scholarly journals KASPspoon: an in vitro and in silico PCR analysis tool for high-throughput SNP genotyping

2019 ◽  
Vol 35 (17) ◽  
pp. 3187-3190 ◽  
Author(s):  
Alsamman M Alsamman ◽  
Shafik D Ibrahim ◽  
Aladdin Hamwieh
Keyword(s):  
High Throughput ◽  
In Silico ◽  
Genetic Maps ◽  
Supplementary Information ◽  
Pcr Analysis ◽  
Analysis Tool ◽  
Data Types ◽  
Kasp Assay ◽  
In Silico Pcr

Abstract Motivation Fine mapping becomes a routine trial following quantitative trait loci (QTL) mapping studies to shrink the size of genomic segments underlying causal variants. The availability of whole genome sequences can facilitate the development of high marker density and predict gene content in genomic segments of interest. Correlations between genetic and physical positions of these loci require handling of different experimental genetic data types, and ultimately converting them into positioning markers using a routine and efficient tool. Results To convert classical QTL markers into KASP assay primers, KASPspoon simulates a PCR by running an approximate-match searching analysis on user-entered primer pairs against the provided sequences, and then comparing in vitro and in silico PCR results. KASPspoon reports amplimers close to or adjoining genes/SNPs/simple sequence repeats and those that are shared between in vitro and in silico PCR results to select the most appropriate amplimers for gene discovery. KASPspoon compares physical and genetic maps, and reports the primer set genome coverage for PCR-walking. KASPspoon could be used to design KASP assay primers to convert QTL acquired by classical molecular markers into high-throughput genotyping assays and to provide major SNP resource for the dissection of genotypic and phenotypic variation. In addition to human-readable output files, KASPspoon creates Circos configurations that illustrate different in silico and in vitro results. Availability and implementation Code available under GNU GPL at (http://www.ageri.sci.eg/index.php/facilities-services/ageri-softwares/kaspspoon). Supplementary information Supplementary data are available at Bioinformatics online.

A novel compound active against SARS-CoV-2 targeting Uridylate-specific endoribonuclease (NendoU/NSP15): In silico and in vitro investigations

2021 ◽  
Author(s):  
Sumit Kumar ◽  
Yash Gupta ◽  
Samantha Zak ◽  
Charu Upadhyay ◽  
Neha Sharma ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Specific Enzyme ◽  
Cyclic Phosphates ◽  
High Throughput Virtual Screening

NendoU (NSP15) is an Mn(2+)-dependent, uridylate-specific enzyme, which leaves 2'-3'-cyclic phosphates 5' to the cleaved bond. Our in-house library was subjected to high throughput virtual screening (HTVS) to identify compounds...

scholarly journals High Throughput in Silico Identification of Novel Phytochemical Inhibitors for the Master Regulator of Inflammation (TNFα)

2021 ◽  
Author(s):  
Pratap Kumar Parida ◽  
Dipak Paul ◽  
Debamitra Chakravorty
Keyword(s):  
Small Molecule ◽  
High Throughput ◽  
In Silico ◽  
Small Molecule Inhibitors ◽  
Binding Free Energy ◽  
Natural Compounds ◽  
Free Energy Calculations ◽  
Compound Libraries ◽  
Relative Binding

<p><a>The over expression of Tumor necrosis factor-α (TNFα) has been implicated in a variety of disease and is classified as a therapeutic target for inflammatory diseases (Crohn disease, psoriasis, psoriatic arthritis, rheumatoid arthritis).Commercially available therapeutics are biologics which are associated with several risks and limitations. Small molecule inhibitors and natural compounds (saponins) were identified by researchers as lead molecules against TNFα, however, </a>they were often associated with high IC50 values which can lead to their failure in clinical trials. This warrants research related to identification of better small molecule inhibitors by screening of large compound libraries. Recent developments have demonstrated power of natural compounds as safe therapeutics, hence, in this work, we have identified TNFα phytochemical inhibitors using high throughput <i>in silico </i>screening approaches of 6000 phytochemicals followed by 200 ns molecular dynamics simulations and relative binding free energy calculations. The work yielded potent hits that bind to TNFα at its dimer interface. The mechanism targeted was inhibition of oligomerization of TNFα upon phytochemical binding to restrict its interaction with TNF-R1 receptor. MD simulation analysis resulted in identification of two phytochemicals that showed stable protein-ligand conformations over time. The two compounds were triterpenoids: Momordicilin and Nimbolin A with relative binding energy- calculated by MM/PBSA to be -190.5 kJ/Mol and -188.03 kJ/Mol respectively. Therefore, through this work it is being suggested that these phytochemicals can be used for further <i>in vitro</i> analysis to confirm their inhibitory action against TNFα or can be used as scaffolds to arrive at better drug candidates.</p>

scholarly journals In Silico De Novo Curcuminoid Derivatives From the Compound Library of Natural Products Research Laboratories Inhibit COVID-19 3CLpro Activity

