RESUMO
eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i)â structure-based bioactivity data mapping, ii)â structure-based identification of scaffold replacement strategies for ligand design, iii)â ligand-based target prediction, iv)â protein sequence-based binding site identification and ligand repurposing, and v)â structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.
Assuntos
Desenho Assistido por Computador , Descoberta de Drogas/métodos , Processamento de Imagem Assistida por Computador , Internet , Inibidores de Proteínas Quinases/química , Ligantes , Estrutura MolecularRESUMO
3D-e-Chem-VM is an open source, freely available Virtual Machine ( http://3d-e-chem.github.io/3D-e-Chem-VM/ ) that integrates cheminformatics and bioinformatics tools for the analysis of protein-ligand interaction data. 3D-e-Chem-VM consists of software libraries, and database and workflow tools that can analyze and combine small molecule and protein structural information in a graphical programming environment. New chemical and biological data analytics tools and workflows have been developed for the efficient exploitation of structural and pharmacological protein-ligand interaction data from proteomewide databases (e.g., ChEMBLdb and PDB), as well as customized information systems focused on, e.g., G protein-coupled receptors (GPCRdb) and protein kinases (KLIFS). The integrated structural cheminformatics research infrastructure compiled in the 3D-e-Chem-VM enables the design of new approaches in virtual ligand screening (Chemdb4VS), ligand-based metabolism prediction (SyGMa), and structure-based protein binding site comparison and bioisosteric replacement for ligand design (KRIPOdb).
Assuntos
Informática/métodos , Desenho de Fármacos , Ligantes , Proteínas Quinases/metabolismo , Receptores Acoplados a Proteínas G/metabolismo , Software , Interface Usuário-ComputadorRESUMO
sn-1-Diacylglycerol lipase α (DAGL-α) is the main enzyme responsible for the production of the endocannabinoid 2-arachidonoylglycerol in the central nervous system. Glycine sulfonamides have recently been identified by a high throughput screening campaign as a novel class of inhibitors for this enzyme. Here, we report on the first structure-activity relationship study of glycine sulfonamide inhibitors and their brain membrane proteome-wide selectivity on serine hydrolases with activity-based protein profiling (ABPP). We found that (i) DAGL-α tolerates a variety of biaryl substituents, (ii) the sulfonamide is required for inducing a specific orientation of the 2,2-dimethylchroman substituent, and (iii) a carboxylic acid is essential for its activity. ABPP revealed that the sulfonamide glycine inhibitors have at least three off-targets, including α/ß-hydrolase domain 6 (ABHD6). Finally, we identified LEI-106 as a potent, dual DAGL-α/ABHD6 inhibitor, which makes this compound a potential lead for the discovery of new molecular therapies for diet-induced obesity and metabolic syndrome.
Assuntos
Glicina/análogos & derivados , Glicina/química , Lipase Lipoproteica/antagonistas & inibidores , Monoacilglicerol Lipases/antagonistas & inibidores , Sulfonamidas/química , Animais , Encéfalo/metabolismo , Glicina/farmacologia , Células HEK293 , Humanos , Camundongos , Modelos Moleculares , Proteoma/metabolismo , Relação Estrutura-Atividade , Sulfonamidas/farmacologiaRESUMO
Science, and the way we undertake research, is changing. The increasing rate of data generation across all scientific disciplines is providing incredible opportunities for data-driven research, with the potential to transform our current practices. The exploitation of so-called 'big data' will enable us to undertake research projects never previously possible but should also stimulate a re-evaluation of all our data practices. Data-driven medicinal chemistry approaches have the potential to improve decision making in drug discovery projects, providing that all researchers embrace the role of 'data scientist' and uncover the meaningful relationships and patterns in available data.
