[BOINC] [生命科学类及其他] World Community Grid

vmzy

Turning virtual results into real-world treatments for schistosoma
By: The Say No to Schistosoma research team
22 四月 2015          

摘要
The research team has combed through the grid computing results of Say No to Schistosoma, using several additional analytical techniques to help them select promising compounds. The three most promising substances are undergoing additional testing with the hope of identifying the most effective potential treatments for schistosomiasis.

Thanks to World Community Grid volunteers who contributed to Say No to Schistosoma, we have selected compounds from the grid-based screening for further testing. We collaborated with various laboratories to perform further computer analysis of 24 compounds using several software tools. These tools help by providing information about numerous features of the compounds such as toxicology, absorption, interactions, pharmacokinetics, and more. Quantitative Structure-activity relationship (QSAR) models were used to further evaluate the results. For more about QSAR, see here and here.

From the analysis, three compounds were identified as the most promising substances. For the next steps, we are continuing with in-vitro testing of these compounds to determine whether they might be viable options for treating schistosomiasis.

We wish to thank and acknowledge the help of World Community Grid volunteers and IBM for making this project possible. We are grateful that we can continue our efforts to find new treatments for schistosomiasis.
大意：
研究团队在对SN2S项目的结果进行分析后找到了3个最有可能的化学物质，进行其他的实验室测试，希望能找到治疗血吸虫病的药物。


System Updates: Sunday, April 26, 2015 at 01:00:00 UTC
23 四月 2015          

摘要
System updates will be performed Sunday, April 26th.

World Community Grid will be performing system updates starting Sunday, April 26, 2015 at 01:00 UTC. The window for this maintenance activity is estimated to be 8 hours, although we anticipate the actual outage time will be less. During the system updates, volunteer devices may not be able to fetch new research tasks or return completed work for a period of time.

No action is required by the members, as the BOINC/World Community Grid software application will automatically reconnect to our servers once the update is over.

Thank you again for your participation in World Community Grid!
大意：
周日4月26日早9点，开始例行维护，预计需要8小时。届时BOINC服务器可能会不稳定（大体上不会太影响任务收发）。

vmzy

Research task availability resumes following unexpected outage
4 五月 2015         

摘要
Starting on May 3rd, no work units were distributed across all World Community Grid projects due to technical issues which have now been resolved.

On May 3rd, World Community Grid experienced a technical issue with the scripts which generate research tasks to distribute to our volunteers. As a result, no new work units were available across all projects. We have now resolved this issue and members should be able to receive new tasks.
We sincerely apologize for this unexpected outage and we will be modifying our work unit generation process accordingly and increase our monitoring going forward.

Thanks for your participation!
大意：
由于技术问题，5月3日wcg服务器断粮了。现已恢复。


Firmware Upgrade: Tuesday, May 5, 2015 at 14:00:00 UTC
4 五月 2015         

摘要
A firmware upgrade will be performed on Tuesday, May 5th.

World Community Grid will be performing a firmware upgrade starting Tuesday, May 5, 2015 at 14:00:00 UTC. The window for this maintenance activity is estimated to be 6 hours, although we anticipate the actual outage time to be very short.

During this firmware upgrade, the website will become unavailable and volunteer devices will not be able to send or receive research tasks for a few short periods of time.

No action is required by the members, as the BOINC/World Community Grid software application will automatically reconnect to our servers once the upgrade is over.

Thank you again for you participation in World Community Grid
大意：
5月6日凌晨WCG将升级硬件固件，预计耗时6小时，届时全站下线（任务收发会受短暂影响）。

vmzy

A milestone and a roadmap: progress in the fight against Ebola
By: The Outsmart Ebola Together research team
5 五月 2015          

摘要
Thanks to the huge level of support from World Community Grid, the team at the Scripps Research Institute has already received most of the matching data for the first target protein of the Ebola virus. While this data is being analyzed, the search now moves to another related protein with potential to help the fight against hemorrhagic fevers.

Outsmart Ebola Together, a long-term scientific project whose goal is to find new drugs for curing Ebola and related life-threatening viral hemorrhagic fevers, is still in its early stages, but we've already reached a major milestone. Our first target was the newly revealed receptor-binding site of the Ebola surface protein, GP. GP is the molecule Ebola virus uses to fuse with a human cell and force its way inside. Armed with a new model of the binding site, and with the vast resources of World Community Grid, we set out to test this site against drugs that could potentially bond with it and prevent Ebola infection. This stage of work is now close to complete:  we have received back from World Community Grid most of the data for the planned matchings of the Ebola surface protein against 5.4 million candidate chemical compounds.

We are now analyzing this data. Drugs that simulations predict will bind well with the Ebola surface protein will go on to a next round of experiments, conducted in the lab with actual proteins and actual drug molecules. Our analysis may also yield general insights about how classes of drugs interact with viral proteins.

Moreover, we are excited to announce that we are beginning work on a second target protein, the Lassa virus nucleoprotein.

Like Ebola, Lassa is a "Group V" virus: in other words, both are viruses that have at their core a genome composed of "negative-sense", single-stranded RNA. Both viruses produce a deadly hemorrhagic fever. While Lassa has received less publicity than Ebola, it is a more consistent killer. There are hundreds of thousands of cases of Lassa Fever every year in Western Africa, with tens of thousands of deaths. It is also the viral hemorrhagic fever most frequently transported out of Africa to the United States and Europe. There are no treatments approved for use in Lassa virus infection. Identification of a potent inhibitor of Lassa virus is imperative for public health.

The Lassa virus's nucleoprotein (NP) is so named because its first discovered function is to bind with, and so enclose and protect, the virus's central strand of RNA. However, Lassa NP is a complex beast that has other functions as well. In particular, our lab discovered that the NP (almost paradoxically) is also responsible for digesting double-stranded RNA (dsRNA) created by the virus itself. Having gained entry to a human cell, the Lassa virus must copy its single-stranded RNA in order to produce viral proteins and replicate itself. This requires creating double-stranded RNA. However, the virus must keep this work secret. The presence of double-stranded RNA in the cytoplasm is a clear sign of a viral infection, and human cells are smart enough to detect this, triggering an effective immune response. Hence the importance of the Lassa NP, which rips apart the virus's own dsRNA byproducts in order to keep its activities secret.

