scholarly journals In silico Identification of Characteristics Spike Glycoprotein of SARS-CoV-2 in the Development Novel Candidates for COVID-19 Infectious Diseases

2020 ◽  
Vol 6 (2) ◽  
pp. 48-52
Author(s):  
Taufik Muhammad Fakih ◽  
Mentari Luthfika Dewi
Keyword(s):  
Infectious Diseases ◽  
In Silico ◽  
Three Dimensional ◽  
Protein Docking ◽  
Dimensional Structure ◽  
Spike Glycoprotein ◽  
Angiotensin Converting Enzyme 2 ◽  
Docking Simulations ◽  
Discovery Studio ◽  
Identification Evaluation

Background: The emergence of infectious diseases caused by SARS-CoV-2 has resulted in more than 90,000 infections and 3,000 deaths. The coronavirus spike glycoprotein encourages the entry of SARS-CoV-2 into cells and is the main target of antivirals. SARS-CoV-2 uses ACE2 to enter cells with an affinity similar to SARS-CoV, correlated with the efficient spread of SARS-CoV-2 among humans.Objective: In the research, identification, evaluation, and exploration of the structure of SARS-CoV and SARS-CoV-2 spike glycoprotein macromolecules and their effects on Angiotensin-Converting Enzyme 2 (ACE-2) using in silico studies.Methods: The spike glycoproteins of the two coronaviruses were prepared using the BIOVIA Discovery Studio 2020. Further identification of the three-dimensional structure and sequencing of the macromolecular spike glycoprotein structure using Chimera 1.14 and Notepad++. To ensure the affinity and molecular interactions between the SARS-CoV and SARS-CoV-2 spike glycoproteins against ACE-2 protein-protein docking simulations using PatchDock was accomplished. The results of the simulations were verified using the BIOVIA Discovery Studio 2020.Results: Based on the results of the identification of the macromolecular structure of the spike glycoprotein, it was found that there are some similarities in characteristics between SARS-CoV and SARS-CoV-2. Protein-protein docking simulations resulted that SARS-COV-2 spike glycoprotein has the strongest bond with ACE-2, with an ACE score of −1509.13 kJ/mol.Conclusion: Therefore, some information obtained from the results of this research can be used as a reference in the development of SARS-CoV-2 spike glycoprotein inhibitor candidates for the treatment of infectious diseases of COVID-19.

scholarly journals In Silico Characterization of Surface Glycoprotein [QHD43416] of Severe Acute Respiratory Syndrome-Coronavirus 2

2020 ◽  
Vol 3 (2) ◽  
pp. 32-36
Author(s):  
Rajneesh Prajapat ◽  
◽  
Suman Jain ◽  
Manish K Vaishnav ◽  
Sonal Sogani ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
In Silico ◽  
Three Dimensional ◽  
Surface Glycoprotein ◽  
Dimensional Structure ◽  
Model Quality ◽  
Core Section ◽  
Novel Coronavirus ◽  
And Function ◽  
Tools And Techniques

The novel coronavirus (SARS-CoV-2) reported from Wuhan, China, that spread rapidly and cause severe acute respiratory syndrome. The disease associated with infection of SARS-CoV-2 that is referred as COVID-19 (Coronavirus Disease 2019). In the present study, the surface glycoprotein [QHD43416] of SARS-CoV-2 was characterized for structure analysis and validation to provide information about its three-dimensional structure by using in silico tools and techniques. The surface glycoprotein [QHD43416] sequence of SARS-CoV-2 was retrieved from NCBI and its PDB file was designed by using phyre2 server. The RAMPAGE and UCLA-DOE (Verify 3D) was used for analysis and validation of structure model of protein. The model quality estimation based on the ProSA. Alignment of surface glycoprotein [QHD43416], revealed homology (72% identity) with spike protein of bat coronavirus [BM48-31/BGR/2008]. The model corresponding to probability conformation with 90.5% residue of core section, 9.1 % of allowed section and 0.4 % residue of outer section in φ-ψ plot, that specifies accuracy of prediction model. The Verify 3D results shows that 59.53% residues have average 3D-1D score >= 0.2 this determines compatibility of 3D model with its amino acid sequence (1D). ProSA Z-score -11.19 represents the good quality of the model. The structure and function of coronavirus surface glycoprotein could be predicted by in silico modeling studies. The protein model will be further used for designing of vaccine / drug development against coronavirus infection.

