RESUMEN
Opioids are potent painkillers, however, their therapeutic use requires close medical monitoring to diminish the risk of severe adverse effects. The G-protein biased agonists of the µ-opioid receptor (MOR) have shown safer therapeutic profiles than non-biased ligands. In this work, we performed extensive all-atom molecular dynamics simulations of two markedly biased ligands and a balanced reference molecule. From those simulations, we identified a protein-ligand interaction fingerprint that characterizes biased ligands. Then, we built and virtually screened a database containing 68,740 ligands with proven or potential GPCR agonistic activity. Exemplary molecules that fulfill the interacting pattern for biased agonism are showcased, illustrating the usefulness of this work for the search of biased MOR ligands and how this contributes to the understanding of MOR biased signaling.
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Receptores Opioides mu/agonistas , Algoritmos , Analgésicos Opioides/farmacología , Proteínas de Unión al GTP/metabolismo , Ligandos , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Unión Proteica , Receptores Opioides mu/metabolismo , Transducción de Señal/efectos de los fármacosRESUMEN
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.
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Antivirales/farmacología , Proteasas 3C de Coronavirus/antagonistas & inhibidores , Inhibidores de Proteasas/farmacología , SARS-CoV-2 , COVID-19 , Ligandos , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Péptido Hidrolasas , SARS-CoV-2/efectos de los fármacosRESUMEN
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.
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Preparaciones Farmacéuticas , Proteínas , Humanos , Ligandos , Aprendizaje Automático , Electricidad EstáticaRESUMEN
Tissue softening accompanies the ripening of many fruits and initiates the processes of irreversible deterioration. Expansins are plant cell wall proteins that have been proposed to disrupt hydrogen bonds within the cell wall polymer matrix. Several authors have shown that FaEXPA2 is a key gene that shows an increased expression level during ripening and softening of the strawberry fruit. For this reason, FaEXPA2 is frequently used as a molecular marker of softening in strawberry fruit, and changes in its relative expression have been related to changes in fruit firmness. In this context, we previously reported that FaEXPA2 has a high accumulation rate during fruit ripening in four different strawberry cultivars; however, the molecular mechanism of FaEXPA2 or expansins in general is not yet clear. Herein, a 3D model of the FaEXPA2 protein was built by comparative modeling to understand how FaEXPA2 interacts with different cell wall components at the molecular level. First, the structure was shown to display two domains characteristic of the other expansins that were previously described. The protein-ligand interaction was evaluated by molecular dynamic (MD) simulation using four different long ligands (a cellulose fiber, two of the more important xyloglucan (XG) fibers found in strawberry (XXXG and XXFG type), and a pectin (homogalacturonic acid type)). The results showed that FaEXPA2 formed a more stable complex with cellulose than other ligands via the different residues present in the open groove surface of its two domains, while FaEXPA2 did not interact with the pectin ligand.
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Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects nearly eight million people worldwide. There are currently only limited treatment options, which cause several side effects and have drug resistance. Thus, there is a great need for a novel, improved Chagas treatment. Bifunctional enzyme dihydrofolate reductase-thymidylate synthase (DHFR-TS) has emerged as a promising pharmacological target. Moreover, some human dihydrofolate reductase (HsDHFR) inhibitors such as trimetrexate also inhibit T. cruzi DHFR-TS (TcDHFR-TS). These compounds serve as a starting point and a reference in a screening campaign to search for new TcDHFR-TS inhibitors. In this paper, a novel virtual screening approach was developed that combines classical docking with protein-ligand interaction profiling to identify drug repositioning opportunities against T. cruzi infection. In this approach, some food and drug administration (FDA)-approved drugs that were predicted to bind with high affinity to TcDHFR-TS and whose predicted molecular interactions are conserved among known inhibitors were selected. Overall, ten putative TcDHFR-TS inhibitors were identified. These exhibited a similar interaction profile and a higher computed binding affinity, compared to trimetrexate. Nilotinib, glipizide, glyburide and gliquidone were tested on T. cruzi epimastigotes and showed growth inhibitory activity in the micromolar range. Therefore, these compounds could lead to the development of new treatment options for Chagas disease.
