Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.

BACKGROUND: The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery. OBJECTIVE: Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity. METHOD: We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space. RESULTS: The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces. CONCLUSION: The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.

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.

Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2.

BACKGROUND: The main protease of SARS-CoV-2 (Mpro) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies. OBJECTIVE: Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the Mpro using the program AutoDock4. METHODS: We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for Mpro to analyze intermolecular interactions and reviewed the methods used to search for inhibitors. RESULTS: The application of docking against the structures available for the Mpro found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of Mpro that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity. CONCLUSION: The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of Mpro. Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to Mpro. This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19.

The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity.

BACKGROUND: One of the main challenges in the early stages of drug discovery is the computational assessment of protein-ligand binding affinity. Machine learning techniques can contribute to predicting this type of interaction. We may apply these techniques following two approaches. Firstly, using the experimental structures for which affinity data is available. Secondly, using protein-ligand docking simulations. OBJECTIVE: In this review, we describe recently published machine learning models based on crystal structures, for which binding affinity and thermodynamic data are available. METHOD: We used experimental structures available at the protein data bank and binding affinity and thermodynamic data was accessed through BindingDB, Binding MOAD, and PDBbind databases. We reviewed machine learning models to predict binding created using open source programs, such as SAnDReS and Taba. RESULTS: Analysis of machine learning models trained against datasets, composed of crystal structure complexes indicated the high predictive performance of these models when compared with classical scoring functions. CONCLUSION: The rapid increase in the number of crystal structures of protein-ligand complexes created a favorable scenario for developing machine learning models to predict binding affinity. These models rely on experimental data from two sources, the structural and the affinity data. The combination of experimental data generates computational models that outperform the classical scoring functions.

Electrostatic Potential Energy in Protein-Drug Complexes.

BACKGROUND: Electrostatic interactions are one of the forces guiding the binding of molecules to proteins. The assessment of this interaction through computational approaches makes it possible to evaluate the energy of protein-drug complexes. OBJECTIVE: Our purpose here is to review some of the methods used to calculate the electrostatic energy of protein-drug complexes and explore the capacity of these approaches for the generation of new computational tools for drug discovery using the abstraction of scoring function space. METHODS: Here, we present an overview of the AutoDock4 semi-empirical scoring function used to calculate binding affinity for protein-drug complexes. We focus our attention on electrostatic interactions and how to explore recently published results to increase the predictive performance of the computational models to estimate the energetics of protein- drug interactions. Public data available at Binding MOAD, BindingDB, and PDBbind were used to review the predictive performance of different approaches to predict binding affinity. RESULTS: A comprehensive outline of the scoring function used to evaluate potential energy available in docking programs is presented. Recent developments of computational models to predict protein-drug energetics were able to create targeted-scoring functions to predict binding to these proteins. These targeted models outperform classical scoring functions and highlight the importance of electrostatic interactions in the definition of the binding. CONCLUSION: Here, we reviewed the development of scoring functions to predict binding affinity through the application of a semi-empirical free energy scoring function. Our studies show the superior predictive performance of machine learning models when compared with classical scoring functions and the importance of electrostatic interactions for binding affinity.

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.

Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes.

Background Breast cancer is highly prevalent among women worldwide. It is classified into three main subtypes: estrogen receptor positive (ER+), human epidermal growth factor receptor 2 positive (HER2+), and triple negative breast cancer (TNBC). This study has evaluated the effects of aspirin and metformin, isolated or in a combination, in breast cancer cells of the different subtypes. Methods The breast cancer cell lines MCF-7, MDA-MB-231, and SK-BR-3 were treated with aspirin and/or metformin (0.01 mM - 10 mM); functional in vitro assays were performed. The interactions with the estrogen receptors (ER) were evaluated in silico. Results Metformin (2.5, 5 and 10 mM) altered the morphology and reduced the viability and migration of the ER+ cell line MCF-7, whereas aspirin triggered this effect only at 10 mM. A synergistic effect for the combination of metformin and aspirin (2.5, 5 or 10 mM each) was observed in the TNBC cell subtype MDA-MB-231, according to the evaluation of its viability and colony formation. Partial inhibitory effects were observed for either of the drugs in the HER2+ cell subtype SK-BR-3. The effects of metformin and aspirin partly relied on cyclooxygenase-2 (COX-2) upregulation, without the production of lipoxins. In silico, metformin and aspirin bound to the ERα receptor with the same energy. Conclusion We have provided novel evidence on the mechanisms of action of aspirin and metformin in breast cancer cells, showing favorable outcomes for these drugs in the ER+ and TNBC subtypes.

Halogen atoms in the modern medicinal chemistry: hints for the drug design.

A significant number of drugs and drug candidates in clinical development are halogenated structures. For a long time, insertion of halogen atoms on hit or lead compounds was predominantly performed to exploit their steric effects, through the ability of these bulk atoms to occupy the binding site of molecular targets. However, halogens in drug - target complexes influence several processes rather than steric aspects alone. For example, the formation of halogen bonds in ligand-target complexes is now recognized as a kind of intermolecular interaction that favorably contributes to the stability of ligand-target complexes. This paper is aimed at introducing the fascinating versatility of halogen atoms. It starts summarizing the prevalence of halogenated drugs and their structural and pharmacological features. Next, we discuss the identification and prediction of halogen bonds in protein-ligand complexes, and how these bonds should be exploited. Interesting results of halogen insertions during the processes of hit-to-lead or lead-to-drug conversions are also detailed. Polyhalogenated anesthetics and protein kinase inhibitors that bear halogens are analyzed as cases studies. Thereby, this review serves as one guide for the virtual screening of libraries containing halogenated compounds and may be a source of inspiration for the medicinal chemists.

Molecular models for shikimate pathway enzymes of Xylella fastidiosa.

The Xylella fastidiosa is a bacterium that is the cause of citrus variegated chlorosis (CVC). The shikimate pathway is of pivotal importance for production of a plethora of aromatic compounds in plants, bacteria, and fungi. Putative structural differences in the enzymes from the shikimate pathway, between the proteins of bacterial origin and those of plants, could be used for the development of a drug for the control of CVC. However, inhibitors for shikimate pathway enzymes should have high specificity for X. fastidiosa enzymes, since they are also present in plants. In order to pave the way for structural and functional efforts towards antimicrobial agent development, here we describe the molecular modeling of seven enzymes of the shikimate pathway of X. fastidiosa. The structural models of shikimate pathway enzymes, complexed with inhibitors, strongly indicate that the previously identified inhibitors may also inhibit the X. fastidiosa enzymes.