FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction.

Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especially for drug design and discovery. In this study, we advanced a novel deep learning architecture, termed FP-GNN (fingerprints and graph neural networks), which combined and simultaneously learned information from molecular graphs and fingerprints for molecular property prediction. To evaluate the FP-GNN model, we conducted experiments on 13 public datasets, an unbiased LIT-PCBA dataset and 14 phenotypic screening datasets for breast cell lines. Extensive evaluation results showed that compared to advanced deep learning and conventional machine learning algorithms, the FP-GNN algorithm achieved state-of-the-art performance on these datasets. In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model. Analysis of the anti-noise ability and interpretation ability also indicated that FP-GNN was competitive in real-world situations. Collectively, FP-GNN algorithm can assist chemists, biologists and pharmacists in predicting and discovering better molecules with desired functions or properties.

A Review of FDA-Approved Anti-HIV-1 Drugs, Anti-Gag Compounds, and Potential Strategies for HIV-1 Eradication.

Acquired immunodeficiency syndrome (AIDS) is an enormous global health threat stemming from human immunodeficiency virus (HIV-1) infection. Up to now, the tremendous advances in combination antiretroviral therapy (cART) have shifted HIV-1 infection from a fatal illness into a manageable chronic disorder. However, the presence of latent reservoirs, the multifaceted nature of HIV-1, drug resistance, severe off-target effects, poor adherence, and high cost restrict the efficacy of current cART targeting the distinct stages of the virus life cycle. Therefore, there is an unmet need for the discovery of new therapeutics that not only bypass the limitations of the current therapy but also protect the body's health at the same time. The main goal for complete HIV-1 eradication is purging latently infected cells from patients' bodies. A potential strategy called "lock-in and apoptosis" targets the budding phase of the life cycle of the virus and leads to susceptibility to apoptosis of HIV-1 infected cells for the elimination of HIV-1 reservoirs and, ultimately, for complete eradication. The current work intends to present the main advantages and disadvantages of United States Food and Drug Administration (FDA)-approved anti-HIV-1 drugs as well as plausible strategies for the design and development of more anti-HIV-1 compounds with better potency, favorable pharmacokinetic profiles, and improved safety issues.

De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update.

Nowadays, machine learning and deep learning approaches are widely utilized for generative chemistry and computer-aided drug design and discovery such as de novo peptide and protein design, where target-specific peptide-based/protein-based therapeutics have been suggested to cause fewer adverse effects than the traditional small-molecule drugs. In light of current advancements in deep learning techniques, generative adversarial network (GAN) algorithms are being leveraged to a wide variety of applications in the process of generative chemistry and computer-aided drug design and discovery. In this review, we focus on the up-to-date developments for de novo peptide and protein design research using GAN algorithms in the interdisciplinary fields of generative chemistry, machine learning, deep learning, and computer-aided drug design and discovery. First, we present various studies that investigate GAN algorithms to fulfill the task of de novo peptide and protein design in the drug development pipeline. In addition, we summarize the drawbacks with respect to the previous studies in de novo peptide and protein design using GAN algorithms. Finally, we depict a discussion of open challenges and emerging problems for future research.

Targeting cyclin-dependent kinase 6 by vanillin inhibits proliferation of breast and lung cancer cells: Combined computational and biochemical studies.

Cyclin-dependent kinase 6 (CDK6) is a member of serine/threonine kinase family, and its overexpression is associated with cancer development. Thus, it is considered as a potential drug target for anticancer therapies. This study showed the CDK6 inhibitory potential of vanillin using combined experimental and computational methods. Structure-based docking and 200 ns molecular dynamics simulation studies revealed that the binding of vanillin stabilizes the CDK6 structure and provides mechanistic insights into the binding mechanism. Enzyme inhibition and fluorescence-binding studies showed that vanillin inhibits CDK6 with an half maximal inhibitory concentration = 4.99 µM and a binding constant (K) 4.1 × 107 M-1 . Isothermal titration calorimetry measurements further complemented our observations. Studies on human cancer cell lines (MCF-7 and A549) showed that vanillin decreases cell viability and colonization properties. The protein expression studies have further revealed that vanillin reduces the CDK6 expression and induces apoptosis in the cancer cells. In conclusion, our study presents the CDK6-mediated therapeutic implications of vanillin for anticancer therapies.

