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发表于 2018-10-1 16:44:51 | 显示全部楼层
Paper Published

Dr. Wilma Groenwald, one of the founding researchers for the HSTB project, recently published a paper describing some of the precursor work to the project. The paper, which discusses the folding behavior of mycolic acids, is now freely available on ChemRXiv.

We hope to have Athina’s first papers with World Community Grid data available later in the year, and will keep you updated.

Thank you to all volunteers for your support.
By: Dr. Anna Croft
University of Nottingham, UK
28 九月 2018


抗击肺结核子项目于近期发表了一些前期成果,并以霉菌酸的折叠行为作为重点。
论文下载请前往  ChemRXiv

附:论文标题《通过高级模拟分析揭示结核分枝杆菌分子酸的溶剂依赖性折叠行为

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发表于 2018-10-1 21:01:11 | 显示全部楼层
zhouxiaobo 发表于 2018-10-1 16:44
Paper Published

Dr. Wilma Groenwald, one of the founding researchers for the HSTB project, recently ...

还以为在划水
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发表于 2018-10-4 10:50:26 | 显示全部楼层
Outsmart Ebola Together Ends Current Phase of Work to Process Data, Look for Additional Collaborators
By: Dr. Erica Ollmann Saphire, PhD
The Scripps Research Institute
3 十月 2018          

摘要
The Outsmart Ebola Together research team is moving on to the data analysis phase of the project. Read more in this message from principal investigator Dr. Erica Ollmann Saphire.


Dear Volunteers,

When we launched the Outsmart Ebola Together project at the end of 2014, everyone in the world had become aware of the virus after it infected more than 14,000 people--and killed more than 5,000 people--in West Africa. The news cycle may have moved on, and the more recent Ebola outbreaks have not been as widespread. But Ebola continues to be a killer, with the most recent outbreaks (as of this writing) reported in the Democratic Republic of the Congo.

Your response to Outsmart Ebola Together has been terrific, and you have returned more than 350 million results to help find new drug targets against Ebola virus. Thank you!

We now have more than enough data to analyze to find new directions for antiviral drugs. So, we are ending this phase of the project to finish processing the suggestions your computers, phones, and tablets have sent us. This is also a good time for us to make any necessary adjustments to our analysis strategy, look for additional collaborators on the project, and determine if/when we'll need additional computational work.

We will be stopping work on World Community Grid in the next three or four weeks.

We will be in touch with more information and results!


Erica Ollmann Saphire
大意:
OET的任务都已经算的差不多了(大概还有3-4周就能算完),现在开始征询合作伙伴对返回的3.5亿个结果文件进行分析处理。

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发表于 2018-10-9 09:17:07 | 显示全部楼层
Planned Maintenance on Thursday, October 11
8 十月 2018          

摘要
We are updating the operating system on our servers on Thursday, October 11, beginning at 14:00 UTC.

We will be applying an important operating system update to our servers on Thursday, October 11, beginning at 14:00 UTC.  We anticipate that the work will take approximately four hours.

During some of this time, volunteers will not be able to upload or download new work, and the website will not be accessible.

Volunteers will not need to take any particular action, as your devices will automatically retry their connections after the maintenance work is completed.
大意:
10月11日(周四)晚22点开始进行例行维护,升级操作系统,大约耗时4小时,届时全站(包括任务下载/上传)下线。
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发表于 2018-11-27 09:33:50 | 显示全部楼层
Planned Maintenance on Thursday, November 29
26 十一月 2018          

摘要
We are updating the operating system on our servers on Thursday, November 29, beginning at 20:00 UTC.

We will be applying an important operating system update to our servers on Thursday, November 29, beginning at 20:00 UTC. We anticipate that the work will take approximately four hours.

During some of this time, volunteers will not be able to upload or download new work, and the website will not be accessible.

Volunteers will not need to take any particular action, as your devices will automatically retry their connections after the maintenance work is completed.

We appreciate your patience and participation.
大意:
11月30号(周五)凌晨4点对服务器操作系统进行升级,预计需要4个小时,届时全站下线。
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发表于 2019-7-10 11:25:05 | 显示全部楼层
OpenZika Nears End of Work on World Community Grid
By: The OpenZika research team
9 七月 2019          

摘要
The OpenZika researchers are making ambitious plans to analyze the data that has been processed by World Community Grid volunteers over the past three years. Learn about the next steps for the project in this comprehensive update.

Work ending on World Community Grid

Recently we evaluated the work that has been done for the OpenZika project, and came to the conclusion that we have nearly completed what we proposed to do—namely, to do virtual screening for nearly all Zika virus proteins from the largest dataset of compounds available (the ZINC 15 library, with approximately 30 million compounds). Thanks to the volunteers who supported the project, we have many, many results to evaluate.

Over the next few weeks, we’ll submit a few additional structures of the Zika virus and dengue virus NS5 polymerase (RNA and active site) and NS5 methyltransferase (GTP site) to finish our project. When the World Community Grid development team creates work units for these structures, this should create approximately 20,000 batches. Once these are completed, we will turn our full attention to analyzing the data.

Progress on selecting and testing compounds

We virtually screened the ChemBridge commercial database, with more than 1 million compounds, against Zika virus (ZIKV) proteins: envelope, protease, helicase, polymerase and methyltransferase.

