Mechanisms and Neuroprotective Activities of Stigmasterol Against Oxidative Stress-Induced Neuronal Cell Death via Sirtuin Family.

Background: Accumulating studies have confirmed that oxidative stress leads to the death of neuronal cells and is associated with the progression of neurodegenerative diseases, including Alzheimer's disease (AD). Despite the compelling evidence, there is a drawback to the use of the antioxidant approach for AD treatment, partly due to limited blood-brain barrier (BBB) permeability. Phytosterol is known to exhibit BBB penetration and exerts various bioactivities such as antioxidant and anticancer effects, and displays a potential treatment for dyslipidemia, cardiovascular disease, and dementia. Objective: In this study, the protective effects of stigmasterol, a phytosterol compound, on cell death induced by hydrogen peroxide (H2O2) were examined in vitro using human neuronal cells (SH-SY5Y cells). Methods: MTT assay, reactive oxygen species measurement, mitochondrial membrane potential assay, apoptotic cell measurement, and protein expression profiles were performed to determine the neuroprotective properties of stigmasterol. Results: H2O2 exposure significantly increased the levels of reactive oxygen species (ROS) within the cells thereby inducing apoptosis. On the contrary, pretreatment with stigmasterol maintained ROS levels inside the cells and prevented oxidative stress-induced cell death. It was found that pre-incubation with stigmasterol also facilitated the upregulation of forkhead box O (FoxO) 3a, catalase, and anti-apoptotic protein B-cell lymphoma 2 (Bcl-2) in the neurons. In addition, the expression levels of sirtuin 1 (SIRT1) were also increased while acetylated lysine levels were decreased, indicating that SIRT1 activity was stimulated by stigmasterol, and the result was comparable with the known SIRT1 activator, resveratrol. Conclusion: Taken together, these results suggest that stigmasterol could be potentially useful to alleviate neurodegeneration induced by oxidative stress.

Interpretable SMILES-based QSAR Model of Inhibitory Activity of Sirtuins 1 and 2.

BACKGROUND: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+-dependent histone deacetylases which play important functional roles in the removal of the acetyl group of acetyllysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. OBJECTIVE: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. METHODS: Simplified molecular-input line-entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The dataset was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration, and external sets. RESULTS: Statistical indices for the evaluation of QSAR models suggested the good statistical quality of models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e., promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved in Sirt1 and Sirt2 inhibitors. CONCLUSION: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

Rational design of novel sirtuin 1 activators via structure-activity insights from application of QSAR modeling.

Sirtuin 1 (SIRT1) enzyme regulates major cell activities, and its activation offers lucrative therapeutic potentials for aging diseases including Alzheimer's disease (AD). Regarding the global aging society, continual attention has been given to various chemical scaffolds as a source for the discovery of novel SIRT1 activators since the discovery of the pioneer activator, resveratrol. Understanding structure-activity relationship (SAR) is essential for screening, designing as well as improving the properties of drugs. In this study, an in silico approach based on quantitative structure-activity relationship (QSAR) modeling, was employed for understanding the SAR of currently available SIRT1 fused-aromatic activators (i.e., imidazothiazole, oxazolopyridine, and azabenzimidazole analogs). Three QSAR models constructed using multiple linear regression (MLR) provided good predictive performance (R 2 LOOCV = 0.729 - 0.863 and RMSE LOOCV = 0.165 - 0.325). An additional novel set of 181 structurally modified compounds were rationally designed according to key descriptors deduced from the QSAR findings and their SIRT1 activities were predicted using the constructed models. In overview, the study provides insightful SAR findings of currently available SIRT1 activators that would be useful for guiding the rational design, screening, and development of further potent SIRT1 activators for managing age-related clinical conditions. A series of promising compounds as well as important scaffolds and molecular properties for potent SIRT1 activator were highlighted. This study demonstrated the efficacious role of QSAR-driven structural modification for the rational design of novel leads.

THPep: A machine learning-based approach for predicting tumor homing peptides.

In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.

Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling.

Drug metabolism determines the fate of a drug when it enters the human body and is a critical factor in defining their absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Among the various drug metabolizing enzymes, cytochrome P450s (CYP450) constitute an important protein family that aside from functioning in xenobiotic metabolism, is also responsible for a diverse array of other roles encompassing steroid and cholesterol biosynthesis, fatty acid metabolism, calcium homeostasis, neuroendocrine functions and growth regulation. Although CYP450 typically converts xenobiotics into safe metabolites, there are some situations whereby the metabolite is more toxic than its parent molecule. Computational modeling has been instrumental in CYP450 research by rationalizing the nature of the binding event (i.e. inhibit or induce CYP450s) or metabolic stability of query compounds of interest. A plethora of computational approaches encompassing ligand, structure and systems based approaches have been utilized to model CYP450-ligand interactions. This review provides a brief background on the CYP450 family (i.e. its roles, advantages and disadvantages as well as its modulators) and then discusses the various computational approaches that have been used to model CYP450-ligand interaction. Particular focus was given to the use of quantitative structure-activity relationship (QSAR) and more recent proteochemometric modeling studies. Finally, a perspective on the current state of the art and future trends of the field is also provided.

Exploring the epigenetic drug discovery landscape.

INTRODUCTION: Epigenetic modification has been implicated in a wide range of diseases and the ability to modulate such systems is a lucrative therapeutic strategy in drug discovery. Areas covered: This article focuses on the concepts and drug discovery aspects of epigenomics. This is achieved by providing a survey of the following concepts: (i) factors influencing epigenetics, (ii) diseases arising from epigenetics, (iii) epigenetic enzymes as druggable targets along with coverage of existing FDA-approved drugs and pharmacological agents, and (iv) drug repurposing/repositioning as a means for rapid discovery of pharmacological agents targeting epigenetics. Expert opinion: Despite significant interests in targeting epigenetic modifiers as a therapeutic route, certain classes of target proteins are heavily studied while some are less characterized. Thus, such orphan target proteins are not yet druggable with limited report of active modulators. Current research points towards a great future with novel drugs directed to the many complex multifactorial diseases of humans, which are still often poorly understood and difficult to treat.