HDAC6 as privileged target in drug discovery: A perspective.

HDAC6, a class IIB HDAC isoenzyme, stands unique in its structural and physiological functions. Besides histone modification, largely due to its cytoplasmic localization, HDAC6 also targets several non-histone proteins including Hsp90, α-tubulin, cortactin, HSF1, etc. Thus, it is one of the key regulators of different physiological and pathological disease conditions. HDAC6 is involved in different signaling pathways associated with several neurological disorders, various cancers at early and advanced stage, rare diseases and immunological conditions. Therefore, targeting HDAC6 has been found to be effective for various therapeutic purposes in recent years. Though several HDAC6 inhibitors (HDAC6is) have been developed till date, only two ACY-1215 (ricolinostat) and ACY-241 (citarinostat) are in the clinical trials. A lot of work is still needed to pinpoint strictly selective as well as potent HDAC6i. Considering the recent crystal structure of HDAC6, novel HDAC6is of significant therapeutic value can be designed. Notably, the canonical pharmacophore features of HDAC6is consist of a zinc binding group (ZBG), a linker function and a cap group. Significant modifications of cap function may lead to achieve better selectivity of the inhibitors. This review details the study about the structural biology of HDAC6, the physiological and pathological role of HDAC6 in several disease states and the detailed structure-activity relationships (SARs) of the known HDAC6is. This detailed review will provide key insights to design novel and highly effective HDAC6i in the future.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Design, synthesis, and biological evaluation of novel benzimidazole derivatives as sphingosine kinase 1 inhibitor.

Sphingosine kinase 1 (SphK1) has emerged as an attractive drug target for different diseases. Recently, discovered SphK1 inhibitors have been recommended in cancer therapeutics; however, selectivity and potency are great challenges. In this study, a novel series of benzimidazoles was synthesized and evaluated as SphK1 inhibitors. Our design strategy is twofold: It aimed first to study the effect of replacing the 5-position of the benzimidazole ring with a polar carboxylic acid group on the SphK1-inhibitory activity and cytotoxicity. Our second aim was to optimize the structures of the benzimidazoles through the elongation of the chain. The enzyme inhibition potentials against all the synthesized compounds toward SphK1 were evaluated, and the results revealed that most of the studied compounds inhibited SphK1 effectively. The binding affinity of the benzimidazole derivatives toward SphK1 was measured by fluorescence binding and molecular docking. Compounds 33, 37, 39, 41, 42, 43, and 45 showed an appreciable binding affinity. Therefore, the SphK1-inhibitory potentials of compounds 33, 37, 39, 41, 42, 43, and 45 were studied and IC50 values were determined, to reveal high potency. The study showed that these compounds inhibited SphK1 with effective IC50 values. Among the studied compounds, compound 41 was the most effective one with the lowest IC50 value and a high cytotoxicity on a wide spectrum of cell lines. Molecular docking revealed that most of these compounds fit well into the ATP-binding site of SphK1 and form hydrogen bond interactions with catalytically important residues. Overall, the findings suggest the therapeutic potential of benzimidazoles in the clinical management of SphK1-associated diseases.

In Vitro and In Vivo Efficacy of a Novel Glucose-Methotrexate Conjugate in Targeted Cancer Treatment.

Methotrexate (MTX) is a commonly used antimetabolite, which inhibits folate and DNA synthesis to be effective in the treatment of various malignancies. However, MTX therapy is hindered by the lack of target tumor selectivity. We have designed, synthesized and evaluated a novel glucose-methotrexate conjugate (GLU-MTX) both in vitro and in vivo, in which a cleavable linkage allows intracellular MTX release after selective uptake through glucose transporter-1 (GLUT1). GLU-MTX inhibited the growth of colorectal (DLD-1), breast (MCF-7) and lung (A427) adenocarcinomas, squamous cell carcinoma (SCC-25), osteosarcoma (MG63) cell lines, but not in WI-38 healthy fibroblasts. In tumor cells, GLU-MTX uptake increased 17-fold compared to unconjugated MTX. 4,6-O-ethylidene-α-D-glucose (EDG), a GLUT1 inhibitor, significantly interfered with GLU-MTX induced growth inhibition, suggesting a glucose-mediated drug uptake. Glu-MTX also caused significant tumor growth delay in vivo in breast cancer-bearing mice. These results show that our GLUT-MTX conjugate can be selectively uptake by a range of tumor cells to cause their significant growth inhibition in vitro, which was also confirmed in a breast cancer model in vivo. GLUT1 inhibitor EDG interfered with these effects verifying the selective drug uptake. Accordingly, GLU-MTX offers a considerable tumor selectivity and may offer cancer growth inhibition at reduced toxicity.

