scholarly journals An In-Silico Approach for Designing a Potential Antagonistic Molecule Targeting β2-adrenoreceptor Having Therapeutic Significance

2020 ◽  
Vol 10 (2) ◽  
pp. 2063-2069
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Active Sites ◽  
De Novo ◽  
Cellular Level ◽  
Ligand Molecule ◽  
Best Fitting ◽  
Β2 Adrenergic Receptor ◽  
External Stimuli ◽  
G Protein Coupled

One of the largest families of membrane proteins, the G protein-coupled receptors (GPCRs) has been a very important target of drug discovery as they are involved in having a regulatory role in a variety of signaling pathways at the cellular level in response to external stimuli. Modern in-silico and crystallographic approaches have further made it easier to peep into their structures. In this study, β2 adrenergic receptor (β2AR) has been targeted, and a new ligand molecule using the de-novo approach has been proposed. Using 1-Amino-3-(2,3-dihydro-1H-indol-4-yloxy)-propan-2-ol, the best fitting binding fragments were established with a significant dissociation constant value of 5-7 nanomolar. The flexibility of specific active sites was also investigated, and it was observed that residues 114 (V), 117 (V), 203 (S), 286 (W), and 289 (F) played a crucial role in accommodating ligand for the best binding. Upon examination of the bioavailability parameters, the ligand var9 exhibited significant inhibitory characteristics having lower toxicity values and high drug likeliness properties. Findings certainly hold significance in terms of targeting GPCRs in getting insight into structure-based drug designing and drug discovery.

scholarly journals NUOVI APPROCCI TEORICI E SPERIMENTALI ALLA SCOPERTA DI FARMACI

2012 ◽  
Author(s):  
Martino Bolognesi
Keyword(s):  
Crystal Structure ◽  
Drug Discovery ◽  
In Silico ◽  
Active Sites ◽  
Yellow Fever Virus ◽  
Enzyme Inhibitor ◽  
Therapeutic Interventions ◽  
Research Activity ◽  
In Silico Docking ◽  
Structural And Functional Properties

New theoretic and experimental approaches to drug discovery. Environmental, demographic and ecological reasons suggest that either novel or known viruses will continue to emerge worldwide, posing new threats to the human population. Additionally, therapeutic interventions present different outcomes; for example, vaccination campaigns in the Third World often encounter local distribution problems and reach an insufficient fraction of the population. For many viruses no vaccine is available, such that the toll death is high, particularly in tropical countries and among infants. On the other hand, resistance of bacteria to know antibiotics is increasingly a serious threat, often associated to nosocomial infections. As a result, new ideas and approaches to the discovery or design of new effective drugs are a high priority in all civilized countries, to prevent and be ready to face potential pandemics. In this context, our group at the University of Milano, in collaboration with several European and extra- European labs, has been exploring the structural and functional properties of enzymes involved in the replication of (+)stranded RNA viruses as targets for the design of antiviral drugs. The rationale behind the choice of specific target viral is the idea that these are structurally and functionally sufficiently different from their human counterparts; thus, blocking the virus enzyme with a new drug should not be reflected by adverse reactions in the human host. The discovery approach applied in our laboratory has been based on a series of specific experimental steps: i) the analysis of crystal structure of the free enzymes, through X-ray crystallography; ii) in silico (computational) preliminary screening of selected enzyme regions towards which the drug search is targeted (e.g. mostly the enzyme active sites); iii) biochemical and biophysical tests of enzyme inhibition; iv) crystal structure analyses of enzyme/inhibitor complexes; v) in cell/in vivo testing; vi) inference for drug-lead optimization. This research method proved effective in discovering low molecular weight inhibitors of two key enzymes from Yellow fever virus (and partly for Dengue virus), and for Norovirus. Specifically, we targeted Norovirus studying the long known drug Suramin (used in the therapy of ‘sleeping sickness’), which was selected through the procedure described above through our in silico docking screenings. Our crystallographic and inhibition assays allowed to highlight the inhibitor binding mode and satisfactory functional inhibitory parameters. Subsequently, in the context of an international collaboration, we could test a series of Suramin molecular fragments, in search of new active compounds endowed with suitable pharmacological parameters. The described research activity, which is based on new conceptual and multidisciplinary approaches to drug discovery, has led to the production of several small molecules that will be further developed into antiviral compounds.

