Cyclin-Dependent Kinase 2 in Cellular Senescence and Cancer. A Structural and Functional Review

Deficiency of ATF2 retards senescence induced by replication stress and pamidronate in mouse jaw bone marrow stem cells

The aging process is associated with a loss of bone mass and an accumulation of senescent cells, which is under epigenetic control. Morphological and molecular analysis revealed a notable reduction in bone mass and alveolar crest height in aged mice, accompanied by increased levels of senescent mouse jaw bone marrow stem cells (mJBMSCs). To investigate whether specific transcription factors are involved, assay for transposase-accessible chromatin with sequencing (ATAC-seq) was performed on mJBMSCs isolated from 2-, 4-, 8-, and 20-month-old mice. In 20-month-old mJBMSCs, increased chromatin accessibility was observed alongside elevated expression of activating transcription factor 2 (ATF2) in both cells and alveolar bone. Silencing Atf2 in mJBMSCs failed to reverse physiological aging, but delayed replication stress and pamidronate (PAM) induced senescence. The analysis of ATAC-seq and RNA sequencing indicated that the differentially expressed genes upregulated by PAM but downregulated by ATF2 deficiency were related to some key biological processes, including negative regulation of cell proliferation, inflammatory response, adipogenesis, and cellular senescence. The dual-luciferase assay was conducted to demonstrate that ATF2 enhances Cdkn2a transcription by binding to its promoter region. Our findings suggest significant chromatin alterations in aged mJBMSCs, positioning ATF2 as a potential target for combating externally induced senescence.

Targeting CDK2 Confers Vulnerability to Lenvatinib Via Driving Senescence in Anaplastic Thyroid Cancer

Anaplastic thyroid cancer (ATC) is the most lethal tumor arising from thyroid follicular epithelium. Lenvatinib is an off-label use option for ATC patients in many countries but an approved prescription in Japan. However, lenvatinib resistance is a substantial clinical challenge. Clinical ATC samples including lenvatinib-resistant tumors are used to build patient-derived cells and patient-derived xenografts. High-throughput drug screening and synergy analyses are performed to identify an effective combination partner for lenvatinib. Cellular functions are detected by cell senescence, apoptosis, cell cycle, cell viability and colony formation assays. CDK2 inhibition showed the significant synthetic lethality with lenvatinib via inhibiting G1/S transition and inducing cell senescence in ATC. High expression of CDK2 is associated with lenvatinib resistance and poor clinical outcomes of ATC patients. Lenvatinib increased protein expression of CDK2 in lenvatinib-resistant ATC cells. Mechanistically, lenvatinib inhibited protein degradation of CDK2 via reducing CDK2's interaction with the RACK1-FBW7 complex, which is involved in ubiquitination and subsequent proteasomal degradation of CDK2. Combination of CDK2 inhibitors in clinical trials (Dinaciclib or PF-07104091) and lenvatinib markedly suppressed growth of xenograft tumors from the lenvatinib-resistant patient. The findings support the combination therapy strategy of lenvatinib and CDK2 inhibitor for lenvatinib-resistant ATC patients with high CDK2 expression.

Homoharringtonine in the treatment of acute myeloid leukemia: A review

Acute myeloid leukemia (AML) is a hematological malignancy characterized by the accumulation of immature myeloid precursor cells. Over half of AML patients fail to achieve long-term disease-free survival under existing therapy, and the overall prognosis is poor, necessitating the urgent development of novel therapeutic approaches. The plant alkaloid homoharringtonine (HHT), which has anticancer properties, was first identified more than 40 years ago. It works in a novel method of action that prevents the early elongation phase of protein synthesis. HHT has been widely utilized in the treatment of AML, with strong therapeutic effects, few toxic side effects, and the ability to enhance AML patients' prognoses. In AML, HHT can induce cell apoptosis through multiple pathways, exerting synergistic antitumor effects, according to clinical and pharmacological research. About its modes of action, some findings have been made recently. This paper reviews the development of research on the mechanisms of HHT in treating AML to offer insights for further research and clinical therapy.

SAnDReS 2.0: Development of machine-learning models to explore the scoring function space

Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.

The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform

Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmacology effects and provides unique pharmacological insights for drug repurposing development of existing kinase inhibitor drugs. However, experimental profiling of whole kinome selectivity is still time-consuming and resource-demanding. Here, we report a deep learning classification model using an in-house built data set of inhibitors against 191 well-representative kinases constructed based on a novel strategy by systematically cleaning and integrating six public data sets. This model, a multitask deep neural network, predicts the kinome selectivity profiles of compounds with novel structures. The model demonstrates excellent predictive performance, with auROC, prc-AUC, Accuracy, and Binary_cross_entropy of 0.95, 0.92, 0.90, and 0.37, respectively. It also performs well in a priori testing for inhibitors targeting different categories of proteins from internal compound collections, significantly improving over similar models on data sets from practical application scenarios. Integrated to subsequent machine learning-enhanced virtual screening workflow, novel CDK2 kinase inhibitors with potent kinase inhibitory activity and excellent kinome selectivity profiles are successfully identified. Additionally, we developed a free online web server, KinomePro-DL, to predict the kinome selectivity profiles and kinome-wide polypharmacology effects of small molecules (available on kinomepro-dl.pharmablock.com). Uniquely, our model allows users to quickly fine-tune it with their own training data sets, enhancing both prediction accuracy and robustness.

Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics

Introduction

Heart failure (HF) and kidney failure (KF) are closely related conditions that often coexist, posing a complex clinical challenge. Understanding the shared mechanisms between these two conditions is crucial for developing effective therapies.

Methods

This study employed transcriptomic analysis to unveil molecular signatures and novel biomarkers for both HF and KF. A total of 2869 shared differentially expressed genes (DEGs) were identified in patients with HF and KF compared to healthy controls. Functional enrichment analysis was performed to explore the common mechanisms underlying these conditions. A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. These genes were further analyzed using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA), with their diagnostic values validated in both training and validation sets. Molecular docking studies were conducted. Additionally, immune cell infiltration and correlation analyses were performed to assess the relationship between immune responses and the identified biomarkers.

Results

The functional enrichment analysis indicated that the common mechanisms are associated with cellular homeostasis, cell communication, cellular replication, inflammation, and extracellular matrix (ECM) production, with the PI3K-Akt signaling pathway being notably enriched. The PPI network revealed two key protein clusters related to the cell cycle and inflammation. CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. Additionally, docking studies with CDK2 and CCND1 were performed to evaluate potential drug candidates. Immune cell infiltration and correlation analyses highlighted the immune microenvironment, and that CDK2 and CCND1 are associated with immune responses in HF and KF.

Discussion

This study identifies CDK2 and CCND1 as novel biomarkers linking cell cycle regulation and inflammation in heart and kidney failure. These findings offer new insights into the molecular mechanisms of HF and KF and present potential targets for diagnosis and therapy.

RNF26-mediated ubiquitination of TRIM21 promotes bladder cancer progression

RNF26 is an important E3 ubiquitin ligase that has been associated with poor prognosis in bladder cancer. However, the underlying mechanisms of RNF26 in bladder cancer tumorigenesis are not fully understood. In the present study, we found that RNF26 expression level was significantly upregulated in the bladder cancer tissues, and higher RNF26 expression is closely associated with poorer prognosis, lower immune cell infiltration, and more sensitive to immune checkpoint blockade drugs and chemotherapy drugs, including cisplatin, VEGFR-targeting drugs and MET-targeting drugs. RNF26 knockdown in UMUC3 and T24 cell lines inhibited cell growth, colony formation and migratory capacity. Meanwhile, RNF26 overexpression had the opposite effects. Mechanistically, RNF26 exerts its oncogenic function by binding to TRIM21 and promoting its ubiquitination and subsequent degradation. Moreover, we revealed ZHX3 as a downstream target of RNF26/TRIM21 pathway in bladder cancer. Taken together, we identified a novel RNF26/TRIM21/ZHX3 axis that promotes bladder cancer progression. Thus, the RNF26/TRIM21/ZHX3 axis constitutes a potential efficacy predictive marker and may serve as a therapeutic target for the treatment of bladder cancer.

Curcumin in Cancer and Inflammation: An In-Depth Exploration of Molecular Interactions, Therapeutic Potentials, and the Role in Disease Management

This paper delves into the diverse and significant roles of curcumin, a polyphenolic compound from the Curcuma longa plant, in the context of cancer and inflammatory diseases. Distinguished by its unique molecular structure, curcumin exhibits potent biological activities including anti-inflammatory, antioxidant, and potential anticancer effects. The research comprehensively investigates curcumin's molecular interactions with key proteins involved in cancer progression and the inflammatory response, primarily through molecular docking studies. In cancer, curcumin's effectiveness is determined by examining its interaction with pivotal proteins like CDK2, CK2α, GSK3β, DYRK2, and EGFR, among others. These interactions suggest curcumin's potential role in impeding cancer cell proliferation and survival. Additionally, the paper highlights curcumin's impact on inflammation by examining its influence on proteins such as COX-2, CRP, PDE4, and MD-2, which are central to the inflammatory pathway. In vitro and clinical studies are extensively reviewed, shedding light on curcumin's binding mechanisms, pharmacological impacts, and therapeutic application in various cancers and inflammatory conditions. These studies are pivotal in understanding curcumin's functionality and its potential as a therapeutic agent. Conclusively, this review emphasizes the therapeutic promise of curcumin in treating a wide range of health issues, attributed to its complex chemistry and broad pharmacological properties. The research points towards curcumin's growing importance as a multi-faceted natural compound in the medical and scientific community.

Differential gene expression of immunity and inflammation genes in colorectal cancer using targeted RNA sequencing

Introduction

Colorectal cancer (CRC) is a heterogeneous disease caused by molecular changes, as driver mutations, gene methylations, etc., and influenced by tumor microenvironment (TME) pervaded with immune cells with both pro- and anti-tumor effects. The studying of interactions between the immune system (IS) and the TME is important for developing effective immunotherapeutic strategies for CRC. In our study, we focused on the analysis of expression profiles of inflammatory and immune-relevant genes to identify aberrant signaling pathways included in carcinogenesis, metastatic potential of tumors, and association of Kirsten rat sarcoma virus (KRAS) gene mutation.

Methods

A total of 91 patients were enrolled in the study. Using NGS, differential gene expression analysis of 11 tumor samples and 11 matching non-tumor controls was carried out by applying a targeted RNA panel for inflammation and immunity genes containing 475 target genes. The obtained data were evaluated by the CLC Genomics Workbench and R library. The significantly differentially expressed genes (DEGs) were analyzed in Reactome GSA software, and some selected DEGs were used for real-time PCR validation.