Mutual Support of Ligand- and Structure-Based Approaches-To What Extent We Can Optimize the Power of Predictive Model? Case Study of Opioid Receptors.

The process of modern drug design would not exist in the current form without computational methods. They are part of every stage of the drug design pipeline, supporting the search and optimization of new bioactive substances. Nevertheless, despite the great help that is offered by in silico strategies, the power of computational methods strongly depends on the input data supplied at the stage of the predictive model construction. The studies on the efficiency of the computational protocols most often focus on global efficiency. They use general parameters that refer to the whole dataset, such as accuracy, precision, mean squared error, etc. In the study, we examined machine learning predictions obtained for opioid receptors (mu, kappa, delta) and focused on cases for which the predictions were the most accurate and the least accurate. Moreover, by using docking, we tried to explain prediction errors. We attempted to develop a rule of thumb, which can help in the prediction of compound activity towards opioid receptors via docking, especially those that have been incorrectly predicted by machine learning. We found out that although the combination of ligand- and structure-based path can be beneficial for the prediction accuracy, there still remain cases that cannot be reliably predicted by any available modeling method. In addition to challenging ligand- and structure-based predictions, we also examined the role of the application of machine-learning methods in comparison to simple statistical methods for both standard ligand-based representations (molecular fingerprints) and interaction fingerprints. All approaches were confronted in both classification (where compounds were assigned to the group of active and inactive group constructed on the basis of Ki values) and regression (where exact Ki value was predicted) experiments.

The Effect of a New Glucose-Methotrexate Conjugate on Acute Lymphoblastic Leukemia and Non-Hodgkin's Lymphoma Cell Lines.

Patients with hematologic malignancies require intensive therapies, including high-dose chemotherapy. Antimetabolite-methotrexate (MTX) has been used for many years in the treatment of leukemia and in lymphoma patients. However, the lack of MTX specificity causes a significant risk of morbidity, mortality, and severe side effects that impairs the quality of patients' life. Therefore, novel targeted therapies based on the malignant cells' common traits have become an essential treatment strategy. Glucose transporters have been found to be overexpressed in neoplastic cells, including hematologic malignancies. In this study, we biologically evaluated a novel glucose-methotrexate conjugate (Glu-MTX) in comparison to a free MTX. The research aimed to assess the effectiveness of Glu-MTX on chosen human lymphoma and leukemia cell lines. Cell cytotoxicity was verified by MTT viability test and flow cytometry. Moreover, the cell cycle and cellular uptake of Glu-MTX were evaluated. Our study reveals that conjugation of methotrexate with glucose significantly increases drug uptake and results in similar cytotoxicity of the synthesized compound. Although the finding has been confined to in vitro studies, our observations shed light on a potential therapeutic approach that increases the selectivity of chemotherapeutics and can improve leukemia and lymphoma patients' outcomes.

Ellagic Acid Controls Cell Proliferation and Induces Apoptosis in Breast Cancer Cells via Inhibition of Cyclin-Dependent Kinase 6.

Cyclin-Dependent Kinase 6 (CDK6) plays an important role in cancer progression, and thus, it is considered as an attractive drug target in anticancer therapeutics. This study presents an evaluation of dietary phytochemicals, capsaicin, tocopherol, rosmarinic acid, ursolic acid, ellagic acid (EA), limonene, caffeic acid, and ferulic acid for their potential to inhibit the activity of CDK6. Molecular docking and fluorescence binding studies revealed appreciable binding affinities of these compounds to the CDK6. Among them, EA shows the highest binding affinity for CDK6, and thus a molecular dynamics simulation study of 200 ns was performed to get deeper insights into the binding mechanism and stability of the CDK6-EA complex. Fluorescence binding studies revealed that EA binds to the CDK6 with a binding constant of K = 107 M-1 and subsequently inhibits its enzyme activity with an IC50 value of 3.053 µM. Analysis of thermodynamic parameters of CDK6-EA complex formation suggested a hydrophobic interaction driven process. The treatment of EA decreases the colonization of cancer cells and induces apoptosis. Moreover, the expression of CDK6 has been downregulated in EA-treated human breast cancer cell lines. In conclusion, this study establishes EA as a potent CDK6 inhibitor that can be further evaluated in CDK6 directed anticancer therapies.

