عنوان مقاله
بازدارنده های بالقوه دارویی شکل نورامینیداز آنفلونزا گروه 1 شناسایی شده به روش طراحی دارو به کمک کامپیوتر
فهرست مطالب
چکیده
مقدمه
مواد و روش ها
نتایج و بحث
نتیجه گیری
بخشی از مقاله
مرحله پس از پردازش ترکیبات تولید شده با AutoGrow
به علت استفاده از ورژن بتا AutoGrow برای تولید لیگاندها، به منظور تصحیح خطاهای ساختاری موقتی، لازم بود ترکیبات بیشتر پردازش شوند. 10 لیگاند برتر از هر سه دور آزمایشAutoGrow به صورت بصری مورد بازبینی قرار گرفتند. وقتی دو جزء متمایز به هم خیلی نزدیک بودند، اجزاء مربوطه به منظور تشکیل حلقه باهم پیوند برقرار می کردند. وقتی به اشتباه دو جزء از طریق هیدروژن پیوند دهنده اضافه شدند، در صورت نیاز اتم های بیشتری حذف شدند.
کلمات کلیدی:
Potential drug-like inhibitors of Group 1 influenza neuraminidase identified through computer-aided drug design Jacob D. Durrant a,∗, J. Andrew McCammonb,c,d a Biomedical Sciences Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0365, United States b Department of Chemistry & Biochemistry, NSF Center for Theoretical Biological Physics, National Biomedical Computation Resource, University of California San Diego, La Jolla, CA 92093, United States c Department of Pharmacology, University of California San Diego, La Jolla, CA 92093, United States d Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92093, United States article info Article history: Received 1 October 2009 Received in revised form 15 January 2010 Accepted 26 March 2010 Keywords: Influenza Neuraminidase Drug design Oseltamivir Flu H1N1 abstract Pandemic (H1N1) influenza poses an imminent threat. Nations have stockpiled inhibitors of the influenza protein neuraminidase in hopes of protecting their citizens, but drug-resistant strains have already emerged, and novel therapeutics are urgently needed. In the current work, the computer program AutoGrow is used to generate novel predicted neuraminidase inhibitors. Given the great flexibility of the neuraminidase active site, protein dynamics are also incorporated into the computer-aided drug-design process. Several potential inhibitors are identified that are predicted to bind to neuraminidase better than currently approved drugs. © 2010 Elsevier Ltd. All rights reserved. 1. Introduction Influenza is caused by RNA viruses of the family Orthomyxoviridae. While not generally life threatening in healthy adults, the virus occasionally mutates into more deadly forms and has been responsible for several pandemics in the last century. Recently, a new strain of pandemic influenza (H1N1) capable of infecting humans has been identified (Dawood et al., 2009), with a U.S. hospitalization rate of about 9%. Additionally, a distinct strain of avian influenza (H5N1) arose in 1997 that may cause a similar global pandemic in the future (Abdel-Ghafar et al., 2008). In preparation for pandemic influenza,many nations have stockpiled inhibitors of the influenza protein neuraminidase (Oxford et al., 2004). Following formation, influenza viral particles remain bound to cell membranes via sialic-acid residues. Neuraminidase cleaves these residues, releasing the virus and enabling viral propagation (De Clercq and Neyts, 2007). Neuraminidase is the target of several FDA-approved drugs, including zanamivir and oseltamivir (Oxford et al., 2004), because it is essential for viral propagation and has a well-conserved active site (Kobasa et al., 1999). Unfortunately, drug-resistant strains have recently emerged (Kiso et al., ∗ Corresponding author. Tel.: +1 858 822 0169; fax: +1 858 534 4974. E-mail address: jdurrant@ucsd.edu (J.D. Durrant). 2004; Beigel et al., 2005; de Jong et al., 2005; De Clercq, 2006), and the need for novel inhibitors is great. Motivated by the urgent need for new influenza therapeutics, we used AutoGrow (Durrant et al., 2009), a recently developed computer-aided drug-design program, to guide the design of several potential neuraminidase inhibitors predicted to bind better than currently approved drugs. 