We approach Lassa NP armed with our lab's crystallographic structures, which clearly identify the shape of the NP and the site where the NP carries out its function of destroying double-stranded RNA. This site is a large cavity in the side of the protein; it is negatively charged, but is also bordered by a positively charged, protruding protein "arm". These distinctive features are key to the site's binding with dsRNA, and, we believe, should make it a good candidate for screenings against possible drugs.

Figure: Our lab's structure for the Lassa NP protein. Portions important to the protein's function of digesting double-stranded RNA include the "cavity" (glowing, particularly a manganese atom that helps bond RNA) and the adjacent "arm" (yellow).


We will now prepare this target protein for matchings against millions of drugs using the resources of the World Community Grid. As with our previous matchings against the Ebola surface protein, drugs that do well in this "virtual screening" will go on to further tests with actual proteins in the lab. While this work is difficult and carries no guarantees, we hope that it will lead to the discovery of a drug that can prevent the Lassa NP from hiding the virus's double-stranded RNA. We have already determined that doing this would allow human cells to detect and act against the Lassa virus more promptly and effectively, potentially saving lives.

It’s amazing to us that we’ve been able to receive so many results so quickly, and we want to say thank you to everyone in the World Community Grid family who helped make this possible. There is much work ahead, but it’s immensely encouraging to know that we have the resources available to carry it out.
大意：
感谢大家的帮助，OET已经处理完了第一个埃博拉靶蛋白。现在开始处理拉沙出血热核蛋白。
拉沙热和埃博拉比较类似，但不为大众所知。它主要出现在西非，每年造成成千上万人死亡，经常会传播到欧美地区，目前暂没有特效药可以治疗。

vmzy

Post grid calculations continue to yield progress and inspire new methods against deadly diseases
By: Dr. Alexander Luke Perryman
Research Teaching Specialist III
5 六月 2015          

摘要
The GO Fight Against Malaria project team has been making good use of the calculations which were conducted by our volunteers and that concluded in summer 2013. Their findings have sparked progress against not only malaria, but tuberculosis as well. They have one paper published and two more about to be submitted. They continue their work to publish their findings, after which they will resume the analysis on and experimental assessment of the massive amount of data generated by World Community Grid volunteers.

Left to right: Dr. Shao-Gang Li, Tom Stratton, Dr. Alex Perryman, Xin Wang, and Professor Joel Freundlich. Not pictured: Dr. Steven Paget

Dear fellow World Community Grid volunteers,

The Global Online Fight Against Malaria (GO FAM) project was launched on the IBM World Community Grid on November 16, 2011. The first phase of the docking calculations was completed in July of 2013, and since then we have been busy analyzing, testing, publishing, and extending these results, which show promise against both malaria and tuberculosis. For a summary of the project, see our last project update from July 2014.

Tuberculosis, a bacterial disease caused by Mycobacterium tuberculosis, kills 1.5 million people each year. A third of the world’s population already has the latent form of the infection, which creates an immense reservoir for re-activation and the emergence of new mutated versions of this deadly bacteria. Although drug-resistant “superbug” versions of tuberculosis were not a significant problem a couple decades ago, Mycobacterium tuberculosis becomes more and more resistant to the current drug treatments each year. There is thus an urgent need to discover and develop new types of drugs that can defeat these drug-resistant tuberculosis “superbugs.”

Update on: GO FAM experiment leads to the discovery of new hits against a key drug target for tuberculosis

In our last update in July of 2014 (see the link above), we mentioned that we were halfway done writing our first research paper that utilized GO FAM results. On January 30, 2015, that paper was published in the Journal of Chemical Information and Modeling. In relation to this paper, we also created an animation of the predicted binding mode of the best inhibitor we discovered in that GO FAM experiment versus a critical drug target for treating tuberculosis.

In March of 2015, Professor Peter J. Tonge and I submitted this manuscript to “PubMed Central,” which means that a free version of this paper will be available to the public next January or February.

Update on: Discovery of additional new hits against a target for drug-resistant tuberculosis

Last July we discussed the initial progress from analyzing and testing the results of GO FAM experiment 9: specifically, the subset of these results that were generated against a key drug target for treating tuberculosis, which is an enzyme called “InhA.” We mentioned that derivatives of the best two “hits” we discovered had been designed in the Freundlich lab. Dr. Shao-Gang Li and Dr. Steven Paget have now synthesized several different “analogs” of these two new hits. These “analogs” are slightly different chemical compounds that involve removing or replacing different regions of the inhibitors, to learn which structural features are involved in increasing or decreasing the potency of the original “parent” compound. These analogs of the top two hits were then tested in enzyme inhibition assays by Tom Stratton (a top-notch undergraduate researcher in the lab) and Xin Wang (a very skilled graduate student in the lab). Xin Wang also tested these compounds in assays that measure the inhibition of Mycobacterium tuberculosis growth and in a different type of assay that measures toxicity to mammalian cells. Our best hit overall is able to inhibit the growth of Mycobacterium tuberculosis, and it is relatively non-toxic to mammalian cells. To enable us to submit these results for publication in one of the best journals, Tom, Shao-Gang, and Steve also performed additional quality control measures on the compounds that were synthesized in the Freundlich lab (i.e., the analogs and locally re-synthesized versions of the original hits). These quality control measurements included “high resolution mass spectroscopy,” LC-MS (liquid chromatography coupled to mass spectrometry), and NMR (nuclear magnetic resonance spectroscopy), which established the molecular weight, purity, and structures of the compounds that were synthesized in the Freundlich lab. We are now almost done writing the paper on these exciting new results and promising compounds, which we were able to discover using a very novel computational workflow, including the work on World Community Grid. We plan to submit this paper within the next month. As soon as it is published, we will let you know.

Creating a computational tool that predicts metabolic stability

“Translational research” is the process of advancing research from the basic science (fundamental background studies) stage to the development of new drugs or other tools that can improve public health. A critical hurdle to the progress of translational research against many different diseases, including malaria, tuberculosis, cancer, and Alzheimer’s disease, involves proving that a compound is both effective and non-toxic in animal studies that serve as an early model/surrogate for the afflicted human patients. For a compound to have any chance of becoming part of a pill that can treat humans, one key feature that it must possess is sufficient metabolic stability (the compound must not be degraded too rapidly after it is administered to a mammal). Consequently, we used “machine learning” techniques to construct, test, and validate new computational models that can help us increase the efficiency of discovering and developing new compounds that display a sufficient amount of metabolic stability. This research (and the previous section on GO FAM experiment 9) was performed in collaboration with Dr. Sean Ekins, of Collaborations in Chemistry (Fuquay-Varina, North Carolina) and Collaborative Drug Discovery (Burlingame, California).