scholarly journals G protein-coupled receptors: In silico drug discovery in 3D

2004 ◽  
Vol 101 (31) ◽  
pp. 11304-11309 ◽  
Author(s):  
O. M. Becker ◽  
Y. Marantz ◽  
S. Shacham ◽  
B. Inbal ◽  
A. Heifetz ◽  
...  
Keyword(s):  
Drug Discovery ◽  
G Protein ◽  
In Silico ◽  
G Protein Coupled Receptors ◽  
G Protein Coupled

scholarly journals De-novo Drug Design, Molecular Docking and In-Silico Molecular Prediction of AChEI Analogues through CADD Approaches as Anti-Alzheimer’s Agents

2020 ◽  
Vol 16 (1) ◽  
pp. 54-72 ◽  
Author(s):  
Surabhi Pandey ◽  
B.K. Singh
Keyword(s):  
Molecular Docking ◽  
Drug Design ◽  
Inhibitory Activity ◽  
In Silico ◽  
Active Sites ◽  
De Novo ◽  
Docking Studies ◽  
Target Protein ◽  
Design And Development ◽  
Reference Drug

Background: There are over 44 million persons who suffer with Alzheimer’s disease (AD) worldwide, no existence of cure and only symptomatic treatments are available for it. The aim of this study is to evaluate the anti-Alzheimer potential of designed AChEI analogues using computer simulation docking studies. AChEIs are the most potential standards for treatment of AD, because they have proven efficacy. Among all AChEIs donepezil possesses lowest adverse effects, it can treat mildmoderate- severe AD and only once-daily dosing is required. Therefore, donepezil is recognized as a significant prototype for design and development of new drug molecule. Methods: In this study the Inhibitory potential of the design compounds on acetylcholinesterase enzyme has been evaluated. Docking studies has been performed which further analyzed by in-silico pharmacokinetic evaluation through pharmacopredicta after that Interaction modes with enzyme active sites were determined. Docking studies revealed that there is a strong interaction between the active sites of AChE enzyme and analyzed compounds. Results: As a result 26 compounds have been indicates better inhibitory activity on AChE enzyme and all the screening parameters have also been satisfied by all 26 compounds. From these 26 compounds, six compounds 17, 18, 24, 30, 36 and 56 are found to be the most potent inhibitors of this series by insilico study through INVENTUS v 1.1 software, having highest bio-affinities i.e. - 8.51, - 7.67, - 8.30, - 7.59, - 8.71 and -7.62 kcal/mol respectively, while the standard or reference drug donepezil had binding affinity of - 6.32 kcal/mol. Conclusion: Computer aided drug design approach has been playing an important role in the design and development of novel anti- AD drugs. With the help of structure based drug design some novel analogues of donepezil have been designed and the molecular docking studies with structure based ADME properties prediction studies is performed for prediction of AChE inhibitory activity. The binding mode of proposed compounds with target protein i.e. AChE has been evaluated and the resulting data from docking studies explains that all of the newly designed analogues had significantly high affinity towards target protein compared to donepezil as a reference ligand.

scholarly journals Masked graph modeling for molecule generation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Omar Mahmood ◽  
Elman Mansimov ◽  
Richard Bonneau ◽  
Kyunghyun Cho
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
De Novo ◽  
Graph Model ◽  
Material Design ◽  
Challenging Problem ◽  
Conditional Distributions ◽  
Trade Off ◽  
Graph Modeling ◽  
Different Parts

