Potential drug discovery for COVID-19 treatment targeting Cathepsin L using a deep learning-based strategy

Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery

Farnesoid X receptor (FXR) is a key regulator of bile acid, lipid, and glucose homeostasis, making it a promising target for treating metabolic diseases. FXR antagonists have shown therapeutic potential in cholestasis, metabolic disorders, and certain cancers, while clinically approved FXR antagonists remain unavailable and underrepresented in current treatment strategies. To address this, we developed deep learning models for predicting FXR antagonistic activity (ANTCL) and toxicity (TOXCL). Screening 217,345 compounds from the HMDB database identified eleven human metabolite candidates with significant FXR binding potential. Molecular dynamics simulations and binding free energy calculations revealed five more stable complexes compared to the reference compound Gly-MCA, with HMDB0253354 (Fulvestrant) and HMDB0242367 (ZM 189154) standing out for their binding free energies. Hydrophobic interactions, particularly involving residues MET328, PHE329, and ALA291, contributed to their stability. These results demonstrate the effectiveness of deep learning in FXR antagonist discovery and highlight the potential of HMDB0253354 and HMDB0242367 as promising candidates for metabolic disease treatment.

Discovering novel Cathepsin L inhibitors from natural products using artificial intelligence

Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders. Current pharmacological interventions targeting CTSL have demonstrated potential in reducing body weight gain, serum insulin levels, and improving glucose tolerance. However, the clinical application of CTSL inhibitors remains limited. In this study, we used a combination of artificial intelligence and experimental methods to identify new CTSL inhibitors from natural products. Through a robust deep learning model and molecular docking, we screened 150 molecules from natural products for experimental validation. At a concentration of 100 µM, we found that 36 of them exhibited more than 50 % inhibition of CTSL. Notably, 13 molecules displayed over 90 % inhibition and exhibiting concentration-dependent effects. The molecular dynamics simulation on the two most potent inhibitors, Plumbagin and Beta-Lapachone, demonstrated stable interaction at the CTSL active site. Enzyme kinetics studies have shown that these inhibitors exert an uncompetitive inhibitory effect on CTSL. In conclusion, our research identifies Plumbagin and Beta-Lapachone as potential CTSL inhibitors, offering promising candidates for the treatment of metabolic disorders and illustrating the effectiveness of artificial intelligence in drug discovery.

Recent discovery of natural substances with cathepsin L-inhibitory activity for cancer metastasis suppression

Cathepsin L (CTSL), a cysteine cathepsin protease of the papain superfamily, plays a crucial role in cancer progression and metastasis. Dysregulation of CTSL is frequently observed in tumor malignancies, leading to the degradation of extracellular matrix and facilitating epithelial-mesenchymal transition (EMT), a key process in malignant cancer metastasis. This review mainly provides a comprehensive information about recent findings on natural inhibitors targeting CTSL and their anticancer effects, which have emerged as potent anticancer therapeutic agents or metastasis-suppressive adjuvants. Specifically, inhibitors are categorized into small-molecule and macromolecule inhibitors, with a particular emphasis on cathepsin propeptide-type macromolecules. Additionally, the article explores the molecular mechanisms of CTSL involvement in cancer metastasis, highlighting its regulation at transcriptional, translational, post-translational, and epigenetic levels. This work underscores the importance of understanding natural CTSL inhibitors and provides researchers with practical insights to advance the relevant fields and discover novel CTSL-targeting inhibitors from natural sources.

Structural Elucidation and Antiviral Activity of Covalent Cathepsin L Inhibitors

Emerging RNA viruses, including SARS-CoV-2, continue to be a major threat. Cell entry of SARS-CoV-2 particles via the endosomal pathway involves cysteine cathepsins. Due to ubiquitous expression, cathepsin L (CatL) is considered a promising drug target in the context of different viral and lysosome-related diseases. We characterized the anti-SARS-CoV-2 activity of a set of carbonyl- and succinyl epoxide-based inhibitors, which were previously identified as inhibitors of cathepsins or related cysteine proteases. Calpain inhibitor XII, MG-101, and CatL inhibitor IV possess antiviral activity in the very low nanomolar EC50 range in Vero E6 cells and inhibit CatL in the picomolar Ki range. We show a relevant off-target effect of CatL inhibition by the coronavirus main protease α-ketoamide inhibitor 13b. Crystal structures of CatL in complex with 14 compounds at resolutions better than 2 Å present a solid basis for structure-guided understanding and optimization of CatL inhibitors toward protease drug development.

