scholarly journals Protein X-ray Crystallography and Drug Discovery

Molecules ◽  
2020 ◽  
Vol 25 (5) ◽  
pp. 1030 ◽  
Author(s):  
Laurent Maveyraud ◽  
Lionel Mourey
Keyword(s):  
Drug Discovery ◽  
Structural Information ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
X Ray ◽  
Drug Candidates ◽  
X Ray Crystallography ◽  
Compound Libraries ◽  
Fragment Library ◽  
Invaluable Tool

With the advent of structural biology in the drug discovery process, medicinal chemists gained the opportunity to use detailed structural information in order to progress screening hits into leads or drug candidates. X-ray crystallography has proven to be an invaluable tool in this respect, as it is able to provide exquisitely comprehensive structural information about the interaction of a ligand with a pharmacological target. As fragment-based drug discovery emerged in the recent years, X-ray crystallography has also become a powerful screening technology, able to provide structural information on complexes involving low-molecular weight compounds, despite weak binding affinities. Given the low numbers of compounds needed in a fragment library, compared to the hundreds of thousand usually present in drug-like compound libraries, it now becomes feasible to screen a whole fragment library using X-ray crystallography, providing a wealth of structural details that will fuel the fragment to drug process. Here, we review theoretical and practical aspects as well as the pros and cons of using X-ray crystallography in the drug discovery process.

scholarly journals Fragment Library of Natural Products and Compound Databases for Drug Discovery

Biomolecules ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1518 ◽  
Author(s):  
Ana L. Chávez-Hernández ◽  
Norberto Sánchez-Cruz ◽  
José L. Medina-Franco
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Synthetic Compounds ◽  
Drug Candidates ◽  
Compound Libraries ◽  
Chemical Database ◽  
Fragment Library ◽  
Further Development ◽  
Fragment Libraries ◽  
Fragment Based Drug Discovery

Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.

scholarly journals In Silico Drug Repositioning Against Human NRP1 to Block SARS-CoV-2 Host Entry

2020 ◽  
Author(s):  
Seref Gul
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Structural Information ◽  
Drug Repositioning ◽  
Experimental Studies ◽  
Md Simulations ◽  
Drug Binding ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
S Protein

Despite COVID-19 turned into a pandemic, no approved drug for the treatment or globally available vaccine is out yet. In such a global emergency, drug repurposing approach that bypasses a costly and long-time demanding drug discovery process is an effective way in search of finding drugs for the COVID-19 treatment. Recent studies showed that SARS-CoV-2 uses neuropilin-1 (NRP1) for host entry. Here I took advantage of structural information of the NRP1 in complex with C-terminal of spike (S) protein of SARS-CoV-2 to identify drugs that may inhibit NRP1 and S protein interaction. U.S. Food and Drug Administration (FDA) approved drugs were screened using docking simulations. Among top drugs, well-tolerated drugs were selected for further analysis. Molecular dynamics (MD) simulations of drugs-NRP1 complexes were run for 100 ns to assess the persistency of binding. MM/GBSA calculations from MD simulations showed that eltrombopag, glimepiride, sitagliptin, dutasteride, and ergotamine stably and strongly bind to NRP1. In silico Alanine scanning analysis revealed that Tyr<sup>297</sup>, Trp<sup>301</sup>, and Tyr<sup>353</sup> amino acids of NRP1 are critical for drug binding. Validating the effect of drugs analyzed in this paper by experimental studies and clinical trials will expedite the drug discovery process for COVID-19.

Lead Optimization in the Drug Discovery Process

2019 ◽  
pp. 62-79
Author(s):  
S. Lakshmana Prabu ◽  
Rathinasabapathy Thirumurugan
Keyword(s):  
Drug Discovery ◽  
High Throughput Screening ◽  
Lead Optimization ◽  
Drug Candidate ◽  
Pharmacokinetic Parameters ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Compound Libraries ◽  
Lead Selection ◽  
Clinical Drug

Discovering a new drug molecule against disease is the main objective of drug discovery. Lead optimization is one of the important steps and acts as a starting point. Over the years, it has significantly changed the drug discovery process. Its main focus is the development of preclinical candidates from “Hit” or “Lead.” Lead optimization comprises lead selection and optimization, drug candidate confirmation, and preclinical drug characterization. Lead optimization process can improve the effectiveness towards its target potency, selectivity, protein binding, pharmacokinetic parameters, and to develop a good preclinical candidate. Lead optimization from high-throughput screening to identification of clinical drug candidate is a seamless process that draws new techniques for accelerated synthesis, purification, screening from iterative compound libraries, validation, and to deliver clinical drug candidate with limited human resources. In conclusion, lead optimization phase is done under the suggestion that the optimized lead molecule will have activity against a particular disease.