We also screened the ChemBridge database against dengue proteins protease and helicase. After the docking calculations on OpenZika, we ran the compounds through the free energy filter and the ZIKV machine learning models filter. We also performed a medicinal chemistry-based inspection of the most promising candidates.

This new round of compounds allowed us to select 55 new candidates for Zika and 20 candidates for dengue (Figure 1). The compounds were purchased and sent to the University of California in San Diego at Dr. Jair Siqueira-Neto’s laboratory, for experimental evaluation. They are performing cell-based assays in human neural stem cells (hNSCs) with ZIKV. We will also perform enzymatic assays in ZIKV NS3 helicase and protease proteins with our collaborators at the Physics Institute of Sao Carlos, University of Sao Paulo (Brazil), at Dr. Glaucius Oliva’s laboratory, to validate the predicted enzymatic candidates’ activities.


Figure 1.Workflow of the virtual screening experiments performed for ZIKV envelope (E), NS2B-NS3 protease, NS3 helicase, NS5 polymerase and NS5 methyltransferase as well as for DENV NS2B-NS3 protease and NS3 helicase, using the ChemBridge database (~1 million compounds).
Additionally, we performed virtual screening of two in-house natural products and semi-synthetic compounds datasets from two collaborators: Dr. Laster at North Carolina State University (NC State University), and Dr. Regasini at São Paulo State University (UNESP). We virtually screened the compounds against ZIKV NS3 protease and helicase proteins and ran the compounds through the free energy filter. Afterwards, we performed a target prediction analysis of the promising hits. The extracted and isolated compounds were then tested using in vitro assays, cell-based assays and enzymatic assays, to validate the in silico study (Figure 2).


Figure 2.Workflow of the virtual screening experiments performed for ZIKV NS2B-NS3 protease and NS3 helicase, using in housenatural and semi-synthetic compounds databases.
Status of the calculations

In total, we have submitted almost 8.57 billion docking jobs, which involved 427 different target sites. Our initial screens used an older library of 6 million commercially available compounds, and our current experiments utilize the new ZINC15 library of 30.2 million compounds.

Thus far, the > 80,000 volunteers who have donated their spare computing power to OpenZika have given us > 84,711 CPU years’worth of docking calculations, at a current average of 75.1 CPU years per day!  Thank you all very much for your help!!

Except for a few stragglers, we have received all of the results for our experiments that involve docking 30.2 million compounds versus NS1, NS3 helicase (both the RNA binding site and the ATP site), NS5 RNA polymerase (NTP and RNA pocket), NS5 methyltransferase (SAM site), NS2B/NS3 protease, capsid (binding pockets 1 and 2) and envelope protein.  

Upcoming publications

We recently published a review entitled “High Throughput and Computational Repurposing for Neglected Diseases” in the journal Pharmaceutical Research. This paper describes the many drug repurposing efforts that have been going on in different labs around the world to try to find treatments for the many tropical diseases, including Zika and dengue infections.

OpenZika researchers Dr. Melina Mottin, Dr. Roosevelt Silva, Msc. Bruna Sousa and Paulo Ramos submitted abstracts for consideration for oral and/or poster presentation at the 9thBrazMedChem2019 conference, the major medicinal chemistry conference in Latin America. Dr. Mottin and Bruna will present the studies related to natural compounds: “Discovery of flavonoids from Pterogyne nitens with potent activity against Zika virus protease and helicase” and “Discovery of new Zika virus candidates: natural products from Angelica keiskei with activity against NS2B-NS3 protease.” Paulo will present the work “Integrative Similarity analysis, Docking and Machine Learning models for identifying new Zika NS5 hits guided by dengue NS5 inhibitors.” Dr. Roosevelt will present the ZIKV NS1 molecular dynamics study: “Dynamic Behavior of dengue and Zika viruses NS1 protein reveals monomer-monomer interaction mechanisms and insights to rational drug design.”

Papers being written – to be submitted soon:

We are preparing the following papers regarding the exciting results of OpenZika project:

One paper reporting the results (virtual and experimental) of the first round of compounds selected against ZIKV NS3 helicase that presented anti-ZIKV activity in human neural stem cells (hNSCs)
Three papers reporting the great results from the virtual screening and experimental evaluation of natural products (two from Brazilian collaborations and one from NC State University collaboration), from plants from Brazil and traditional Chinese medicine, respectively, which presented anti-ZIKV activity, inhibiting ZIKV proteins protease and/or helicase
One paper reporting the results for approved drugs/clinical collections compounds, that have anti-malarial and anti-Ebola activity, that presented anti-ZIKV activities against ZIKV helicase protein, candidates for drug repurposing
One paper reporting the computational results of ZIKV NS1 protein molecular dynamics simulations and insights regarding binding pockets and drug design. This paper has been submitted to Journal of Biomolecular Structure & Dynamics, titled “Dynamic Behavior of Dengue and Zika viruses NS1 protein reveals monomer-monomer interaction mechanisms and insights to rational drug design”, and is under consideration for publication.
The team has also collaborated on a chapter for a book on trypanosomal diseases to be published in Burger’s Medicinal Chemistry.
These papers will be soon submitted to high-impact scientific journals.

