近日,，上海交通大學(xué)Bio-X研究院生物信息中心和美國(guó)食品及藥物管理局（FDA）的研究人員通力合作，構(gòu)建了搜索小分子藥物潛在治療用途的搜索引擎,，在老藥新用研究方面取得重大進(jìn)展,，相關(guān)研究成果已發(fā)表在近期的《核酸研究》（Nucleic Acids Research）上。
在這項(xiàng)研究中,，研究人員利用藥物-蛋白互作組（Chemical-Protein Interactome, CPI）在超級(jí)計(jì)算機(jī)上預(yù)測(cè)老藥新用途,。該超級(jí)機(jī)上收錄重大意義的蛋白和小分子互作信息的背景分布，研究通過(guò)分子熱力學(xué)模擬構(gòu)建了小分子蛋白互作的指紋圖譜,，并對(duì)麻省理工學(xué)院預(yù)測(cè)小分子關(guān)聯(lián)的算法進(jìn)行改進(jìn),，借鑒Google的搜索引擎算法，比較不同的藥物和蛋白之間的互作指紋來(lái)搜索藥物分子潛在治療效果,。
該項(xiàng)研究由賀林院士領(lǐng)導(dǎo),，主要參與人員還包括美國(guó)食品藥品監(jiān)督管理局IBM BlueMeadow(R)超級(jí)計(jì)算中心的麥克·米凱洛夫博士，葛蘭素史克（費(fèi)城）研發(fā)中心項(xiàng)目組長(zhǎng)Lun Yang博士,，以及上海交通大學(xué)Bio-X研究院生物信息研究中心的羅衡和陳劍等,。(生物谷Bioon.com)
生物谷提供英文摘要索引：
Nucl. Acids Res. (2011) doi: 10.1093/nar/gkr299
DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome
Heng Luo1, Jian Chen1, Leming Shi2, Mike Mikailov3, Huang Zhu1, Kejian Wang1, Lin He1,4,5,* and Lun Yang1,2,4,*
1Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China, 2National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, 3Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA, 4Institutes of Biomedical Sciences, Fudan University and 5Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
Identifying new indications for existing drugs (drug repositioning) is an efficient way of maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death among hospitalized patients. As both new indications and ADRs are caused by unexpected chemical–protein interactions on off-targets, it is reasonable to predict these interactions by mining the chemical–protein interactome (CPI). Making such predictions has recently been facilitated by a web server named DRAR-CPI. This server has a representative collection of drug molecules and targetable human proteins built up from our work in drug repositioning and ADR. When a user submits a molecule, the server will give the positive or negative association scores between the user’s molecule and our library drugs based on their interaction profiles towards the targets. Users can thus predict the indications or ADRs of their molecule based on the association scores towards our library drugs. We have matched our predictions of drug–drug associations with those predicted via gene-expression profiles, achieving a matching rate as high as 74%. We have also successfully predicted the connections between anti-psychotics and anti-infectives, indicating the underlying relevance of anti-psychotics in the potential treatment of infections, vice versa. This server is freely available at http://cpi.bio-x.cn/drar/.