X-ray free electron laser: opportunities for drug discovery

2017 ◽  
Vol 61 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Robert K.Y. Cheng ◽  
Rafael Abela ◽  
Michael Hennig
Keyword(s):  
Conformational Changes ◽  
Structure Determination ◽  
Free Electron ◽  
Free Electron Laser ◽  
Structural Information ◽  
Crystal Sample ◽  
X Ray ◽  
Drug Candidates ◽  
X Ray Crystallography ◽  
The Impact

Past decades have shown the impact of structural information derived from complexes of drug candidates with their protein targets to facilitate the discovery of safe and effective medicines. Despite recent developments in single particle cryo-electron microscopy, X-ray crystallography has been the main method to derive structural information. The unique properties of X-ray free electron laser (XFEL) with unmet peak brilliance and beam focus allow X-ray diffraction data recording and successful structure determination from smaller and weaker diffracting crystals shortening timelines in crystal optimization. To further capitalize on the XFEL advantage, innovations in crystal sample delivery for the X-ray experiment, data collection and processing methods are required. This development was a key contributor to serial crystallography allowing structure determination at room temperature yielding physiologically more relevant structures. Adding the time resolution provided by the femtosecond X-ray pulse will enable monitoring and capturing of dynamic processes of ligand binding and associated conformational changes with great impact to the design of candidate drug compounds.

scholarly journals Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Zhang ◽  
Taoyu Ye ◽  
Hui Xi ◽  
Mario Juhas ◽  
Junyi Li
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Severe Acute Respiratory Syndrome ◽  
Infectious Diseases ◽  
Broad Spectrum ◽  
Antimicrobial Compounds ◽  
Discovery Process ◽  
Future Perspectives ◽  
Drug Discovery Process ◽  
Drug Candidates

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

Machine Learning and Perturbation Theory Machine Learning (PTML) in Medicinal Chemistry, Biotechnology, and Nanotechnology

2021 ◽  
Vol 21 ◽  
Author(s):  
Diana M. Herrera-Ibatá
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Drug Discovery ◽  
Medicinal Chemistry ◽  
Molecular Descriptors ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Drug Candidates ◽  
Protein Research ◽  
New Compounds

: Recently different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. The aim of the models is to find relations between the molecular descriptors and the biological characteristics to predict key properties of new compounds. An area where the ML has been very useful is the drug discovery process. The entire process of drug discovery leads to the generation of lots of data, and it is also a costly and time-consuming process. ML comes with the opportunity of analyzing great amounts of chemical data obtaining outcomes to find potential drug candidates.

Fragment-based drug discovery and its application to challenging drug targets

2017 ◽  
Vol 61 (5) ◽  
pp. 475-484 ◽  
Author(s):  
Amanda J. Price ◽  
Steven Howard ◽  
Benjamin D. Cons
Keyword(s):  
Molecular Weight ◽  
Drug Discovery ◽  
Drug Targets ◽  
Structural Information ◽  
New Drugs ◽  
Low Molecular Weight ◽  
Related Factor ◽  
X Ray ◽  
X Ray Crystallography ◽  
Fragment Based Drug Discovery

Fragment-based drug discovery (FBDD) is a technique for identifying low molecular weight chemical starting points for drug discovery. Since its inception 20 years ago, FBDD has grown in popularity to the point where it is now an established technique in industry and academia. The approach involves the biophysical screening of proteins against collections of low molecular weight compounds (fragments). Although fragments bind to proteins with relatively low affinity, they form efficient, high quality binding interactions with the protein architecture as they have to overcome a significant entropy barrier to bind. Of the biophysical methods available for fragment screening, X-ray protein crystallography is one of the most sensitive and least prone to false positives. It also provides detailed structural information of the protein–fragment complex at the atomic level. Fragment-based screening using X-ray crystallography is therefore an efficient method for identifying binding hotspots on proteins, which can then be exploited by chemists and biologists for the discovery of new drugs. The use of FBDD is illustrated here with a recently published case study of a drug discovery programme targeting the challenging protein–protein interaction Kelch-like ECH-associated protein 1:nuclear factor erythroid 2-related factor 2.

scholarly journals Pyrimidone inhibitors targeting Chikungunya Virus nsP3 macrodomain by fragment-based drug design