Past publications and outreach

Our Drug Discovery Today keynote review “The A–Z of Zika drug discovery” was published on June 20 2018. This is a comprehensive review of the recent advances in ZIKV drug discovery efforts, highlighting drug repositioning and computationally guided compounds, including recently discovered viral and host cell inhibitors. Promising ZIKV molecular targets are also described and discussed, as well as targets belonging to the host cell, as new opportunities for ZIKV drug discovery. All this knowledge is not only crucial for advancing the fight against the Zika virus and other flaviviruses, but it will also help the scientific community prepare for the next emerging virus outbreak to which we will have to respond.

Our paper “Computational drug discovery for the Zika virus” was published in a special issue of the Brazilian Journal of Pharmaceutical Sciences. In this paper, we summarize current computational drug discovery efforts and their application to the discovery of anti-ZIKV drugs. We also present successful examples of the use of computational approaches to ZIKV drug discovery, including our OpenZika project.

Dr. Sean Ekins presented a poster at Cell Symposia: Emerging and Re-emerging Viruses, on October 1-3, 2017, in Arlington, VA, USA, titled “OpenZika: Opening up the discovery of new antiviral candidates against Zika virus”.

Our PLoS Neglected Tropical Diseases paper, "OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery,"was published on October 20 2016, and it has already been viewed over 5,200 times. Anyone can access and read this paper for free. Another research paper “Illustrating and homology modeling the proteins of the Zika virus” was published in F1000Research and viewed > 4,200 times.

We have also published another research paper entitled “Molecular Dynamics simulations of Zika Virus NS3 helicase: Insights into RNA binding site activity” in a special issue on flaviviruses for the journal Biochemical and Biophysical Research Communications. This study of the NS3 helicase system helped us learn more about this promising target for blocking Zika replication. The results will help guide how we analyze the virtual screens that we performed against NS3 helicase, and the molecular dynamics simulations generated new conformations of this system that we have been using as targets in new virtual screens that we performed as part of OpenZika.

The OpenZika project results were presented at the 256th ACS National Meeting, on August 19-23, 2018,  in Boston, MA, USA. Dr. Mottin gave an oral presentation and presented a poster entitled “OpenZika: Discovery of new antiviral candidates against Zika virus,; in the session “Chemoinformatics Approaches to Enhance Drug Discovery Based on Natural Products.”

Dr. Mottin also gave an oral presentation “Applying Molecular Dynamics to Drug Discovery for Zika Virus and Schistosoma mansoni,” in the South American Initiative for Cooperation on Molecular Simulations (SAIMS) meeting, held at Institut Pasteur, Montevideo, Uruguay, November 4-7, 2018.The meeting was a great opportunity to exchange experiences and collaborate with South American researchers who work with Zika.

Information about BrazMedChem

The Principal Investigator of OpenZika, Prof. Carolina Horta Andrade, also Director of the Medicinal Chemistry Division of the Brazilian Chemical Society (SBQ), is organizing the major medicinal chemistry & drug discovery conference in Latin America, the 9thBrazMedChem2019, to be held September 1-4, 2019, in Pirenópolis, Goias, Brazil.

The main theme of this year’s conference is "Bridging the gap between Academia and Pharmaceutical Industries for Advancing Drug Discovery." The Organizing and Scientific Committees are working to keep the outstanding quality of the past editions, trying to enable the effective engagement and participation of the scientific community, in a modern structure that mixes high level science and social activities to congregate participants.

We expect to host around 500 people, mainly graduate students, researchers and professors working in the field of medicinal chemistry from Brazil and Latin American countries, in an interactive and collaborative atmosphere to exchange of experiences and information to meet the challenges of the medicinal chemistry of the 21st century.

Moreover, Dr. Sean Ekins, co-PI of the OpenZika project, is going to 9thBrazMedChem to give a talk regarding our work on OpenZika as well as on his work on Chagas disease and Ebola drug discovery, which represents just a few of the neglected disease collaborations he is involved with.

New project team members

Paulo Ramos is a new undergraduate student who joined Prof. Andrade’s laboratory in January 2019. In his project work, he first searched for dengue virus (DENV) NS5 inhibitors in the PubChem and ChEMBL databases. He also performed an integrative similarity analysis of dengue and Zika NS5 proteins, docking of DENV known inhibitors on Zika NS5 sites and machine learning (ML) models, to prioritize the best hits. He found 156 compounds reported as dengue NS5 inhibitors, that were docked in the Zika NS5 sites and scored by ML models. Twenty-two compounds were selected as showing the best results. Then, he performed a similar search with the hits in a commercial database (e-molecules) and screened the similar compounds using the docking and ML filters. The 67 virtual hits for Zika NS5 will be validated by cell-based Zika assays.


OpenZika team of LabMol: Melina, Carolina, Bruna and Paulo in the Lab in Federal University of Goias, Brazil (Spring 2019)

Collaborations Pharmeceuticals (left to right): Sean Ekins, Daniel Foil, Kimberley Zorn, Ana Puhl Rubio, Jennifer Klein, Maggie Hupcey, Thomas Lane, Andrea Barry (business advisor)


In Dr. Ekins’ lab, Kim and Daniel (shown above) have been involved in scoring compounds for the OpenZika projects with our machine learning models developed for different datasets. Collaborations Pharmaceuticals continues to address neglected diseases through collaborations with scientists around the world. If you would be interested in collaborating with us, please contact sean@collaborationspharma.com.