2020 ◽  
Vol 15 (9) ◽  
pp. 1934578X2095326
Author(s):  
Jai-Sing Yang ◽  
Jo-Hua Chiang ◽  
Shih‑Chang Tsai ◽  
Yuan-Man Hsu ◽  
Da-Tian Bau ◽  
...  
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
Binding Affinity ◽  
High Throughput ◽  
In Silico ◽  
De Novo ◽  
Compound Library ◽  
Therapeutic Success ◽  
High Throughput Virtual Screening

The coronavirus disease 2019 (COVID‐19) outbreak caused by the 2019 novel coronavirus (2019-nCOV) is becoming increasingly serious. In March 2019, the Food and Drug Administration (FDA) designated remdesivir for compassionate use to treat COVID-19. Thus, the development of novel antiviral agents, antibodies, and vaccines against COVID-19 is an urgent research subject. Many laboratories and research organizations are actively investing in the development of new compounds for COVID-19. Through in silico high-throughput virtual screening, we have recently identified compounds from the compound library of Natural Products Research Laboratories (NPRL) that can bind to COVID-19 3Lpro polyprotein and block COVID-19 3Lpro activity through in silico high-throughput virtual screening. Curcuminoid derivatives (including NPRL334, NPRL339, NPRL342, NPRL346, NPRL407, NPRL415, NPRL420, NPRL472, and NPRL473) display strong binding affinity to COVID-19 3Lpro polyprotein. The binding site of curcuminoid derivatives to COVID-19 3Lpro polyprotein is the same as that of the FDA-approved human immunodeficiency virus protease inhibitor (lopinavir) to COVID-19 3Lpro polyprotein. The binding affinity of curcuminoid derivatives to COVID-19 3Lpro is stronger than that of lopinavir and curcumin. Among curcuminoid derivatives, NPRL-334 revealed the strongest binding affinity to COVID-19 3Lpro polyprotein and is speculated to have an anti-COVID-19 effect. In vitro and in vivo ongoing experiments are currently underway to confirm the present findings. This study sheds light on the drug design for COVID-19 3Lpro polyprotein. Basing on lead compound development, we provide new insights on inhibiting COVID-19 attachment to cells, reducing COVID-19 infection rate and drug side effects, and increasing therapeutic success rate.

Screening for phospholipidosis induced by central nervous drugs: Comparing the predictivity of an in vitro assay to high throughput in silico assays

2010 ◽  
Vol 24 (5) ◽  
pp. 1417-1425 ◽  
Author(s):  
Natalie Mesens ◽  
Margino Steemans ◽  
Erik Hansen ◽  
Geert R. Verheyen ◽  
Freddy Van Goethem ◽  
...  
Keyword(s):  
High Throughput ◽  
In Silico ◽  
In Vitro Assay ◽  
Vitro Assay

scholarly journals Distribution of Biofilm-associated Genes among Acinetobacter baumannii by in-silico PCR

2021 ◽  
pp. 140-149
Author(s):  
A. Aldrin Joshua ◽  
A. S. Smiline Girija ◽  
P. Sankar Ganesh ◽  
J. Vijayashree Priyadharsini
Keyword(s):  
Acinetobacter Baumannii ◽  
In Silico ◽  
Target Genes ◽  
Multidrug Resistant ◽  
Pcr Analysis ◽  
Evolutionary Analysis ◽  
Ompa Gene ◽  
Clinical Strains ◽  
Tract Infections ◽  
In Silico Pcr

Background: Acinetobacter baumannii is a coccobacillus that is Gram negative, non motile, non fermentative and oxidase negative. It is the most common and successful nosocomial pathogen recognised by WHO. This dreadful pathogen causes urinary tract infections, ventilator associated pneumonia (VAP), bacteremia, etc., These infections are most common in hospital wards especially Intensive Care Unit (ICU). The infections are due to biofilm formation by the virulent genes of A. baumannii, and the common biofilm-associated genes of A. baumannii were bap, csuE, fimH, epsA, bfmS, ptk, pgaB, ompA, blaPER-1. Among these, bap, epsA and ompA genes are highly prevalent among the clinical strains of A. baumannii. Aim:  To detect the three vital biofilm-associated genes of A. baumannii by in-silico PCR analysis. Materials and Methods: 19 isolates of A. baumannii were selected and 3 target genes, namely epsA, ompA and bap gene were used for the amplification process through in-silico PCR simulation tools. Evolutionary analysis was done for the ompA gene. Results: The epsA gene was expressed in 10.52% of the total strains selected with the highest occurrence of ompA gene as 57.89%. bap gene was not observed from the study strains included. From evolutionary analysis based on ompA distributed strains, the Acinetobacter baumannii SDF and Acinetobacter baumannii BJAB0715 might be the parental strains where the evolution of strains would have started. Through successive generations, the Acinetobacter baumannii MDR-ZJ06 and Acinetobacter baumannii TYTH-1 had become the multidrug resistant strains present in the environment. Conclusion: The findings of the study confirms the distribution of epsA and ompA genes among the 19 different strains of A. baumannii. The study suggests periodical monitoring of biofilm based virulence genes among the clinical strains and to curtail the A. baumannii infections.