Results

After prioritization, the most significant differences in gene expression were shown by the genes TNFRSF4, IRF7, IL6R, NR3CI, EIF2AK2, MIF, CCL5, TNFSF10, CCL20, CXCL11, RIPK2, and BLNK. Validation analyses on 91 samples showed a correlation between RNA-seq data and qPCR for TNFSF10, RIPK2, and BLNK gene expression. The top differently regulated signaling pathways between the studied groups (cancer vs. control, metastatic vs. primary CRC and KRAS positive and negative CRC) belong to immune system, signal transduction, disease, gene expression, DNA repair, and programmed cell death.

Conclusion

Analyzed data suggest the changes at more levels of CRC carcinogenesis, including surface receptors of epithelial or immune cells, its signal transduction pathways, programmed cell death modifications, alterations in DNA repair machinery, and cell cycle control leading to uncontrolled proliferation. This study indicates only basic molecular pathways that enabled the formation of metastatic cancer stem cells and may contribute to clarifying the function of the IS in the TME of CRC. A precise identification of signaling pathways responsible for CRC may help in the selection of personalized pharmacological treatment.

Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy

Cyclin-dependent kinase 2 (CDK2) is a promising target for cancer treatment, developing new effective CDK2 inhibitors is of great significance in anticancer therapy. The involvement of CDK2 in tumorigenesis has been debated, but recent evidence suggests that specifically inhibiting CDK2 could be beneficial in treating certain tumors. This approach remains attractive in the development of anticancer drugs. Several small-molecule inhibitors targeting CDK2 have reached clinical trials, but a selective inhibitor for CDK2 is yet to be discovered. In this study, we conducted machine learning-based drug designing to search for a drug candidate for CDK2. Machine learning models, including k-NN, SVM, RF, and GNB, were created to detect active and inactive inhibitors for a CDK2 drug target. The models were assessed using 10-fold cross-validation to ensure their accuracy and reliability. These methods are highly suitable for classifying compounds as either active or inactive through the virtual screening of extensive compound libraries. Subsequently, machine learning techniques were employed to analyze the test dataset obtained from the zinc database. A total of 25 compounds with 98% accuracy were predicted as active against CDK2. These compounds were docked into CDK2's active site. Finally, three compounds were selected based on good docking score, and, along with a reference compound, underwent MD simulation. The Gaussian naïve Bayes model yielded superior results compared to other models. The top three hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as CDK2 inhibitors.

The emerging roles of TRIM21 in coordinating cancer metabolism, immunity and cancer treatment

Tripartite motif containing-21 (TRIM21), an E3 ubiquitin ligase, was initially found to be involved in antiviral responses and autoimmune diseases. Recently studies have reported that TRIM21 plays a dual role in cancer promoting and suppressing in the occurrence and development of various cancers. Despite the fact that TRIM21 has effects on multiple metabolic processes, inflammatory responses and the efficacy of tumor therapy, there has been no systematic review of these topics. Herein, we discuss the emerging role and function of TRIM21 in cancer metabolism, immunity, especially the immune response to inflammation associated with tumorigenesis, and also the cancer treatment, hoping to shine a light on the great potential of targeting TRIM21 as a therapeutic target.

Structure-Based Design of 2-Aminopurine Derivatives as CDK2 Inhibitors for Triple-Negative Breast Cancer

Cyclin-dependent kinase 2 (CDK2) regulates the progression of the cell cycle and is critically associated with tumor growth. Selective CDK2 inhibition provides a potential therapeutic benefit against certain tumors. Purines and related heterocycle (e.g., R-Roscovitine) are important scaffolds in the development of CDK inhibitors. Herein, we designed a new series of 2-aminopurine derivatives based on the fragment-centric pocket mapping analysis of CDK2 crystal structure. Our results indicated that the introduction of polar substitution at the C-6 position of purine would be beneficial for CDK2 inhibition. Among them, compound 11l showed good CDK2 inhibitory activity (IC₅₀ = 19 nM) and possessed good selectivity against other CDKs. Further in vitro tests indicated that compound 11l possesses anti-proliferation activity in triple-negative breast cancer (TNBC) cells. Moreover, molecular dynamics simulation suggested the favorable binding mode of compound 11l, which may serve as a new lead compound for the future development of CDK2 selective inhibitors.

Inhibition of the CDK2 and Cyclin A complex leads to autophagic degradation of CDK2 in cancer cells

Cyclin-dependent kinase 2 (CDK2) complex is significantly over-activated in many cancers. While it makes CDK2 an attractive target for cancer therapy, most inhibitors against CDK2 are ATP competitors that are either nonspecific or highly toxic, and typically fail clinical trials. One alternative approach is to develop non-ATP competitive inhibitors; they disrupt interactions between CDK2 and either its partners or substrates, resulting in specific inhibition of CDK2 activities. In this report, we identify two potential druggable pockets located in the protein-protein interaction interface (PPI) between CDK2 and Cyclin A. To target the potential druggable pockets, we perform a LIVS in silico screening of a library containing 1925 FDA approved drugs. Using this approach, homoharringtonine (HHT) shows high affinity to the PPI and strongly disrupts the interaction between CDK2 and cyclins. Further, we demonstrate that HHT induces autophagic degradation of the CDK2 protein via tripartite motif 21 (Trim21) in cancer cells, which is confirmed in a leukemia mouse model and in human primary leukemia cells. These results thus identify an autophagic degradation mechanism of CDK2 protein and provide a potential avenue towards treating CDK2-dependent cancers.

