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.

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 Structural insights into targeting of the colchicine binding site by ELR510444 and parbendazole to achieve rational drug design

RSC Advances ◽  
2021 ◽  
Vol 11 (31) ◽  
pp. 18938-18944
Author(s):  
Jia-Hong Lei ◽  
Ling-Ling Ma ◽  
Jing-Hong Xian ◽  
Hai Chen ◽  
Jian-Jian Zhou ◽  
...  
Keyword(s):  
Drug Design ◽  
Binding Site ◽  
Crystal Structures ◽  
Rational Drug Design ◽  
Colchicine Binding Site ◽  
Rational Drug ◽  
Structural Insights

Crystal structures of tubulin complexed with ELR510444 and parbendazole facilitate the design of novel colchicine binding site inhibitors.

scholarly journals Inhibitory mechanism of reveromycin A at the tRNA binding site of a class I synthetase

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bingyi Chen ◽  
Siting Luo ◽  
Songxuan Zhang ◽  
Yingchen Ju ◽  
Qiong Gu ◽  
...  
Keyword(s):  
Binding Site ◽  
Catalytic Domain ◽  
Trna Synthetase ◽  
Rational Drug Design ◽  
Class I ◽  
Binding Assays ◽  
Trna Synthetases ◽  
Aminoacyl Trna Synthetases ◽  
Rational Drug ◽  
Trna Binding

AbstractThe polyketide natural product reveromycin A (RM-A) exhibits antifungal, anticancer, anti-bone metastasis, anti-periodontitis and anti-osteoporosis activities by selectively inhibiting eukaryotic cytoplasmic isoleucyl-tRNA synthetase (IleRS). Herein, a co-crystal structure suggests that the RM-A molecule occupies the substrate tRNAIle binding site of Saccharomyces cerevisiae IleRS (ScIleRS), by partially mimicking the binding of tRNAIle. RM-A binding is facilitated by the copurified intermediate product isoleucyl-adenylate (Ile-AMP). The binding assays confirm that RM-A competes with tRNAIle while binding synergistically with l-isoleucine or intermediate analogue Ile-AMS to the aminoacylation pocket of ScIleRS. This study highlights that the vast tRNA binding site of the Rossmann-fold catalytic domain of class I aminoacyl-tRNA synthetases could be targeted by a small molecule. This finding will inform future rational drug design.

scholarly journals NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Homa MohammadiPeyhani ◽  
Anush Chiappino-Pepe ◽  
Kiandokht Haddadi ◽  
Jasmin Hafner ◽  
Noushin Hadadi ◽  
...  
Keyword(s):  
Drug Targets ◽  
Drug Repurposing ◽  
Rational Drug Design ◽  
Bioactive Molecules ◽  
Lead Discovery ◽  
Minimal Impact ◽  
Practical Applications ◽  
Rational Drug ◽  
Golden Standard ◽  
And Performance

The discovery of a drug requires over a decade of intensive research and financial investments – and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug–drug and drug–metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

scholarly journals Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies

2020 ◽  
Author(s):  
Austė Kanapeckaitė ◽  
Claudia Beaurivage ◽  
Matthew Hancock ◽  
Erik Verschueren
Keyword(s):  
Dihedral Angle ◽  
Target Selection ◽  
Gaussian Mixture Models ◽  
Drug Repurposing ◽  
Gaussian Mixture ◽  
Rational Drug Design ◽  
Drug Candidates ◽  
Novel Approach ◽  
Protein Fingerprinting ◽  
Rational Drug

AbstractTarget evaluation is at the centre of rational drug design and biologics development. In order to successfully engineer antibodies, T-cell receptors or small molecules it is necessary to identify and characterise potential binding or contact sites on therapeutically relevant target proteins. Currently, there are numerous challenges in achieving a better docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinting approach based on dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. In addition, we showed that the entire protein regions or individual structural subsets can be profiled using our derived fi-score based on amino acid dihedral angle and B-factor distribution. We further described a method to assess the structural profile and extract information on sites of importance using machine learning Gaussian mixture models. In combination, these biophysical analytical methods could potentially help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites which would greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets.

