Structural Basis for Inhibition of Enoyl-[Acyl Carrier Protein] Reductase (InhA) from Mycobacterium tuberculosis.

BACKGROUND: The enzyme trans-enoyl-[acyl carrier protein] reductase (InhA) is a central protein for the development of antitubercular drugs. This enzyme is the target for the pro-drug isoniazid, which is catalyzed by the enzyme catalase-peroxidase (KatG) to become active. OBJECTIVE: Our goal here is to review the studies on InhA, starting with general aspects and focusing on the recent structural studies, with emphasis on the crystallographic structures of complexes involving InhA and inhibitors. METHOD: We start with a literature review, and then we describe recent studies on InhA crystallographic structures. We use this structural information to depict protein-ligand interactions. We also analyze the structural basis for inhibition of InhA. Furthermore, we describe the application of computational methods to predict binding affinity based on the crystallographic position of the ligands. RESULTS: Analysis of the structures in complex with inhibitors revealed the critical residues responsible for the specificity against InhA. Most of the intermolecular interactions involve the hydrophobic residues with two exceptions, the residues Ser 94 and Tyr 158. Examination of the interactions has shown that many of the key residues for inhibitor binding were found in mutations of the InhA gene in the isoniazid-resistant Mycobacterium tuberculosis. Computational prediction of the binding affinity for InhA has indicated a moderate uphill relationship with experimental values. CONCLUSION: Analysis of the structures involving InhA inhibitors shows that small modifications on these molecules could modulate their inhibition, which may be used to design novel antitubercular drugs specific for multidrug-resistant strains.

Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase.

In this study, we describe the development of new machine learning models to predict inhibition of the enzyme 3-dehydroquinate dehydratase (DHQD). This enzyme is the third step of the shikimate pathway and is responsible for the synthesis of chorismate, which is a natural precursor of aromatic amino acids. The enzymes of shikimate pathway are absent in humans, which make them protein targets for the design of antimicrobial drugs. We focus our study on the crystallographic structures of DHQD in complex with competitive inhibitors, for which experimental inhibition constant data is available. Application of supervised machine learning techniques was able to elaborate a robust DHQD-targeted model to predict binding affinity. Combination of high-resolution crystallographic structures and binding information indicates that the prevalence of intermolecular electrostatic interactions between DHQD and competitive inhibitors is of pivotal importance for the binding affinity against this enzyme. The present findings can be used to speed up virtual screening studies focused on the DHQD structure.

Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC50) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores.

Understanding the Structural Basis for Inhibition of Cyclin-Dependent Kinases. New Pieces in the Molecular Puzzle.

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.