Berberine Metabolites Stimulate GLP-1 Secretion by Alleviating Oxidative Stress and Mitochondrial Dysfunction

Berberine (BBR) is a principal component of Rhizoma coptidis known for its therapeutic potential in treating diseases such as type 2 diabetes mellitus (T2DM) and obesity. Despite the trace levels of BBR in plasma, it's believed that its metabolites play a pivotal role in its biological activities. While BBR is recognized to promote GLP-1 production in intestinal L cells, the cytoprotective effects of its metabolites on these cells are yet to be explored. The present study investigates the effects of BBR metabolites on GLP-1 secretion and the underlying mechanisms. Our results revealed that, out of six BBR metabolites, berberrubine (BBB) and palmatine (PMT) significantly increased the production and glucose-stimulated secretion of GLP-1 in GLUTag cells. Notably, both BBB and PMT could facilitate GLP-1 and insulin secretion and enhance glucose tolerance in standard mice. Moreover, a single dose of PMT could markedly increase plasma GLP-1 and improve glucose tolerance in mice with obesity induced by a high-fat diet. In palmitic acid or TNF[Formula: see text]-treated GLUTag cells, BBB and PMT alleviated cell death, oxidative stress, and mitochondrial dysfunction. Furthermore, they could effectively reverse inflammation-induced inhibition of the Akt signaling pathway. In general, these insights suggest that the beneficial effects of orally administered BBR on GLP-1 secretion are largely attributed to the pharmacological activity of BBB and PMT by their above cytoprotective effects on L cells, which provide important ideas for stimulating GLP-1 secretion and the treatment of T2DM.

Targeting SARS-CoV-2 entry processes: The promising potential and future of host-targeted small-molecule inhibitors

The COVID-19 pandemic, caused by SARS-CoV-2, has had a huge impact on global health. To respond to rapidly mutating viruses and to prepare for the next pandemic, there is an urgent need to develop small molecule therapies that target critical stages of the SARS-CoV-2 life cycle. Inhibiting the entry process of the virus can effectively control viral infection and play a role in prevention and treatment. Host factors involved in this process, such as ACE2, TMPRSS2, ADAM17, furin, PIKfyve, TPC2, CTSL, AAK1, V-ATPase, HSPG, and NRP1, have been found to be potentially good targets with stability. Through further exploration of the cell entry process of SARS-CoV-2, small-molecule drugs targeting these host factors have been developed. This review focuses on the structural functions of potential host cell targets during the entry of SARS-CoV-2 into host cells. The research progress, chemical structure, structure-activity relationship, and clinical value of small-molecule inhibitors against COVID-19 are reviewed to provide a reference for the development of small-molecule drugs against COVID-19.

Calpeptin is a potent cathepsin inhibitor and drug candidate for SARS-CoV-2 infections

Several drug screening campaigns identified Calpeptin as a drug candidate against SARS-CoV-2. Initially reported to target the viral main protease (Mpro), its moderate activity in Mpro inhibition assays hints at a second target. Indeed, we show that Calpeptin is an extremely potent cysteine cathepsin inhibitor, a finding additionally supported by X-ray crystallography. Cell infection assays proved Calpeptin's efficacy against SARS-CoV-2. Treatment of SARS-CoV-2-infected Golden Syrian hamsters with sulfonated Calpeptin at a dose of 1 mg/kg body weight reduces the viral load in the trachea. Despite a higher risk of side effects, an intrinsic advantage in targeting host proteins is their mutational stability in contrast to highly mutable viral targets. Here we show that the inhibition of cathepsins, a protein family of the host organism, by calpeptin is a promising approach for the treatment of SARS-CoV-2 and potentially other viral infections.