This project on predicting metabolic stability was mainly funded by the “Center to develop therapeutic countermeasures to high-threat bacterial agents,” which is a multi-lab grant from the NIH Centers of Excellence for Translational Research.

These models did not use any GO FAM data, but these tools might enhance the pace and fruitfulness of our future research projects that use GO FAM data to advance the discovery of new compounds that can help fight tuberculosis or malaria. Since the training sets and the detailed protocols that we used to make these models will be shared with the research community when we publish this study, it might also help other scientists advance their own research against these and many other diseases. We are almost done writing the paper on this project. We plan to submit it within the next week or two. As soon as it is published, we will definitely share the news with you.

Sharing our progress with other scientists

An important aspect of being a scientist is sharing your results with the rest of the research community. Different projects, labs, and disciplines are able to assist, motivate, and inspire new directions in each other’s research. Getting critical feedback also helps one hone the research and how it is analyzed, presented, and extended. To support these goals, Dr. Perryman will be presenting the data that was summarized in this update at the 250th National Meeting of the American Chemical Society (ACS), which takes place this August in Boston, USA. Tom Stratton, Dr. Shao-Gang Li, and Professor Joel Freundlich will also be presenting research at this ACS conference as well.

Inspiring aspiring new scientists

To help Ph.D. students learn about drug discovery, and to try to inspire them to pursue research that can help advance the fight against infectious diseases, Dr. Perryman recently taught some sections of Professor Joel Freundlich’s new class on “Critical Readings in the Chemical Biology of Pathogens” at the Rutgers University-NJ Medical School.

Thank you to all our supporters

We hope you’re as excited as we are to see the many different ways that your donated computing time has helped advance important scientific research. As is so often the case, the eventual impact of the research wasn’t known at the beginning, and we only arrived at these beneficial outcomes because you cared enough to support our project. On behalf of the entire team, thank you, and please check back for future updates!
大意：
GFAM一阶项目开始于2011年11月16日，完成于2013年7月。随后我们对结果进行了分析，找到了可以用于治疗肺结核的分子。
去年7月我们把最好的两个计算结果分子进行了合成，并在实验室进行了测试，发现它们的确能抑制肺结核的生长并且对动物细胞没有毒性。
此外我们还在利用‘机器学习’技术开发新的计算工具，开展药代动力学稳定性的‘转化研究’。因为一个药物是否有效，跟它在动物体内的药物代谢及衰减速度有着很大的关系。虽然这个研究与GFAM项目本身的数据没有多少关系。但它对未来的药物研发有着至关重要的作用。
在今年8月的第250界美国化学学会上，我们将把精炼的研究数据与科学家们共享。
最后由衷的感谢大家的无私奉献。

vmzy

深入挖掘纳米技术潜力，改善数百万人难以获取清洁水的现状
2015/7/6
清水计算团队发现了使水以更简单的方式流过微型碳纳米管的新方法。这一突破性发现可用于开发更高效的水过滤和淡化技术，从而改善数百万人难以获取清洁水的现状。该发现还可用于清洁能源和医疗行业。全球最知名的纳米技术期刊《自然纳米技术》已经发表了这一研究成果。

我们团队的这一发现将推动清洁水技术的发展，从而使有迫切需求的人们获得清洁水。清洁水是生命的基础，目前全球有近 10 亿人无法获得清洁水。这不仅仅是便捷性问题：每年逾百万人死于由于水质问题引发的疾病。随着人口增长和气候变化，这一问题将愈发严重。现有的水过滤技术通常价格高昂，需要清洁水的人们大都无法承受。您所支持的清水计算研究有助于改变这一现状。全球最知名的纳米技术期刊《自然纳米技术》已经发表了这些激动人心的研究成果。

我们从根本上发现了如何将碳纳米管用于制造更高效、更低成本的滤水器。碳纳米管采用单层原子厚度的碳原子板制成，这种材料被称为石墨烯，经弯曲后形成细管状，直径仅为几纳米，相当于人的头发直径的万分之一。碳纳米管的大小足以使水分子通过，但会挡住较大的病原菌和污染物，使水得到净化。碳纳米管的直径极小，以至于科学界最初认为水通过碳纳米管的速度极慢，因此不具有实用性。但早期的实验表明，水通过碳纳米管的速度有时会大大超过预期。

水的流速加快意味着更高效的过滤，但由于缺少足够的计算能力，科学家通过计算机模拟的数据与在实验中取得的实际测量结果存在巨大差异。我们的研究工作重点是努力缩小这一差异。通过全球网格大同盟的巨大计算模拟能力，我们发现了一种称为“声子”的自然振动。在特定的条件下，这种振动能够使通过碳纳米管的水分扩散（一种水流）速度提高 300% 以上。重要的是，这些振动源于所有物体本身具有的热能，无需外部能量就能够自然发生。

这一发现对于未来研究有何意义？从模拟中获得的新发现可以立即用于设计更高效的滤水器。如果实验能够验证我们的预测，这种过滤器将可以帮助改善全球数百万人难以获取清洁水的现状。我们的预测还可能会降低海水淡化技术的成本。海水淡化技术是一种从海水中获得淡水的工艺。

我们可以利用这一纳米层面的发现制造出滤膜与过滤器，为水或其它液体相关工艺和工业带来变革。比如，这一发现可以揭示化学品和药品如何穿过活细胞壁上的小通道。通过进一步研究，这些发现还可用于改进海水盐差能工艺，即通过混合淡水与海水来生产清洁能源。

没有您的慷慨参与，这些无数可能性都将无从谈起：没有研究团队拥有如此巨大的计算能力，以运行如此精密的模拟计算，从而直接比较过滤器中的实际水流状态。通过与全球网格大同盟以及参与本项目的 15 万名志愿者合作，我们才能进行前所未有的详细水流状态模拟研究，从而揭示在此前的研究中从未被发现的现象。

这一发现是我们与中国、瑞士、以色列、英国和澳大利亚研究人员共同努力的结果。由于您的参与，我们才能够在短短几年时间里完成了一台计算机需要 40,000 年才能够完成的研究。我仅代表整个团队，感谢帮助我们开展此项研究的 15 万名全球网格大同盟志愿者。这一突破性的研究成果同样也属于你们。

vmzy

Exceptional early results in the fight against Leishmaniasis
By: Dr. Carlos Muskus López
Coordinator, Molecular Biology and Computational Unit, PECET University of Antioquia
12 七月 2015          

摘要
The Drug Search for Leishmaniasis team has completed in vitro lab testing of the 10 top-rated compounds identified during screening, and have found that 4 of those 10 have very interesting properties that could point the way to new therapies. The post-processing of results continues, with the hope of identifying even more promising compounds for future lab and in vivo testing.