scholarly journals In silico and in vitro evaluation of primers for molecular differentiation of Leishmania species

2021 ◽  
Vol 30 (1) ◽  
Author(s):  
Beatriz Batista Trigo ◽  
Fernanda Muller de Oliveira-Rovai ◽  
Marco Milanesi ◽  
Pier Kenji Rauschkolb Katsuda Ito ◽  
Yuri Tani Utsunomiya ◽  
...  
Keyword(s):  
In Silico ◽  
Wide Spectrum ◽  
Clinical Signs ◽  
Signs And Symptoms ◽  
Laboratory Diagnosis ◽  
Leishmania Infection ◽  
Pcr Analysis ◽  
In Vitro Evaluation ◽  
Leishmania Spp

Abstract Leishmaniasis is a zoonotic disease caused by over 20 species of protozoan parasites of the genus Leishmania. Infection is commonly spread by sandflies and produces a wide spectrum of clinical signs and symptoms. Therefore, from an epidemiological and therapeutic standpoint, it is important to detect and differentiate Leishmania spp. The objective of this study was to combinate in silico and in vitro strategies to evaluate the analytical specificity of primers previously described in the literature. According to electronic PCR (e-PCR) analysis, 23 out of 141 pairs of primers selected through literature search matched their previously reported analytical specificity. In vitro evaluation of nine of these primer pairs by quantitative PCR (qPCR) confirmed the analytical specificity of five of them at the level of Leishmania spp., L. mexicana complex or Leishmania and Viannia subgenera. Based on these findings, the combination of e-PCR and qPCR is suggested to be a valuable approach to maximize the specificity of new primer pairs for the laboratory diagnosis of infections with Leishmania spp.

scholarly journals In silico homology modelling and identification of Tousled-like kinase 1 inhibitors for glioblastoma therapy via high throughput virtual screening protein-ligand docking

2016 ◽  
Author(s):  
Kamariah Ibrahim ◽  
Abubakar Danjuma ◽  
Chyan Leong Ng ◽  
Nor Azian Abdul Murad ◽  
Roslan Harun ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Energy Minimization ◽  
In Silico ◽  
Homology Modelling ◽  
Ligand Docking ◽  
Molegro Virtual Docker ◽  
Homology Models ◽  
High Throughput Virtual Screening

Background: Glioblastoma multiforme (GBM) is a grade IV brain tumor that arises from star-shaped glial cells supporting neural cells called astrocytes. The survival of GBM patients remains poor despite many specific molecular targets that have been developed and used for therapy. Tousled-like kinase 1 (TLK1), a serine-threonine kinase, was identified to be overexpressed in cancers such as GBM. TLK1 plays an important role in controlling chromosomal aggregation, cell survival and proliferation. In vitro studies suggested that TLK1 is a potential target for some cancers; hence, the identification of suitable molecular inhibitors for TLK1 is warranted as a new therapeutic agents in GBM. To date, there is no structure available for TLK1. In this study, we aimed to create a homology model of TLK1 and to identify suitable molecular inhibitors or compounds that are likely to bind and inhibit TLK1 activity via in silico high-throughput virtual screening (HTVS) protein-ligand docking. Methods: 3D homology models of TLK1 were derived from various servers including HOmology ModellER, i-Tasser, Psipred and Swiss Model. All models were evaluated using Swiss Model Q-Mean server. Only one model was selected for further analysis. Further validation was performed using PDBsum, 3d2go, ProSA, Procheck analysis and ERRAT. Energy minimization was performed using YASARA energy minimization server. Subsequently, HTVS was performed using Molegro Virtual Docker 6.0 and candidate ligands from ligand.info database. Ligand-docking procedures were analyzed at the putative catalytic site of TLK1. Drug-like molecules were filtered using FAF-Drugs3, which is an ADME-Tox filtering program. Results and conclusion: High quality homology models were obtained from the Aurora B kinase (PDB ID:4B8M) derived from Xenopus levias structure that share 33% sequence identity to TLK1. From the HTVS ligand-docking, two compounds were identified to be the potential inhibitors as it did not violate the Lipinski rule of five and the CNS-based filter as a potential drug-like molecule for GBM.