AbstractDe novo, in-silico design of molecules is a challenging problem with applications in drug discovery and material design. We introduce a masked graph model, which learns a distribution over graphs by capturing conditional distributions over unobserved nodes (atoms) and edges (bonds) given observed ones. We train and then sample from our model by iteratively masking and replacing different parts of initialized graphs. We evaluate our approach on the QM9 and ChEMBL datasets using the GuacaMol distribution-learning benchmark. We find that validity, KL-divergence and Fréchet ChemNet Distance scores are anti-correlated with novelty, and that we can trade off between these metrics more effectively than existing models. On distributional metrics, our model outperforms previously proposed graph-based approaches and is competitive with SMILES-based approaches. Finally, we show our model generates molecules with desired values of specified properties while maintaining physiochemical similarity to the training distribution.

scholarly journals Roadmap for the use of base editors to decipher drug mechanism of action

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257537
Author(s):  
Estel Aparicio-Prat ◽  
Dong Yan ◽  
Marco Mariotti ◽  
Michael Bassik ◽  
Gaelen Hess ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Mechanism Of Action ◽  
Large Scale ◽  
Reporter System ◽  
Experimental Conditions ◽  
Drug Mechanism ◽  
Β2 Adrenergic Receptor ◽  
Set Up ◽  
G Protein Coupled ◽  
Early Drug

CRISPR base editors are powerful tools for large-scale mutagenesis studies. This kind of approach can elucidate the mechanism of action of compounds, a key process in drug discovery. Here, we explore the utility of base editors in an early drug discovery context focusing on G-protein coupled receptors. A pooled mutagenesis screening framework was set up based on a modified version of the CRISPR-X base editor system. We determine optimized experimental conditions for mutagenesis where sgRNAs are delivered by cell transfection or viral infection over extended time periods (>14 days), resulting in high mutagenesis produced in a short region located at -4/+8 nucleotides with respect to the sgRNA match. The β2 Adrenergic Receptor (B2AR) was targeted in this way employing a 6xCRE-mCherry reporter system to monitor its response to isoproterenol. The results of our screening indicate that residue 184 of B2AR is crucial for its activation. Based on our experience, we outline the crucial points to consider when designing and performing CRISPR-based pooled mutagenesis screening, including the typical technical hurdles encountered when studying compound pharmacology.

scholarly journals De novo assembly and functional annotation of blood transcriptome of loggerhead turtle, and in silico characterization of peroxiredoxins and thioredoxins

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12395
Author(s):  
Javier Hernández-Fernández ◽  
Andrés Mauricio Pinzón Velasco ◽  
Ellie Anne López Barrera ◽  
María Del Pilar Rodríguez Becerra ◽  
José Luis Villanueva-Cañas ◽  
...  
Keyword(s):  
In Silico ◽  
Functional Annotation ◽  
Active Sites ◽  
De Novo ◽  
Loggerhead Turtle ◽  
Loggerhead Sea Turtles ◽  
Illumina Hiseq ◽  
Human Proteins ◽  
Amino Acid Mutations ◽  
Blood Transcriptome

The aim of this study was to generate and analyze the atlas of the loggerhead turtle blood transcriptome by RNA-seq, as well as identify and characterize thioredoxin (Tnxs) and peroxiredoxin (Prdxs) antioxidant enzymes of the greatest interest in the control of peroxide levels and other biological functions. The transcriptome of loggerhead turtle was sequenced using the Illumina Hiseq 2000 platform and de novo assembly was performed using the Trinity pipeline. The assembly comprised 515,597 contigs with an N50 of 2,631 bp. Contigs were analyzed with CD-Hit obtaining 374,545 unigenes, of which 165,676 had ORFs encoding putative proteins longer than 100 amino acids. A total of 52,147 (31.5%) of these transcripts had significant homology matches in at least one of the five databases used. From the enrichment of GO terms, 180 proteins with antioxidant activity were identified, among these 28 Prdxs and 50 putative Tnxs. The putative proteins of loggerhead turtles encoded by the genes Prdx1, Prdx3, Prdx5, Prdx6, Txn and Txnip were predicted and characterized in silico. When comparing Prdxs and Txns of loggerhead turtle with homologous human proteins, they showed 18 (9%), 52 (18%) 94 (43%), 36 (16%), 35 (33%) and 74 (19%) amino acid mutations respectively. However, they showed high conservation in active sites and structural motifs (98%), with few specific modifications. Of these, Prdx1, Prdx3, Prdx5, Prdx6, Txn and Txnip presented 0, 25, 18, three, six and two deleterious changes. This study provides a high quality blood transcriptome and functional annotation of loggerhead sea turtles.

