2) and LPS. Macrophages stimulated by mygalin plus LPS showed suppressed gene expression of tumor necrosis factor (TNF-α), interleukine 6 (IL-6), cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS), as well as inhibition of signaling protein p65 of the nuclear factor κB (NF-κB), resulting in decreased production of nitric oxide (NO) and TNF-α. These results indicate that mygalin has anti-inflammatory potential, being an attractive option to be explored. In addition, we reinforce the importance of virtual screening analysis to assist in the discovery of new drugs.

Morphological Profiling Identifies a Common Mode of Action for Small Molecules with Different Targets

Unbiased morphological profiling of bioactivity, for example, in the cell painting assay (CPA), enables the identification of a small molecule's mode of action based on its similarity to the bioactivity of reference compounds, irrespective of the biological target or chemical similarity. This is particularly important for small molecules with nonprotein targets as these are rather difficult to identify with widely employed target-identification methods. We employed morphological profiling using the CPA to identify compounds that are biosimilar to the iron chelator deferoxamine. Structurally different compounds with different annotated cellular targets provoked a shared physiological response, thereby defining a cluster based on their morphological fingerprints. This cluster is based on a shared mode of action and not on a shared target, that is, cell-cycle modulation in the S or G2 phase. Hierarchical clustering of morphological fingerprints revealed subclusters that are based on the mechanism of action and could be used to predict target-related bioactivity.

In silico study for prediction of novel bioactivities of the endophytic fungal alkaloid, mycoleptodiscin B for human targets

Mycoleptodiscin B is a natural product extracted from the endophytic fungus Mycoleptodiscus sp. found in Sri Lanka and Panama with experimentally unexplored activities for human targets. In this study, a computational methodology was applied to determine druggable targets of mycoleptodiscin B. According to the computational toxicity and pharmacokinetics assessment, mycoleptodiscin B was proven to be a suitable drug candidate. Druggable targets for this compound, aromatase, acidic plasma glycoprotein and androgen receptor, were predicted using reverse docking. A two-step validation of those targets was performed using conventional molecular docking and molecular dynamic (MD) simulations, resulting in aromatase being determined as the potential therapeutic target. Based on molecular mechanics/Generalized Born Surface Area (GBSA) free energies and ligand stability inside the active site cavity during its 120 ns MD run, it can be concluded that mycoleptodiscin B is a potent aromatase inhibitor and could be subjected to further in vitro and in vivo experiments in the drug development pipeline. Consequently, natural product chemists can quickly identify the hidden medicinal properties of their miracle compounds using the computational approach applied in this research.

Computational/in silico methods in drug target and lead prediction

Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.

Combining experimental strategies for successful target deconvolution

Investment in phenotypic drug discovery has led to increased demand for rapid and robust target deconvolution to aid successful drug development. Although methods for target identification and mechanism of action (MoA) discovery are flourishing, they typically lead to lists of putative targets. Validating which target(s) are involved in the therapeutic mechanism of a compound poses a significant challenge, requiring direct binding, target engagement, and functional studies in relevant physiological contexts. A combination of orthogonal approaches can allow target identification beyond the proteome as well as aid prioritisation for resource-intensive target validation studies.

Identification of the Effects of Aspirin and Sulindac Sulfide on the Inhibition of HMGA2-Mediated Oncogenic Capacities in Colorectal Cancer

Distant metastatic colorectal cancer (CRC) is present in approximately 25% of patients at initial diagnosis, and eventually half of CRC patients will develop metastatic disease. The 5-year survival rate for patients with metastatic CRC is a mere 12.5%; thus, there is an urgent need to investigate the molecular mechanisms of cancer progression in CRC. High expression of human high-mobility group A2 (HMGA2) is related to tumor progression, a poor prognosis, and a poor response to therapy for CRC. Therefore, HMGA2 is an attractive target for cancer therapy. In this study, we identified aspirin and sulindac sulfide as novel potential inhibitors of HMGA2 using a genome-wide mRNA signature-based approach. In addition, aspirin and sulindac sulfide induced cytotoxicity of CRC cells stably expressing HMGA2 by inhibiting cell proliferation and migration. Moreover, a gene set enrichment analysis (GSEA) revealed that gene sets related to inflammation were positively correlated with HMGA2 and that the main molecular function of these genes was categorized as a G-protein-coupled receptor (GPCR) activity event. Collectively, this is the first study to report that aspirin and sulindac sulfide are novel potential inhibitors of HMGA2, which can induce cytotoxicity of CRC cells stably expressing HMGA2 by inhibiting cell proliferation and migration through influencing inflammatory-response genes, the majority of which are involved in GPCR signaling.

SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules

SwissTargetPrediction is a web tool, on-line since 2014, that aims to predict the most probable protein targets of small molecules. Predictions are based on the similarity principle, through reverse screening. Here, we describe the 2019 version, which represents a major update in terms of underlying data, backend and web interface. The bioactivity data were updated, the model retrained and similarity thresholds redefined. In the new version, the predictions are performed by searching for similar molecules, in 2D and 3D, within a larger collection of 376 342 compounds known to be experimentally active on an extended set of 3068 macromolecular targets. An efficient backend implementation allows to speed up the process that returns results for a druglike molecule on human proteins in 15-20 s. The refreshed web interface enhances user experience with new features for easy input and improved analysis. Interoperability capacity enables straightforward submission of any input or output molecule to other on-line computer-aided drug design tools, developed by the SIB Swiss Institute of Bioinformatics. High levels of predictive performance were maintained despite more extended biological and chemical spaces to be explored, e.g. achieving at least one correct human target in the top 15 predictions for >70% of external compounds. The new SwissTargetPrediction is available free of charge (www.swisstargetprediction.ch).

A practical guide to secondary pharmacology in drug discovery

Secondary pharmacological profiling is increasingly applied in pharmaceutical drug discovery to address unwanted pharmacological side effects of drug candidates before entering the clinic. Regulators, drug makers and patients share a demand for deep characterization of secondary pharmacology effects of novel drugs and their metabolites. The scope of such profiling has therefore expanded substantially in the past two decades, leading to the implementation of broad in silico profiling methods and focused in vitro off-target screening panels, to identify liabilities, but also opportunities, as early as possible. The pharmaceutical industry applies such panels at all stages of drug discovery routinely up to early development. Nevertheless, target composition, screening technologies, assay formats, interpretation and scheduling of panels can vary significantly between companies in the absence of dedicated guidelines. To contribute towards best practices in secondary pharmacology profiling, this review aims to summarize the state-of-the art in this field. Considerations are discussed with respect to panel design, screening strategy, implementation and interpretation of the data, including regulatory perspectives. The cascaded, or integrated, use of in silico and off-target profiling allows to exploit synergies for comprehensive safety assessment of drug candidates.

On the in silico and in vitro anticancer activity of sulfonamide chalcones: potential JNKK3 inhibitors

Although many compound classes have been studied as JNK inhibitors, we are interested in using chalcones for this purpose. Do different groups drive to different bindings modes to JNK?