We are incredibly grateful for all the volunteers who are donating their unused computing time to this project!  Thank you very much!!
大意:
通过大家3年的不懈努力,我们的任务(总共大概85.7亿个)都算的差不多了,接下来我们将再提交大概2万组任务。就结束计算,工作重心全面转到结果分析上去。
基于当前的结果,我们筛选出了55种寨卡候选分子、20种登革热候选分子,她们都将拿到实验室进行测试/验证。

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发表于 2019-11-23 00:10:37 | 显示全部楼层
能坚持到现在的都是神...
我只是因为最近天冷了...回来取暖...

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vincentdark + 10 欢迎回来取暖~

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发表于 2020-1-11 16:51:31 | 显示全部楼层
本帖最后由 qysnn 于 2020-1-11 16:55 编辑

OpenZika’s Work on World Community Grid is Complete
Now that OpenZika’s work on World Community Grid is complete, the researchers are beginning the next phase of their work while continuing to spread the word about their early findings.


Progress on selecting and testing compounds
Continuing the progress we mentioned in our last project update earlier this year, we virtually selected and purchased 75 compounds--55 candidates for Zika virus (ZIKV) and 20 candidates for dengue virus (DENV--to be experimentally evaluated at Dr. Siqueira-Neto’s laboratory at the University ofCalifornia in San Diego. Dr. Siqueira-Neto’s lab performed cell-based assays in glioblastoma stem cells (hGSC) with ZIKV.
From the experimental results obtained so far, two compounds selected for NS2B-NS3 protease presented inhibitory activity against cells infected with ZIKV and low cytotoxicity (Figure 1). One of them presented activity in the nanomolar range and is therefore a promising candidate.
Figure 1. Virtual screening for ZIKV and DENV NS2B-NS3 protease.

Moreover, from the seven compounds selected for NS3 helicase RNA site, three were for the ATP site and inhibited cells infected with ZIKV and possessed low cytotoxicity (Figure 2).
Figure 2. Virtual screening for ZIKV NS3 helicase, ATP site and RNA site.

Two compounds were virtually selected for the NS5 methyltransferase and had inhibitory activity against cells infected with ZIKV (one for the active site and one for the SAM site) and had low cytotoxicity (Figure 3).

Figure 3. Virtual screening for the ZIKV NS5 methyltransferase, active, GTP and SAM site.

None of the compounds virtually selected for NS5 polymerase showed inhibitory activity against cells infected with ZIKV (Figure 4).
Figure 4. Virtual screening for ZIKV NS5 polymerase, NTP and RNA site.

One compound virtually selected for the ZIKV envelope protein and was an inhibitor of cells infected with ZIKV and low cytotoxicity (Figure 5).
Figure 5. Virtual screening for ZIKV Envelope protein.

The enzymatic assays with the ZIKV NS3 helicase, NS2B-NS3 protease and NS5 polymerase proteins will be performed at the Physics Institute of Sao Carlos, University of Sao Paulo (Brazil), at Prof. Glaucius Oliva's laboratory, to validate the predicted enzymatic candidates' activities.

Two compounds virtually selected for DENV NS2B-NS3 protease were an inhibitor against cells infected with ZIKV and had low cytotoxicity (Figure 6). The active sites of DENV and ZIKV protease have more than 90% sequence identity. These compounds will be also tested against cells infected with DENV at the laboratory of Prof. Jose Modena, another OpenZika collaborator, at the University of Campinas(UNICAMP) in Brazil.
Figure 6. Virtual screening against DENV NS2B-NS3 protease.

Two compounds virtually selected for DENV NS3 helicase were an inhibitor of cells infected with ZIKV and also had low cytotoxicity (Figure 7). The ATP and RNA sites of DENV and ZIKV protease are conserved and have more than 90% sequence identity. These compounds will be also tested against cells infected with DENV at Prof. José Modena’s laboratory at UNICAMP.
Figure 7. Virtual screening against DENV NS3 helicase ATP site and RNA site.

In another project conducted by the undergraduate student Paulo Ramos, we used integrative similarity analysis, docking and machine learning (ML) models for identifying new ZIKV NS5 inhibitors candidates guided by known DENV NS5 inhibitors. Comparing the primary sequence of NS5 ZIKV and NS5 DENV binding sites (SAM, GTP, RNA-site, catalytic site and N-Pocket), we found high sequence identity for all of them. We searched for DENV NS5 inhibitors in the PubChem and ChEMBL databases and screened through these ML models for Zika.
We found 145 compounds reported as DENV NS5 inhibitors that were screened through the ML filter (Figure 8). From this filter, 74 compounds were prioritized for the molecular docking calculations. The 32 compounds that presented free energy scores from the docking < -7.0 kcal/mol were searched for similarity to structures in the E-molecules commercial database which resulted in 6,053 similar compounds. The ML and docking process were repeated for these similar compounds. The compounds were also screened through Bayesian ML models for ZIKV and cytotoxicity, and 58 were prioritized. Finally, these compounds were screened through the STopTox1 and Pred-hERG2 servers and 38 compounds were predicted as non-toxic for the studied endpoints. These compounds will be purchased and submitted for experimental evaluation.
Figure 8. Virtual screening against ZIKV NS5 guided by Dengue NS5 inhibitors.