Last year, we updated you on the progress our team has made in filtering the computational results from our time on World Community Grid and identifying promising candidate compounds to test in the lab. Since then, we have completed the filtering process and identified over 100 compounds that the simulations predicted should bind effectively to the target PDB:3MJY protein in the Leishmaniasis parasite. However, the only way to be certain is to move those compounds into in vitro testing. Due to limited funding, we focused on the 10 compounds with the highest predicted rating, and found that 4 of them do in fact show positive results in in vitro tests, with one showing an exceptionally promising result. This means that in vitro the compounds kill the parasites efficiently while not affecting human cells.

Table 1. In vitro results from 4 of the best selected compounds against the protein PDB:3MJY according to the virtual screening strategy

Compound        Cytotoxicity (LC50)        Antiparasite (EC50)        Selectivity (IS)
3MJYsZ04        7.5 ± 1.1                1.6 ± 0.3                4.7
3MJYcZ03        >200                        19.2 ± 2.3                >10.4
3MJYcZ10        >200                        13.4 ± 1.0                >14.9
3MJYcZ01        >200                        0.7 ± 1.0                >285.7
Amphotericine B        42.1 ± 2.0                0.04 ± 0.01                1052.5


The compound 3MJYcZ01 is a very good candidate, and will be evaluated and optimized in further experiments. The results of these tests form the basis of a paper we’re presently finishing and about to submit to an academic journal.

Due to the high success rate of our screen (i.e. 40% of the compounds we tested showed good in vitro results), we think it would be very worthwhile to test the remaining compounds that we identified in our original screen, and we are still seeking funding to make that possible. In addition, the next step for the compounds already tested in vitro is to move to in vivo testing, and this process requires additional time and money. Unfortunately there are no crowdsourced initiatives like World Community Grid that can support actual in-lab testing!

Supporting the research community

Another important aspect of our work is making the results available to other researchers, so we have already presented our findings at both national and international events. Furthermore, we plan to present a full round-up of our work at the 3rd Colombian Congress on Computational Biology and Bioinformatics, in Medellin, in September 2015. To accommodate those who cannot attend, we will provide access to a public database with the respective affinity predictions for all the receptors and compounds analyzed, after the paper publication and the experimental validations of some hits.

In addition, one of the graduate student members of our team is planning to use pharmacokinetics and pharmacodynamics models and simulations to analyze several of the promising anti-leishmanial compounds. This will allow us to computationally predict the availability and distribution of the compounds in the human body. However, this evaluation will depend on public availability of pharmacological data of the compounds, and the quality of some predictions.

Team news

As our project carries on from year to year, it is inevitable that some team members will reach personal milestones along the way.

Andres Florez is finishing his PhD in Germany and is applying for a Post doc in Netherlands. Rodrigo Ochoa has finished his Master’s study and is currently working on measuring the stability of interactions between test compounds and the Leishmania protein.

Thank you

As we continue with our research, we’re constantly reminded of how valuable it is to have received such support from World Community Grid volunteers. Thank you for helping the fight against Leishmaniasis; we hope to have future work that can benefit from volunteer computing as well.
大意：
在DSFL的初筛中，我们选出了100多种潜在分子，但由于经费限制，我们只选了10个进行活体细胞测试，结果发现4个有效，其中3MJYcZ01结果数据最好。相关的测试结果，我们已经整理成论文，准备近期提交给相关的学术期刊。
接下来我们将筹集经费对剩下的潜在分子进行活体测试。在9月我们会公开所有研究结果。另外，我们有个研究生打算利用药代动力学和药效学模型对这些潜在分子进行计算研究。
最后，十分感谢大家为本项目做出的无私奉献。

vmzy

Security upgrade, Monday, July 20, 2015
14 七月 2015          

摘要
We will be updating our security certificates on Monday, July 20th, 2015. Volunteers using older versions of the software may need to upgrade.

World Community Grid will be upgrading our SSL (security) certificates on Monday, July 20th, 2015. The majority of our volunteers will not be impacted by this change.

SSL certificates are used to encrypt communications between your web browser and our servers, and between the software client and our servers. SSL certificates must be renewed periodically and we are therefore updating our current certificates.

Certain older versions of BOINC or World Community Grid software will not be compatible with our new certificates. After we upgrade the certificates, volunteers using older versions of the software may not be able upload, download, request or report research work. To ensure that you will not be impacted, please upgrade to the latest version of the software.

If you need assistance, please post in the BOINC Agent Support forum.
大意：
7月20日升级SSL加密证书。届时老BOINC客户端可能需要升级到最新版客户端，否则无法正常使用。

vmzy

Unlocking new potential for improving access to clean water
16 七月 2015          

摘要
An exciting video about the recent Computing for Clean Water breakthrough.

https://www.youtube.com/embed/pDKXHOgdFoE

With the help of World Community Grid volunteers, researchers reveal their breakthrough discovery of how nanotechnology could be used to improve access to clean water for the 1 billion people around the world who lack it.

Please check out this News article for more details on this exciting discovery.
大意：
C4CW项目的视频，其中包含志愿者采访。

vmzy

One step closer to identifying lung cancer biomarkers
By: The Mapping Cancer Markers research team
19 八月 2015          

摘要
Although work continues, enough data has already been processed to let the Mapping Cancer Markers team begin identifying high-scoring signatures and associating them with particular lung cancer biomarkers. The ultimate goal is to find signatures that distinguish many types of cancer, giving physicians and researchers another tool to improve detection, treatment and patient outcomes.

A new stage of MCM lung cancer biomarker discovery

After a long first stage of exploratory analysis, Mapping Cancer Markers (MCM) began a new, more targeted stage of lung cancer analysis in April 2015. Processing results from the first stage revealed a subset of approximately 1% of the biomarkers that frequently occur in high-scoring signatures. The second stage of the MCM lung cancer study will focus on signatures drawn from this subset of biomarkers.