Assuntos
Química Farmacêutica/tendências , Descoberta de Drogas/tendências , Estatística como Assunto/tendências , Animais , Química Farmacêutica/métodos , Descoberta de Drogas/métodos , Humanos , Estatística como Assunto/métodosAssuntos
Encéfalo/enzimologia , Inibidores Enzimáticos/química , Lipase Lipoproteica/metabolismo , Animais , Ácidos Araquidônicos/metabolismo , Sítios de Ligação , Encéfalo/metabolismo , Membrana Celular/metabolismo , Endocanabinoides/metabolismo , Inibidores Enzimáticos/metabolismo , Glicerídeos/metabolismo , Células HEK293 , Humanos , Lipase Lipoproteica/antagonistas & inibidores , Lipase Lipoproteica/genética , Camundongos , Ligação Proteica , Estrutura Terciária de Proteína , Proteoma , Relação Estrutura-AtividadeRESUMO
The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.
RESUMO
Glucocorticoids (GCs) such as prednisolone are potent immunosuppressive drugs but suffer from severe adverse effects, including the induction of insulin resistance. Therefore, development of so-called Selective Glucocorticoid Receptor Modulators (SGRM) is highly desirable. Here we describe a non-steroidal Glucocorticoid Receptor (GR)-selective compound (Org 214007-0) with a binding affinity to GR similar to that of prednisolone. Structural modelling of the GR-Org 214007-0 binding site shows disturbance of the loop between helix 11 and helix 12 of GR, confirmed by partial recruitment of the TIF2-3 peptide. Using various cell lines and primary human cells, we show here that Org 214007-0 acts as a partial GC agonist, since it repressed inflammatory genes and was less effective in induction of metabolic genes. More importantly, in vivo studies in mice indicated that Org 214007-0 retained full efficacy in acute inflammation models as well as in a chronic collagen-induced arthritis (CIA) model. Gene expression profiling of muscle tissue derived from arthritic mice showed a partial activity of Org 214007-0 at an equi-efficacious dosage of prednisolone, with an increased ratio in repression versus induction of genes. Finally, in mice Org 214007-0 did not induce elevated fasting glucose nor the shift in glucose/glycogen balance in the liver seen with an equi-efficacious dose of prednisolone. All together, our data demonstrate that Org 214007-0 is a novel SGRMs with an improved therapeutic index compared to prednisolone. This class of SGRMs can contribute to effective anti-inflammatory therapy with a lower risk for metabolic side effects.
Assuntos
Anti-Inflamatórios não Esteroides/farmacologia , Dibenzazepinas/farmacologia , Receptores de Glucocorticoides/agonistas , Tiadiazóis/farmacologia , Animais , Anti-Inflamatórios não Esteroides/química , Anti-Inflamatórios não Esteroides/uso terapêutico , Artrite Experimental/tratamento farmacológico , Artrite Experimental/genética , Glicemia , Dibenzazepinas/uso terapêutico , Feminino , Regulação da Expressão Gênica/efeitos dos fármacos , Humanos , Cinética , Fígado/efeitos dos fármacos , Fígado/enzimologia , Masculino , Camundongos , Simulação de Acoplamento Molecular , Prednisolona/farmacologia , Prednisolona/uso terapêutico , Ligação Proteica , Receptores de Glucocorticoides/química , Receptores de Glucocorticoides/metabolismo , Tiadiazóis/uso terapêuticoRESUMO
We present the systematic prospective evaluation of a protein-based and a ligand-based virtual screening platform against a set of three G-protein-coupled receptors (GPCRs): the ß-2 adrenoreceptor (ADRB2), the adenosine A(2A) receptor (AA2AR), and the sphingosine 1-phosphate receptor (S1PR1). Novel bioactive compounds were identified using a consensus scoring procedure combining ligand-based (frequent substructure ranking) and structure-based (Snooker) tools, and all 900 selected compounds were screened against all three receptors. A striking number of ligands showed affinity/activity for GPCRs other than the intended target, which could be partly attributed to the fuzziness and overlap of protein-based pharmacophore models. Surprisingly, the phosphodiesterase 5 (PDE5) inhibitor sildenafil was found to possess submicromolar affinity for AA2AR. Overall, this is one of the first published prospective chemogenomics studies that demonstrate the identification of novel cross-pharmacology between unrelated protein targets. The lessons learned from this study can be used to guide future virtual ligand design efforts.