CDK2 can drive the proliferation of cancer cells. Here, the authors screened for a non-ATP competitive inhibitor of the CDK2/cylinA complex and find that Homoharringtonine can disrupt the complex and promote the degradation of CDK2.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

BACKGROUND

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity.

OBJECTIVE

This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2.

METHOD

We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs.

RESULTS

Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina.

CONCLUSION

All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

BACKGROUND

Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs.

OBJECTIVE

Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes.

METHODS

SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions.

RESULTS

Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions.

CONCLUSION

Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.

Alcoholism and Osteoimmunology

BACKGROUND

Chronic consumption of alcohol has an adverse effect on the skeletal system, which may lead to osteoporosis, delayed fracture healing and osteonecrosis of the femoral head. Currently, the treatment is limited, therefore, there is an urgent need to determine the underline mechanism and develop a new treatment. It is well-known that normal bone remodeling relies on the balance between osteoclast-mediated bone resorption and - mediated bone formation. Various factors can destroy the balance, including the dysfunction of the immune system. In this review, we summarized the relevant research in the alcoholic osteopenia with a focus on the abnormal osteoimmunology signals. We provided a new theoretical basis for the prevention and treatment of the alcoholic bone.

METHODS

We searched PubMed for publications from 1 January 1980 to 1 February 2020 to identify relevant and recent literature, summarizing evaluation and the prospect of alcoholic osteopenia. Detailed search terms were 'alcohol', 'alcoholic osteoporosis', 'alcoholic osteopenia' 'immune', 'osteoimmunology', 'bone remodeling', 'osteoporosis treatment' and 'osteoporosis therapy'.

RESULTS

A total of 135 papers are included in the review. About 60 papers described the mechanisms of alcohol involved in bone remodeling. Some papers were focused on the pathogenesis of alcohol on bone through osteoimmune mechanisms.

CONCLUSION

There is a complex network of signals between alcohol and bone remodeling and intercellular communication of osteoimmune may be a potential mechanism for alcoholic bone. Studying the osteoimmune mechanism is critical for drug development specific to the alcoholic bone disorder.

Targeting Mammalian Target of Rapamycin: Prospects for the Treatment of Inflammatory Bowel Diseases

Inflammatory bowel disease (IBD) is a general term for a group of chronic and progressive disorders. Several cellular and biomolecular pathways are implicated in the pathogenesis of IBD, yet the etiology is unclear. Activation of the mammalian target of rapamycin (mTOR) pathway in the intestinal epithelial cells was also shown to induce inflammation. This review focuses on the inhibition of the mTOR signaling pathway and its potential application in treating IBD. We also provide an overview of plant-derived compounds that are beneficial for the IBD management through modulation of the mTOR pathway. Data were extracted from clinical, in vitro and in vivo studies published in English between 1995 and May 2019, which were collected from PubMed, Google Scholar, Scopus and Cochrane library databases. Results of various studies implied that inhibition of the mTOR signaling pathway downregulates the inflammatory processes and cytokines involved in IBD. In this context, a number of natural products might reverse the pathological features of the disease. Furthermore, mTOR provides a novel drug target for IBD. Comprehensive clinical studies are required to confirm the efficacy of mTOR inhibitors in treating IBD.

Advances in Drug Therapy for Systemic Lupus Erythematosus

BACKGROUND

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a local or systemic inflammatory response. At present, the increasing research results show that the pathogenesis of the disease is complex, and the methods of clinical treatment also show diversity. This review analyzes and summarizes the existing mechanism research and drug treatment methods in order to provide a reference value for further drug research and development.

METHOD

We carried out a thorough literature search using databases. According to the main purpose of the article, irrelevant articles were excluded after further examination and directly relevant articles were included. Finally, the information related to the article was summarized.

RESULT

In this article, seventy-four articles are included. According to related articles, there are mainly four kinds of drugs, namely antimalarial drugs, glucocorticoids, immunosuppressive agents and biological agents. About fifty-five articles summarized the drugs for the treatment of systemic lupus erythematosus. The rest of the articles were related to the research progress of the mechanism of systemic lupus erythematosus.

CONCLUSION

This article describes the pathogenesis of systemic lupus erythematosus, and summarizes the traditional and new therapeutic drugs, which is not only beneficial to the treatment of lupus erythematosus patients, but also plays a vital reference significance for the future development of new systemic lupus erythematosus drugs.

Impact of Epigenetic Alterations in the Development of Oral Diseases

BACKGROUND

Epigenetic mechanisms alter gene expression and regulate vital cellular processes that contribute to the onset and progression of major dental diseases. Their reversible character may prove beneficial for therapeutic targeting. This review aims to provide an update on the main epigenetic changes that contribute to the pathogenesis of Oral Squamous Cell Carcinoma (OSCC), pulpitis and periodontitis as well as dental caries and congenital orofacial malformations, in an effort to identify potential therapeutic targets.

