中科院上海药物所蒋华良课题组与华东理工大学药学院李洪林课题组合作，继反向对接方法—TarFisDock后，该研究团队构建了药效团靶标库—PharmTargetDB（包含7000余个重要靶标结构的信息和药效团模型，涵盖了349种生物功能和110种临床适应症）。通过药效团匹配方法，课题组刘晓峰博士发展了以活性小分子为探针、搜寻潜在药物靶标、进而预测化合物生物活性的“反向药效团匹配方法”；并建立了相应的公共网络服务器PharmMapper，相关结果发表在《核酸研究》杂志上。
药物潜在靶标的识别对于早期药物分子的研发、安全性评价和老药新用等领域都有着非常重要的意义，但是受制于通量、精度和费用的影响，实验手段的应用难以广泛开展。作为一种快速而低成本的手段，计算机辅助的靶标识别算法的开发正在受到越来越多的重视，发展快速、精确的靶标识别预测方法对于靶向性药物开发、药物—靶标相互作用网络图谱的构建和小分子调控网络的分析都具有十分重要的意义。
该研究团队发展了一系列新的计算生物学方法和数据库，用于靶标发现研究。发展的反向分子对接方法及其服务器TarFisDock，现有来自50多个国家和地区800多个用户，许多理论预测新靶标获得了实验验证。作为TarFisDock的重要补充，PharmMapper在计算速度方面较反向对接方法有了明显的提高。基于该方法的靶标预测结果可以在数分钟至数十分钟内完成，为药物新靶标发现提供信息技术支撑，有力地促进药物靶标发现研究。目前，与国内外多家单位课题组合作，该研究团队已针对数十个天然产物进行靶标搜寻和实验验证，期望通过PharmMapper找寻到这些天然产物化合物的潜在作用靶标，明确其作用机理。
该研究项目得到了国家科技部、国家自然科学基金委及上海市科委的资助。(生物谷Bioon.net)
生物谷推荐原文出处：
Nucleic Acids Research doi:10.1093/nar/gkq300
PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach
Xiaofeng Liu1,2, Sisheng Ouyang1, Biao Yu3, Yabo Liu3, Kai Huang3, Jiayu Gong3, Siyuan Zheng4, Zhihua Li3, Honglin Li2,3,* and Hualiang Jiang1,2,*
1Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, 2State Key Laboratory of Bioreactor Engineering & Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, 3School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237 and 4Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
In silico drug target identification, which includes many distinct algorithms for finding disease genes and proteins, is the first step in the drug discovery pipeline. When the 3D structures of the targets are available, the problem of target identification is usually converted to finding the best interaction mode between the potential target candidates and small molecule probes. Pharmacophore, which is the spatial arrangement of features essential for a molecule to interact with a specific target receptor, is an alternative method for achieving this goal apart from molecular docking method. PharmMapper server is a freely accessed web server designed to identify potential target candidates for the given small molecules (drugs, natural products or other newly discovered compounds with unidentified binding targets) using pharmacophore mapping approach. PharmMapper hosts a large, in-house repertoire of pharmacophore database (namely PharmTargetDB) annotated from all the targets information in TargetBank, BindingDB, DrugBank and potential drug target database, including over 7000 receptor-based pharmacophore models (covering over 1500 drug targets information). PharmMapper automatically finds the best mapping poses of the query molecule against all the pharmacophore models in PharmTargetDB and lists the top N best-fitted hits with appropriate target annotations, as well as respective molecule’s aligned poses are presented. Benefited from the highly efficient and robust triangle hashing mapping method, PharmMapper bears high throughput ability and only costs 1 h averagely to screen the whole PharmTargetDB. The protocol was successful in finding the proper targets among the top 300 pharmacophore candidates in the retrospective benchmarking test of tamoxifen.