Assuntos
Bases de Dados Factuais , Desenho de Fármacos , Modelos Moleculares , Relação Quantitativa Estrutura-Atividade , Receptores A2 de Adenosina/química , Receptores Adrenérgicos beta 2/química , Receptores de Lisoesfingolipídeo/química , Agonistas do Receptor A2 de Adenosina/química , Antagonistas do Receptor A2 de Adenosina/química , Agonistas de Receptores Adrenérgicos beta 2/química , Antagonistas de Receptores Adrenérgicos beta 2/química , Animais , Células CHO , Cricetinae , Cricetulus , Agonismo Parcial de Drogas , Células HEK293 , Ensaios de Triagem em Larga Escala , Humanos , Ligantes , Estrutura Molecular , Inibidores da Fosfodiesterase 5/química , Piperazinas/química , Piperazinas/metabolismo , Purinas/química , Purinas/metabolismo , Ensaio Radioligante , Receptores A2 de Adenosina/metabolismo , Receptores Adrenérgicos beta 2/metabolismo , Receptores de Lisoesfingolipídeo/agonistas , Receptores de Lisoesfingolipídeo/metabolismo , Citrato de Sildenafila , Processos Estocásticos , Sulfonas/química , Sulfonas/metabolismoRESUMO
We present here the x-ray structures of the progesterone receptor (PR) in complex with two mixed profile PR modulators whose functional activity results from two differing molecular mechanisms. The structure of Asoprisnil bound to the agonist state of PR demonstrates the contribution of the ligand to increasing stability of the agonist conformation of helix-12 via a specific hydrogen-bond network including Glu(723). This interaction is absent when the full antagonist, RU486, binds to PR. Combined with a previously reported structure of Asoprisnil bound to the antagonist state of the receptor, this structure extends our understanding of the complex molecular interactions underlying the mixed agonist/antagonist profile of the compound. In addition, we present the structure of PR in its agonist conformation bound to the mixed profile compound Org3H whose reduced antagonistic activity and increased agonistic activity compared with reference antagonists is due to an induced fit around Trp(755), resulting in a decreased steric clash with Met(909) but inducing a new internal clash with Val(912) in helix-12. This structure also explains the previously published observation that 16α attachments to RU486 analogs induce mixed profiles by altering the binding of 11ß substituents. Together these structures further our understanding of the steric and electrostatic factors that contribute to the function of steroid receptor modulators, providing valuable insight for future compound design.
Assuntos
Estrenos/química , Mifepristona/química , Oximas/química , Receptores de Progesterona/agonistas , Receptores de Progesterona/química , Cristalografia por Raios X , Humanos , Ligantes , Estrutura Secundária de Proteína , Estrutura Terciária de ProteínaRESUMO
The progesterone receptor is able to bind to a large number and variety of ligands that elicit a broad range of transcriptional responses ranging from full agonism to full antagonism and numerous mixed profiles inbetween. We describe here two new progesterone receptor ligand binding domain x-ray structures bound to compounds from a structurally related but functionally divergent series, which show different binding modes corresponding to their agonistic or antagonistic nature. In addition, we present a third progesterone receptor ligand binding domain dimer bound to an agonist in monomer A and an antagonist in monomer B, which display binding modes in agreement with the earlier observation that agonists and antagonists from this series adopt different binding modes.