In a collaborative project with Profs. Scott Laster and Frank Scholle from North Carolina State University, we performed docking calculations for natural compounds from Ashitaba (Angelica keiskei), which is used in Asiatic medicine. These compounds were described in the literature as viral inhibitors. We screened the extracts from Ashitaba through cell based and cytotoxic assays (Figure 9). They also presented antiviral activity against ZIKV. Thus, we isolated the compounds and three chalcones presented anti-ZIKV activity with IC50s in the range of 5-20 µM and low cytotoxicity in mammalian cells (CC50 of 100 µM). Target prediction was performed, and we predicted that protease is the most probable target of these compounds. We also performed molecular docking studiesagainst all ZIKV proteins in the OpenZika project. Agreeing with the target prediction, docking suggested that ZIKV protease is the main target. Protease enzymatic assays showed that the compounds can inhibit ZIKV protease, validating the in silico predictions.
Figure 9. Screening of extracts from Ashitaba through cell based and computational approaches found three chalcones-like ZIKV NS3 inhibitors.

Accepted and upcoming publications
Our paper entitled “A diarylamine derived from anthranilic acid inhibits ZIKV replication” was accepted at the Scientific Reports journal. This study was a collaborative work with Prof. Ana Carolina Jardim, from the Federal University of Uberlandia, Brazil, and it reports a series of analogs from anthranilic acid screened by cell-based assays. Then, docking calculations performed in the OpenZika project suggested that NS3 helicase was the most probable target, which was experimentally validated by enzymatic assays.
We recently submitted a paper entitled “Natural products from Angelica keiskei with activity against Zika vírus NS2B-NS3 protease” to Antiviral Research. This study describes three compounds extracted from Angelica keiskei, commonly used in Asian medicine, that presented anti-ZIKV activity at cell-based assays. Target prediction and molecular docking suggested NS2B-NS3 protease as the most probable target. Enzymatic assays validated the in silico predictions.

Papers being written – to be submitted soon:
We are preparing the following papers regarding the exciting results of OpenZika project:
  • One paper reporting the results (virtual and experimental) of the first round of compounds selected against ZIKV NS3 helicase that presented anti-ZIKV activity in human glioblastoma stem cells (hGSCs);
  • One paper reporting the results from the virtual screening and experimental evaluation of natural products from Pterogyne nitens, which presented anti-ZIKV activity, inhibiting ZIKV proteins protease and helicase;
  • One paper reporting the results for approved drugs/clinical collections compounds, that have anti-malarial and anti-ebola activity, that presented anti-ZIKV activities against ZIKV helicase protein, candidates for drug

These papers will be soon submitted to high-impact scientific journals.

Past publications and outreach
We published a review entitled “High Throughput and Computational Repurposing for Neglected Diseases” in the journal Pharmaceutical Research. This paper describes the many drug repurposing efforts that have been going on in different labs around the world to try to find treatments for the many tropical diseases, including Zika and dengue infections.
Our Drug Discovery Today keynote review “The A–Z of Zika drug discovery” was published on June 20 2018. This is a comprehensive review of the recent advances in ZIKV drug discovery efforts, highlighting drug repositioning and computationally guided compounds, including recently discovered viral and host cell inhibitors. Promising ZIKV molecular targets are also described and discussed, as well as targets belonging to the host cell, as new opportunities for ZIKV drug discovery. All this knowledge is not only crucial to advancing the fight against the Zika virus and other flaviviruses, but it will also help the scientific community prepare for the next emerging virus outbreak to which we will have to respond.
Our paper “Computational drug discovery for the Zika virus” was published in a special issue of the Brazilian Journal of Pharmaceutical Sciences. In this paper, we summarize current computational drug discovery efforts and their application to the discovery of anti-ZIKV drugs. We also present successful examples of the use of computational approaches to ZIKV drug discovery, including our OpenZika project.
Our PLoS Neglected Tropical Diseases paper, "OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery," was published on October 20 2016, and it has already been viewed over 5,200 times. Anyone can access and read this paper for free. Another research paper, “Illustrating and homology modeling the proteins of the Zika virus,” was published in F1000Research and viewed > 4,200 times.
We have also published another research paper entitled “Molecular Dynamics simulations of Zika Virus NS3 helicase: Insights into RNA binding site activity” in a special issue on Flaviviruses for the journal Biochemical and Biophysical Research Communications. This study of the NS3 helicase system helped us learn more about this promising target for blocking Zika replication. The results will help guide how we analyze the virtual screens that we performed against NS3 helicase, and the Molecular Dynamics simulations generated new conformations of this system that we have been using as targets in new virtual screens that we performed as part of OpenZika.
Dr. Sean Ekins presented a poster at Cell Symposia: Emerging and Re-emerging Viruses, on October 1-3, 2017, in Arlington, VA, USA, entitled “OpenZika: Opening up the discovery of new antiviral candidates against Zika virus.”
The OpenZika researchers Dr. Melina Mottin, Dr. Roosevelt Silva, Msc. Bruna Sousa and Paulo Ramos presented posters at the 9th BrazMedChem 2019conference, the major medicinal chemistry conference in Latin America, which was organized by the OpenZika PI, Prof. Carolina Horta Andrade.
Dr. Melina and Bruna presented the studies related to natural compounds: “Discovery of flavonoids from Pterogyne nitens with potent activity against Zika virus protease and helicase” and “Discovery of new Zika virus candidates: natural products from Angelica keiskei with activity against NS2B-NS3 protease,” respectively. Paulo presented the work "Integrative Similarity analysis, Docking and Machine Learning models for identifying new Zika NS5 hits guided by Dengue NS5 inhibitors.”  Paulo also presented this work at the 16th CONPEEX (Research, Teaching and Extension Congress of Federal University of Goiás - UFG) and the work was selected for the UFG Undergraduate award.
Dr. Roosevelt presented the ZIKV NS1 molecular dynamics poster: “Dynamic Behavior of Dengue and Zika viruses NS1 protein reveals monomer-monomer interaction mechanisms and insights to rational drug design."
Dr. Sean Ekins also participated in the 9th BrazMedChem conference as the Keynote Speaker, presenting the talk “The Next Era of Pharmaceutical Research: From Bayesian Models to Deep Learning regarding updated results of the OpenZika Project as well as his work on Chagas disease and Ebola.
Figure 10. The LabMol team and Dr. Sean Ekins (wearing white trousers) at the BrazMedChem symposium.