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245013
Author(s):  
Sixue Zhang ◽  
Atefeh Garzan ◽  
Nicole Haese ◽  
Robert Bostwick ◽  
Yohanka Martinez-Gzegozewska ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Carboxylic Acid ◽  
Antiviral Activity ◽  
Viral Replication ◽  
Chikungunya Virus ◽  
X Ray ◽  
X Ray Crystallography ◽  
Small Molecule Drugs ◽  
Fragment Library ◽  
Fragment Based Drug Discovery

The macrodomain of nsP3 (nsP3MD) is highly conserved among the alphaviruses and ADP-ribosylhydrolase activity of Chikungunya Virus (CHIKV) nsP3MD is critical for CHIKV viral replication and virulence. No small molecule drugs targeting CHIKV nsP3 have been identified to date. Here we report small fragments that bind to nsP3MD which were discovered by virtually screening a fragment library and X-ray crystallography. These identified fragments share a similar scaffold, 2-pyrimidone-4-carboxylic acid, and are specifically bound to the ADP-ribose binding site of nsP3MD. Among the fragments, 2-oxo-5,6-benzopyrimidine-4-carboxylic acid showed anti-CHIKV activity with an IC50 of 23 μM. Our fragment-based drug discovery approach provides valuable information to further develop a specific and potent nsP3 inhibitor of CHIKV viral replication based on the 2-pyrimidone-4-carboxylic acid scaffold. In silico studies suggest this pyrimidone scaffold could also bind to the macrodomains of other alphaviruses and coronaviruses and thus, have potential pan-antiviral activity.

scholarly journals In Silico Drug Repositioning Against Human NRP1 to Block SARS-CoV-2 Host Entry

2020 ◽  
Author(s):  
Seref Gul
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Structural Information ◽  
Drug Repositioning ◽  
Experimental Studies ◽  
Md Simulations ◽  
Drug Binding ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
S Protein

Despite COVID-19 turned into a pandemic, no approved drug for the treatment or globally available vaccine is out yet. In such a global emergency, drug repurposing approach that bypasses a costly and long-time demanding drug discovery process is an effective way in search of finding drugs for the COVID-19 treatment. Recent studies showed that SARS-CoV-2 uses neuropilin-1 (NRP1) for host entry. Here I took advantage of structural information of the NRP1 in complex with C-terminal of spike (S) protein of SARS-CoV-2 to identify drugs that may inhibit NRP1 and S protein interaction. U.S. Food and Drug Administration (FDA) approved drugs were screened using docking simulations. Among top drugs, well-tolerated drugs were selected for further analysis. Molecular dynamics (MD) simulations of drugs-NRP1 complexes were run for 100 ns to assess the persistency of binding. MM/GBSA calculations from MD simulations showed that eltrombopag, glimepiride, sitagliptin, dutasteride, and ergotamine stably and strongly bind to NRP1. In silico Alanine scanning analysis revealed that Tyr<sup>297</sup>, Trp<sup>301</sup>, and Tyr<sup>353</sup> amino acids of NRP1 are critical for drug binding. Validating the effect of drugs analyzed in this paper by experimental studies and clinical trials will expedite the drug discovery process for COVID-19.

Counting on Fragment Based Drug Design Approach for Drug Discovery

2019 ◽  
Vol 18 (27) ◽  
pp. 2284-2293 ◽  
Author(s):  
Aanchal Kashyap ◽  
Pankaj Kumar Singh ◽  
Om Silakari
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Structural Information ◽  
Binding Mode ◽  
New Drugs ◽  
Design Approach ◽  
X Ray ◽  
X Ray Crystallography ◽  
Success Stories

Fragment based drug design (FBDD) is a structure guided ligand design approach used in the process of drug discovery. It involves identification of low molecular weight fragments as hits followed by determination of their binding mode using X-ray crystallography and/or NMR spectroscopy. X-ray protein crystallography is one of the most sensitive biophysical methods used for screening and is least prone to false positives. It also provides detailed structural information of the protein–fragment complex at the atomic level. The retrieved binding information facilitates the optimization of fragments into drug like molecules. These identified molecules bind efficiently with the target proteins and form high quality binding interactions. Fragment-based screening using X-ray crystallography is, therefore, an efficient method for identifying binding hotspots on proteins that can be further exploited by chemists and biologists for the discovery of new drugs. The recent advancements in FBDD technique are illustrated in this review along with recently published success stories of FBDD technique in drug discovery.

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