中科院上海藥物所蔣華良課題組與華東理工大學(xué)藥學(xué)院李洪林課題組合作,，繼反向?qū)臃椒?mdash;TarFisDock后，該研究團(tuán)隊(duì)構(gòu)建了藥效團(tuán)靶標(biāo)庫—PharmTargetDB（包含7000余個(gè)重要靶標(biāo)結(jié)構(gòu)的信息和藥效團(tuán)模型,，涵蓋了349種生物功能和110種臨床適應(yīng)癥）,。通過藥效團(tuán)匹配方法，課題組劉曉峰博士發(fā)展了以活性小分子為探針,、搜尋潛在藥物靶標(biāo),、進(jìn)而預(yù)測化合物生物活性的“反向藥效團(tuán)匹配方法”；并建立了相應(yīng)的公共網(wǎng)絡(luò)服務(wù)器PharmMapper,，相關(guān)結(jié)果發(fā)表在《核酸研究》雜志上,。
藥物潛在靶標(biāo)的識(shí)別對于早期藥物分子的研發(fā)、安全性評價(jià)和老藥新用等領(lǐng)域都有著非常重要的意義,，但是受制于通量,、精度和費(fèi)用的影響，實(shí)驗(yàn)手段的應(yīng)用難以廣泛開展,。作為一種快速而低成本的手段,，計(jì)算機(jī)輔助的靶標(biāo)識(shí)別算法的開發(fā)正在受到越來越多的重視，發(fā)展快速,、精確的靶標(biāo)識(shí)別預(yù)測方法對于靶向性藥物開發(fā),、藥物—靶標(biāo)相互作用網(wǎng)絡(luò)圖譜的構(gòu)建和小分子調(diào)控網(wǎng)絡(luò)的分析都具有十分重要的意義。
該研究團(tuán)隊(duì)發(fā)展了一系列新的計(jì)算生物學(xué)方法和數(shù)據(jù)庫,，用于靶標(biāo)發(fā)現(xiàn)研究,。發(fā)展的反向分子對接方法及其服務(wù)器TarFisDock，現(xiàn)有來自50多個(gè)國家和地區(qū)800多個(gè)用戶，許多理論預(yù)測新靶標(biāo)獲得了實(shí)驗(yàn)驗(yàn)證,。作為TarFisDock的重要補(bǔ)充,，PharmMapper在計(jì)算速度方面較反向?qū)臃椒ㄓ辛嗣黠@的提高?；谠摲椒ǖ陌袠?biāo)預(yù)測結(jié)果可以在數(shù)分鐘至數(shù)十分鐘內(nèi)完成,，為藥物新靶標(biāo)發(fā)現(xiàn)提供信息技術(shù)支撐，有力地促進(jìn)藥物靶標(biāo)發(fā)現(xiàn)研究,。目前,，與國內(nèi)外多家單位課題組合作，該研究團(tuán)隊(duì)已針對數(shù)十個(gè)天然產(chǎn)物進(jìn)行靶標(biāo)搜尋和實(shí)驗(yàn)驗(yàn)證,，期望通過PharmMapper找尋到這些天然產(chǎn)物化合物的潛在作用靶標(biāo),，明確其作用機(jī)理。
該研究項(xiàng)目得到了國家科技部,、國家自然科學(xué)基金委及上海市科委的資助,。(生物谷Bioon.net)
生物谷推薦原文出處：
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.