Promising terpenes as SARS-CoV-2 spike receptor-binding domain (RBD) attachment inhibitors to the human ACE2 receptor: Integrated computational approach.

The spike protein receptor binding domain (S-RBD) is a necessary corona-viral protein for binding and entry of coronaviruses (COVs) into the host cells. Hence, it has emerged as an attractive antiviral drug target. Therefore, present study was aimed to target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) S-RBD with novel bioactive compounds to retrieve potential candidates that could serve as anti-coronavirus disease 2019 (COVID-19) drugs. In this paper, computational approaches were employed, especially the structure-based virtual screening followed by molecular dynamics (MD) simulation as well as binding energy analysis for the computational identification of specific terpenes from the medicinal plants, which can block SARS-CoV-2 S-RBD binding to Human angiotensin-converting enzyme 2 (H-ACE2) and can act as potent anti-COVID-19 drugs after further advancements. The screening of focused terpenes inhibitors database composed of ~1000 compounds with reported therapeutic potential resulted in the identification of three candidate compounds, NPACT01552, NPACT01557 and NPACT00631. These three compounds established conserved interactions, which were further explored through all-atom MD simulations, free energy calculations, and a residual energy contribution estimated by MM-PB(GB)SA method. All these compounds showed stable conformation and interacted well with the hot-spot residues of SARS-CoV-2 S-RBD. Conclusively, the reported SARS-CoV-2 S-RBD specific terpenes could serve as seeds for developing potent anti-COVID-19 drugs. Importantly, the experimentally tested glycyrrhizin (NPACT00631) against SARS-CoV could be used further in the fast-track drug development process to help curb COVID-19.

Identification and Evaluation of Inhibitors of Lipase from Malassezia restricta using Virtual High-Throughput Screening and Molecular Dynamics Studies.

Recent studies revealed the role of lipase in the pathogenicity of Malassezia restricta in dandruff and seborrheic dermatitis (D/SD). The lipase from M. restricta (Mrlip1) is considered a potential target for dandruff therapy. In this work, we performed structure-based virtual screening in Zinc database to find the natural bioactive inhibitors of Mrlip1. We identified three compounds bearing superior affinity and specificity from the Traditional Chinese Medicine database (~60,000 compounds), and their binding patterns with Mrlip1 were analyzed in detail. Additionally, we performed three sets of 100 ns MD simulations of each complex in order to understand the interaction mechanism of Mrlip1 with known inhibitor RHC80267 and the newly identified compounds such as ZINC85530919, ZINC95914464 and ZINC85530320, respectively. These compounds bind to the active site cavity and cause conformational changes in Mrlip1. The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) studies suggested that the average binding energy was stronger in the case of Mrlip1-ZINC85530919 and Mrlip1-ZINC95914464. The selected natural inhibitors might act as promising lead drugs against Mrlip1. Further, the present study will contribute to various steps involved in developing and creating potent drugs for several skin diseases including dandruff.

Identification of High-Affinity Inhibitors of Cyclin-Dependent Kinase 2 Towards Anticancer Therapy.

Cyclin-dependent kinase 2 (CDK2) is an essential protein kinase involved in the cell cycle regulation. The abnormal activity of CDK2 is associated with cancer progression and metastasis. Here, we have performed structure-based virtual screening of the PubChem database to identify potent CDK2 inhibitors. First, we retrieved all compounds from the PubChem database having at least 90% structural similarity with the known CDK2 inhibitors. The selected compounds were subjected to structure-based molecular docking studies to investigate their pattern of interaction and estimate their binding affinities with CDK2. Selected compounds were further filtered out based on their physicochemical and ADMET properties. Detailed interaction analysis revealed that selected compounds interact with the functionally important residues of the active site pocket of CDK2. All-atom molecular dynamics simulation was performed to evaluate conformational changes, stability and the interaction mechanism of CDK2 in-complex with the selected compound. We found that binding of 6-N,6-N-dimethyl-9-(2-phenylethyl)purine-2,6-diamine stabilizes the structure of CDK2 and causes minimal conformational change. Finally, we suggest that the compound (PubChem ID 101874157) would be a promising scaffold to be further exploited as a potential inhibitor of CDK2 for therapeutic management of cancer after required validation.

