scholarly journals Making the invisible enemy visible

2020 ◽  
Author(s):  
Tristan Croll ◽  
Kay Diederichs ◽  
Florens Fischer ◽  
Cameron Fyfe ◽  
Yunyun Gao ◽  
...  
Keyword(s):  
Life Cycle ◽  
Drug Design ◽  
Structure Determination ◽  
Task Force ◽  
Protein Structures ◽  
Structural Models ◽  
Viral Proteins ◽  
Human Cells ◽  
Structure Based Drug Design ◽  
The Eu

AbstractDuring the COVID-19 pandemic, structural biologists rushed to solve the structures of the 28 proteins encoded by the SARS-CoV-2 genome in order to understand the viral life cycle and enable structure-based drug design. In addition to the 204 previously solved structures from SARS-CoV-1, 548 structures covering 16 of the SARS-CoV-2 viral proteins have been released in a span of only 6 months. These structural models serve as the basis for research to understand how the virus hijacks human cells, for structure-based drug design, and to aid in the development of vaccines. However, errors often occur in even the most careful structure determination - and may be even more common among these structures, which were solved quickly and under immense pressure.The Coronavirus Structural Task Force has responded to this challenge by rapidly categorizing, evaluating and reviewing all of these experimental protein structures in order to help downstream users and original authors. In addition, the Task Force provided improved models for key structures online, which have been used by Folding@Home, OpenPandemics, the EU JEDI COVID-19 challenge and others.

scholarly journals Insights into Structures and Dynamics of Flavivirus Proteases from NMR Studies

2020 ◽  
Vol 21 (7) ◽  
pp. 2527 ◽  
Author(s):  
Qingxin Li ◽  
CongBao Kang
Keyword(s):  
Nmr Spectroscopy ◽  
Drug Design ◽  
Conformational Changes ◽  
Structural Information ◽  
Protein Structures ◽  
Rational Drug Design ◽  
Solution Nmr ◽  
Nmr Studies ◽  
Structure Based Drug Design ◽  
Additional Information

Nuclear magnetic resonance (NMR) spectroscopy plays important roles in structural biology and drug discovery, as it is a powerful tool to understand protein structures, dynamics, and ligand binding under physiological conditions. The protease of flaviviruses is an attractive target for developing antivirals because it is essential for the maturation of viral proteins. High-resolution structures of the proteases in the absence and presence of ligands/inhibitors were determined using X-ray crystallography, providing structural information for rational drug design. Structural studies suggest that proteases from Dengue virus (DENV), West Nile virus (WNV), and Zika virus (ZIKV) exist in open and closed conformations. Solution NMR studies showed that the closed conformation is predominant in solution and should be utilized in structure-based drug design. Here, we reviewed solution NMR studies of the proteases from these viruses. The accumulated studies demonstrated that NMR spectroscopy provides additional information to understand conformational changes of these proteases in the absence and presence of substrates/inhibitors. In addition, NMR spectroscopy can be used for identifying fragment hits that can be further developed into potent protease inhibitors.

scholarly journals 2.8 Å resolution reconstruction of the Thermoplasma acidophilum 20S proteasome using cryo-electron microscopy

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Melody G Campbell ◽  
David Veesler ◽  
Anchi Cheng ◽  
Clinton S Potter ◽  
Bridget Carragher
Keyword(s):  
Electron Microscopy ◽  
Drug Design ◽  
Single Particle ◽  
Protein Structures ◽  
Rational Drug Design ◽  
20S Proteasome ◽  
Thermoplasma Acidophilum ◽  
Structure Based Drug Design ◽  
Cryo Electron Microscopy ◽  
Pharmaceutical Industries

Recent developments in detector hardware and image-processing software have revolutionized single particle cryo-electron microscopy (cryoEM) and led to a wave of near-atomic resolution (typically ∼3.3 Å) reconstructions. Reaching resolutions higher than 3 Å is a prerequisite for structure-based drug design and for cryoEM to become widely interesting to pharmaceutical industries. We report here the structure of the 700 kDa Thermoplasma acidophilum 20S proteasome (T20S), determined at 2.8 Å resolution by single-particle cryoEM. The quality of the reconstruction enables identifying the rotameric conformation adopted by some amino-acid side chains (rotamers) and resolving ordered water molecules, in agreement with the expectations for crystal structures at similar resolutions. The results described in this manuscript demonstrate that single particle cryoEM is capable of competing with X-ray crystallography for determination of protein structures of suitable quality for rational drug design.

scholarly journals The Growing Role of Electron Microscopy in Anti-parasitic Drug Discovery

2019 ◽  
Vol 25 (39) ◽  
pp. 5279-5290
Author(s):  
R.M. Johnson ◽  
S. Rawson ◽  
M.J. McPhillie ◽  
C.W.G. Fishwick ◽  
S.P. Muench
Keyword(s):  
Electron Microscopy ◽  
Drug Discovery ◽  
High Resolution ◽  
Drug Design ◽  
Structural Information ◽  
Protein Structures ◽  
Parasite Protein ◽  
Structure Based Drug Design ◽  
Dramatic Rise ◽  
Review Reports

Background: Parasite diseases are a huge burden on human health causing significant morbidity and mortality. However, parasite structure based drug discovery programmes have been hindered by a lack of high resolution structural information from parasite derived proteins and have often relied upon homology models from mammalian systems. The recent renaissance in electron microscopy (EM) has caused a dramatic rise in the number of structures being determined at high resolution and subsequently enabled it to be thought of as a tool in drug discovery. Results: In this review, we discuss the challenges associated with the structural determination of parasite proteins including the difficulties in obtaining sufficient quantities of protein. We then discuss the reasons behind the resurgence in EM, how it may overcome some of these challenges and provide examples of EM derived parasite protein structures. Finally, we discuss the challenges which EM needs to overcome before it is used as a mainstream technique in anti-parasite drug discovery. Conclusions: This review reports the progress that has been made in obtaining sufficient quantities of proteins for structural studies and the role EM may play in future structure based drug design programs. The outlook for future structure based drug design programs against some of the most devastating parasite diseases looks promising.