Figure 11. Dr. Sean Ekins speaking during the 9th BrazMedChem Symposium in Brazil about the latest results of OpenZika Project.

Dr. Melina Mottin recently presented updates on the OpenZika project as oral presentation at RENORBIO 2019: II Meeting Biotechnology of Northeast in Fortaleza, Brazil, on November 28, 2019.
Figure 12. Dr. Melina presenting the updated results of the OpenZika Project at the RENORBIO conference.

Recently, Prof. Carolina Horta Andrade gave the opening conference at the III Young Medicinal Chemist Workshopin Salvador, BA, Brazil, on November 21, 2019. In her lecture,she showed the OpenZika Project and our latest and exciting results.
Figure 13. Dr. Carolina Andrade presenting the updated results of the OpenZika Project at the III Young Medicinal Chemist Workshop.

Status of the calculations
In total, we have submitted almost 9.29 billion docking jobs, which involved 427 different target sites. Our initial screens used an older library of 6 million commercially available compounds, and our current experiments utilize the new ZINC15 library of 30.2 million compounds.
The 80,000 volunteers who donated their spare computing power to OpenZika gave us 92,696 CPU years' worth of docking calculations! Thank you all very much for your help!!
We have received all of the results for our experiments that involve docking 30.2 million compounds versus NS1,NS helicase (both the RNA binding site and the NTPsite), NS5 RNA polymerase (NTP and RNA pocket), NS5 methyltransferase(SAM and GTP site), NS2B/NS3 protease, capsid (binding pockets 1 and 2) and envelope protein.
We are incredibly grateful for all the volunteers who donated their unused computing time to this project! Thank you very much!!

References
  • STOPTOX 1.0. LabMol, 2016. Available at: http://stoptox.labmol.com.br/. Access on: Dec 02. of 2019.
  • Pred-hERGA novel web-accessible computational tool for predicting cardiac toxicity. Braga, R.C.; Alves, V.M.; Silva, M.F.B.; Muratov, E.; Fourches, D.; Liao, L.M.; Tropsha, A.; Andrade, C.H. Mol. Inf.2015,34, 698-701.10.1002/minf.201500040





2019年12月13日
OpenZika项目正式结束!


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发表于 2020-2-13 14:06:14 | 显示全部楼层
Mapping Cancer Markers Researchers Analyzing Lung Cancer Data
By: The Mapping Cancer Markers research team
31 一月 2020          

摘要
The Mapping Cancer Markers team discusses the past (lung cancer), present (ovarian cancer), and future (sarcoma) of the project in this comprehensive update.


Background
The Mapping Cancer Markers (MCM) project was designed to identify the markers associated with various types of cancer, and by refining the process of identifying these markers, identify such biomarkers for other diseases more efficiently. We aimed to analyze multiple cancer datasets in order to identify potential biomarkers for these cancers, which could eventually help scientists and physicians detect cancers earlier and create personalized treatments. The first three datasets in MCM plan are lung, ovarian, and sarcoma, representing the past, present and future of MCM. Lung processing is complete. Ovarian marker processing is underway, but nearing completion. We are now preparing for a switch to sarcoma.

Processing a dataset on World Community Grid over months and years produces a huge amount of data, and this data is not directly usable, but must then be collated, filtered, and analyzed in different ways. We have been focusing on this post-processing step in our lab.

In this update, we will mainly discuss some of the work done with the processed lung dataset, but first, we will take a quick glance at the future.

Final preparations for sarcoma dataset
The upcoming sarcoma dataset will be MCM’s most complex dataset to-date. It contains potential biomarkers drawn from multiple sources: measurements of RNA, DNA, and protein activity, mutations, and other biological modalities.

With such detailed information about each sample in the dataset, it took some effort to reduce the dataset and result sizes to practical levels. We are currently testing work units of our draft dataset and are planning the work.