METHODS

We undertook a structured search of bibliographic databases (PubMed and MEDLINE) for peer-reviewed epigenetic research studies focused on oral diseases in the last ten years. A qualitative content analysis was performed in screened papers and a critical discussion of main findings is provided.

RESULTS

Several epigenetic modifications have been associated with OSCC pathogenesis, including promoter methylation of genes involved in DNA repair, cell cycle regulation and proliferation leading to malignant transformation. Additionally, epigenetic inactivation of tumor suppressor genes, overexpression of histone chaperones and several microRNAs are implicated in OSCC aggressiveness. Changes in the methylation patterns of IFN-γ and trimethylation of histone Η3Κ27 have been detected in pulpitis, along with an aberrant expression of several microRNAs, mainly affecting cytokine production. Chronic periodontal disease has been associated with modifications in the methylation patterns of Toll-Like Receptor 2, Prostaglandin synthase 2, E-cadherin and some inflammatory cytokines, along with the overexpression of miR-146a and miR155. Furthermore, DNA methylation was found to regulate amelogenesis and has been implicated in the pathogenesis of dental caries as well as in several congenital orofacial malformations.

CONCLUSION

Strong evidence indicates that epigenetic changes participate in the pathogenesis of oral diseases and epigenetic targeting may be considered as a complementary therapeutic scheme to the current management of oral health.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

BACKGROUND

The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities.

OBJECTIVE

Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures.

METHODS

We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models.

RESULTS

Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data.

CONCLUSION

Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

Inhibiting CDK6 Activity by Quercetin Is an Attractive Strategy for Cancer Therapy

Cyclin-dependent kinase 6 (CDK6) is a potential drug target that plays an important role in the progression of different types of cancers. We performed in silico and in vitro screening of different natural compounds and found that quercetin has a high binding affinity for the CDK6 and inhibits its activity with an IC50 = 5.89 μM. Molecular docking and a 200 ns whole atom simulation of the CDK6-quercetin complex provide insights into the binding mechanism and stability of the complex. Binding parameters ascertained by fluorescence and isothermal titration calorimetry studies revealed a binding constant in the range of 107 M-1 of quercetin to the CDK6. Thermodynamic parameters associated with the formation of the CDK6-quercetin complex suggested an electrostatic interaction-driven process. The cell-based protein expression studies in the breast (MCF-7) and lung (A549) cancer cells revealed that the treatment of quercetin decreases the expression of CDK6. Quercetin also decreases the viability and colony formation potential of selected cancer cells. Moreover, quercetin induces apoptosis, by decreasing the production of reactive oxygen species and CDK6 expression. Both in silico and in vitro studies highlight the significance of quercetin for the development of anticancer leads in terms of CDK6 inhibitors.

Two dimensional proteomic analysis of serum shows immunological proteins exclusively expressed in sulfur mustard exposed patients with long term pulmonary complications

BACKGROUND

Despite more than 30 years after utilization of sulfur mustard or bis (2-chloroethyl) sulfide (SM) by Iraqi troops against Iranian military members and civilians, there are a lot of reported delayed complications for the exposed people. Nonetheless, the molecular mechanism of action from this chemical warfare agent is not recognized yet.

MATERIAL AND METHOD

In this study, we employed two dimensional gel electrophoresis (2DE) technique to investigate the serum proteins from chemical exposed people compared to non-exposed individuals to provide an inside into molecular mechanism of this chemical agent. Each group was divided into two subgroups including individuals with, and without respiratory complications. For each group, 10 individuals were included after informed consent.

RESULT

The results showed protein spots, which were exclusively/mainly expressed in chemical exposed patients with complications, including T cell receptor alpha, and hematopoietic cell signal transducer. Also there were protein spots that were expressed only in all exposed groups (with and without complications). On the other hand, we could identify protein spots that were exclusively expressed/altered only in non-exposed group with complications including Pre T-cell antigen receptor, CD40 ligand, and multidrug and toxin extrusion proteins.

CONCLUSION

Our investigation could result in identification of proteins that are associated to chemical exposure, as well as those specific for respiratory complications irrespective of chemical exposure. These candidate proteins can be used as biomarker, as well as a base for understanding the molecular mechanism of this chemical agent.

NOB1: A Potential Biomarker or Target in Cancer

Human NIN1/RPN12 binding protein 1 homolog (NOB1), an RNA binding protein, is expressed ubiquitously in normal tissues such as the lung, liver, and spleen. Its core physiological function is to regulate protease activities and participate in maintaining RNA metabolism and stability. NOB1 is overexpressed in a variety of cancers, including pancreatic cancer, non-small cell lung cancer, ovarian cancer, prostate carcinoma, osteosarcoma, papillary thyroid carcinoma, colorectal cancer, and glioma. Although existing data indicate that NOB1 overexpression is associated with cancer growth, invasion, and poor prognosis, the molecular mechanisms behind these effects and its exact roles remain unclear. Several studies have confirmed that NOB1 is clinically relevant in different cancers, and further research at the molecular level will help evaluate the role of NOB1 in tumors. NOB1 has become an attractive target in anticancer therapy because it is overexpressed in many cancers and mediates different stages of tumor development. Elucidating the role of NOB1 in different signaling pathways as a potential cancer treatment will provide new ideas for existing cancer treatment methods. This review summarizes the research progress made into NOB1 in cancer in the past decade; this information provides valuable clues and theoretical guidance for future anticancer therapy by targeting NOB1.

