Nature:計算機預測技術對數十億種藥物-蛋白質相互作用進行排序

Drugable.com通過計算機分析，預測藥物作用機制。

幾十年以來，藥物的研發過程通常就像試驗與錯誤之間的一場較量，數以百萬計的候選藥物中最終只有極少數能夠被成功研發出來。近日，研究者們利用計算機技術，為藥物的研發開啟了一個良好的開端。通過分析藥物的化學結構，研究者們就可以了解它是否可能與某個生物學靶標（例如蛋白質）結合或「對接」。這樣的算法尤為有用，有利於發現那些由於藥物與結構相似的非靶標性蛋白質意外結合所引發的潛在毒副作用。

近日，研究者們提出了一個計算機模擬工作計劃，擬以公共資料庫中存儲的藥物信息和蛋白質信息為基礎，對數十億種可能的藥物與蛋白質之間的對接進行評價。紐約大學（New York University）朗格醫學中心（Langone Medical Center）的藥理學家Timothy Cardozo曾經於11月19日參加了美國國立衛生研究院（National Institutes of Health）在馬裡蘭州貝塞斯達舉辦的「高風險-高回報」研討會（High Risk–High Reward Symposium），並在會上展現了這種計算機模擬技術；他指出，這是人類有史以來所做的最大的藥物對接計算機模擬工作。這項工作最終創建了一個名為Drugable（drugable.com）的網站，該網站獲得了美國國家醫學圖書館（National Library of Medicine, NLM）的支持；雖然Drugable仍然處在測試階段，但它最終會向公眾開放，從而使研究者們僅僅根據化合物的化學結構，就能夠預測它們如何在體內發揮作用以及在何處發揮作用（請參閱「藥物信息挖掘」）。

藥物信息挖掘

研究者們利用谷歌的超級計算機檢測數十億種藥物-蛋白質相互作用。

Cardozo承認道，計算機模擬僅僅是新藥研發過程中的一個初始步驟。在預測了某種蛋白質是否能與化合物結合之後，藥物研發者們必須在細胞中檢測藥物對這種蛋白質的作用，從而觀察藥物對蛋白質功能的實際影響，以及藥物在不同情況下所需要的劑量等。隨後再進行動物實驗；如果幸運的話，研究者們接下來還可以進行人體試驗。但是加利福尼亞大學（University of California）舊金山分校的計算生物學家Brian Shoichet指出，醫藥公司往往持有這些額外數據的專利。他指出，儘管一些公共資料庫（例如由NLM進行維護的PubChem）持有藥物與酵母細胞蛋白質相互作用的自動化檢測結果，但是其中卻包含有一些不準確的結果和假陽性結果。

儘管如此，科學家們還是已經證實計算機模擬方法能夠為藥物研發提供一些捷徑。2012年，Shoichet與馬塞諸塞州劍橋市諾華生物醫學研究所（Novartis Institutes for BioMedical Research）的研究人員合作，聯合開發了一種能夠根據藥物之間化學結構的相似性來預測藥物副作用的算法。當研究者們對656種已獲得批准的藥物和73個生物靶標之間的相互作用進行測試時，他們發現該算法能夠預測出數百種前所未知的相互作用，並且大約一半的預測結果都證實這些副作用是真的（E. Lounkine et al. Nature 486. 361-367；2012）。而Shoichet也指出，對於已知的藥物，這類計算提供了一種快捷的方法來確認藥物-蛋白質相互作用，以便進行深入研究。

Drugable嘗試預測那些未經試驗的化合物是如何與體內蛋白質相互作用的，而這種預測工作更具挑戰性。在建立Drugable網站時，Cardozo研究團隊從PubChem資料庫和歐洲生物信息研究所（European Bioinformatics Institute）的ChEMBL資料庫（該資料庫收納了數百萬種公開型化合物的資料）中選擇了大約60萬個化合物分子，評價了這些分子與資料庫中人類蛋白質的7000個結構「口袋」的結合能力強弱。計算領域的巨頭谷歌（Google）向研究者們提供了相當於一億個小時的超級計算機處理器時間，協助他們完成這項龐大的工作。

該研究團隊提出了利用對接分數對40億多個潛在的藥物-蛋白質相互作用的強弱程度進行排序。隨後，該研究團隊將自己預測的靶蛋白與NLM基因表達綜合資料庫（Gene Expression Omnibus）中的靶蛋白進行了相互參照，從而表明不同蛋白質編碼基因在體內的表達部位。Cardozo指出，這使得研究者們可以預測藥物發揮作用的可能部位：如果Drugable發現某種藥物與某個部位中高表達的蛋白質之間存在著相互作用，那麼這種藥物就很有可能會對這種組織發揮作用。

諾華研究所的研究員Jeremy Jenkins指出，多年以來醫藥公司一直都在開展類似的計算機預測工作。但是他也指出：諾華研究所雖然擁有一個包含150萬種公共型化合物和專利型化合物的文庫，但卻從來沒有像Drugable那樣，一次性分析過那麼多種蛋白質和藥物。

Cardozo希望Drugable能夠在抗精神病藥物的評價方面提供特殊的幫助，因為此類藥物通常都以難以衡量的方式發揮作用。作為示範，Cardozo研究團隊採用Drugable算法對兩種常常被用來治療精神分裂症的藥物——氯氮平（clozapine）和氯丙嗪（chlorpromazine）進行了靶標相互作用的預測。

