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      Determination of tyrosinase-cyanidin-3- O-glucoside and (−/+)-catechin binding modes reveal mechanistic differences in tyrosinase inhibition

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          Abstract

          Tyrosinase, exquisitely catalyzes the phenolic compounds into brown or black pigment, inhibition is used as a treatment for dermatological or neurodegenerative disorders. Natural products, such as cyanidin-3- O-glucoside and (−/+)-catechin, are considered safe and non-toxic food additives in tyrosinase inhibition but their ambiguous inhibitory mechanism against tyrosinase is still elusive. Thus, we presented the mechanistic insights into tyrosinase with cyanidin-3- O-glucoside and (−/+)-catechin using computational simulations and in vitro assessment. Initial molecular docking results predicted ideal docked poses (− 9.346 to − 5.795 kcal/mol) for tyrosinase with selected flavonoids. Furthermore, 100 ns molecular dynamics simulations and post-simulation analysis of docked poses established their stability and oxidation of flavonoids as substrate by tyrosinase. Particularly, metal chelation via catechol group linked with the free 3-OH group on the unconjugated dihydropyran heterocycle chain was elucidated to contribute to tyrosinase inhibition by (−/+)-catechin against cyanidin-3- O-glucoside. Also, predicted binding free energy using molecular mechanics/generalized Born surface area for each docked pose was consistent with in vitro enzyme inhibition for both mushroom and murine tyrosinases. Conclusively, (−/+)-catechin was observed for substantial tyrosinase inhibition and advocated for further investigation for drug development against tyrosinase-associated diseases.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            The Protein Data Bank.

            The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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              Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments.

              Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.
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                Author and article information

                Contributors
                keun126@ynu.ac.kr
                shiv.bharadwaj@ibt.cas.cz
                asahoo@iiita.ac.in
                u_yadava@yahoo.com
                kangsg@ynu.ac.kr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 December 2021
                30 December 2021
                2021
                : 11
                : 24494
                Affiliations
                [1 ]GRID grid.413028.c, ISNI 0000 0001 0674 4447, Department of Biotechnology, Institute of Biotechnology, College of Life and Applied Sciences, , Yeungnam University, ; 280 Daehak-Ro, Gyeongsan, 38541 Gyeongbuk Korea
                [2 ]GRID grid.417946.9, ISNI 0000 0001 0572 6888, Department of Applied Sciences, , Indian Institute of Information Technology Allahabad, ; Allahabad, 211015 Uttar Pradesh India
                [3 ]GRID grid.411985.0, ISNI 0000 0001 0662 4146, Department of Physics, , Deen Dayal Upadhyay Gorakhpur University, ; Gorakhpur, India
                [4 ]GRID grid.413028.c, ISNI 0000 0001 0674 4447, Stemforce, 313 Institute of Industrial Technology, , Yeungnam University, ; 280 Daehak-Ro, Gyeongsan, 38541 Gyeongbuk Korea
                [5 ]GRID grid.448014.d, Present Address: Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences, , BIOCEV Research Center, ; Vestec, Czech Republic
                Article
                3569
                10.1038/s41598-021-03569-1
                8718538
                34969954
                40f77dca-6774-4b47-8ebf-20609f318260
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 September 2021
                : 6 December 2021
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                © The Author(s) 2021

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                virtual screening,biochemistry,computational biology and bioinformatics,drug discovery,biophysics,computational biophysics

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