A Computational and Literature-Based Evaluation for a Combination of Chiral Anti-CoV Drugs to Block and Eliminate SARS-CoV-2 Safely

2021 ◽  
Vol 20 (04) ◽  
pp. 417-432
Author(s):  
Mohd. Suhail
Keyword(s):  
Drug Design ◽  
Jc Virus ◽  
Computational Study ◽  
Human Cells ◽  
The Body ◽  
Chiral Drugs ◽  
Structure Based Drug Design ◽  
Drug Candidates ◽  
Main Protease ◽  
The World

It has been a great challenge for scientists to develop an anti-Covid drug/vaccine with fewer side effects, since the coronavirus pandemic began. Of course, the prescription of chiral drugs (chloroquine or hydroxychloroquine) has been proved wrong because these chiral drugs neither kill the virus nor eliminate it from the body, but block SARS-CoV-2 from binding to human cells. Another hurdle facing the world is not only the positive test of the patient recovered from coronavirus, but also the second wave of Covid-19. Hence, the world demands such a drug or drug combination which not only prevents the entry of SARS-CoV-2 in the human cell but also ejects it or its material from the body completely. The current computational study not only utilizes a structure-based drug design approach to find possible drug candidates but also explains (i) why the prescription of chiral drugs was not satisfactory, (ii) what types of modification can make their prescription satisfactory, (iii) the mechanism of action of chiral drugs (chloroquine and hydroxychloroquine) to block SARS-CoV-2 from binding to human cells, and (iv) the strength of mefloquine to eliminate SARS-CoV-2. As the main protease (M[Formula: see text]) of microbes is considered as an effective target for drug design and development, the binding affinities of mefloquine with the M[Formula: see text] of JC virus and SARS-CoV-2 were calculated, and then compared to know the eliminating strength of mefloquine against SARS-CoV-2. The M[Formula: see text] of JC virus was taken because mefloquine has already shown a tremendous result of eliminating it from the body. The prescription of a combination of S-[Formula: see text]-hydroxychloroquine and [Formula: see text]-mefloquine is considered as a boon by the predicted study.

Structure-Based Drug Design Strategies and Challenges

2018 ◽  
Vol 18 (12) ◽  
pp. 998-1006 ◽  
Author(s):  
Xin Wang ◽  
Ke Song ◽  
Li Li ◽  
Lijiang Chen
Keyword(s):  
Drug Design ◽  
Protein Structures ◽  
Three Dimensional ◽  
Design Strategies ◽  
Structure Based Drug Design ◽  
The Past ◽  
Three Dimensional Representation ◽  
Potent Drug ◽  
The Cost ◽  
Gene Information

Over the past ten years, the number of three-dimensional protein structures identified by advanced science and technology increases, and the gene information becomes more available than ever before as well. The development of computing science becomes another driving force which makes it possible to use computational methods effectively in various phases of the drug design and research. Now Structure-Based Drug Design (SBDD) tools are widely used to help researchers to predict the position of small molecules within a three-dimensional representation of the protein structure and estimate the affinity of ligands to target protein with considerable accuracy and efficiency. They also accelerate discovery speed of potent drug and reduce the cost and times for drug research. Here we present an overview of SBDD used in drug discovery and highlight its recent successes and major challenges to current SBDD methodologies.

scholarly journals Structure-based drug design: aiming for a perfect fit

2017 ◽  
Vol 61 (5) ◽  
pp. 431-437 ◽  
Author(s):  
Rob L.M. van Montfort ◽  
Paul Workman
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Structural Biology ◽  
Protein Structures ◽  
Three Dimensional ◽  
Integral Membrane Protein ◽  
Open Science ◽  
Dimensional Structure ◽  
Structure Based Drug Design ◽  
X Ray

Knowledge of the three-dimensional structure of therapeutically relevant targets has informed drug discovery since the first protein structures were determined using X-ray crystallography in the 1950s and 1960s. In this editorial we provide a brief overview of the powerful impact of structure-based drug design (SBDD), which has its roots in computational and structural biology, with major contributions from both academia and industry. We describe advances in the application of SBDD for integral membrane protein targets that have traditionally proved very challenging. We emphasize the major progress made in fragment-based approaches for which success has been exemplified by over 30 clinical drug candidates and importantly three FDA-approved drugs in oncology. We summarize the articles in this issue that provide an excellent snapshot of the current state of the field of SBDD and fragment-based drug design and which offer key insights into exciting new developments, such as the X-ray free-electron laser technology, cryo-electron microscopy, open science approaches and targeted protein degradation. We stress the value of SBDD in the design of high-quality chemical tools that are used to interrogate biology and disease pathology, and to inform target validation. We emphasize the need to maintain the scientific rigour that has been traditionally associated with structural biology and extend this to other methods used in drug discovery. This is particularly important because the quality and robustness of any form of contributory data determines its usefulness in accelerating drug design, and therefore ultimately in providing patient benefit.

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

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

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

<b>Introduction:</b> 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.<br><b>Objectives:</b> 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.<br><b>Methods:</b> 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.<br><b>Results: </b>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.<br><b>Conclusions:</b> 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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