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      Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

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          Abstract

          JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein–ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using in vitro and in vivo methods.

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          GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

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            Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes.

            A novel scoring function to estimate protein-ligand binding affinities has been developed and implemented as the Glide 4.0 XP scoring function and docking protocol. In addition to unique water desolvation energy terms, protein-ligand structural motifs leading to enhanced binding affinity are included: (1) hydrophobic enclosure where groups of lipophilic ligand atoms are enclosed on opposite faces by lipophilic protein atoms, (2) neutral-neutral single or correlated hydrogen bonds in a hydrophobically enclosed environment, and (3) five categories of charged-charged hydrogen bonds. The XP scoring function and docking protocol have been developed to reproduce experimental binding affinities for a set of 198 complexes (RMSDs of 2.26 and 1.73 kcal/mol over all and well-docked ligands, respectively) and to yield quality enrichments for a set of fifteen screens of pharmaceutical importance. Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.
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              g_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations.

              Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA), a method to estimate interaction free energies, has been increasingly used in the study of biomolecular interactions. Recently, this method has also been applied as a scoring function in computational drug design. Here a new tool g_mmpbsa, which implements the MM-PBSA approach using subroutines written in-house or sourced from the GROMACS and APBS packages is described. g_mmpbsa was developed as part of the Open Source Drug Discovery (OSDD) consortium. Its aim is to integrate high-throughput molecular dynamics (MD) simulations with binding energy calculations. The tool provides options to select alternative atomic radii and different nonpolar solvation models including models based on the solvent accessible surface area (SASA), solvent accessible volume (SAV), and a model which contains both repulsive (SASA-SAV) and attractive components (described using a Weeks-Chandler-Andersen like integral method). We showcase the effectiveness of the tool by comparing the calculated interaction energy of 37 structurally diverse HIV-1 protease inhibitor complexes with their experimental binding free energies. The effect of varying several combinations of input parameters such as atomic radii, dielectric constant, grid resolution, solute-solvent dielectric boundary definition, and nonpolar models was investigated. g_mmpbsa can also be used to estimate the energy contribution per residue to the binding energy. It has been used to identify those residues in HIV-1 protease that are most critical for binding a range of inhibitors.
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                Author and article information

                Contributors
                Journal
                Front Pharmacol
                Front Pharmacol
                Front. Pharmacol.
                Frontiers in Pharmacology
                Frontiers Media S.A.
                1663-9812
                21 April 2022
                2022
                : 13
                : 837369
                Affiliations
                [1] 1 Computational Biology Lab , Department of Genetic Engineering , School of Bioengineering , SRM Institute of Science and Technology , SRM Nagar , Kattankulathur, India
                [2] 2 Department of Clinical Immunology , Jawaharlal Institute of Post-Graduate Medical Education and Research , Pondicherry, India
                [3] 3 Department of Chemistry and Department of Carbon Materials , Chosun University , Gwangju, South Korea
                Author notes

                Edited by: Leonardo L. G. Ferreira, University of São Paulo, Brazil

                Reviewed by: Prasanth Kumar, Gujarat University, India

                Mariana Laureano De Souza, Universidade de São Paulo São Carlos, Brazil

                *Correspondence: Thirumurthy Madhavan, thiru.murthyunom@ 123456gmail.com ; Honglae Sohn, hsohn@ 123456chosun.ac.kr

                This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology

                Article
                837369
                10.3389/fphar.2022.837369
                9068899
                35529449
                637d5e01-3772-4531-9dfa-e5f43edd6d00
                Copyright © 2022 Babu, Nagarajan, Sathish, Negi, Sohn and Madhavan.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 16 December 2021
                : 07 March 2022
                Funding
                Funded by: Department of Science and Technology, Ministry of Science and Technology, India , doi 10.13039/501100001409;
                Funded by: National Research Foundation of Korea , doi 10.13039/501100003725;
                Funded by: Chosun University , doi 10.13039/501100002457;
                Categories
                Pharmacology
                Original Research

                Pharmacology & Pharmaceutical medicine
                jak1,pharmacophore modeling,virtual screening,molecular docking,molecular dynamics simulation,density function theory

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