Multiparameter Kinetic Analysis for Covalent Fragment Optimization by Using Quantitative Irreversible Tethering (qIT)

Chemical probes that covalently modify cysteine residues in a protein‐specific manner are valuable tools for biological investigations. Covalent fragments are increasingly implemented as probe starting points, but the complex relationship between fragment structure and binding kinetics makes covalent fragment optimization uniquely challenging. We describe a new technique in covalent probe discovery that enables data‐driven optimization of covalent fragment potency and selectivity. This platform extends beyond the existing repertoire of methods for identifying covalent fragment hits by facilitating rapid multiparameter kinetic analysis of covalent structure–activity relationships through the simultaneous determination of K_i, k_inact and intrinsic reactivity. By applying this approach to develop novel probes against electrophile‐sensitive kinases, we showcase the utility of the platform in hit identification and highlight how multiparameter kinetic analysis enabled a successful fragment‐merging strategy.

Aging of the cells: Insight into cellular senescence and detection Methods

Cellular theory of aging states that human aging is the result of cellular aging, in which an increasing proportion of cells reach senescence. Senescence, from the Latin word senex, means "growing old," is an irreversible growth arrest which occurs in response to damaging stimuli, such as DNA damage, telomere shortening, telomere dysfunction and oncogenic stress leading to suppression of potentially dysfunctional, transformed, or aged cells. Cellular senescence is characterized by irreversible cell cycle arrest, flattened and enlarged morphology, resistance to apoptosis, alteration in gene expression and chromatin structure, expression of senescence associated- β-galactosidase (SA-β-gal) and acquisition of senescence associated secretory phenotype (SASP). In this review paper, different types of cellular senescence including replicative senescence (RS) which occurs due to telomere shortening and stress induced premature senescence (SIPS) which occurs in response to different types of stress in cells, are discussed. Biomarkers of cellular senescence and senescent assays including BrdU incorporation assay, senescence associated- β-galactosidase (SA-β-gal) and senescence-associated heterochromatin foci assays to detect senescent cells are also addressed.

Molecular Dynamics Simulations with NAMD2

X-ray diffraction crystallography is the primary technique to determine the three-dimensional structures of biomolecules. Although a robust method, X-ray crystallography is not able to access the dynamical behavior of macromolecules. To do so, we have to carry out molecular dynamics simulations taking as an initial system the three-dimensional structure obtained from experimental techniques or generated using homology modeling. In this chapter, we describe in detail a tutorial to carry out molecular dynamics simulations using the program NAMD2. We chose as a molecular system to simulate the structure of human cyclin-dependent kinase 2.

Docking with AutoDock4

AutoDock is one of the most popular receptor-ligand docking simulation programs. It was first released in the early 1990s and is in continuous development and adapted to specific protein targets. AutoDock has been applied to a wide range of biological systems. It has been used not only for protein-ligand docking simulation but also for the prediction of binding affinity with good correlation with experimental binding affinity for several protein systems. The latest version makes use of a semi-empirical force field to evaluate protein-ligand binding affinity and for selecting the lowest energy pose in docking simulation. AutoDock4.2.6 has an arsenal of four search algorithms to carry out docking simulation including simulated annealing, genetic algorithm, and Lamarckian algorithm. In this chapter, we describe a tutorial about how to perform docking with AutoDock4. We focus our simulations on the protein target cyclin-dependent kinase 2.

Higher Accuracy Achieved for Protein-Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking

Molecular docking plays an indispensable role in predicting the receptor-ligand interactions in which the protein receptor is usually kept rigid, whereas the ligand is treated as being flexible. Because of the inherent flexibility of proteins, the binding pocket of apo receptors might undergo significant conformational rearrangement upon ligand binding, which limits the prediction accuracy of docking. Here, we present an iterative anisotropic network model (iterANM)-based ensemble docking approach, which generates multiple holo-like receptor structures starting from the apo receptor and incorporates protein flexibility into docking. In a validation data set consisting of 233 chemically diverse cyclin-dependent kinase 2 (CDK2) inhibitors, the iterANM-based ensemble docking achieves higher capacity to reproduce native-like binding poses compared with those using single apo receptor conformation or conformational ensemble from molecular dynamics simulations. The prediction success rate within the top5-ranked binding poses produced by the iterANM can further be improved through reranking with the molecular mechanics-Poisson-Boltzmann surface area method. In a smaller data set with 58 CDK2 inhibitors, the iterANM-based ensemble shows a higher success rate compared with the flexible receptor-based docking procedure AutoDockFR and other receptor conformation generation approaches. Further, an additional docking test consisting of 10 diverse receptor-ligand combinations shows that the iterANM is robustly applicable for different receptor structures. These results suggest the iterANM-based ensemble docking as an accurate, efficient, and practical framework to predict the binding mode of a ligand for receptors with flexibility.

Exploring the computational methods for protein-ligand binding site prediction

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

Transcriptional activation of CBFβ by CDK11p110 is necessary to promote osteosarcoma cell proliferation

BACKGROUND

Aberrant expression of cyclin-dependent protein kinases (CDK) is a hallmark of cancer. CDK11 plays a crucial role in cancer cell growth and proliferation. However, the molecular mechanisms of CDK11 and CDK11 transcriptionally regulated genes are largely unknown.