Design, synthesis, in silico and biological evaluation of novel 2-(4-(4-substituted piperazin-1-yl)benzylidene)hydrazine carboxamides.

A series of novel 2-(4-(4-substituted piperazin-1-yl)benzylidene)hydrazinecarboxamide derivatives has been successfully designed and synthesized to evaluate their potential as carbonic anhydrase (CA) inhibitors. The inhibitory potential of synthesized compounds against human CAI and CAII was evaluated. Compounds 3a-n exhibited [Formula: see text] values between [Formula: see text] against CAI and [Formula: see text] against CAII. Compound 3g was the most active inhibitor, with an [Formula: see text] value of [Formula: see text] against CAII. Molecular docking studies of compound 3g with CAII showed this compound fits nicely in the active site of CAII and it interacts with the zinc ion ([Formula: see text]) along with three histidine residues in the active site. Molecular dynamics simulation studies of compound 3g complexed with CAII also showed essential interactions which were maintained up to 40 ns of simulation. In vivo sub-acute toxicity study using 3g (300 mg/kg) was found non-toxic in adult Wistar rats.

Structure-based investigation of pyruvate dehydrogenase kinase-3 inhibitory potential of thymoquinone, targeting lung cancer therapy.

Protein kinases are an attractive therapeutic target for cardiovascular, cancer and neurodegenerative diseases. Cancer cells demand energy generation through aerobic glycolysis, surpassing "oxidative phosphorylation" (OXPHOS) in mitochondria. The pyruvate dehydrogenase kinases (PDKs) have many regulatory roles in energy generation balance by controlling the pyruvate dehydrogenase complex. Overexpression of PDKs is associated with the overall survival of cancer. PDK3, an isoform of PDK is highly expressed in various cancer types, is targeted for inhibition in this study. PDK3 has been shown to binds strongly with a natural compound, thymoquinone (TQ), which is known to exhibit anti-cancer potential. Detailed interaction between the PDK3 and TQ was carried out using spectroscopic and docking methods. The overall changes in the protein's structures after TQ binding were estimated by UV-Vis spectroscopy, circular dichroism and fluorescence binding studies. The kinase activity assay was also carried out to see the kinase inhibitory potential of TQ. The enzyme inhibition assay suggested an excellent inhibitory potential of TQ towards PDK3 (IC50 = 5.49 µM). We observed that TQ forms a stable complex with PDK3 without altering its structure and can be a potent PDK3 inhibitor which may be implicated in cancer therapy after desired clinical validation.

Advances in anti-EV-A71 drug development research.

BACKGROUND: Enterovirus A71 (EV-A71) is capable of causing hand, foot and mouth disease (HFMD), which may lead to neurological sequelae and even death. As EV-A71 is resistant to environmental changes and mutates easily, there is still a lack of effective treatments or globally available vaccines. AIM OF REVIEW: For more than 50 years since the HFMD epidemic, related drug research has been conducted. Progress in this area can promote the further application of existing potential drugs and develop more efficient and safe antiviral drugs, and provide useful reference for protecting the younger generation and maintaining public health security. KEY SCIENTIFIC CONCEPTS OF REVIEW: At present, researchers have identified hundreds of EV-A71 inhibitors based on screening repurposed drugs, targeted structural design, and rational modification of previously effective drugs as the main development strategies. This review systematically introduces the current potential drugs to inhibit EV-A71 infection, including viral inhibitors targeting key sites such as the viral capsid, RNA-dependent RNA polymerase (RdRp), 2C protein, internal ribosome entry site (IRES), 3C proteinase (3Cpro), and 2A proteinase (2Apro), starting from each stage of the viral life cycle. Meanwhile, the progress of host-targeting antiviral drugs and their development are summarized in terms of regulating host immunity, inhibiting autophagy or apoptosis, and regulating the cellular redox environment. In addition, the current clinical methods for the prevention and treatment of HFMD are summarized and discussed with the aim of providing support and recommendations for the treatment of enterovirus infections including EV-A71.

