Adhesion behavior of silica nanoparticles with bacteria: Spectroscopy measurements based on kinetics, and molecular docking

A 3D model of Cyclin-dependent kinase 5 (CDK5) (Accession Number: Q543f6) is generated based on crystal structure of P. falciparum PFPK5-indirubin-5-sulphonate ligand complex (PDB ID: 1V0O) at 2.30 Å resolution was used as template. Protein-ligand interaction studies were performed with flavonoids to explore structural features and binding mechanism of flavonoids as CDK5 (Cyclin-dependent kinase 5) inhibitors. The modelled structure was selected on the basis of least modeler objective function. The model was validated by PROCHECK. The predicted 3D model is reliable with 93.0% of amino acid residues in core region of the Ramachandran plot. Molecular docking studies with flavonoids viz., Diosmetin, Eriodictyol, Fortuneletin, Apigenin, Ayanin, Baicalein, Chrysoeriol and Chrysosplenol-D with modelled protein indicate that Diosmetin is the best inhibitor containing docking score of -8.23 kcal/mol. Cys83, Lys89, Asp84. The compound Diosmetin shows interactions with Cys83, Lys89, and Asp84.

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Exploring the Active Compounds of Traditional Mongolian Medicine Agsirga in Intervention of Novel Coronavirus (2019-nCoV) Based on HPLC-Q-Exactive-MS/MS and Molecular Docking Method

10.26434/chemrxiv.11955273 ◽

2020 ◽

Author(s):

Jie Cheng ◽

Yuchen Tang ◽

Baoquan Bao ◽

Ping Zhang

Keyword(s):

Mass Spectrometry ◽

Molecular Docking ◽

High Resolution ◽

Mass Spectra ◽

High Resolution Mass Spectrometry ◽

Structural Formula ◽

Active Compounds ◽

High Resolution Mass ◽

Q Exactive ◽

Resolution Mass

<a></a><a></a><a></a><a>Objective</a>: To screen all compounds of Agsirga based on the HPLC-Q-Exactive high-resolution mass spectrometry and find potential inhibitors that can respond to 2019-nCoV from active compounds of Agsirga by molecular docking technology. Methods: HPLC-Q-Exactive high-resolution mass spectrometry was adopted to identify the complex components of Mongolian medicine Agsirga, and separated by the high-resolution mass spectrometry Q-Exactive detector. Then the Orbitrap detector was used in tandem high-resolution mass spectrometry, and the related molecular and structural formula were found by using the chemsipider database and related literature, combined with precise molecular formulas (errors ≤ 5 × 10−6) , retention time, primary mass spectra, and secondary mass spectra information, The fragmentation regularities of mass spectra of these compounds were deduced. Taking ACE2 as the receptor and deduced compounds as the ligand, all of them were pretreated by discover studio, autodock and Chem3D. The molecular docking between the active ingredients and the target protein was studied by using AutoDock molecular docking software. The interaction between ligand and receptor is applied to provide a choice for screening anti-2019-nCoV drugs. Result: Based on the fragmentation patterns of the reference compounds and consulting literature, a total of 96 major alkaloids and stilbenes were screened and identified in Agsirga by the HPLC-Q-Exactive-MS/MS method. Combining with molecular docking, a conclusion was got that there are potential active substances in Mongolian medicine Agsirga which can block the binding of ACE2 and 2019-nCoV at the molecular level.

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Structure-Based Virtual Screening of Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) as Endocrine Disruptors of Androgen Receptor Activity Using Molecular Docking and Machine Learning

10.26434/chemrxiv.11886702.v1 ◽

2020 ◽

Author(s):

Azhagiya Singam Ettayapuram Ramaprasad ◽

Phum Tachachartvanich ◽

Denis Fourches ◽

Anatoly Soshilov ◽

Jennifer C.Y. Hsieh ◽

...

Keyword(s):

Machine Learning ◽

Molecular Docking ◽

Androgen Receptor ◽

Endocrine Disruptors ◽

Hormone Receptors ◽

Steroid Hormone Receptors ◽

Machine Learning Techniques ◽

Support Vector ◽

Polyfluoroalkyl Substances ◽

Perfluoroalkyl And Polyfluoroalkyl Substances

Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew’s correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.

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