Among the first research questions we are aiming to answer in the second stage are those about the nature of successful signatures and the reduced signature space. Will the selected subset of biomarkers in the targeted stage perform better at distinguishing lung cancer in tissue samples? Will the effect of signature length (number of biomarkers) on signature performance that we noticed in the exploratory stage also appear in this narrowed signature space? Which patterns of biomarkers characterize the top-performing cancer signatures? Most biological function is achieved by multiple genes (or proteins) participating in a coordinated network or signaling cascade (pathway), so can we discover pairs or larger groups of biomarkers that frequently co-occur in successful signatures? Will these groups of biomarkers correspond to known biological networks, or do successful signatures necessarily draw their members from multiple networks?

Enough second-stage results have been returned to allow us to start the preliminary analysis. One main goal of the second stage is to discover high-performing cancer signatures. We used results from the first stage to narrow the field of potential biomarkers from 22,000+ to a subset of 223. Figure 1 shows how the average cancer-distinguishing ability of the stage-2 gene signatures has improved considerably, compared to signatures discovered in the initial stage.

Figure 1. Distribution of signature scores, stage 1 vs. stage 2, by size. Signature frequencies are drawn in blue for stage 1, black for stage 2. Note the increase in the quality of scores in both stages between signatures of length 20 vs. length 10, as well as increase in frequency of higher quality scores.

Shorter or longer signatures?

One of the questions that the volunteer community might be asking is why we continue to focus on shorter gene signatures when the trend in the data shows that the longer gene signatures are performing better. Despite this trend, a larger gene signature may be more predictive but not always better. One reason is practical. Much of the work in the field of biomarker identification has the ultimate goal of producing a signature that can be translated into a clinical test. Feasibility and economics will play an important role at that stage. The process of moving a research-based result through testing and approval is lengthy and complex, so a 10 gene signature is easier and cheaper to translate than a 65 gene signature. The viability of gene signature sizes has roughly guided how we define our lower and upper size search limits for the MCM project.

Biomarker pairs

One of our goals in the analysis of gene signatures is to look at smaller combinations of genes, and identify groups of genes that relate to patient outcome in a similar manner (i.e. they may provide alternative choices for the signature). This is important for a variety of reasons. From the analytical side, if we can find two genes that perform almost exactly the same, then a successful gene signature will likely have only one of those. This will help us reduce search space, but also to find alternatives. From the practical side, one of the two (or more) alternatives may be easier to bring to clinical practice. Thus, we aim to find multiple signatures, and characterize them with respect to their relationships. Another reason to look at combinations of genes involves seeing if two genes may have a biological reason for being related. Is this particular cancer affecting two genes at the same time? Is a particular biological pathway compromised? These kinds of questions might explain differences between patients or why certain people respond better to particular therapies. These are questions that are much further down the research path but we wanted to touch on them so that the community is aware of where in the pipeline your contributions have helped and also what still needs to be done. If a pair (or larger group) of genes is related by disease, signatures containing those genes or related biomarkers should perform well. Figure 2 looks at the rate at which stage-2 biomarker pairs co-occur in high-scoring signatures.

Figure 2: Frequencies of stage-2 biomarker pairs. The frequency of biomarkers i and j co-occurirng in high-scoring signatures is represented by the color of the row-i, column-j element of the matrix. Higher-frequency pairs are colored lighter blue. Note the horizontal and vertical stripes indicating specific biomarkers that perform well regardless of their pairing. Also, very bright single spots highlight biomarker combinations that are exceptionally promising.

A note on run times of research tasks

Some of you may have noticed above-average run times of work units in this new stage of MCM. We are working to make run times more consistent and predictable; however, this job is made more difficult as this stage of the research requires changing work unit designs more frequently than before. The design of new work units will also depend in part on results of earlier second-stage results. Consequently, the turnaround time for benchmarking and calibrating work units may limit our success at stabilizing run times. We trust that our wonderful volunteers will be able to continue contributing results no matter what work units we provide, but we wanted to let you know what to expect. Once again, thank you for making our research possible, and please stay tuned for future announcements!