正如所預期的那樣，Drugable預測結果表明兩種藥物與神經遞質羥色胺（serotonin）和多巴胺（dopamine）的受體的結合能力最強，而這兩種神經遞質受體通常表達於進行高級信息處理的大腦區域中。但是Drugable發現，也可用於治療情緒障礙性疾病（如抑鬱症）的氯氮平能夠強烈地結合於一種名為DRD4的多巴胺受體，而這種特殊的受體主要表達於大腦的情緒調節區域——松果體（pineal gland）中。

該研究團隊也發現，氯氮平能夠與大腦中調節唾液分泌區域內的受體結合；而唾液分泌過多恰恰是氯氮平的一種已知的副作用。雖然在之前就有研究者從生物化學的角度對氯氮平的情緒調節和唾液分泌作用進行了解釋，但是Cardozo認為Drugable就能夠用於揭示最合理的藥物作用機制。(生物谷Bioon.com)

生物谷推薦的英文原文



Nature   doi:10.1038/503449a

Project ranks billions of drug interactions

Sara Reardon

For decades, drug development was mostly a game of trial and error, with brute-force candidate screens throwing up millions more duds than winners. Researchers are now using computers to get a head start. By analysing the chemical structure of a drug, they can see if it is likely to bind to, or 『dock』 with, a biological target such as a protein. Such algorithms are particularly useful for finding potentially toxic side effects that may come from unintended dockings to structurally similar, but untargeted, proteins.

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Last week, researchers presented a computational effort that assesses billions of potential dockings on the basis of drug and protein information held in public databases. 「It’s the largest computational docking ever done by mankind,」 says Timothy Cardozo, a pharmacologist at New York University’s Langone Medical Center, who presented the project on 19 November at the US National Institutes of Health’s High Risk–High Reward Symposium in Bethesda, Maryland. The result, a website called Drugable (drugable.com) that is backed by the US National Library of Medicine (NLM), is still in testing, but it will eventually be available for free, allowing researchers to predict how and where a compound might work in the body, purely on the basis of chemical structure (see 『Mining for drugs』).

Cardozo acknowledges that the computations are just an initial step in drug discovery. After predicting whether a protein can bind to a compound, drug developers must test the drug’s action on the same protein in a cell to see what actually happens to the protein’s function, as well as how much of the drug is needed and under what conditions. Then come animal trials and, if researchers are lucky, human trials. But these extra data are often proprietary and held by pharmaceutical companies, says Brian Shoichet, a computational biologist at the University of California, San Francisco. Some public databases such as PubChem, maintained by the NLM, hold the results of automated tests of drugs on proteins in yeast cells, but they contain inaccuracies and false positives, he says.

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Still, scientists have already shown that the computational approach can provide some short cuts. In 2012, Shoichet and researchers at the Novartis Institutes for BioMedical Research in Cambridge, Massachusetts, developed an algorithm that predicts side effects on the basis of similarities between drugs』 chemical structures. When the researchers tested the program on 656 approved drugs and 73 biological targets, they found that it predicted hundreds of previously unknown interactions — and that these side effects turned out to be real about half of the time (E. Lounkine et al. Nature 486, 361–367; 2012). For known drugs, Shoichet says, this type of computation provides a quick way to identify interactions that should be investigated further.

Predicting how untested compounds will interact with proteins in the body, as Drugable attempts to do, is more challenging. In setting up the website, Cardozo’s group selected about 600,000 molecules from PubChem and the European Bioinformatics Institute’s ChEMBL, which together catalogue millions of publicly available compounds. The group evaluated how strongly these molecules would bind to 7,000 structural 『pockets』 on human proteins also described in the databases. Computing giant Google awarded the researchers the equivalent of more than 100 million hours of processor time on its supercomputers for the mammoth effort.

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The team came up with ranked docking scores describing some 4 billion potential drug–protein interactions. Then the group cross-referenced the target proteins with those in the NLM’s Gene Expression Omnibus database, which shows where in the body different genes that code for proteins are expressed. This allowed them to predict where the drug might act, says Cardozo: if Drugable finds an interaction for a protein that is highly expressed in a certain tissue, chances are good that the effect would manifest itself in that tissue.

Pharmaceutical companies have been doing similar computational predictions for years, says Jeremy Jenkins, a researcher at the Novartis Institutes. But he says that Novartis, which has a library of 1.5 million public and proprietary compounds, has never attempted to analyse as many proteins and drugs at once as Drugable has done.

Cardozo hopes that Drugable will be particularly helpful in evaluating psychiatric drugs, which often act in ways that are difficult to measure. As a demonstration, Cardozo’s group applied Drugable’s algorithm to clozapine and chlorpromazine, two drugs often prescribed to treat schizophrenia.

As expected, Drugable showed that the two drugs bind most strongly to receptors for the neurotransmitters serotonin and dopamine, which are expressed in the parts of the brain involved in higher information processing. But it found that clozapine, which also stabilizes mood disorders such as depression, binds strongly to a particular dopamine receptor called DRD4, which is expressed in the brain’s pineal gland — a known mood regulator.

The group also found that clozapine binds to a receptor in the part of the brain that regulates saliva production; excessive salivation is a known side effect of clozapine. Although the biochemical explanations for mood regulation and salivation have been proposed before, Cardozo says that Drugable can be used to reveal the most plausible mechanisms.