Improving quality and harvest period of protein crystals for structure-based drug design: effects of a gel and a magnetic field on bovine adenosine deaminase crystals

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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Technology of high quality protein crystals’ obtaining aboard the ISS at execution of joint Japanese–Russian experiments

Space engineering and technology ◽

10.33950/spacetech-2308-7625-2020-2-5-25 ◽

2020 ◽

pp. 5-25

Author(s):

Koji INAKA ◽

Saori ICHIMIZU ◽

Izumi YOSHIZAKI ◽

Kiyohito KIHIRA ◽

Elena G. LAVRENKO ◽

...

Keyword(s):

Drug Design ◽

International Space Station ◽

Space Station ◽

Protein Crystals ◽

Diffusion Method ◽

X Ray Diffraction ◽

High Quality ◽

International Space ◽

X Ray ◽

High Quality Protein

A series of space experiments aboard the International Space Station (ISS) associated with high-quality Protein Crystal Growth (PCG) in microgravity conditions can be considered as a unique and one of the best examples of fruitful collaboration between Japanese and Russian scientists and engineers in space, which includes also other ISS International Partners. X-ray diffraction is still the most powerful tool to determine the protein three dimensional structure necessary for Structure based drug design (SBDD). The major purpose of the experiment is to grow high quality protein crystals in microgravity for X-ray diffraction on Earth. Within one and a half decade, Japan and Russia have established an efficient process over PCG in space to support latest developments over drug design and structural biology. One of the keys for success of the experiment lies in how precisely pre-launch preparations are made. Japanese party provides flight equipment for crystallization and ensures the required environment to support the experiment aboard of the ISS’s Kibo module, and also mainly takes part of the experiment ground support such as protein sample characterization, purification, crystallization screening, and solution optimization for microgravity experiment. Russian party is responsible for integration of the flight items equipped with proteins and precipitants on board Russian transportation space vehicles (Soyuz or Progress), for delivery them at the ISS, transfer to Kibo module, and returning the experiments’ results back on Earth aboard Soyuz manned capsule. Due to close cooperation of the parties and solid organizational structure, samples can be launched at the ISS every half a year if the ground preparation goes smoothly. The samples are crystallized using counter diffusion method at 20 degree C for 1–2.5 months. After samples return, the crystals are carefully taken out from the capillary, and frozen for X-ray diffraction at SPring8 facility in Japan. Extensive support of researchers from both countries is also a part of this process. The paper analyses details of the PCG experiment scheme, unique and reliable technology of its execution, and contains examples of the application. Key words: International Space Station, Protein crystals, Microgravity, International collaboration.

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Faculty Opinions recommendation of Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK).

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.12934957.14822055 ◽

2012 ◽

Author(s):

John Proudfoot

Keyword(s):

Drug Design ◽

Anaplastic Lymphoma Kinase ◽

Dual Inhibitor ◽

Structure Based Drug Design ◽

Anaplastic Lymphoma ◽

Mesenchymal Epithelial Transition Factor ◽

Factor C ◽

Mesenchymal Epithelial Transition

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Faculty Opinions recommendation of Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK).

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.12934957.14323054 ◽

2011 ◽

Author(s):

John Lowe

Keyword(s):

Drug Design ◽

Anaplastic Lymphoma Kinase ◽

Dual Inhibitor ◽

Structure Based Drug Design ◽

Anaplastic Lymphoma ◽

Mesenchymal Epithelial Transition Factor ◽

Factor C ◽

Mesenchymal Epithelial Transition

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Faculty Opinions recommendation of Free Ligand 1D NMR Conformational Signatures to Enhance Structure Based Drug Design of a Mcl-1 inhibitor (AZD5991) and other Synthetic Macrocycles.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.736292329.793563510 ◽

2019 ◽

Author(s):

John Lowe

Keyword(s):

Drug Design ◽

Free Ligand ◽

Structure Based Drug Design

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Preface ( Hot Topics: ADMET / Tox Informatics: Adding Innovation to Structure-Based Drug Design Guest Editor: Roberta Bursi ).

Current Topics in Medicinal Chemistry ◽

10.2174/1568026033451943 ◽

2003 ◽

Vol 3 (11) ◽

pp. 1-2

Author(s):

R. Bursi

Keyword(s):

Drug Design ◽

Guest Editor ◽

Structure Based Drug Design

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Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/156802662019200701164759 ◽

2020 ◽

Vol 20 (19) ◽

pp. 1651-1660

Author(s):

Anuraj Nayarisseri

Keyword(s):

Drug Discovery ◽

Drug Design ◽

Binding Affinity ◽

Chemical Biology ◽

De Novo ◽

Structure Based Drug Design ◽

Computational Approaches ◽

Drug Designing ◽

Traditional Drug ◽

Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

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