Recent publications, presentations and media coverage

Publications
Navab, R., Strumpf, D., Jurisica, I., Walker, C. G., Gullberg, D., Tsao, M.S. Integrin a11b1 regulates cancer stromal stiffness and promotes tumorigenecity in non-small cell lung cancer, Oncogene, 2015. In press.
Stewart, E.L., Mascaux, C., Pham, N-A, Sakashita, S., Sykes, J., Kim, L., Yanagawa, N., Allo, G., Ishizawa, K., Wang, D., Zhu, C.Q., Li, M., Ng, C., Liu, N., Pintilie, M., Martin, P., John, T., Jurisica, I., Leighl, N.B., Neel, B.G., Waddell, T.K., Shepherd, F.A., Liu, G., Tsao, M-S. Clinical Utility of Patient Derived Xenografts to Determine Biomarkers of Prognosis and Map Resistance Pathways in EGFR-Mutant Lung Adenocarcinoma, J Clin Oncol, 2015. In press. CJCO/2014/601492.
Camargo, J. F., Resende, M., Zamel, R., Klement, W., Bhimji, A., Huibner, S., Kumar, D., Humar, A., Jurisica, I., Keshavjee, S., Kaul, R., Husain, S. Potential role of CC chemokine receptor 6 (CCR6) in prediction of late-onset CMV infection following solid organ transplant. Clinical Transplantation, 2015. In press. doi: 10.1111/ctr.12531
Fortney, K., Griesman, G., Kotlyar, M., Pastrello, C., Angeli, M., Tsao, M.S., Jurisica, I. Prioritizing therapeutics for lung cancer: An integrative meta-analysis of cancer gene signatures and chemogenomic data, PLoS Comp Biol, 11(3): e1004068, 2015.
Starmans, M.H., Pintilie, M., Chan-Seng-Yue, M., Moon, N.C., Haider, S., Nguyen, F., Lau, S.K., Liu, N., Kasprzyk, A., Wouters, B.G., Der, S.D., Shepherd, F.A., Jurisica, I., Penn, L.Z., Tsao, M.S., Lambin, P., Boutros, P.C. Integrating RAS status into prognostic signatures for adenocarcinomas of the lung. Clin Cancer Res, 21(6): 1477-86, 2015.
Wong, S. W. H., Cercone, N., Jurisica, I. Comparative network analysis via differential graphlet communities, Special Issue of Proteomics dedicated to Signal Transduction, Proteomics, 15(2-3):608-17, 2015. E-pub 2014/10/07. doi: 10.1002/pmic.201400233
Editorial
Hoeng J, Peitsch MC, Meyer, P. and Jurisica, I. Where are we at regarding Species Translation? A review of the sbv IMPROVER Challenge, Bioinformatics, 31(4):451-452, 2015.
Presentations
Keynote: Life of an orphan protein, Symposium on Computational Biology, eScience approaches for biomedical data analysis, University of Southern Denmark, Odense, June 10-12
Invited presentation: High-performance computing in integrative cancer informatics. Fathoming cancer by data-driven medicine, Advanced Computing and Analytics in Medical Research Symposium, University of Ottawa, May 11-12.
Invited presentation: Scalable visual data mining.
HPC and “big data” in integrative cancer informatics. OCE Discovery Conference, the Metro Toronto Convention Centre, April 28.
Invited presentation: High-performance computing in integrative cancer informatics. Challenges and opportunities in intelligent molecular medicine, Systems Biology Ireland Seminar Series, University College Dublin, The College of Health Sciences, Dublin, Ireland, March 6
Keynote presentation: Integrative cancer informatics - moving personalized medicine to preventive interventions, Cancer Care Ontario Workshop - PREVENTION INTERVENTION STUDIES TO IMPROVE THE HEALTH OF ADULT CANCER SURVIVORS.
Scalable visual data mining video and demo, Compute Ontario highlight at OCE Discovery Conference, Toronto, April 27-28
Scalable visual data mining video, High Performance Computing Conference, Montreal, June
Media
The Jurisica lab and the MCM1 project scientists were recently interviewed for a Drug Discovery News article about the difficulties of cancer biomarker discovery and validation: Signs of intelligent biomarkers by Randall C Willis, DDNews.
Igor was also interviewed for the NewsTalk Radio 1010 in June 2015 about the work on discovering prognostic and predictive cancer signatures.
World Community Grid was also covered by Genevieve Roberts in The Independent on June 10: In 10 years, 'crowdsourced computing' has changed the world; now it's tackling Ebola
大意：
MCM项目快找到肺癌标记了
在一阶段的结果分析过程中，我们海选出了不少疑似肺癌标记（2.2万个），接下来我们将对这些标记进行精选（223个）。我们需要找尽可能小、简单（易用于临床）的标记。
我们将根据研究结果不断的调整、改进WU，所以今后我们任务的时长会很不稳定。希望大家能理解。
最后十分感谢大家的无私奉献和支持。

vmzy

Pioneering new techniques in the fight against HIV
By: Dr. Arthur Olson
Professor, The Scripps Research Institute
30 九月 2015         

摘要
The team behind FightAIDS@Home is launching Phase 2 of the project, putting to use a more accurate simulation tool to help them determine which of the Phase 1 results merit further investigation. Phase 2 will also be applying this analysis technique at an unprecedented scale, which if proven successful, can benefit medical research not only for HIV but many other diseases as well.


Model of a complete HIV Virion with all of the component molecules.
There have been some amazing advances in the fight against the human immunodeficiency virus (HIV), including treatments that have improved and extended millions of lives. But the fight continues - HIV is continually mutating, and as it does it evolves resistance to existing treatments. With tens of millions of people currently living with HIV, and millions more infected every year, the search for more effective HIV treatments is as critical as ever. Our team is therefore launching a new phase of HIV research to build on the success of the first phase and more accurately analyze the most promising drug candidates we've identified so far.

For almost a decade, FightAIDS@Home has contributed to this fight by exploring different ways of disabling the virus. World Community Grid members have provided my team an unprecedented amount of computing power, enabling us to investigate a huge number of potential cures. To date, volunteers have performed over 20 billion comparisons between candidate chemicals and different binding sites on the virus. Along the way, our team has improved the tools used in the fight, by developing - and validating - software tools to simulate chemical binding, and discovering new potential binding sites for drugs to attack. These tools have even supported other medical research efforts, both on World Community Grid and elsewhere.

The massive success of FightAIDS@Home has also generated a new challenge: thousands of potential 'hits' (chemicals that might form the basis of effective drugs) - a handful of which we're synthesizing for additional testing. But because there are so many, it is prohibitively expensive and time consuming to synthesize and lab test all of those chemicals. The project now needs a new computational method to double-check the promising Phase 1 results, and ensure that only the most thoroughly vetted and probable candidate compounds proceed for further investigation. Phase 2 of FightAIDS@Home will address both of these goals: refining the Phase 1 results and validating the technology needed to make more accurate simulations.

Specifically, Phase 2 uses a new analysis technique called BEDAM (Binding Energy Distribution Analysis Method), which is implemented using software called Academic IMPACT developed by our collaborators at Temple University. BEDAM has proven effective at carrying out more accurate simulations in computational contests, but thanks to World Community Grid volunteers, we now have an opportunity to apply it to analyze molecules at an unprecedented scale. This is important because if successful, these techniques can be applied to other drug discovery searches beyond HIV.


Collaborating labs for the HIVE Center. Prof. Art Olson directs the Center and collaborators include Prof. Ron Levy, who is partnering with the FightAIDS@Home project.

Phase 2 is more radical than its name suggests - World Community Grid volunteers have the opportunity to help us validate a new promising research paradigm that can help the search for treatments for many diseases, not just HIV. It's only because of the commitment shown by volunteers that FightAIDS@Home has been able to accomplish so much thus far. We hope we can count on your continued support as we continue this important journey.

To contribute to FightAIDS@Home - Phase 2, join World Community Grid, or if you are already a volunteer, make sure the project is selected on your My Projects page.
大意：
采用新技术抗艾
FAAH将进入二阶段，届时将采用更精确的模拟计算工具对1阶段的优选结果进行复核。
全球大概有上千万HIV携带者，而且HIV还在不断变异，对药物产生抗药性，使得传统疗法逐渐失效。
在志愿者的齐心协力下，经过10年的计算，我们对药物进行了200亿次筛选。同时我们也在对模拟计算程序不断进行优化。
由于一期筛选出的候选药物过多，出于时间和成本方面的考虑，不可能一一对他们进行实验室药检。所以二期我们要对这些候选分子进行二次筛选。
二期将采用新开发的算法BEDAM，我们将对这个算法进行验证。如果验证通过，今后其他项目也就可以使用这个算法。
最后感谢大家的无私奉献。

vmzy

Finding new avenues to attack Ebola  
By: Dr. Erica Ollmann Saphire, PhD
The Scripps Research Institute
6 十月 2015

摘要
Efforts to simulate matches between candidate compounds and one key Ebola virus protein are largely complete. Simulations of matches against another, newly discovered target protein are beginning now. Even as simulation work continues, the team is beginning to analyze these results and home in on compounds that could form the basis for effective new drugs against Ebola and other related diseases. Thanks to your help, and a new grant, the work is proceeding well.


