The impact of GPCR structures on pharmacology and structure-based drug design

Abstract Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule—along with any bound ligand(s)—within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method—coupled with our XModeScore method for experimental tautomer/protomer state determination—to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.

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Drug design and repurposing with DockThor-VS web server focusing on SARS-CoV-2 therapeutic targets and their non-synonym variants

Scientific Reports ◽

10.1038/s41598-021-84700-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Isabella A. Guedes ◽

Leon S. C. Costa ◽

Karina B. dos Santos ◽

Ana L. M. Karl ◽

Gregório K. Rocha ◽

...

Keyword(s):

Drug Design ◽

Therapeutic Targets ◽

Web Server ◽

Drug Repurposing ◽

World Health ◽

Design Strategies ◽

Structure Based Drug Design ◽

N Protein ◽

The Impact ◽

The Web

AbstractThe COVID-19 caused by the SARS-CoV-2 virus was declared a pandemic disease in March 2020 by the World Health Organization (WHO). Structure-Based Drug Design strategies based on docking methodologies have been widely used for both new drug development and drug repurposing to find effective treatments against this disease. In this work, we present the developments implemented in the DockThor-VS web server to provide a virtual screening (VS) platform with curated structures of potential therapeutic targets from SARS-CoV-2 incorporating genetic information regarding relevant non-synonymous variations. The web server facilitates repurposing VS experiments providing curated libraries of currently available drugs on the market. At present, DockThor-VS provides ready-for-docking 3D structures for wild type and selected mutations for Nsp3 (papain-like, PLpro domain), Nsp5 (Mpro, 3CLpro), Nsp12 (RdRp), Nsp15 (NendoU), N protein, and Spike. We performed VS experiments of FDA-approved drugs considering the therapeutic targets available at the web server to assess the impact of considering different structures and mutations to identify possible new treatments of SARS-CoV-2 infections. The DockThor-VS is freely available at www.dockthor.lncc.br.

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The impact of crystallization conditions on structure-based drug design: A case study on the methylene blue/acetylcholinesterase complex

Protein Science ◽

10.1002/pro.2923 ◽

2016 ◽

Vol 25 (6) ◽

pp. 1096-1114 ◽

Cited By ~ 19

Author(s):

Orly Dym ◽

Wanling Song ◽

Clifford Felder ◽

Esther Roth ◽

Valery Shnyrov ◽

...

Keyword(s):

Methylene Blue ◽

Drug Design ◽

Structure Based Drug Design ◽

Crystallization Conditions ◽

The Impact

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Drug Design and Repurposing with DockThor-VS Web Server: Virtual Screening focusing on SARS-CoV-2 Therapeutic Targets and their Non-Synonym Variants

10.21203/rs.3.rs-96789/v1 ◽

2020 ◽

Author(s):

Isabella A. Guedes ◽

Leon S. C. Costa ◽

Karina B. dos Santos ◽

Ana L. M. Karl ◽

Gregório K. Rocha ◽

...

Keyword(s):

Virtual Screening ◽

Drug Design ◽

Therapeutic Targets ◽

Web Server ◽

Drug Repurposing ◽

World Health ◽

Design Strategies ◽

Structure Based Drug Design ◽

The Impact ◽

The Web

Abstract The COVID-19 caused by the SARS-CoV-2 virus was declared as a pandemic disease in March 2020 by the World Health Organization (WHO). Structure-Based Drug Design strategies based on docking methodologies have been widely used for both new drug development and drug repurposing to find effective treatments against this disease. In this work, we present the developments implemented in the DockThor-VS web server to provide a virtual screening (VS) platform with curated structures of potential therapeutic targets from SARS-CoV-2 incorporating genetic information regarding relevant non-synonymous variations. The web server facilitates repurposing VS experiments providing curated libraries of currently available drugs on the market. Currently, DockThor-VS provides ready-for-docking 3D structures for wild type and selected mutations for Nsp3 (papain-like, PLpro domain), Nsp5 (Mpro, 3CLpro), Nsp12 (RdRp), Nsp15 (NendoU), N protein and Spike. We performed VS experiments of FDA-approved drugs considering the therapeutic targets available at the web server to assess the impact of considering different structures and mutations in the identification of possible new treatments of SARS-CoV-2 infections. The DockThor-VS is freely available at www.dockthor.lncc.br.

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The impact of crystallisation conditions on structure-based drug design

Acta Crystallographica Section A Foundations and Advances ◽

10.1107/s2053273317095237 ◽

2017 ◽

Vol 73 (a2) ◽

pp. C47-C47

Author(s):

Orly Dym

Keyword(s):

Drug Design ◽

Structure Based Drug Design ◽

The Impact

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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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