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.

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.

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.

Protein structure by solid-state NMR spectroscopy

1987 ◽  
Vol 19 (1-2) ◽  
pp. 7-49 ◽  
Author(s):  
S. J. Opella ◽  
P. L. Stewart ◽  
K. G. Valentine
Keyword(s):  
Solid State ◽  
Nmr Spectroscopy ◽  
Solid State Nmr ◽  
Structural Information ◽  
Biological Systems ◽  
Genetic Regulation ◽  
Protein Structures ◽  
Crystalline Solid ◽  
Solid State Nmr Spectroscopy ◽  
Nmr Methods

The three-dimensional structures of proteins are among the most valuable contributions of biophysics to the understanding of biological systems (Dickerson & Geis, 1969; Creighton, 1983). Protein structures are utilized in the description and interpretation of a wide variety of biological phenomena, including genetic regulation, enzyme mechanisms, antibody recognition, cellular energetics, and macroscopic mechanical and structural properties of molecular assemblies. Virtually all of the information currently available about the structures of proteins at atomic resolution has been obtained from diffraction studies of single crystals of proteins (Wyckoff et al, 1985). However, recently developed NMR methods are capable of determining the structures of proteins and are now being applied to a variety of systems, including proteins in solution and other non-crystalline environments that are not amenable for X-ray diffraction studies. Solid-state NMR methods are useful for proteins that undergo limited overall reorientation by virtue of their being in the crystalline solid state or integral parts of supramolecular structures that do not reorient rapidly in solution. For reviews of applications of solid-state NMR spectroscopy to biological systems see Torchia and VanderHart (1979), Griffin (1981), Oldfield et al. (1982), Opella (1982), Torchia (1982), Gauesh (1984), Torchia (1984) and Opella (1986). This review describes how solid-state NMR can be used to obtain structural information about proteins. Methods applicable to samples with macroscopic orientation are emphasized.

scholarly journals Structure-Function of Neuronal Nicotinic Acetylcholine Receptor Inhibitors Derived From Natural Toxins

2020 ◽  
Vol 14 ◽  
Author(s):  
Thao N. T. Ho ◽  
Nikita Abraham ◽  
Richard J. Lewis
Keyword(s):  
Drug Design ◽  
Ligand Binding ◽  
Nicotinic Acetylcholine Receptors ◽  
Acetylcholine Receptors ◽  
Molecular Mechanisms ◽  
Rational Drug Design ◽  
Comprehensive Overview ◽  
Nicotinic Acetylcholine ◽  
Structure Based Drug Design ◽  
Selective Ligand

Neuronal nicotinic acetylcholine receptors (nAChRs) are prototypical cation-selective, ligand-gated ion channels that mediate fast neurotransmission in the central and peripheral nervous systems. nAChRs are involved in a range of physiological and pathological functions and hence are important therapeutic targets. Their subunit homology and diverse pentameric assembly contribute to their challenging pharmacology and limit their drug development potential. Toxins produced by an extensive range of algae, plants and animals target nAChRs, with many proving pivotal in elucidating receptor pharmacology and biochemistry, as well as providing templates for structure-based drug design. The crystal structures of these toxins with diverse chemical profiles in complex with acetylcholine binding protein (AChBP), a soluble homolog of the extracellular ligand-binding domain of the nAChRs and more recently the extracellular domain of human α9 nAChRs, have been reported. These studies have shed light on the diverse molecular mechanisms of ligand-binding at neuronal nAChR subtypes and uncovered critical insights useful for rational drug design. This review provides a comprehensive overview and perspectives obtained from structure and function studies of diverse plant and animal toxins and their associated inhibitory mechanisms at neuronal nAChRs.

Rational Drug Design

2014 ◽  
Vol 4 (1) ◽  
pp. 59-85 ◽  
Author(s):  
Khaled H. Barakat ◽  
Michael Houghton ◽  
D. Lorne Tyrrel ◽  
Jack A. Tuszynski
Keyword(s):  
Drug Design ◽  
Rational Drug Design ◽  
Dimensional Structure ◽  
Computational Tools ◽  
Structure Based Drug Design ◽  
Practical Applications ◽  
3 Dimensional ◽  
Drug Candidates ◽  
The Past ◽  
Rational Drug

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

scholarly journals A Deep-Learning Proteomic-Scale Approach for Drug Design

2021 ◽  
Vol 14 (12) ◽  
pp. 1277
Author(s):  
Brennan Overhoff ◽  
Zackary Falls ◽  
William Mangione ◽  
Ram Samudrala
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Protein Structures ◽  
Holistic Approach ◽  
Rational Drug Design ◽  
P Value ◽  
Experimental Drugs ◽  
Rational Drug ◽  
Drug Compound ◽  
Novel Drug

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

scholarly journals Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases

2019 ◽  
Vol 20 (3) ◽  
pp. 548 ◽  
Author(s):  
Yunhui Peng ◽  
Emil Alexov ◽  
Sankar Basu
Keyword(s):  
Drug Design ◽  
Structural Information ◽  
Radical Change ◽  
Biological Macromolecules ◽  
Free Energies ◽  
Biophysical Properties ◽  
Structure Based Drug Design ◽  
Promising Alternative ◽  
Intrinsically Disordered ◽  
Binding Free Energies

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations—whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico–chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.

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