首页 | 本学科首页   官方微博 | 高级检索  
     


Modelling the binding mode of macrocycles: Docking and conformational sampling
Affiliation:1. Wolfson Institute for Biomedical Research, University College London, Gower Street, London WC1E 6BT, UK;2. Vernalis (R&D) Ltd., Granta Park, Abington, Cambridge CB21 6GB, UK;1. Key Laboratory of Computational Chemistry-Based Natural Antitumor Drug Research & Development, Liaoning Province, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang 110016, People''s Republic of China;2. School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, People''s Republic of China;1. Department of Pharmacy, Health and Nutritional Sciences, DoE 2018-2022, University of Calabria, Edificio Polifunzionale, 87036 Rende (CS), Italy;2. Department of Biotechnology, Chemistry and Pharmacy, DoE 2018-2022, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy;3. Department of Life Sciences, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy;1. Biodesign Center for BioEnergetics, Arizona State University, Tempe, AZ 85287, USA;2. Department of Central Laboratory, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, PR China;3. School of Medcine, Taizhou University, Taizhou, Zhejiang, PR China;1. Department of Phytochemistry, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, G. C., Evin, 1983963113 Tehran, Iran;2. Swiss Tropical and Public Health Institute, Basel, Switzerland;3. University of Basel, Basel, Switzerland;4. National and Medical Sciences Research Center, University of Nizwa, P.O. Box 33, Birkat Al-Mauz, Nizwa 611, Oman;1. Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, Sichuan Industrial Institute of Antibiotics, Chengdu University, Chengdu 610052, People’s Republic of China;2. Sichuan Institute for Food and Drug Control, Chengdu 611731, People’s Republic of China;3. Department of Chemistry, School of Science, Beijing Technology and Business University, Beijing 100048, People’s Republic of China;1. Chemical Biology Laboratory, Graduate School of Arts and Sciences, Iwate University, Morioka 020-8550, Japan;2. Hiroshima Research Center for Healthy Aging, Hiroshima University, Higashi-Hiroshima 739-8530, Japan;3. Division of Biological and Life Sciences, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima 739-8530, Japan;4. RIKEN Center for Sustainable Resource Science, Wako 351-0198, Japan;5. Department of Biological Chemistry and Food Science, Iwate University, Morioka 020-8550, Japan
Abstract:Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.
Keywords:Drug discovery  Computational chemistry  Conformers  Docking  Macrocycle  Molecular recognition  CCDC"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0040"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Cambridge Crystallographic Data Centre  CSD"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0050"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Cambridge Structural Database  GA"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0060"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Genetic Algorithm  GUI"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0070"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Graphical User Interface  LowModeMD"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0080"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  search method combining low-mode moves and molecular dynamics  MT/LMOD"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0090"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  mixed torsional/Low-mode  MOE"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0100"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Molecular Operating Environment  MW"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0110"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  molecular weight  NMR"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0120"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Nuclear Magnetic Resonance  NRot"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0130"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Oprea Number of Rotatable bonds  OPLS"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0140"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Optimized Potential for Liquid Simulations force-field  PDB"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0150"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Protein Data Bank  RMSD"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0160"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Root Mean Square Deviation  SAR"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0170"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Structure-Activity Relationship  SBDD"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0180"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Structure-Based Drug Design  SD"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0190"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Standard Deviation  TPSA"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0200"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  Mean Topological Polar Surface Area  2D"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0210"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  two-dimensional  3D"  },{"  #name"  :"  keyword"  ,"  $"  :{"  id"  :"  k0220"  },"  $$"  :[{"  #name"  :"  text"  ,"  _"  :"  three-dimensional
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号