scholarly journals Analisis In-Silico Struktur Tiga Dimensi Reseptor Trk A dan Trk B Protein Neurotrophin 3 Pada Gallus gallus (Chicken)

2020 ◽  
Vol 12 (2) ◽  
pp. 78-84
Author(s):  
Muhammad F. Rahman ◽  
Amiruddin Kasim ◽  
Muchlis L. Djirimu ◽  
I. Made Budiarsa
Keyword(s):  
Amino Acids ◽  
In Silico ◽  
Three Dimensional ◽  
Gallus Gallus ◽  
Dimensional Structure ◽  
Neurotrophin 3 ◽  
Trk A ◽  
And Function ◽  
Trk B ◽  
Ucsf Chimera

NT3 protein is expressed by Neurotrophin 3 (NTF-3) which plays a role in the process of differentiation, survival of peripheral and neuropathological of neurons. The information of structure and function of NT-3 proteins is still very limited, especially in Gallus gallus. This study aims to predict the three-dimensional structure of the Trk A and Trk B proteins in Gallus gallus. The target protein obtained from the UniProt server with access codes Q91009 (Trk A) and Q91987 (Trk B) using the 6kzc 1.A (PDB ID) template was analyzed in silico through a homology approach and describing the structural assessment using Chimera UCSF software. The analysis showed that the Trk A protein had a QMEAN value of -0.08, composed of 778 amino acids, mass 87334.30 Daltons, and Seq Identity 79.93%. Trk B had a QMEAN value of 0.16, consisting of 818 amino acids, mass 91732.05 Daltons, and Seq Identity 84.30%. Key words: NT3; homology; UCSF chimera; G. gallus

scholarly journals Quantitative Computational Prediction of the Consensus B-cell Epitopes of 2019-nCoV

2020 ◽  
Vol 1 (1) ◽  
pp. 42-47
Author(s):  
Raúl Isea
Keyword(s):  
B Cell ◽  
Three Dimensional ◽  
Similarity Index ◽  
Computational Prediction ◽  
Dimensional Structure ◽  
Spike Glycoprotein ◽  
B Cell Epitopes ◽  
Three Dimensional Structure ◽  
Mathematical Algorithms

The goal of this paper is to obtain the numerical consensus of B cell epitopes from the three-dimensional structure of the prefusion spike glycoprotein of the new betacoronavirus that could lead to the development of a vaccine to 2019-nCoV. In order to do that, we first calculated the B-cell epitopes that are predicted using fourteen different mathematical algorithms. Later, we obtained the consensus of B-cell epitopes according to the Similarity Index, and finally selecting the best candidates according to the results of a function called <F> which is evaluated for the glycoprotein. The best candidates that we obtained in order to design a vaccine are SSANNCT, PLQSYGFQPT, TESNKKFLP, NNSYEC, AENS, LPDPSK and YDPLQPE.

In silico study examining new phenylpropanoids targets with antidepressant activity

2020 ◽  
Vol 21 ◽  
Author(s):  
Luciana Scotti ◽  
Poliane da Silva Calixto ◽  
Mirian G. S. Stiebbe Salvadori ◽  
Reinaldo Nóbrega de Almeida ◽  
Mayara dos Santos Maia ◽  
...  
Keyword(s):  
In Silico ◽  
Late Onset ◽  
Clinical Psychopharmacology ◽  
Three Dimensional ◽  
Public Health Problem ◽  
Antidepressant Activity ◽  
Docking Studies ◽  
Dimensional Structure ◽  
Molegro Virtual Docker ◽  
Stereochemical Quality