A future update will announce the launch of a new phase of the MCM project, focusing on sarcoma and provide more details.

Results from the lung dataset
The biomarkers in the MCM lung dataset measure the activity of thousands of genes. Collectively, these biomarkers cover most of the human genome. The majority of MCM lung work processed on World Community Grid surveyed signatures randomly drawn from the entire set of biomarkers. A shorter, second phase of MCM lung drew signatures from optimized subsets of those biomarkers.

The contribution of compute cycles to the project was extraordinary. World Community Grid members processed 4.5 trillion candidate lung cancer signatures in the main phase of MCM lung, 220 billion in an initial experimental phase, and 1.6 trillion signatures in the optimizing phase.

We will discuss some findings from the main phase of MCM lung in this update.

The question of signature size

MCM lung surveyed signatures of multiple sizes. Sizes varied from 5 biomarkers to 100, with the greatest focus on signatures in the range of 10-20 biomarkers. For a cancer signature to succeed in clinical use, signature size is a compromise between diagnostic power, complexity, and cost. Every biomarker can potentially add diagnostic information to a signature, increasing accuracy, but too many biomarkers can also add noise and unnecessarily increase cost and complexity for practical use in the clinic.

The figure below shows the effect of signature size on peak accuracy. For almost any size, a signature built from randomly-chosen biomarkers will have poor accuracy, but by testing enough such signatures, and then looking at the accuracy of the top fraction (say, the top 0.01%), we see the effect made by signature size. Carefully engineered signatures should achieve the same accuracy using fewer biomarkers.

Figure 1A


Figure 1B



Figures 1A and 1B: Size affects a signature’s potential accuracy. (A) The score distribution from successful signatures of different sizes. (B) A closer look at the effect of size of score accuracy. Peak accuracy of is found in signatures between 40-80 biomarkers.

Which biomarkers are most successful?

In the main phase of MCM lung, signatures were built from biomarkers chosen randomly from the dataset. As such, every biomarker had an equal chance of appearing in each new signature. This does not mean, though, that all biomarkers are equally useful – as we said above, a random signature will most likely have low accuracy. If, however, we take only the most accurate fraction of signatures, and see which biomarkers they contain, we see that a few biomarkers appear frequently, and that the rest are relatively rare. (We may even notice patterns in ways that certain groups of biomarkers appear together, as we discussed in a previous update.) We can determine then how effective or useful each biomarker is from how often it appears in these top signatures.

Figure 2


Figure 2: As signature size increases, we see a decrease in the number of genes enriched by any factor (e.g., 5x above normal).

After analyzing the full set of MCM lung results, we can confirm an effect that we had noticed in earlier, preliminary studies: the effectiveness of each biomarker depends on the signature size, affecting each biomarker differently. The figure below illustrates the effect for some of the top-ranked biomarkers.

Figure 3


Figure 3: The size of the lung cancer signature determines how useful a biomarker may be. As the signature size grows, individual biomarkers may become more or less effective.

Pathway enrichment among the top biomarkers

To get a higher-level view of the biomarkers discovered in the lung dataset, we examined them from the pathway perspective. A pathway is a group of genes that cooperate to perform the same biological function. We fed lists of top-1% biomarkers into our lab’s pathDIP database [1], [2]. pathDIP is a comprehensive, integrated database of known pathways (signaling cascades), and given a list of genes, it will find all pathways associated with any gene in the list. Most usefully, it will measure the enrichment of each pathway in your gene list – the degree to which pathway has an above-average connection to your list. Using such analysis, we aim to find biologically meaningful interpretation of our identified biomarkers.

The figure below shows the results from pathDIP.

            

Figure 4



Across a large number of signature sizes, pathDIP consistently found five pathways enriched in our gene lists:

Cyclophosphamide Pathway, Pharmacodynamics
Ifosfamide Pathway, Pharmacodynamics
Ethanol oxidation
Fatty Acid Omega oxidation
Oxidative Stress Regulatory Pathway (Erythrocyte)
All five enriched pathways relate to metabolism, meaning the breakdown of chemicals in the body. Curiously, the first two pathways relate specifically to metabolism of chemotherapy drugs, Cyclophosphamide and Ifosfamide. The last three relate to either oxidation or the prevention of oxidative stress (free radicals) in red blood cells.

Using the Gene Ontology Resource to describe top biomarkers

We can get a related view from the Gene Ontology Resource (GO). GO categorizes each gene from three different perspectives: biological process, molecular function, and cellular component. The figures below show terms in GO categories that appear frequently in top 1% biomarkers.

Figure 5



Figure 6



Figure 7



Many of the terms reflect the themes found in pathways: oxidation, alcohol, and red-blood-cell chemistry.

Looking ahead
We are in the process of expanding and combining multiple additional analyses of the main-phase lung data, and substantial analyses of the second phase lung results. After that, the ovarian data awaits. For ovarian, some of the same techniques will apply, but some will need to be adapted, and some we’ll need to develop.

In short, the MCM project will keep us busy for a long time. In the meantime, we would like to thank you for your interest and for your generous donation of computing power to this and other World Community Grid projects. We will provide updates more frequently now.