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Enfermedad de Chagas/enzimología , Antagonistas del Ácido Fólico/farmacología , Tripanocidas/farmacología , Enfermedad de Chagas/tratamiento farmacológico , Simulación por Computador , Reposicionamiento de Medicamentos , Antagonistas del Ácido Fólico/química , Glipizida/química , Glipizida/farmacología , Gliburida/química , Gliburida/farmacología , Humanos , Ligandos , Simulación del Acoplamiento Molecular , Estructura Molecular , Pirimidinas/química , Pirimidinas/farmacología , Relación Estructura-Actividad , Compuestos de Sulfonilurea/química , Compuestos de Sulfonilurea/farmacología , Tripanocidas/química , Trypanosoma cruzi/efectos de los fármacosRESUMEN
BACKGROUND: In the field of protein engineering and biotechnology, the discovery and characterization of structural patterns is highly relevant as these patterns can give fundamental insights into protein-ligand interaction and protein function. This paper presents GSP4PDB, a bioinformatics web tool that enables the user to visualize, search and explore protein-ligand structural patterns within the entire Protein Data Bank. RESULTS: We introduce the notion of graph-based structural pattern (GSP) as an abstract model for representing protein-ligand interactions. A GSP is a graph where the nodes represent entities of the protein-ligand complex (amino acids and ligands) and the edges represent structural relationships (e.g. distances ligand - amino acid). The novel feature of GSP4PDB is a simple and intuitive graphical interface where the user can "draw" a GSP and execute its search in a relational database containing the structural data of each PDB entry. The results of the search are displayed using the same graph-based representation of the pattern. The user can further explore and analyse the results using a wide range of filters, or download their related information for external post-processing and analysis. CONCLUSIONS: GSP4PDB is a user-friendly and efficient application to search and discover new patterns of protein-ligand interaction.
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Ligandos , Proteínas/metabolismo , Interfaz Usuario-Computador , Animales , Bases de Datos de Proteínas , Humanos , Enlace de Hidrógeno , Mapas de Interacción de Proteínas , Estructura Secundaria de Proteína , Estructura Terciaria de Proteína , Proteínas/química , Dedos de ZincRESUMEN
Computational methods are a powerful and consolidated tool in the early stage of the drug lead discovery process. Among these techniques, high-throughput molecular docking has proved to be extremely useful in identifying novel bioactive compounds within large chemical libraries. In the docking procedure, the predominant binding mode of each small molecule within a target binding site is assessed, and a docking score reflective of the likelihood of binding is assigned to them. These methods also shed light on how a given hit could be modified in order to improve protein-ligand interactions and are thus able to guide lead optimization. The possibility of reducing time and cost compared to experimental approaches made this technology highly appealing. Due to methodological developments and the increase of computational power, the application of quantum mechanical methods to study macromolecular systems has gained substantial attention in the last decade. A quantum mechanical description of the interactions involved in molecular association of biomolecules may lead to better accuracy compared to molecular mechanics, since there are many physical phenomena that cannot be correctly described within a classical framework, such as covalent bond formation, polarization effects, charge transfer, bond rearrangements, halogen bonding, and others, that require electrons to be explicitly accounted for. Considering the fact that quantum mechanics-based approaches in biomolecular simulation constitute an active and important field of research, we highlight in this work the recent developments of quantum mechanical-based molecular docking and high-throughput docking.
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Descubrimiento de Drogas/métodos , Preparaciones Farmacéuticas/química , Ligandos , Simulación del Acoplamiento Molecular , Proteínas/química , Teoría CuánticaRESUMEN
Hsp90s are key proteins in cellular homeostasis since they interact with many client proteins. Several studies indicated that Hsp90s are potential targets for treating diseases, such as cancer or malaria. It has been shown that Hsp90s from different organisms have peculiarities despite their high sequence identity. Therefore, a detailed comparative analysis of several Hsp90 proteins is relevant to the overall understanding of their activity. Accordingly, the goal of this work was to evaluate the interaction of either ADP or ATP with recombinant Hsp90s from different organisms (human α and ß isoforms, Plasmodium falciparum, Leishmania braziliensis, yeast and sugarcane) by isothermal titration calorimetry. The measured thermodynamic signatures of those interactions indicated that despite the high identity among all Hsp90s, they have specific thermodynamic characteristics. Specifically, the interactions with ADP are driven by enthalpy but are opposed by entropy, whereas the interaction with ATP is driven by both enthalpy and entropy. Complimentary structural and molecular dynamics studies suggested that specific interactions with ADP that differ from those with ATP may contribute to the observed enthalpies and entropies. Altogether, the data suggest that selective inhibition may be more easily achieved using analogues of the Hsp90-ADP bound state than those of Hsp90-ATP bound state.