Background: Natural products, such as phenylpropanoids, which are found in essential oils derived from aromatic plants, have been explored during non-clinical psychopharmacology studies, to discover new molecules with relevant pharmacological activities in the central nervous system, especially antidepressant and anxiolytic activities. Major depressive disorder is a highly debilitating psychiatric disorder and is considered to be a disabling public health problem, worldwide, as a primary factor associated with suicide. Current clinically administered antidepressants have late-onset therapeutic actions, are associated with several side effects, and clinical studies have reported that some patients do not respond well to treatment or reach complete remission. Objective: To review important new targets for antidepressant activity and to select phenylpropanoids with antidepressant activity, using Molegro Virtual Docker and Ossis Data Warris, and to verify substances with more promising antidepressant activity. Results and Conclusion: We conducted an in silico molecular modeling study, based on homology, to determine the three-dimensional structure the 5-hydroxytryptamine 2A receptor (5-HT2AR), then performed molecular docking studies and examined the predisposition for cytotoxicity risk among identified molecules. We obtained a model for 5-HT2AR homology, with satisfactory results, indicating the good stereochemical quality of the model. The phenylpropanoid 4- allyl-2,6-dimethoxyphenol showed the lowest binding energy for 5-HT2AR, with results relevant to the L-arginine/nitric oxide (NO)/cGMP pathway, and showed no toxicity within the parameters of mutagenicity, carcinogenicity, reproductive system toxicity, and skin-tissue irritability, when evaluated in silico; therefore, this molecule can be considered promising for the investigation of antidepressant activity.

Pharmaceutical interest of in-silico approaches

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Dinesh Kumar ◽  
Pooja Sharma ◽  
Ayush Mahajan ◽  
Ravi Dhawan ◽  
Kamal Dua
Keyword(s):  
Molecular Modeling ◽  
In Silico ◽  
Three Dimensional ◽  
Gene Sequencing ◽  
New Drugs ◽  
Dimensional Structure ◽  
Pharmaceutical Applications ◽  
Drug Designing ◽  
Different Types

Abstract The virtual environment within the computer using software performed on the computer is known as in-silico studies. These drugs designing software play a vital task in discovering new drugs in the field of pharmaceuticals. These designing programs and software are employed in gene sequencing, molecular modeling, and in assessing the three-dimensional structure of the molecule, which can further be used in drug designing and development. Drug development and discovery is not only a powerful, extensive, and an interdisciplinary system but also a very complex and time-consuming method. This book chapter mainly focused on different types of in-silico approaches along with their pharmaceutical applications in numerous diseases.

Protein-ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2

2021 ◽  
Vol 28 ◽  
Author(s):  
Walter Filgueira de Azevedo Junior ◽  
Gabriela Bitencourt-Ferreira ◽  
Joana Retzke Godoy ◽  
Hilda Mayela Aran Adriano ◽  
Wallyson André dos Santos Bezerra ◽  
...  
Keyword(s):  
Scientific Evidence ◽  
Three Dimensional ◽  
Structural Data ◽  
Ligand Docking ◽  
Dimensional Structure ◽  
Computational Approaches ◽  
Docking Simulations ◽  
Protein Target ◽  
Main Protease ◽  
Docking Protocol

Background: The main protease of SARS-CoV-2 (Mpro) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies. Objective: Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the Mpro using the program AutoDock4. Method: We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for Mpro to analyze intermolecular interactions and reviewed the methods used to search for inhibitors. Results: The application of docking against the structures available for the Mpro found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of Mpro that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity. Conclusion: The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of Mpro. Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to Mpro. This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19.

Myosin B of Plasmodium falciparum (PfMyoB): in silico prediction of its three-dimensional structure and its possible interaction with MTIP

2017 ◽  
Vol 116 (4) ◽  
pp. 1373-1382 ◽  
Author(s):  
Paula C. Hernández ◽  
Liliana Morales ◽  
Isabel C Castellanos ◽  
Moisés Wasserman ◽  
Jacqueline Chaparro-Olaya
Keyword(s):  
Plasmodium Falciparum ◽  
In Silico ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
In Silico Prediction ◽  
Three Dimensional Structure ◽  
Myosin B

scholarly journals COMPUTATIONAL APPROACHES FOR THE PREDICTION OF ANTIMICROBIAL POTENTIAL PEPTIDES FROM OCIMUM TENUIFLORUM.