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 Universal Architectural Concepts Underlying Protein Folding Patterns

2021 ◽  
Vol 7 ◽  
Author(s):  
Arun S. Konagurthu ◽  
Ramanan Subramanian ◽  
Lloyd Allison ◽  
David Abramson ◽  
Peter J. Stuckey ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Data Bank ◽  
Rational Drug Design ◽  
Basis Set ◽  
Sequence Structure ◽  
Information Theoretic ◽  
Protein Architecture ◽  
Structure Correlations ◽  
Rational Drug

What is the architectural “basis set” of the observed universe of protein structures? Using information-theoretic inference, we answer this question with a dictionary of 1,493 substructures—called concepts—typically at a subdomain level, based on an unbiased subset of known protein structures. Each concept represents a topologically conserved assembly of helices and strands that make contact. Any protein structure can be dissected into instances of concepts from this dictionary. We dissected the Protein Data Bank and completely inventoried all the concept instances. This yields many insights, including correlations between concepts and catalytic activities or binding sites, useful for rational drug design; local amino-acid sequence–structure correlations, useful for ab initio structure prediction methods; and information supporting the recognition and exploration of evolutionary relationships, useful for structural studies. An interactive site, Proçodic, at http://lcb.infotech.monash.edu.au/prosodic (click), provides access to and navigation of the entire dictionary of concepts and their usages, and all associated information. This report is part of a continuing programme with the goal of elucidating fundamental principles of protein architecture, in the spirit of the work of Cyrus Chothia.

scholarly journals A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics

2020 ◽  
Author(s):  
Jonas Gossen ◽  
Simone Albani ◽  
Anton Hanke ◽  
Benjamin P. Joseph ◽  
Cathrine Bergh ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Active Sites ◽  
Chemical Space ◽  
Protein Structures ◽  
Rational Drug Design ◽  
In Vitro Assays ◽  
Strong Basis ◽  
High Affinity ◽  
Compound Libraries

AbstractThe SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unfortunately, unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ~30,000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ~200 virtual screenings of compound libraries on selected protein structures, we redefine the protein’s druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

Identification of Potential Inhibitors against SARS-Cov-2 using Computational Drug Repurposing Study

2021 ◽  
Vol 16 ◽  
Author(s):  
Hasan Zulfiqar ◽  
Fu-Ying Dao ◽  
Hao Lv ◽  
Hui Yang ◽  
Peng Zhou ◽  
...  
Keyword(s):  
Envelope Protein ◽  
Nucleocapsid Protein ◽  
Active Sites ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Chemical Compounds ◽  
Host Organism ◽  
Binding Pockets ◽  
Potential Inhibitors ◽  
Severe Type

Background: SARS-Cov-2 is a newly emerged coronavirus that causes a severe type of pneumonia in the host organism. It is an urgent need to find some inhibitors against SARS-Cov-2. Therefore, drug repurposing study is an effective strategy for treating pneumonia to find the inhibitors of SARS-Cov-2 proteins. Method: For this purpose, a library of 2500 verified drug chemical compounds were generated and docked against nucleocapsid, membrane, and envelope protein structures of SARS-Cov-2 to determine the binding affinity of the chemical compounds against targeting binding pockets. Moreover, cheminformatics properties and ADMET of these compounds were assessed to find the drug-likeness behavior of compounds. The chemical compounds with the lowest S-score were identified as potential inhibitors. Results: Our findings showed that the compound IDs 1212, 1019, and 1992 could interact inside the active sites of membrane protein, nucleocapsid protein, and envelope protein. Conclusion: This insilico knowledge will be helpful for the design of novel, safe, and less costly drugs against the SARS-Cov-2.

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