scholarly journals In silico homology modelling and identification of Tousled-like kinase 1 inhibitors for glioblastoma therapy via high throughput virtual screening protein-ligand docking

2016 ◽  
Author(s):  
Kamariah Ibrahim ◽  
Abubakar Danjuma ◽  
Chyan Leong Ng ◽  
Nor Azian Abdul Murad ◽  
Roslan Harun ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Energy Minimization ◽  
In Silico ◽  
Homology Modelling ◽  
Ligand Docking ◽  
Molegro Virtual Docker ◽  
Homology Models ◽  
High Throughput Virtual Screening

Background: Glioblastoma multiforme (GBM) is a grade IV brain tumor that arises from star-shaped glial cells supporting neural cells called astrocytes. The survival of GBM patients remains poor despite many specific molecular targets that have been developed and used for therapy. Tousled-like kinase 1 (TLK1), a serine-threonine kinase, was identified to be overexpressed in cancers such as GBM. TLK1 plays an important role in controlling chromosomal aggregation, cell survival and proliferation. In vitro studies suggested that TLK1 is a potential target for some cancers; hence, the identification of suitable molecular inhibitors for TLK1 is warranted as a new therapeutic agents in GBM. To date, there is no structure available for TLK1. In this study, we aimed to create a homology model of TLK1 and to identify suitable molecular inhibitors or compounds that are likely to bind and inhibit TLK1 activity via in silico high-throughput virtual screening (HTVS) protein-ligand docking. Methods: 3D homology models of TLK1 were derived from various servers including HOmology ModellER, i-Tasser, Psipred and Swiss Model. All models were evaluated using Swiss Model Q-Mean server. Only one model was selected for further analysis. Further validation was performed using PDBsum, 3d2go, ProSA, Procheck analysis and ERRAT. Energy minimization was performed using YASARA energy minimization server. Subsequently, HTVS was performed using Molegro Virtual Docker 6.0 and candidate ligands from ligand.info database. Ligand-docking procedures were analyzed at the putative catalytic site of TLK1. Drug-like molecules were filtered using FAF-Drugs3, which is an ADME-Tox filtering program. Results and conclusion: High quality homology models were obtained from the Aurora B kinase (PDB ID:4B8M) derived from Xenopus levias structure that share 33% sequence identity to TLK1. From the HTVS ligand-docking, two compounds were identified to be the potential inhibitors as it did not violate the Lipinski rule of five and the CNS-based filter as a potential drug-like molecule for GBM.

scholarly journals High Throughput in Silico Identification of Novel Phytochemical Inhibitors for the Master Regulator of Inflammation (TNFα)

2021 ◽  
Author(s):  
Pratap Kumar Parida ◽  
Dipak Paul ◽  
Debamitra Chakravorty
Keyword(s):  
Small Molecule ◽  
High Throughput ◽  
In Silico ◽  
Small Molecule Inhibitors ◽  
Binding Free Energy ◽  
Natural Compounds ◽  
Free Energy Calculations ◽  
Compound Libraries ◽  
Relative Binding

<p><a>The over expression of Tumor necrosis factor-α (TNFα) has been implicated in a variety of disease and is classified as a therapeutic target for inflammatory diseases (Crohn disease, psoriasis, psoriatic arthritis, rheumatoid arthritis).Commercially available therapeutics are biologics which are associated with several risks and limitations. Small molecule inhibitors and natural compounds (saponins) were identified by researchers as lead molecules against TNFα, however, </a>they were often associated with high IC50 values which can lead to their failure in clinical trials. This warrants research related to identification of better small molecule inhibitors by screening of large compound libraries. Recent developments have demonstrated power of natural compounds as safe therapeutics, hence, in this work, we have identified TNFα phytochemical inhibitors using high throughput <i>in silico </i>screening approaches of 6000 phytochemicals followed by 200 ns molecular dynamics simulations and relative binding free energy calculations. The work yielded potent hits that bind to TNFα at its dimer interface. The mechanism targeted was inhibition of oligomerization of TNFα upon phytochemical binding to restrict its interaction with TNF-R1 receptor. MD simulation analysis resulted in identification of two phytochemicals that showed stable protein-ligand conformations over time. The two compounds were triterpenoids: Momordicilin and Nimbolin A with relative binding energy- calculated by MM/PBSA to be -190.5 kJ/Mol and -188.03 kJ/Mol respectively. Therefore, through this work it is being suggested that these phytochemicals can be used for further <i>in vitro</i> analysis to confirm their inhibitory action against TNFα or can be used as scaffolds to arrive at better drug candidates.</p>

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