2018 ◽  
Vol 11 (1) ◽  
pp. 398
Author(s):  
Sunil Kumar Suryawanshi ◽  
Usha Chouhan
Keyword(s):  
Three Dimensional ◽  
Docking Studies ◽  
Dimensional Structure ◽  
Support Vector ◽  
Learning Approach ◽  
Discovery Studio ◽  
Peptide Docking ◽  
Machine Learning Approach ◽  
Ocimum Tenuiflorum ◽  
Novel Target

 Objective: In this study, antimicrobial activity was predicted against novel antimicrobial target 1SDE receptor to understand the structural feature of predicted peptides using machine learning approach from Ocimum tenuiflorum. Methods: Protein sequences from O. tenuiflorum were digested using peptide cutter and further processed for the prediction of antimicrobial peptide (AMP) through AMP predictor tool of CAMP which have multidimensional algorithms such as support vector machine, artificial neural network, random forest, and discriminant analysis. After analyzing various peptides, only four peptides were predicted as antimicrobial in nature. Furthermore, the three-dimensional structure of different potential peptides was generated with the help of Pepfold-3.0 server followed by protein-peptide docking studies with novel target receptor with the help of PatchDock, FireDock webserver, and Hex 8.0 software. Interactions were further visualized using Discovery Studio Client 2.5 software tool.Results: This study revealed that peptide 2 resulted higher score in PatchDock and FireDock and also Hex 8.0 provides E total value of −430.18 which is higher than that of peptide 1 with −381.07, peptide 3 with −416.86, and peptide 4 with −407.94.Conclusion: The peptide predicted in this study has potential to act as effective AMP against target receptor and also utilize other antimicrobial target.

scholarly journals Metal Binding Sites in Plant Soluble Inorganic Pyrophosphatases. An Example of the Use of ROSETTA Design and Hidden Markov Models to Guide the Homology Modeling of Proteins

2017 ◽  
Vol 56 (1) ◽  
Author(s):  
Luis Rosales-León Rosales-León ◽  
Eric Edmundo Hernández-Domínguez ◽  
Samantha Gaytán-Mondragón ◽  
Rogelio Rodríguez-Sotres
Keyword(s):  
Homology Modeling ◽  
Metal Binding ◽  
Binding Sites ◽  
In Silico ◽  
Markov Models ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Magnesium Ions ◽  
Three Dimensional Structure ◽  
Metal Binding Sites

In contrast to their counterparts in bacteria and animals the soluble inorganic pyrophosphatases from plant cells are active as monomers. The isoforms 1 and 4 from <em>Arabidopsis thaliana</em> have been characterized with more detail, but their three-dimensional structure is unavailable. Here, a recently published protocol (ROSETTA design-HMMer), is used to guide well-known techniques for homology-modeling, in the production of reliable models for the three-dimensional structure of these two arabidopsis isoforms. Their interaction with magnesium ions and pyrophosphate is analyzed <em>in silico</em>in silico.

scholarly journals STUDI MOLECULAR DOCKING SENYAWA GOLONGAN FLAVONOL SEBAGAI ANTIBAKTERI

2018 ◽  
Vol 1 (2) ◽  
pp. 20-27
Author(s):  
Isna Wardaniati ◽  
Muhammad Azhari Herli
Keyword(s):  
Molecular Docking ◽  
Binding Energy ◽  
Binding Affinity ◽  
Bioactive Compounds ◽  
In Silico ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
Receptor Interactions

In this paper we studied the bioactive compounds of Flavonol-D-alanil D-alanin dekarboksipeptidase receptor interactions In silico. First, prepared three dimensional structure of D-alanil D-alanin dekarboksipeptidase as receptor. Preparation of fourth bioactive compounds of flavonol which will be as ligands, klokasilin and D-alanil D-alanin as a comparison. The fourth bioactive compounds of flavonol, klokasilin and D-alanil D-alanin were docked with D-alanil D-alanin dekarboksipeptidase until energy values were obtained. The fourth bioactive compounds of flavonol had lesser binding energy values than D-alanil D-alanin, Quercitrine and rutin also predicted to have greater binding energy and binding affinity than klokasilin (antibiotic) and D-alanil D-alanin (nature ligand).

Sign in / Sign up

Export Citation Format

Share Document