Two Protein Data Bank structures for the ribonuclease H domain of HIV reverse transcriptase. We used structural and experimental data for this domain to optimize our analysis protocols for the Lassa NP exonuclease site.

Thanks to the efforts of thousands of World Community Grid members, my team has continued to make progress on Outsmart Ebola Together, a project whose goal is to find new drugs for curing Ebola and related life-threatening viral hemorrhagic fevers.

Outsmart Ebola Together began with a study of potential drug attacks against the receptor-binding site of the Ebola surface glycoprotein (GP). We then announced the start of work on a second drug target: the nucleoprotein (NP) of Lassa Fever virus. Specifically, we are looking for drugs that attack the newly discovered "exonuclease site" of Lassa NP. This exonuclease site helps conceal the virus's presence from the infected human cell by destroying the virus's own excess production of double-stranded RNA.

We have since prepared research tasks for testing the Lassa NP exonuclease site against millions of potential drugs. These tasks are now ready for use, and will be sent out to World Community Grid volunteers over the coming months.

Our lab has also been investigating the Ebola NP and VP35 proteins. NP and VP35 must engage in a series of specific interactions with each other as Ebola virus replicates. These newly discovered interactions could potentially be disrupted by new drugs, making NP and VP35 possible future targets for investigation by Outsmart Ebola Together.

At this stage in the project, we’ve gathered enough data that we need to begin focusing on analysis procedures for the data already returned by World Community Grid volunteers. We must analyze the data for both the Ebola GP receptor-binding site and the Lassa NP exonuclease site; and our analysis procedures must be sufficient to filter out false positives from the large quantity of results returned.

For each viral protein site that we test against potential drugs, we assure the validity of our analysis as follows: We select a substantially analogous site (generally from a different virus) for which there exists experimental data about potential drugs that bind or do not bind to the site. We then tune our analysis protocols so that, when applied to this site, our analysis results closely match the known experimental results. Only when this is done do we feel that we can confidently apply the same analysis protocols to the site of current interest.

In particular, this summer we looked closely at analysis optimization for the Lassa NP exonuclease site. As the analogous well-studied site, we chose the "ribonuclease H domain" of HIV reverse transcriptase, which has strong similarities to the Lassa NP exonuclease site in its protein structure and use of catalytic metal ions. The optimization of our analysis protocols against experimental data for the HIV ribonuclease H domain is now complete, and we are looking forward to the arrival of the Lassa NP exonuclease data as it is processed by World Community Grid volunteers. Candidate drugs that pass the analysis stage will go on to a next round of experiments, conducted in the lab rather than by computer simulation.

We are also happy to announce that a $50,000 grant to support this work has been provided by the Robert Wood Johnson Foundation President’s Grant Fund of the Princeton Area Community Foundation. With this grant and the vast computing resources of World Community Grid, our way to the successful completion of the project is clear.

As always, we close with a thank-you to the volunteers who have run this work for us. As you can see, we’ve already made significant progress but there is much work still to do. Make sure you’re signed up to contribute to this project, and spread the word about our lifesaving work!
大意：
埃博拉病毒1号蛋白的模拟计算已经基本完成，现在开始2号蛋白的计算。

vmzy

Network Updates: Sunday, February 21, 2016 at 4:30am UTC
By: Keith Uplinger
World Community Grid
16 二月 2016          

摘要
Network updates will be performed Sunday, February 21st. The World Community Grid website and BOINC servers may be affected during this update.

World Community Grid will be performing network updates starting Sunday, February, 21 2015 at 04:30am UTC. The window for this maintenance activity is estimated to be 6 hours, although we anticipate the actual outage time will be less. During the network updates, volunteers may not be able to access the website, fetch new research tasks or return completed work for a period of time.

No action is required by the members, as the BOINC/World Community Grid software application will automatically reconnect to our servers once the update is over.

Thank you again for your participation in World Community Grid!
大意：
北京时间2016/2/21 周日中午12点半进行网络升级，预计耗时6小时，届时网络会不稳定，客户端可能连不上服务器。

vmzy

Next-Level Screening: Predicting Material Properties
14 十月 2015          

摘要
A new paper from the Clean Energy Project team reveals that they can now use multi-layer artificial neural networks to predict the electrical properties of novel molecules without actually simulating the entire molecule. This advance was made possible by the enormous number of simulations done for the Clean Energy Project, and promises to enable screening of many more molecules than the team was able to address in their previous work.

Paper Title:
"Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery"

Published in journal:
Advanced Functional Materials

Authors:
Edward O. Pyzer-Knapp, Kewei Li and Alan Aspuru-Guzik

Layman Abstract:

In this paper Edward, from Harvard's Clean Energy Project, shows how techniques from the field of machine learning can be used to speed up materials screening. By using a special class of neural networks, known as multi-layer perceptrons, he is able to predict the properties of a molecule to a high degree of accuracy before any calculations are performed. They show how using this method one can eliminate almost 99% of a screening library or molecules without having to calculate it. By eliminating the molecules unlikely to be useful, they greatly increasing the range of molecules that can be considered by the Clean Energy Project.

Technical Abstract:

Here, the employment of multilayer perceptrons, a type of artificial neural network, is proposed as part of a computational funneling procedure for high-throughput organic materials design. Through the use of state of the art algorithms and a large amount of data extracted from the Harvard Clean Energy Project, it is demonstrated that these methods allow a great reduction in the fraction of the screening library that is actually calculated. Neural networks can reproduce the results of quantum-chemical calculations with a large level of accuracy. The proposed approach allows to carry out large-scale molecular screening projects with less computational time. This, in turn, allows for the exploration of increasingly large and diverse libraries.

