Advances in the Understanding of the Cannabinoid Receptor 1 – Focusing on the Inverse Agonists Interactions

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.

Recent Advances in the Development of Thalidomide-Related Compounds as Anticancer Drugs

INTRODUCTION

Thalidomide is an old well-known drug that was first used as morning sickness relief in pregnant women before being withdrawn from the market due to its severe side effects on normal fetal development, However, over the last few decades, the interest in this old drug has been renewed because of its efficacy in several important disorders for instance, multiple myeloma, breast cancer, and HIV-related diseases due to its antiangiogenic and immunomodulatory properties. Unfortunately, even in these cases, many aftereffects as deep vein thrombosis, peripheral neuropathy, constipation, somnolence, pyrexia, pain, and teratogenicity have been reported, showing the requirement of careful and monitored use. For this reason, research efforts are geared toward the synthesis and optimization of new thalidomide analogues lacking in toxic effects to erase these limits and improve the pharmacological profile.

AIMS

This review aims to examine the state-of-the-art concerning the current studies on thalidomide and its analogues towards cancer diseases (with few hints regarding the antimicrobial activity), focusing the attention on the possible mechanisms of action involved and the lack of toxicity.

CONCLUSION

In the light of the collected data, thalidomide analogues and their ongoing optimization could lead, in the future, to the realization of a promising therapeutic alternative for cancer-fighting.

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.

New paradigms for the treatment of lysosomal storage diseases: targeting the endocannabinoid system as a therapeutic strategy

Over the past three decades the lysosomal storage diseases have served as model for rare disease treatment development. While these efforts have led to considerable success, important challenges remain. For example, no treatments are currently approved for nearly two thirds of all lysosomal diseases, and there is limited impact of the existing drugs on the central nervous system. In addition, the costs of these therapies are extremely high, in part due to the fact that drug development has focused on a "single hit" approach - i.e., one drug for one disease. To overcome these obstacles researchers have begun to focus on defining common disease mechanisms in the lysosomal diseases, particularly in the central nervous system, with the hope of identifying drugs that might be used in several lysosomal diseases rather than an individual disease. With this concept in mind, herein we review a new potential treatment approach for the lysosomal storage diseases that focuses on modulation of the endocannabinoid system. We provide a short introduction to lysosomal storage diseases and the endocannabinoid system, followed by a brief review of data supporting this concept.

Cannabinoids and Cannabinoid Receptors: The Story so Far

Like most modern molecular biology and natural product chemistry, understanding cannabinoid pharmacology centers around molecular interactions, in this case, between the cannabinoids and their putative targets, the G-protein coupled receptors (GPCRs) cannabinoid receptor 1 (CB₁) and cannabinoid receptor 2 (CB₂). Understanding the complex structure and interplay between the partners in this molecular dance is required to understand the mechanism of action of synthetic, endogenous, and phytochemical cannabinoids. This review, with 91 references, surveys our understanding of the structural biology of the cannabinoids and their target receptors including both a critical comparison of the extant crystal structures and the computationally derived homology models, as well as an in-depth discussion about the binding modes of the major cannabinoids. The aim is to assist in situating structural biochemists, synthetic chemists, and molecular biologists who are new to the field of cannabis research.

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Cannabinoid research has greatly expanded

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Structural biology and computational chemistry jointly provide mechanistic insight

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Structural data are being generated at an exponentially increasing rate

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Phytocannabinoid targeting of other GPCR receptors deserves investigation

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.

Biopolymer Substrates in Buccal Drug Delivery: Current Status and Future Trend

BACKGROUND

This paper provides a critical review of biopolymer-based substrates, especially the cellulose derivatives, for their application in buccal drug delivery. Drug delivery to the buccal mucous has the benefits of immobile muscle, abundant vascularization and rapid recovery, but not all the drugs can be administered through the buccal mucosa (e.g., macromolecular drugs), due to the low bioavailability caused by their large molecular size. This shortfall inspired the rapid development of drug-compounding technologies and the corresponding usage of biopolymer substrates.

METHODS

Cellulose derivatives have been extensively developed for drug manufacturing to facilitate its delivery. We engaged in structured research of cellulose-based drug compounding technologies. We summarized the characteristic cellulose derivatives which have been used as the biocompatible substrates in buccal delivery systems. The discussion of potential use of the rapidly-developed nanocellulose (NC) is also notable in this paper.

