RESUMO
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.
Assuntos
Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Software , Trifosfato de Adenosina/química , Quinase 2 Dependente de Ciclina/química , Desenho de Fármacos , Ligação de Hidrogênio , Ligantes , Conformação Molecular , Ligação Proteica , Proteínas/química , Interface Usuário-ComputadorRESUMO
BACKGROUND: One key step in the development of inhibitors for an enzyme is the application of computational methodologies to predict protein-ligand interactions. The abundance of structural and ligand-binding information for HIV-1 protease opens up the possibility to apply computational methods to develop scoring functions targeted to this enzyme. OBJECTIVE: Our goal here is to develop an integrated molecular docking approach to investigate protein-ligand interactions with a focus on the HIV-1 protease. In addition, with this methodology, we intend to build target-based scoring functions to predict inhibition constant (Ki) for ligands against the HIV-1 protease system. METHODS: Here, we described a computational methodology to build datasets with decoys and actives directly taken from crystallographic structures to be applied in evaluation of docking performance using the program SAnDReS. Furthermore, we built a novel function using as terms MolDock and PLANTS scoring functions to predict binding affinity. To build a scoring function targeted to the HIV-1 protease, we have used machine-learning techniques. RESULTS: The integrated approach reported here has been tested against a dataset comprised of 71 crystallographic structures of HIV protease, to our knowledge the largest HIV-1 protease dataset tested so far. Comparison of our docking simulations with benchmarks indicated that the present approach is able to generate results with improved accuracy. CONCLUSION: We developed a scoring function with performance higher than previously published benchmarks for HIV-1 protease. Taken together, we believe that the approach here described has the potential to improve docking accuracy in drug design projects focused on HIV-1 protease.
Assuntos
Inibidores da Protease de HIV/farmacologia , Protease de HIV/metabolismo , Aprendizado de Máquina , Simulação de Acoplamento Molecular , Avaliação Pré-Clínica de Medicamentos , Inibidores da Protease de HIV/síntese química , Inibidores da Protease de HIV/química , LigantesRESUMO
BACKGROUND: Cyclin-dependent kinases (CDKs) comprise an important protein family for development of drugs, mostly aimed for use in treatment of cancer but there is also potential for development of drugs for neurodegenerative diseases and diabetes. Since the early 1990s, structural studies have been carried out on CDKs, in order to determine the structural basis for inhibition of this protein target. OBJECTIVE: Our goal here is to review recent structural studies focused on CDKs. We concentrate on latest developments in the understanding of the structural basis for inhibition of CDKs, relating structures and ligand-binding information. METHOD: Protein crystallography has been successfully applied to elucidate over 400 CDK structures. Most of these structures are complexed with inhibitors. We use this richness of structural information to describe the major structural features determining the inhibition of this enzyme. RESULTS: Structures of CDK1, 2, 4-9, 12 13, and 16 have been elucidated. Analysis of these structures in complex with a wide range of different competitive inhibitors, strongly indicate some common features that can be used to guide the development of CDK inhibitors, such as a pattern of hydrogen bonding and the presence of halogen atoms in the ligand structure. CONCLUSION: Nowadays we have structural information for hundreds of CDKs. Combining the structural and functional information we may say that a pattern of intermolecular hydrogen bonds is of pivotal importance for inhibitor specificity. In addition, machine learning techniques have shown improvements in predicting binding affinity for CDKs.