METHODS

In this study, we performed a global transcriptional analysis using gene array technology to investigate the transcriptional role of CDK11 in osteosarcoma. The promoter luciferase assay, chromatin immunoprecipitation assay, and Gel Shift assay were used to identify direct transcriptional targets of CDK11. Clinical relevance and function of core-binding factor subunit beta (CBFβ) were further accessed in osteosarcoma.

RESULTS

We identified a transcriptional role of protein-DNA interaction for CDK11p110, but not CDK11p58, in the regulation of CBFβ expression in osteosarcoma cells. The CBFβ promoter luciferase assay, chromatin immunoprecipitation assay, and Gel Shift assay confirmed that CBFβ is a direct transcriptional target of CDK11. High expression of CBFβ is associated with poor outcome in osteosarcoma patients. Expression of CBFβ contributes to the proliferation and metastatic behavior of osteosarcoma cells.

CONCLUSIONS

These data establish CBFβ as a mediator of CDK11p110 dependent oncogenesis and suggest that targeting the CDK11- CBFβ pathway may be a promising therapeutic strategy for osteosarcoma treatment.

How Docking Programs Work

Protein-ligand docking simulations are of central interest for computer-aided drug design. Docking is also of pivotal importance to understand the structural basis for protein-ligand binding affinity. In the last decades, we have seen an explosion in the number of three-dimensional structures of protein-ligand complexes available at the Protein Data Bank. These structures gave further support for the development and validation of in silico approaches to address the binding of small molecules to proteins. As a result, we have now dozens of open source programs and web servers to carry out molecular docking simulations. The development of the docking programs and the success of such simulations called the attention of a broad spectrum of researchers not necessarily familiar with computer simulations. In this scenario, it is essential for those involved in experimental studies of protein-ligand interactions and biophysical techniques to have a glimpse of the basics of the protein-ligand docking simulations. Applications of protein-ligand docking simulations to drug development and discovery were able to identify hits, inhibitors, and even drugs. In the present chapter, we cover the fundamental ideas behind protein-ligand docking programs for non-specialists, which may benefit from such knowledge when studying molecular recognition mechanism.

Docking with GemDock

GEMDOCK is a protein-ligand docking software that makes use of an elegant biologically inspired computational methodology based on the differential evolution algorithm. As any docking program, GEMDOCK has two major features to predict the binding of a small-molecule ligand to the binding site of a protein target: the search algorithm and the scoring function to evaluate the generated poses. The GEMDOCK scoring function uses a piecewise potential energy function integrated into the differential evolutionary algorithm. GEMDOCK has been applied to a wide range of protein systems with docking accuracy similar to other docking programs such as Molegro Virtual Docker, AutoDock4, and AutoDock Vina. In this chapter, we explain how to carry out protein-ligand docking simulations with GEMDOCK. We focus this tutorial on the protein target cyclin-dependent kinase 2.

Electrostatic Energy in Protein-Ligand Complexes

Computational analysis of protein-ligand interactions is of pivotal importance for drug design. Assessment of ligand binding energy allows us to have a glimpse of the potential of a small organic molecule as a ligand to the binding site of a protein target. Considering scoring functions available in docking programs such as AutoDock4, AutoDock Vina, and Molegro Virtual Docker, we could say that they all rely on equations that sum each type of protein-ligand interactions to model the binding affinity. Most of the scoring functions consider electrostatic interactions involving the protein and the ligand. In this chapter, we present the main physics concepts necessary to understand electrostatics interactions relevant to molecular recognition of a ligand by the binding pocket of a protein target. Moreover, we analyze the electrostatic potential energy for an ensemble of structures to highlight the main features related to the importance of this interaction for binding affinity.

SAnDReS: A Computational Tool for Docking

Since the early 1980s, we have witnessed considerable progress in the development and application of docking programs to assess protein-ligand interactions. Most of these applications had as a goal the identification of potential new binders to protein targets. Another remarkable progress is taking place in the determination of the structures of protein-ligand complexes, mostly using X-ray diffraction crystallography. Considering these developments, we have a favorable scenario for the creation of a computational tool that integrates into one workflow all steps involved in molecular docking simulations. We had these goals in mind when we developed the program SAnDReS. This program allows the integration of all computational features related to modern docking studies into one workflow. SAnDReS not only carries out docking simulations but also evaluates several docking protocols allowing the selection of the best approach for a given protein system. SAnDReS is a free and open-source (GNU General Public License) computational environment for running docking simulations. Here, we describe the combination of SAnDReS and AutoDock4 for protein-ligand docking simulations. AutoDock4 is a free program that has been applied to over a thousand receptor-ligand docking simulations. The dataset described in this chapter is available for downloading at https://github.com/azevedolab/sandres.

Van der Waals Potential in Protein Complexes

Van der Waals forces are determinants of the formation of protein-ligand complexes. Physical models based on the Lennard-Jones potential can estimate van der Waals interactions with considerable accuracy and with a computational complexity that allows its application to molecular docking simulations and virtual screening of large databases of small organic molecules. Several empirical scoring functions used to evaluate protein-ligand interactions approximate van der Waals interactions with the Lennard-Jones potential. In this chapter, we present the main concepts necessary to understand van der Waals interactions relevant to molecular recognition of a ligand by the binding pocket of a protein target. We describe the Lennard-Jones potential and its application to calculate potential energy for an ensemble of structures to highlight the main features related to the importance of this interaction for binding affinity.

