ChemInform Abstract: Structure-Based Drug Design: Exploring the Proper Filling of Apolar Pockets at Enzyme Active Sites

Background: Antibiotic resistance is currently a serious problem for global public health. To this end, discovery of new antibacterial drugs that interact with novel targets is important. The biosynthesis of lipoproteins is vital to bacterial survival and its inhibitors have shown efficacy against a range of bacteria, thus bacterial lipoprotein biosynthetic pathway is a potential target. Methods: At first, the literature that covered the basic concept of bacterial lipoprotein biosynthetic pathway as well as biochemical characterization of three key enzymes was reviewed. Then, the recently resolved crystal structures of the three enzymes were retrieved from Protein Data Bank (PDB) and the essential residues in the active sites were analyzed. Lastly, all the available specific inhibitors targeting this pathway and their Structure-activity Relationship (SAR) were discussed. Results: We briefly introduce the bacterial lipoprotein biosynthetic pathway and describe the structures and functions of three key enzymes in detail. In addition, we present much knowledge on ligand recognition that may facilitate structure-based drug design. Moreover, we focus on the SAR of LspA inhibitors and discuss their potency and drug-likeness. Conclusion: This review presents a clear background of lipoprotein biosynthetic pathway and provides practical clues for structure-based drug design. In particular, the most up-to-date knowledge on the SAR of lead compounds targeting this pathway would be a good reference for discovery of a novel class of antibacterial agents.

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Structure-Based Drug Design for Cytochrome P450 Family 1 Inhibitors

Bioinorganic Chemistry and Applications ◽

10.1155/2018/3924608 ◽

2018 ◽

Vol 2018 ◽

pp. 1-21 ◽

Cited By ~ 12

Author(s):

Zbigniew Dutkiewicz ◽

Renata Mikstacka

Keyword(s):

Drug Design ◽

Active Sites ◽

Wide Spectrum ◽

Cytochromes P450 ◽

Cancer Chemoprevention ◽

Ligand Docking ◽

Current Status ◽

Structure Based Drug Design ◽

Cytochrome P450 Family ◽

Distinctive Role

Cytochromes P450 are a class of metalloproteins which are responsible for electron transfer in a wide spectrum of reactions including metabolic biotransformation of endogenous and exogenous substrates. The superfamily of cytochromes P450 consists of families and subfamilies which are characterized by a specific structure and substrate specificity. Cytochromes P450 family 1 (CYP1s) play a distinctive role in the metabolism of drugs and chemical procarcinogens. In recent decades, these hemoproteins have been intensively studied with the use of computational methods which have been recently developed remarkably to be used in the process of drug design by the virtual screening of compounds in order to find agents with desired properties. Moreover, the molecular modeling of proteins and ligand docking to their active sites provide an insight into the mechanism of enzyme action and enable us to predict the sites of drug metabolism. The review presents the current status of knowledge about the use of the computational approach in studies of ligand-enzyme interactions for CYP1s. Research on the metabolism of substrates and inhibitors of CYP1s and on the selectivity of their action is particularly valuable from the viewpoint of cancer chemoprevention, chemotherapy, and drug-drug interactions.

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Discovery of Phosphodiesterase-4 Inhibitors: Serendipity and Rational Drug Design

Australian Journal of Chemistry ◽

10.1071/ch14397 ◽

2014 ◽

Vol 67 (12) ◽

pp. 1780 ◽

Cited By ~ 2

Author(s):

Susanne C. Feil ◽

Jessica K. Holien ◽

Craig J. Morton ◽

Nancy C. Hancock ◽

Philip E. Thompson ◽

...

Keyword(s):

Drug Design ◽

Active Sites ◽

Inflammatory Diseases ◽

Critical Role ◽

Rational Drug Design ◽

Chronic Obstructive ◽

Structure Based Drug Design ◽

Obstructive Pulmonary Disease ◽

Phosphodiesterase 4 ◽

Wide Range

Phosphodiesterase 4 (PDE4), the primary cyclic AMP-hydrolysing enzyme in cells, is a promising drug target for a wide range of mental disorders including Alzheimer's and Huntington's diseases, schizophrenia, and depression, plus a range of inflammatory diseases including chronic obstructive pulmonary disease, asthma, and rheumatoid arthritis. However, targeting PDE4 is complicated by the fact that the enzyme is encoded by four very closely related genes, together with 20 distinct isoforms as a result of mRNA splicing, and inhibition of some of these isoforms leads to intolerable side effects in clinical trials. With almost identical active sites between the isoforms, X-ray crystallography has played a critical role in the discovery and development of safer PDE4 inhibitors. Here we describe our discovery of a novel class of highly potent PDE4 via a ‘virtuous’ cycle of structure-based drug design and serendipity.

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ChemInform Abstract: Structure-Based Drug Design of Tricyclic 8H-Indeno[1,2-d][1,3]thiazoles as Potent FBPase Inhibitors.

ChemInform ◽

10.1002/chin.201037114 ◽

2010 ◽

Vol 41 (37) ◽

pp. no-no

Author(s):

Tomoharu Tsukada ◽

Mizuki Takahashi ◽

Toshiyasu Takemoto ◽

Osamu Kanno ◽

Takahiro Yamane ◽

...

Keyword(s):

Drug Design ◽

Structure Based Drug Design ◽

Abstract Structure

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ChemInform Abstract: Structure-Based Drug Design

ChemInform ◽

10.1002/chin.199408333 ◽

2010 ◽

Vol 25 (8) ◽

pp. no-no

Author(s):

J. A. MONTGOMERY ◽

S. NIWAS

Keyword(s):

Drug Design ◽

Structure Based Drug Design ◽

Abstract Structure

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ChemInform Abstract: Structure-Based Drug Design: Inhibitors of Purine Nucleoside Phosphorylase.

ChemInform ◽

10.1002/chin.199412313 ◽

2010 ◽

Vol 25 (12) ◽

pp. no-no

Author(s):

J. A. MONTGOMERY

Keyword(s):

Drug Design ◽

Purine Nucleoside Phosphorylase ◽

Purine Nucleoside ◽

Structure Based Drug Design ◽

Nucleoside Phosphorylase ◽

Abstract Structure

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Antibody- and Peptide Structure-Based Drug Design

PsycEXTRA Dataset ◽

10.1037/e495942006-004 ◽

1993 ◽

Author(s):

Manfred E. Wolff ◽

Keyword(s):

Drug Design ◽

Peptide Structure ◽

Structure Based Drug Design

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Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

10.26434/chemrxiv.9866912 ◽

2019 ◽

Author(s):

Mohammad Rezaei ◽

Yanjun Li ◽

Xiaolin Li ◽

Chenglong Li

Keyword(s):

Deep Learning ◽

Drug Design ◽

Binding Affinity ◽

Benchmark Dataset ◽

Rational Drug Design ◽

Learning Models ◽

Structure Based Drug Design ◽

Binding Affinity Prediction ◽

Affinity Prediction ◽

Rational Drug

Introduction: The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results. Objectives: The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters. Methods: The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models. Results: The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model. Conclusions: The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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