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Adenosina Difosfato/metabolismo , Adenosina Trifosfato/metabolismo , Proteínas HSP90 de Choque Térmico/metabolismo , Secuencia de Aminoácidos , Proteínas HSP90 de Choque Térmico/química , Humanos , Modelos Moleculares , Unión Proteica , Conformación Proteica , TermodinámicaRESUMEN
Acyl-CoA Binding Proteins (ACBP) form a housekeeping family of proteins that is responsible for the buffering of long chain acyl-coenzyme A esters (LCFA-CoA) inside the cell. Even though numerous studies have focused on the characterization of different members of the ACBP family, the knowledge about the impact of both LCFA-CoA and phospholipids on ACBP structure and stability remains scarce. Besides, there are still controversies regarding the possible interaction of ACBP with biological membranes, even though this might be essential for the cargo capture and delivery. In this study, we observed that LCFA-CoA and phospholipids play opposite roles on protein stability and that the interaction with the membrane is dictated by electrostatic interaction. Furthermore, the results support the hypothesis that the LCFA-CoA delivery is driven by the increase of the negative charge on the membrane surface. The combined influence played by the different molecules on ACBP structure is discussed on the light of cargo capture/delivery giving new insights about this important process.
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Acilcoenzima A/química , Acilcoenzima A/farmacología , Inhibidor de la Unión a Diazepam/química , Inhibidor de la Unión a Diazepam/metabolismo , Ésteres/química , Fosfolípidos/química , Acilcoenzima A/metabolismo , Secuencia de Aminoácidos , Inhibidor de la Unión a Diazepam/genética , Mutación , Transición de Fase , Estabilidad Proteica/efectos de los fármacos , Estructura Secundaria de Proteína/efectos de los fármacosRESUMEN
The increasing awareness that drugs may have the clinical effect through the interaction with multiple targets is encouraging the screening of investigational compounds across multiple biological endpoints. As the number and complexity of chemogenomics data sets increase, more computational approaches are being developed for the efficient analysis of structure-multiple activity relationships. In silico methods cover a wide range of applications including visual, qualitative, and quantitative approaches to describe in detail multiple ligand-protein relationships, find associations between targets and, whenever possible, to predict the bioactivity profile of small molecules. Here, we present a commentary of representative computational methods and their applications to characterize structure-multiple activity relationships and conduct the rational design of polypharmacology for the advancement of drug discovery.
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Diseño de Fármacos , Relación Estructura-Actividad , Simulación por Computador , LigandosRESUMEN
BACKGROUND: Hsp90 is a molecular chaperone essential for cell viability in eukaryotes that is associated with the maturation of proteins involved in important cell functions and implicated in the stabilization of the tumor phenotype of various cancers, making this chaperone a notably interesting therapeutic target. Celastrol is a plant-derived pentacyclic triterpenoid compound with potent antioxidant, anti-inflammatory and anticancer activities; however, celastrol's action mode is still elusive. RESULTS: In this work, we investigated the effect of celastrol on the conformational and functional aspects of Hsp90α. Interestingly, celastrol appeared to target Hsp90α directly as the compound induced the oligomerization of the chaperone via the C-terminal domain as demonstrated by experiments using a deletion mutant. The nature of the oligomers was investigated by biophysical tools demonstrating that a two-fold excess of celastrol induced the formation of a decameric Hsp90α bound throughout the C-terminal domain. When bound, celastrol destabilized the C-terminal domain. Surprisingly, standard chaperone functional investigations demonstrated that neither the in vitro chaperone activity of protecting against aggregation nor the ability to bind a TPR co-chaperone, which binds to the C-terminus of Hsp90α, were affected by celastrol. CONCLUSION: Celastrol interferes with specific biological functions of Hsp90α. Our results suggest a model in which celastrol binds directly to the C-terminal domain of Hsp90α causing oligomerization. However, the ability to protect against protein aggregation (supported by our results) and to bind to TPR co-chaperones are not affected by celastrol. Therefore celastrol may act primarily by inducing specific oligomerization that affects some, but not all, of the functions of Hsp90α. GENERAL SIGNIFICANCE: To the best of our knowledge, this study is the first work to use multiple probes to investigate the effect that celastrol has on the stability and oligomerization of Hsp90α and on the binding of this chaperone to Tom70. This work provides a novel mechanism by which celastrol binds Hsp90α.