Cathepsins and SARS-CoV-2 infection: From pathogenic factors to potential therapeutic targets

Coronavirus disease-19 (COVID-19) is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection. The COVID-19 pandemic began in March 2020 and has wrought havoc on health and economic systems worldwide. Efficacious treatment for COVID-19 is lacking: Only preventive measures as well as symptomatic and supportive care are available. Preclinical and clinical studies have indicated that lysosomal cathepsins might contribute to the pathogenesis and disease outcome of COVID-19. Here, we discuss cutting-edge evidence on the pathological roles of cathepsins in SARS-CoV-2 infection, host immune dysregulations, and the possible underlying mechanisms. Cathepsins are attractive drug targets because of their defined substrate-binding pockets, which can be exploited as binding sites for pharmaceutical enzyme inhibitors. Accordingly, the potential modulatory strategies of cathepsin activity are discussed. These insights could shed light on the development of cathepsin-based interventions for COVID-19.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

An approach combining deep learning and molecule docking for drug discovery of cathepsin L

OBJECTIVES

Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders and COVID-19. However, there are still no clinically available CTSL inhibitors. Our objective is to develop an approach for the discovery of potential reversible covalent CTSL inhibitors.

METHODS

The authors combined Chemprop, a deep learning-based strategy, and the Schrödinger CovDock algorithm to identify potential CTSL inhibitors. First, they used Chemprop to train a deep learning model capable of predicting whether a molecule would inhibit the activity of CTSL and performed predictions on ZINC20 in-stock librarie (~9.2 million molecules). Then, they selected the top-200 predicted molecules and performed the Schrödinger covalent docking algorithm to explore the binding patterns to CTSL (PDB: 5MQY). The authors then calculated the binding energies using Prime MM/GBSA and examined the stability between the best two molecules and CTSL using 100ns molecular dynamics simulations.

RESULTS

The authors found five molecules that showed better docking results than the well-known cathepsin inhibitor odanacatib. Notably, two of these molecules, ZINC-35287427 and ZINC-1857528743, showed better docking results with CTSL compared to other cathepsins.

CONCLUSION

Our approach enables drug discovery from large-scale databases with little computational consumption, which will save the cost and time required for drug discovery.

Structure-based lead optimization of peptide-based vinyl methyl ketones as SARS-CoV-2 main protease inhibitors

Despite several major achievements in the development of vaccines and antivirals, the fight against SARS-CoV-2 and the health problems accompanying COVID-19 are still ongoing. SARS-CoV-2 main protease (M^pro), an essential viral cysteine protease, is a crucial target for the development of antiviral agents. A virtual screening analysis of in-house cysteine protease inhibitors against SARS-CoV-2 M^pro allowed us to identify two hits (i.e., 1 and 2) bearing a methyl vinyl ketone warhead. Starting from these compounds, we herein report the development of Michael acceptors targeting SARS-CoV-2 M^pro, which differ from each other for the warhead and for the amino acids at the P2 site. The most promising vinyl methyl ketone-containing analogs showed sub-micromolar activity against the viral protease. SPR38, SPR39, and SPR41 were fully characterized, and additional inhibitory properties towards hCatL, which plays a key role in the virus entry into host cells, were observed. SPR39 and SPR41 exhibited single-digit micromolar EC₅₀ values in a SARS-CoV-2 infection model in cell culture.

Navigating with chemometrics and machine learning in chemistry

Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.

Children with type 1 diabetes in COVID-19 pandemic: Difficulties and solutions

Children/adolescents with type 1 diabetes (T1D) require holistic approach and continuous care. However, the coronavirus disease 2019 (COVID-19) pandemic has made challenges for the T1D children and their caregivers, professionals, and the healthcare system. This minireview aims to consolidate and discuss the difficulties and solutions of children with type 1 diabetes in the COVID-19 pandemic. T1D has been the most common type of diabetes in children and adolescents and the last decades has seen a rapid increase in the prevalence of T1D in youths worldwide, which deserves a public concern particularly in the COVID-19 pandemic. As reported in previous studies, T1D is a risk factor related to severe cases, while the virus may induce new-onset diabetes and serious complications. Moreover, restriction strategies influence medical availability and lifestyle, impact glycemic control and compilation management, and thus pose stress on families and health providers of youths with T1D, especially on those with certain fragile conditions. Therefore, special treatment plans are required for children provided by caregivers and the local health system. Latest health tools such as improved medical devices and telemedicine service, as well as a combined support may benefit in this period. This minireview emphasises that continued medical access and support are required to prevent deteriorated condition of children and adolescents with diabetes throughout this pandemic. Therefore, strategies are supposed to be formulated to mitigate the difficulties and stress among this group, particularly in the most at-risk population. Proposed solutions in this minireview may help individuals and the health system to overcome these problems and help youths with T1D in better diabetes management during such emergency situations.