超级引擎可搜索分子潜在治疗用途

近日，上海交通大学Bio-X研究院生物信息中心和美国食品及药物管理局（FDA）的研究人员通力合作，构建了搜索小分子药物潜在治疗用途的搜索引擎，在老药新用研究方面取得重大进展，相关研究成果已发表在近期的《核酸研究》（Nucleic Acids Research）上。

在这项研究中，研究人员利用药物-蛋白互作组（Chemical-Protein Interactome, CPI）在超级计算机上预测老药新用途。该超级机上收录重大意义的蛋白和小分子互作信息的背景分布，研究通过分子热力学模拟构建了小分子蛋白互作的指纹图谱，并对麻省理工学院预测小分子关联的算法进行改进，借鉴Google的搜索引擎算法，比较不同的药物和蛋白之间的互作指纹来搜索药物分子潜在治疗效果。

该项研究由贺林院士领导，主要参与人员还包括美国食品药品监督管理局IBM BlueMeadow(R)超级计算中心的麦克·米凯洛夫博士，葛兰素史克（费城）研发中心项目组长Lun Yang博士，以及上海交通大学Bio-X研究院生物信息研究中心的罗衡和陈剑等.

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/.