RESULTS

Seventy-eight papers were referenced in this perspective paper with the majority (sixty-five) published later than 2010. Forty-seven papers defined the buccal drug delivery systems and their substrates. Fifteen papers outlined the properties and applications of cellulose derivatives. Nanocellulose was introduced as a leading edge of nanomaterial with sixteen papers highlighted its adaptability in drug compounding for buccal delivery.

CONCLUSION

The findings of this perspective paper proposed the potential use of cellulose derivatives, the typical kind of biopolymers, in the buccal drug delivery system for promoting the bioavailability of macromolecular drugs. Nanocellulose (NC) in particular was proposed as an innovative bio-binder/carrier for the controlled-release of drugs in buccal system.

A transcriptional co-expression network-based approach to identify prognostic biomarkers in gastric carcinoma

Background

Gastric carcinoma is a very diverse disease. The progression of gastric carcinoma is influenced by complicated gene networks. This study aims to investigate the actual and potential prognostic biomarkers related to survival in gastric carcinoma patients to further our understanding of tumor biology.

Methods

A weighted gene co-expression network analysis was performed with a transcriptome dataset to identify networks and hub genes relevant to gastric carcinoma prognosis. Data was obtained from 300 primary gastric carcinomas (GSE62254). A validation dataset (GSE34942 and GSE15459) and TCGA dataset confirmed the results. Gene ontology, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA) were performed to identify the clusters responsible for the biological processes and pathways of this disease.

Results

A brown transcriptional module enriched in the organizational process of the extracellular matrix was significantly correlated with overall survival (HR = 1.586, p = 0.005, 95% CI [1.149–2.189]) and disease-free survival (HR = 1.544, p = 0.008, 95% CI [1.119–2.131]). These observations were confirmed in the validation dataset (HR = 1.664, p = 0.006, 95% CI [1.155–2.398] in overall survival). Ten hub genes were identified and confirmed in the validation dataset from this brown module; five key biomarkers (COL8A1, FRMD6, TIMP2, CNRIP1 and GPR124 (ADGRA2)) were identified for further research in microsatellite instability (MSI) and epithelial-tomesenchymal transition (MSS/EMT) gastric carcinoma molecular subtypes. A high expression of these genes indicated a poor prognosis.

Conclusion

A transcriptional co-expression network-based approach was used to identify prognostic biomarkers in gastric carcinoma. This method may have potential for use in personalized therapies, however, large-scale randomized controlled clinical trials and replication experiments are needed before these key biomarkers can be applied clinically.

Computational Analysis of Dipyrone Metabolite 4-Aminoantipyrine As A Cannabinoid Receptor 1 Agonist

BACKGROUND

Cannabinoid receptor 1 has its crystallographic structure available in complex with agonists and inverse agonists, which paved the way to establish an understanding of the structural basis of interactions with ligands. Dipyrone is a prodrug with analgesic capabilities and is widely used in some countries. Recently some evidence of a dipyrone metabolite acting over the Cannabinoid Receptor 1has been shown.

OBJECTIVE

Our goal here is to explore the dipyrone metabolite 4-aminoantipyrine as a Cannabinoid Receptor 1 agonist, reviewing dipyrone characteristics, and investigating the structural basis for its interaction with the Cannabinoid Receptor 1.

METHOD

We reviewed here recent functional studies related to the dipyrone metabolite focusing on its action as a Cannabinoid Receptor 1 agonist. We also analyzed protein-ligand interactions for this complex obtained through docking simulations against the crystallographic structure of the Cannabinoid Receptor 1.

RESULTS

Analysis of the crystallographic structure and docking simulations revealed that most of the interactions present in the docked pose were also present in the crystallographic structure of Cannabinoid Receptor 1 and agonist.

CONCLUSION

Analysis of the complex of 4-aminoantipyrine and Cannabinoid Receptor 1 revealed the pivotal role played by residues Phe 170, Phe 174, Phe 177, Phe 189, Leu 193, Val 196, and Phe 379, besides the conserved hydrogen bond at Ser 383. The mechanistic analysis and the present computational study suggest that the dipyrone metabolite 4-aminoantipyrine interacts with the Cannabinoid Receptor 1.

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.

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.

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.

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.