2. Material and methods 2.1. Accounting for protein flexibility To account for protein flexibility, we drew upon a molecular dynamics simulation of neuraminidase that has been described previously (Cheng et al., 2008). Protein conformations extracted from this 40-ns simulation were clustered into 27 groups by rootmean-square-deviation (RMSD) conformational clustering using the gromos clustering algorithm, as implemented in the GROMOS++ analysis software (Daura et al., 1999; Christen et al., 2005). In brief, an RMSD distance was calculated for each pair of protein conformations extracted from the MD simulation. Those pairs with associated RMSD distances greater than 1.3 Å were discarded. The single conformation most frequently present in the remaining pairs, together with the other corresponding conformation of each pair, weremerged into a list of conformations called the first cluster.Potential drug-like inhibitors of Group 1 influenza neuraminidase identified through computer-aided drug design Jacob D. Durrant a,∗, J. Andrew McCammonb,c,d a Biomedical Sciences Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0365, United States b Department of Chemistry & Biochemistry, NSF Center for Theoretical Biological Physics, National Biomedical Computation Resource, University of California San Diego, La Jolla, CA 92093, United States c Department of Pharmacology, University of California San Diego, La Jolla, CA 92093, United States d Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92093, United States article info Article history: Received 1 October 2009 Received in revised form 15 January 2010 Accepted 26 March 2010 Keywords: Influenza Neuraminidase Drug design Oseltamivir Flu H1N1 abstract Pandemic (H1N1) influenza poses an imminent threat. Nations have stockpiled inhibitors of the influenza protein neuraminidase in hopes of protecting their citizens, but drug-resistant strains have already emerged, and novel therapeutics are urgently needed. In the current work, the computer program AutoGrow is used to generate novel predicted neuraminidase inhibitors. Given the great flexibility of the neuraminidase active site, protein dynamics are also incorporated into the computer-aided drug-design process. Several potential inhibitors are identified that are predicted to bind to neuraminidase better than currently approved drugs. © 2010 Elsevier Ltd. All rights reserved. 1. Introduction Influenza is caused by RNA viruses of the family Orthomyxoviridae. While not generally life threatening in healthy adults, the virus occasionally mutates into more deadly forms and has been responsible for several pandemics in the last century. Recently, a new strain of pandemic influenza (H1N1) capable of infecting humans has been identified (Dawood et al., 2009), with a U.S. hospitalization rate of about 9%. Additionally, a distinct strain of avian influenza (H5N1) arose in 1997 that may cause a similar global pandemic in the future (Abdel-Ghafar et al., 2008). In preparation for pandemic influenza,many nations have stockpiled inhibitors of the influenza protein neuraminidase (Oxford et al., 2004). Following formation, influenza viral particles remain bound to cell membranes via sialic-acid residues. Neuraminidase cleaves these residues, releasing the virus and enabling viral propagation (De Clercq and Neyts, 2007). Neuraminidase is the target of several FDA-approved drugs, including zanamivir and oseltamivir (Oxford et al., 2004), because it is essential for viral propagation and has a well-conserved active site (Kobasa et al., 1999). Unfortunately, drug-resistant strains have recently emerged (Kiso et al., ∗ Corresponding author. Tel.: +1 858 822 0169; fax: +1 858 534 4974. E-mail address: jdurrant@ucsd.edu (J.D. Durrant). 2004; Beigel et al., 2005; de Jong et al., 2005; De Clercq, 2006), and the need for novel inhibitors is great. Motivated by the urgent need for new influenza therapeutics, we used AutoGrow (Durrant et al., 2009), a recently developed computer-aided drug-design program, to guide the design of several potential neuraminidase inhibitors predicted to bind better than currently approved drugs. 2. Material and methods 2.1. Accounting for protein flexibility To account for protein flexibility, we drew upon a molecular dynamics simulation of neuraminidase that has been described previously (Cheng et al., 2008). Protein conformations extracted from this 40-ns simulation were clustered into 27 groups by rootmean-square-deviation (RMSD) conformational clustering using the gromos clustering algorithm, as implemented in the GROMOS++ analysis software (Daura et al., 1999; Christen et al., 2005). In brief, an RMSD distance was calculated for each pair of protein conformations extracted from the MD simulation. Those pairs with associated RMSD distances greater than 1.3 Å were discarded. The single conformation most frequently present in the remaining pairs, together with the other corresponding conformation of each pair, weremerged into a list of conformations called the first cluster.