Access to Paper:

To view the paper, please click here.
大意：
下一步筛选：预测材料属性
论文标题，利用神经网络加速新材料探索
CEP的新论文声称他们现在可以利用人工智能机器学习技术在不完全模拟整个分子的情况下预测分子的电属性。利用此法可以直接筛除99%的无效分子，极大的减少了不必要的浪费。详见链接。

译注：去年事情太多，好多新闻没时间翻译。现在来慢慢填坑。

vmzy

Analyzing a wealth of data about the natural world
By: Wim Degrave, Ph.D.
Laboratório de Genômica Funcional e Bioinformática Instituto Oswaldo Cruz - Fiocruz
14 十月 2015          

摘要
The Uncovering Genome Mysteries project has already amassed data on over 200 million proteins, with the goal of understanding the common features of life everywhere on earth. There are tens of millions of calculations still to run, but the team is also making preparations for analysis and eventual publication of the data.


For almost a year now, Uncovering Genome Mysteries has been comparing protein sequences derived from the genomes of nearly all living organisms analyzed to date. Thanks to the volunteers that contribute computer time to World Community Grid, more than 34 million results have been returned with data on functional identification and protein similarities. Along with our collaborators in Australia, we’ve paid particular attention to microorganisms from different ecosystems, with special emphasis on marine organisms. More than 200 million proteins have been compared thus far, during the equivalent of 15,000 years of computation. The resulting data are sent to our computer servers at the Fiocruz Foundation in Rio de Janeiro, Brazil and now also to the University of New South Wales, Sydney, Australia. A last set of around 20 million protein sequences, determined over the last year, is now being added to the dataset and will be run on World Community Grid in the coming months.

However, the task of functional mapping and comparison between proteins from all these organisms does not end there. Our team of scientists is, in the meantime, investing more efforts to optimize the algorithms for further analysis and representation of the data generated by World Community Grid volunteers, and preparing for the database systems that will make the results available to the scientific community. Once our data is public, we expect that the scientific community’s understanding of the intricate network of life will gain a completely new perspective, and that results will also contribute to the development of many new applications in health, agriculture and life sciences in general.

This project is a cooperation between World Community Grid, the laboratory of Dr. Torsten Thomas and his team from the School of Biotechnology and Biomolecular Sciences & Centre for Marine Bio-Innovation at the University of New South Wales, Sydney, Australia, and our team at the Laboratory for Functional Genomics and Bioinformatics, at the Oswaldo Cruz Foundation – Fiocruz, in Brazil.
大意：
UGM项目已经处理了2亿多个蛋白质。
过去一年已经算了大概3千4百万个结果，处理2亿多个蛋白质，相当于1万5年的计算。
与此同时，我们的科学家团队，还在优化算法，分析结果数据。并为将来公开结果建立数据库系统。

vmzy

FightAIDS@Home Team Re-Opens Phase 1
By: The FightAIDS@Home research team
1 十二月 2016          

摘要
We are happy to announce that we are re-opening Phase 1 of the Fight AIDS@Home project. In collaboration with World Community Grid, and thanks to their affiliated volunteers around the globe, High Throughput Virtual Screening will be performed by targeting the HIV-1 capsid protein with the goal of discovering new chemical compounds to defeat the AIDS virus (HIV). Read more in this update.

Background

During the maturation of the HIV virus, the HIV-1 capsid protein (CA) assembles with thousands of copies to forms the capsid core [ref 1] with a characteristic conical shape (see Figure 1C). This core encloses the RNA viral genome. Upon the entry of the HIV in host cells, the capsid core is released into the cytoplasm, and it dissociates in connection with the reverse transcription in a not completely understood process. This leads to the importation of DNA viral genome in the host cell’s nucleus, where it is integrated in the host DNA to finalize the infection.

The critical role of CA protein, in early and late stages of the viral replication life cycle, has led to recent efforts on drug development, targeting the mature form of the protein. Currently, none of these molecules are used in clinic, and some face natural polymorphism and resistant mutations [ref 2]. Therefore, continued development of drugs targeting the CA protein is still needed

Different level of the capsid protein structure

CA protein consist of a sequence of 231 amino acids which folds into 3 different domains (Figure 1A): The N-terminal domain (N-ter), the linker, and the C-terminal domain (C-ter). This protein chain complexes with other chains to form hexamers (Figure 1B) or pentamers; which assemble together to form the fullerene-cone shape of the capsid core (Figure 1C). There are several models of the core assembly, but all are composed of ~200 hexamers, and exactly 12 pentamers.


Figure 1: The HIV-1 capsid protein structure
High Throughput Virtual Screening

The FightAIDS@Home team is working with World Community Grid to find active compounds which could attach to the CA proteins and mediate the assembly of the capsid core. This computational experiment will be performed using the docking software AutoDock VINA [ref 3].

Thanks to the volunteers, around 2 million molecules will be screened across ~50 conformations of the capsid protein, and hopefully lead to a reduced selection of molecules. This will be the starting point of a drug discovery process targeting the HIV-1 capsid protein.

With the support of our collaborators from the HIV Interaction and Viral Evolution (HIVE), experimental biding assays and infectivity assays will be conducted to determine if the selected compounds could be optimized as a promising drug candidate.

Four pockets of interest

Based on X-ray structures of CA protein, models of the core, and computational analysis of their flexibility, four pockets of interest have been selected on the surface of the hexamer assembly (see Figure 2).


Figure 2: Four pockets of interest
These pockets involve either one monomer (as pocket 2 along the linker domain), at the interface of two monomers (pocket 1 & 4), or at the six-fold interface (pocket 3).

Mutagenesis experiments revealed that core stability is fine-tuned to allow ordered disassembly during early stage of virus replication cycle [ref 4]. This is why selection of compounds will be done either for molecules which could stabilize or destabilize the hexamer; assuming that both actions could have impacts on the equilibrium of the core.

Our team from The Scripps Research Institute of San Diego, which includes Dr. Pierrick Craveur, Dr. Stefano Forli, and Prof. Arthur Olson, really appreciates the support this project receives from World Community Grid volunteers around the globe.

References

Briggs, J. A. and H. G. Krausslich (2011). "The molecular architecture of HIV." J Mol Biol 410(4): 491-500.
Thenin-Houssier, S. and S. T. Valente (2016). "HIV-1 Capsid Inhibitors as Antiretroviral Agents." Curr HIV Res 14(3): 270-282.
Trott, O. and A. J. Olson (2010). "AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading." J Comput Chem 31(2): 455-461.
Forshey, B. M., U. von Schwedler, et al. (2002). "Formation of a human immunodeficiency virus type 1 core of optimal stability is crucial for viral replication." J Viro
大意：
重启FAAH1项目，对新的4个靶点进行初筛计算。