PF543-like compound, a promising sphingosine kinase 1 inhibitor: Structure-based virtual screening and molecular dynamic simulation approaches.

Sphingosine kinase 1 (SphK1) has been widely recognized as a significant contributor to various types of cancer, including breast, lung, prostate, and hematological cancers. This research aimed to find a potential SphK1 inhibitor through a step-by-step virtual screening of PF543 (a known SphK1 inhibitor)-like compounds obtained from the PubChem library with the Tanimoto threshold of 80 %. The virtual screening process included several steps, namely physicochemical and ADMET evaluation, PAINS filtering, and molecular docking, followed by molecular dynamics (MD) simulation and principal component analysis (PCA). The results showed that compound CID:58293960 ((3R)-1,1-dioxo-2-[[3-[(4-phenylphenoxy)methyl]phenyl]methyl]-1,2-thiazolidine-3-carboxylic acid) demonstrated high potential as SphK1 inhibitor. All-atom MD simulations were performed for 100 ns to evaluate the stability and structural changes of the docked complexes in an aqueous environment. The analysis of the time evolution data of structural deviations, compactness, PCA, and free energy landscape (FEL) indicated that the binding of CID:58293960 with SphK1 is relatively stable throughout the simulation. The results of this study provide a platform for the discovery and development of new anticancer therapeutics targeting SphK1.

Utilization of AlphaFold models for drug discovery: Feasibility and challenges. Histone deacetylase 11 as a case study.

Histone deacetylase 11 (HDAC11), an enzyme that cleaves acyl groups from acylated lysine residues, is the sole member of class IV of HDAC family with no reported crystal structure so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms which complicates the conventional template-based homology modeling. AlphaFold is a neural network machine learning approach for predicting the 3D structures of proteins with atomic accuracy even in absence of similar structures. However, the structures predicted by AlphaFold are missing small molecules as ligands and cofactors. In our study, we first optimized the HDAC11 AlphaFold model by adding the catalytic zinc ion followed by assessment of the usability of the model by docking of the selective inhibitor FT895. Minimization of the optimized model in presence of transplanted inhibitors, which have been described as HDAC11 inhibitors, was performed. Four complexes were generated and proved to be stable using three replicas of 50 ns MD simulations and were successfully utilized for docking of the selective inhibitors FT895, MIR002 and SIS17. For SIS17, The most reasonable pose was selected based on structural comparison between HDAC6, HDAC8 and the HDAC11 optimized AlphaFold model. The manually optimized HDAC11 model is thus able to explain the binding behavior of known HDAC11 inhibitors and can be used for further structure-based optimization.

Structure-guided design and development of cyclin-dependent kinase 4/6 inhibitors: A review on therapeutic implications.

Cyclin-dependent kinase 6 (EC 2.7.11.22) play significant roles in numerous biological processes and triggers cell cycle events. CDK6 controlled the transcriptional regulation. A dysregulated function of CDK6 is linked with the development of progression of multiple tumor types. Thus, it is considered as an effective drug target for cancer therapy. Based on the direct roles of CDK4/6 in tumor development, numerous inhibitors developed as promising anti-cancer agents. CDK4/6 inhibitors regulate the G1 to S transition by preventing Rb phosphorylation and E2F liberation, showing potent anti-cancer activity in several tumors, including HR+/HER2- breast cancer. CDK4/6 inhibitors such as abemaciclib, palbociclib, and ribociclib, control cell cycle, provoke cell senescence, and induces tumor cell disturbance in pre-clinical studies. Here, we discuss the roles of CDK6 in cancer along with the present status of CDK4/6 inhibitors in cancer therapy. We further discussed, how structural features of CDK4/6 could be implicated in the design and development of potential anti-cancer agents. In addition, the therapeutic potential and limitations of available CDK4/6 inhibitors are described in detail. Recent pre-clinical and clinical information for CDK4/6 inhibitors are highlighted. In addition, combination of CDK4/6 inhibitors with other drugs for the therapeutic management of cancer are discussed.