Assuntos
Receptores de Progesterona/agonistas , Receptores de Progesterona/antagonistas & inibidores , Receptores de Progesterona/metabolismo , Animais , Sítios de Ligação , Células CHO , Cricetinae , Cricetulus , Cristalografia por Raios X/métodos , Dimerização , Desenho de Fármacos , Avaliação Pré-Clínica de Medicamentos , Ligantes , Mifepristona/química , Modelos Moleculares , Conformação Molecular , Noretindrona/química , Progesterona/química , Ligação Proteica , Conformação ProteicaRESUMO
The difference between biologically active molecules and drugs is that the latter balance an array of related and unrelated properties required for administration to patients. Inevitability, during optimization, some of these multiple factors will conflict. Although informatics has a crucial role in addressing the challenges of modern compound optimization, it is arguably still undervalued and underutilized. We present here some of the basic requirements of multi-parameter drug design, the crucial role of informatics and examples of favorable practice. The most crucial of these best practices are the need for informaticians to align their technologies and insights directly to discovery projects and for all scientists in drug discovery to become more proficient in the use of in silico methods.
Assuntos
Biologia Computacional/métodos , Simulação por Computador , Desenho de Fármacos , Descoberta de Drogas/métodos , Humanos , Modelos MolecularesRESUMO
Liver X receptors (LXRs) are nuclear receptors that are central regulators of cholesterol homeostasis, and synthetic LXR agonists have shown promise as promoters of reverse cholesterol transport and anti-inflammatory agents. Here, we present three X-ray structures of three different agonists bound to the ligand binding domain of LXRalpha. These compounds are GW3965, F(3)methylAA, and a benzisoxazole urea, and we show that these diverse chemical scaffolds address common structural themes, leading to high binding affinity for LXR. Our structures show the LXRalpha ligand binding domain in its homodimeric form, an arrangement previously thought to be stereochemically difficult. A comparison with existing structures of the LXRbeta homodimer and LXRalpha:RXR (retinoid X receptor) heterodimers explains differences in dimer affinity and leads us to propose a model for allosteric activation in nuclear receptor dimers, in which an unactivated RXR partner provides an inhibitory tail wrap to the cofactor binding pocket of LXR.
Assuntos
Receptores Nucleares Órfãos/química , Transdução de Sinais , Benzoatos/química , Benzoatos/metabolismo , Benzilaminas/química , Benzilaminas/metabolismo , Sítios de Ligação , Cristalografia por Raios X , Dimerização , Isoxazóis/química , Isoxazóis/metabolismo , Ligantes , Receptores X do Fígado , Modelos Moleculares , Receptores Nucleares Órfãos/metabolismo , Fenilacetatos/química , Fenilacetatos/metabolismo , Alinhamento de Sequência , Tiazóis/química , Tiazóis/metabolismoRESUMO
Mutagenicity is one of the numerous adverse properties of a compound that hampers its potential to become a marketable drug. Toxic properties can often be related to chemical structure, more specifically, to particular substructures, which are generally identified as toxicophores. A number of toxicophores have already been identified in the literature. This study aims at increasing the current degree of reliability and accuracy of mutagenicity predictions by identifying novel toxicophores from the application of new criteria for toxicophore rule derivation and validation to a considerably sized mutagenicity dataset. For this purpose, a dataset of 4337 molecular structures with corresponding Ames test data (2401 mutagens and 1936 nonmutagens) was constructed. An initial substructure-search of this dataset showed that most mutagens were detected by applying only eight general toxicophores. From these eight, more specific toxicophores were derived and approved by employing chemical and mechanistic knowledge in combination with statistical criteria. A final set of 29 toxicophores containing new substructures was assembled that could classify the mutagenicity of the investigated dataset with a total classification error of 18%. Furthermore, mutagenicity predictions of an independent validation set of 535 compounds were performed with an error percentage of 15%. Since these error percentages approach the average interlaboratory reproducibility error of Ames tests, which is 15%, it was concluded that these toxicophores can be applied to risk assessment processes and can guide the design of chemical libraries for hit and lead optimization.