scholarly journals A structural keystone for drug design

2006 ◽  
Vol 3 (1) ◽  
pp. 21-31
Author(s):  
Kristian Rother ◽  
Mathias Dunkel ◽  
Elke Michalsky ◽  
Silke Trissl ◽  
Andrean Goede ◽  
...  
Keyword(s):  
Drug Design ◽  
Small Molecules ◽  
Protein Complexes ◽  
Protein Structures ◽  
Binding Activity ◽  
Rational Drug Design ◽  
Biologically Active ◽  
Web Resource ◽  
Fold Family ◽  
Approved Drugs

Abstract 3D-structures of proteins and potential ligands are the cornerstones of rational drug design. The first brick to build upon is selecting a protein target and finding out whether biologically active compounds are known. Both tasks require more information than the structures themselves provide. For this purpose we have built a web resource bridging protein and ligand databases. It consists of three parts: i) A data warehouse on annotation of protein structures that integrates many well-known databases such as Swiss-Prot, SCOP, ENZYME and others. ii) A conformational library of structures of approved drugs. iii) A conformational library of ligands from the PDB, linking the realms of proteins and small molecules. The data collection contains structures of 30,000 proteins, 5,000 different ligands from 70,000 ligand-protein complexes, and 2,500 known drugs. Sets of protein structures can be refined by criteria like protein fold, family, metabolic pathway, resolution and textual annotation. The structures of organic compounds (drugs and ligands) can be searched considering chemical formula, trivial and trade names as well as medical classification codes for drugs (ATC). Retrieving structures by 2D-similarity has been implemented for all small molecules using Tanimoto coefficients. For the drug structures, 110,000 structural conformers have been calculated to account for structural flexibility. Two substances can be compared online by 3D-superimposition, where the pair of conformers that fits best is detected. Together, these web-accessible resources can be used to identify promising drug candidates. They have been used in-house to find alternatives to substances with a known binding activity but adverse side effects.

scholarly journals Recognition of Potential COVID-19 Drug Treatments through the Study of Existing Protein–Drug and Protein–Protein Structures: An Analysis of Kinetically Active Residues

Biomolecules ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1346
Author(s):  
Ognjen Perišić
Keyword(s):  
Small Molecules ◽  
Active Sites ◽  
Protein Structures ◽  
Normal Modes ◽  
Rational Drug Design ◽  
Rational Drug ◽  
In Silico Study ◽  
Drug Treatments ◽  
Protein Pockets ◽  
Approved Drugs

We report the results of our in silico study of approved drugs as potential treatments for COVID-19. The study is based on the analysis of normal modes of proteins. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and α-difluoromethylornithine (DMFO). We applied the tools we developed and standard tools used in the structural biology community. Our results indicate that small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets, and that some prefer hydrophobic sites over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general.

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 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 Binding site matching in rational drug design: algorithms and applications

2018 ◽  
Vol 20 (6) ◽  
pp. 2167-2184 ◽  
Author(s):  
Misagh Naderi ◽  
Jeffrey Mitchell Lemoine ◽  
Rajiv Gandhi Govindaraj ◽  
Omar Zade Kana ◽  
Wei Pan Feinstein ◽  
...  
Keyword(s):  
Small Molecules ◽  
Binding Site ◽  
Protein Structures ◽  
Chemical Properties ◽  
Drug Repurposing ◽  
Rational Drug Design ◽  
Basic Knowledge ◽  
Rational Drug ◽  
Binding Pockets ◽  
Local Protein

Abstract Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.

Protein Structures and Structure-Based Rational Drug Design

2018 ◽  
pp. 73-89
Author(s):  
Paul M. Selzer ◽  
Richard J. Marhöfer ◽  
Oliver Koch
Keyword(s):  
Drug Design ◽  
Protein Structures ◽  
Rational Drug Design ◽  
Rational Drug

scholarly journals Binding of small molecules at interface of protein–protein complex – A newer approach to rational drug design

2017 ◽  
Vol 24 (2) ◽  
pp. 379-388 ◽  
Author(s):  
A.B. Gurung ◽  
A. Bhattacharjee ◽  
M. Ajmal Ali ◽  
F. Al-Hemaid ◽  
Joongku Lee
Keyword(s):  
Drug Design ◽  
Small Molecules ◽  
Protein Complex ◽  
Rational Drug Design ◽  
Rational Drug

scholarly journals A Deep Learning Proteomic Scale Approach for Drug Design

2021 ◽  
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

AbstractComputational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multi-target therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach by computing 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 model, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds are predicted to be significantly (p-value ≤ .05) more behaviorally similar relative to all corresponding controls, and 20/20 are predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design perform significantly better than those derived from natural sources (p-value ≤.05), suggesting that the model has learned an abstraction of rational drug design. We also show that 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 enhance thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. This work represents a significant step forward in automating holistic therapeutic design with machine learning, and subsequently offers a reduction in the time needed to generate novel, effective, and safe drug leads for any indication.

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.

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