Molegro Virtual Docker for Docking

Molegro Virtual Docker is a protein-ligand docking simulation program that allows us to carry out docking simulations in a fully integrated computational package. MVD has been successfully applied to hundreds of different proteins, with docking performance similar to other docking programs such as AutoDock4 and AutoDock Vina. The program MVD has four search algorithms and four native scoring functions. Considering that we may have water molecules or not in the docking simulations, we have a total of 32 docking protocols. The integration of the programs SAnDReS ( https://github.com/azevedolab/sandres ) and MVD opens the possibility to carry out a detailed statistical analysis of docking results, which adds to the native capabilities of the program MVD. In this chapter, we describe a tutorial to carry out docking simulations with MVD and how to perform a statistical analysis of the docking results with the program SAnDReS. To illustrate the integration of both programs, we describe the redocking simulation focused the cyclin-dependent kinase 2 in complex with a competitive inhibitor.

Molecular Docking Simulations with ArgusLab

Molecular docking is the major computational technique employed in the early stages of computer-aided drug discovery. The availability of free software to carry out docking simulations of protein-ligand systems has allowed for an increasing number of studies using this technique. Among the available free docking programs, we discuss the use of ArgusLab ( http://www.arguslab.com/arguslab.com/ArgusLab.html ) for protein-ligand docking simulation. This easy-to-use computational tool makes use of a genetic algorithm as a search algorithm and a fast scoring function that allows users with minimal experience in the simulations of protein-ligand simulations to carry out docking simulations. In this chapter, we present a detailed tutorial to perform docking simulations using ArgusLab.

Homology Modeling of Protein Targets with MODELLER

Homology modeling is a computational approach to generate three-dimensional structures of protein targets when experimental data about similar proteins are available. Although experimental methods such as X-ray crystallography and nuclear magnetic resonance spectroscopy successfully solved the structures of nearly 150,000 macromolecules, there is still a gap in our structural knowledge. We can fulfill this gap with computational methodologies. Our goal in this chapter is to explain how to perform homology modeling of protein targets for drug development. We choose as a homology modeling tool the program MODELLER. To illustrate its use, we describe how to model the structure of human cyclin-dependent kinase 3 using MODELLER. We explain the modeling procedure of CDK3 apoenzyme and the structure of this enzyme in complex with roscovitine.

Exploring the Scoring Function Space

In the analysis of protein-ligand interactions, two abstractions have been widely employed to build a systematic approach to analyze these complexes: protein and chemical spaces. The pioneering idea of the protein space dates back to 1970, and the chemical space is newer, later 1990s. With the progress of computational methodologies to create machine-learning models to predict the ligand-binding affinity, clearly there is a need for novel approaches to the problem of protein-ligand interactions. New abstractions are required to guide the conceptual analysis of the molecular recognition problem. Using a systems approach, we proposed to address protein-ligand scoring functions using the modern idea of the scoring function space. In this chapter, we describe the fundamental concept behind the scoring function space and how it has been applied to develop the new generation of targeted-scoring functions.

Machine Learning to Predict Binding Affinity

Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic coordinates of protein-ligand complexes. These new computational tools made application of a broad spectrum of machine-learning techniques to study protein-ligand interactions possible. The essential aspect of these machine-learning approaches is to train a new computational model by using technologies such as supervised machine-learning techniques, convolutional neural network, and random forest to mention the most commonly applied methods. In this chapter, we focus on supervised machine-learning techniques and their applications in the development of protein-targeted scoring functions for the prediction of binding affinity. We discuss the development of the program SAnDReS and its application to the creation of machine-learning models to predict inhibition of cyclin-dependent kinase and HIV-1 protease. Moreover, we describe the scoring function space, and how to use it to explain the development of targeted scoring functions.

Docking with SwissDock

Protein-ligand docking simulation is central in drug design and development. Therefore, the development of web servers intended to docking simulations is of pivotal importance. SwissDock is a web server dedicated to carrying out protein-ligand docking simulation intuitively and elegantly. SwissDock is based on the protein-ligand docking program EADock DSS and has a simple and integrated interface. The SwissDock allows the user to upload structure files for a protein and a ligand, and returns the results by e-mail. To facilitate the upload of the protein and ligand files, we can prepare these input files using the program UCSF Chimera. In this chapter, we describe how to use UCSF Chimera and SwissDock to perform protein-ligand docking simulations. To illustrate the process, we describe the molecular docking of the competitive inhibitor roscovitine against the structure of human cyclin-dependent kinase 2.

Hydrogen Bonds in Protein-Ligand Complexes

Fast and reliable evaluation of the hydrogen bond potential energy has a significant impact in the drug design and development since it allows the assessment of large databases of organic molecules in virtual screening projects focused on a protein of interest. Semi-empirical force fields implemented in molecular docking programs make it possible the evaluation of protein-ligand binding affinity where the hydrogen bond potential is a common term used in the calculation. In this chapter, we describe the concepts behind the programs used to predict hydrogen bond potential energy employing semi-empirical force fields as the ones available in the programs AMBER, AutoDock4, TreeDock, and ReplicOpter. We described here the 12-10 potential and applied it to evaluate the binding affinity for an ensemble of crystallographic structures for which experimental data about binding affinity are available.