scholarly journals Evaluation of Drug Repositioning by Molecular Docking of Pharmaceutical Resources to Identification of Potential SARS-CoV-2 Viral Inhibitors

2022 ◽  
Author(s):  
Fatemeh Hosseini ◽  
Mehrdad Azin ◽  
Hamideh Ofoghi ◽  
Tahereh Alinejad
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
In Silico ◽  
De Novo ◽  
Drug Repositioning ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Future Perspective

Unfortunately, to date, there is no approved specific antiviral drug treatment against COVID-19. Due to the costly and time-consuming nature of the de novo drug discovery and development process, in recent days, the computational drug repositioning method has been highly regarded for accelerating the drug-discovery process. The selection of drug target molecule(s), preparation of an approved therapeutics agent library, and in silico evaluation of their affinity to the subjected target(s) are the main steps of a molecular docking-based drug repositioning process, which is the most common computational drug re-tasking process. In this chapter, after a review on origin, pathophysiology, molecular biology, and drug development strategies against COVID-19, recent advances, challenges as well as the future perspective of molecular docking-based drug repositioning for COVID-19 are discussed. Furthermore, as a case study, the molecular docking-based drug repurposing process was planned to screen the 3CLpro inhibitor(s) among the nine Food and Drug Administration (FDA)-approved antiviral protease inhibitors. The results demonstrated that Fosamprenavir had the highest binding affinity to 3CLpro and can be considered for more in silico, in vitro, and in vivo evaluations as an effective repurposed anti-COVID-19 drug.

scholarly journals QPoweredTarget2DeNovoDrugPropMax : a novel programmatic tool incorporating deep learning and in silico methods for automated de novo drug design for any target of interest

2021 ◽  
Author(s):  
Ben Geoffrey ◽  
Rafal Madaj ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
In Silico ◽  
Drug Target ◽  
Data Science ◽  
De Novo ◽  
Autodock Vina ◽  
De Novo Drug Design ◽  
Ligand Interaction

The past decade has seen a surge in the range of application data science, machine learning, deep learning, and AI methods to drug discovery. The presented work involves an assemblage of a variety of AI methods for drug discovery along with the incorporation of in silico techniques to provide a holistic tool for automated drug discovery. When drug candidates are required to be identified for aparticular drug target of interest, the user is required to provide the tool target signatures in the form of an amino acid sequence or its corresponding nucleotide sequence. The tool collects data registered on PubChem required to perform an automated QSAR and with the validated QSAR model, prediction and drug lead generation are carried out. This protocol we call Target2Drug. This is followed by a protocol we call Target2DeNovoDrug wherein novel molecules with likely activityagainst the target are generated de novo using a generative LSTM model. It is often required in drug discovery that the generated molecules possess certain properties like drug-likeness, and therefore to optimize the generated de novo molecules toward the required drug-like property we use a deep learning model called DeepFMPO, and this protocol we call Target2DeNovoDrugPropMax. This is followed by the fast automated AutoDock-Vina based in silico modeling and profiling of theinteraction of optimized drug leads and the drug target. This is followed by an automated execution of the Molecular Dynamics protocol that is also carried out for the complex identified with the best protein-ligand interaction from the AutoDock- Vina based virtual screening. The results are stored in the working folder of the user. The code is maintained, supported, and provide for use in thefollowing GitHub repositoryhttps://github.com/bengeof/Target2DeNovoDrugPropMaxAnticipating the rise in the use of quantum computing and quantum machine learning in drug discovery we use the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep learning models into a quantum layer and introduce quantum layers into our classical models to produce a quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the same is provided belowhttps://github.com/bengeof/QPoweredTarget2DeNovoDrugPropMax

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