Additional results
Over the last two years, we have published several original manuscripts and multiple applications using our tools and programs, many of which we have been using to increase the value of MCM analyses:

PMID: 31583635Kennedy, S., Jarboui, M-A, Srihari, S, Raso, C, Bryan, K, Dernayka, L, Charitou. T, Bernal-Llinares, M, Herrera-Montavez, C, Krstic, A, Matallanas, D, Kotlyar, M, Jurisica, I, Curak, J, Wong, V, Stagljar, I, LeBihan, T, Imrie, L, Pillai, P, Lynn, M, Fasterius, E, Szigyarto, C. A-K, Breen, J, Kiel, C, Serrano, L, Rauch, N, Rukhlenko, O, Kholodenko, B, Iglesias-Martinez, L, Ryan, C, Pilkington, R, Cammareri, P, Sansom, O, Shave, S, Auer, M, Horn, N, Klose, F, Ueffing, M, Boldt, K, Lynn, D, Kolch, W, Extensive Rewiring of the EGFR Network in Colorectal Cancer Cells Expressing Transforming Levels of KRASG13D, Nat Commun, 2019. In press.
Enfield, K.S.S., Marshall, E.A., Anderson, C., Ng, K.W., Rahmati, S, Xu, Z. Fuller, M., Milne, K., Lu, D., Shi, R., Rowbotham, D. A., Becker-Santos, D.D., Johnson, F.D., English, J.C., MacAulay, C.E., Lam, S., Lockwood, W.W., Chari, R., Karsan, A., Jurisica, I., Lam, W.L., Epithelial tumor suppressor ELF3 is a lineage-specific amplified oncogene in lung adenocarcinoma, Nat Commun,10(1):5438, 2019. doi:10.1038/s41467-019-13295-y
Rahmati, S., Abovsky, M., Pastrello, C., Kotlyar, M., Lu, R., Cumbaa, C.A., Rahman, P., Chandran, V. and Jurisica, I. pathDIP 4: An extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species, Nucl Acids Res, In press. 2019. https://doi.org/10.1093/nar/gkz989
Holzinger A, Haibe-Kains B, Jurisica I. Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data, Eur J Nucl Med Mol Imaging. 2019 Jun 15. doi: 10.1007/s00259-019-04382-9.
Monette A, Bergeron D, Ben Amor A, Meunier L, Caron C, Mes-Masson AM, Kchir N, Hamzaoui K, Jurisica I, Lapointe R. Immune-enrichment of non-small cell lung cancer baseline biopsies for multiplex profiling define prognostic immune checkpoint combinations for patient stratification, J Immunother Cancer, 7(1):86, 2019. doi: 10.1186/s40425-019-0544-x
Monette A, Morou A, Al-Banna NA, Rousseau L, Lattouf JB, Rahmati S, Tokar T, Routy JP, Cailhier JF, Kaufmann DE, Jurisica I, Lapointe R. Failed immune responses across multiple pathologies share pan-tumor and circulating lymphocytic targets, J Clin Invest, 129(6):2463-2479, 2019. doi: 10.1172/JCI125301
Mohammed Ali Z, Tokar T, Batruch I, Reid S, Tavares-Brum A, Yip P, Cardinal H, Hébert MJ, Li Y, Kim SJ, Jurisica I, John R, Konvalinka A. Urine Angiotensin II Signature Proteins as Markers of Fibrosis in Kidney Transplant Recipients, Transplantation,103(6):e146-e158, 2019. doi: 10.1097/TP.0000000000002676.
Kaufmann KB, Garcia-Prat L, Liu Q, Ng SWK, Takayanagi SI, Mitchell A, Wienholds E, van Galen P, Cumbaa CA, Tsay MJ, Pastrello C, Wagenblast E, Krivdova G, Minden MD, Lechman ER, Zandi S, Jurisica I, Wang JCY, Xie SZ, Dick JE. A stemness screen reveals C3orf54/INKA1 as a promoter of human leukemia stem cell latency, Blood, 133(20):2198-2211, 2019. doi: 10.1182/blood-2018-10-881441
Mandilaras, V, Garg, S, Cabanero, M, Tan, Q, Pastrello, C, Burnier, J, Karakasis, K, Wang, L, Dhani, NC, Butler, MO, Bedard, PL, Siu, LL, Clarke, B, Shaw, PA, Stockley, T, Jurisica, I, Oza, AM. TP53 mutations in high grade serous ovarian cancer and impact on clinical outcomes: a comparison of next generation sequencing and bioinformatics analyses. Int J Gyn Cancer, Jan 18. pii: ijgc-2018-000087. doi: 10.1136/ijgc-2018-000087.
del Toro N, Duesbury M, Koch M, Perfetto L, Shrivastava A, Ochoa D, Wagih O, Piñero J, Kotlyar M, Pastrello C, Beltrao P, Furlong LI, Jurisica I, Hermjakob H, Orchard S, Porras P. Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set. Nat Commun, 10(1): 10, 2019.
Li L, Guturi KKN, Gautreau B, Patel PS, Saad A, Morii M, Mateo F, Palomero L, Barbour H, Gomez A, Ng D, Kotlyar M, Pastrello C, Jackson HW, Khokha R, Jurisica I, Affar EB, Raught B, Sanchez O, Alaoui-Jamali M, Pujana MA, Hakem A, Hakem R., Ubiquitin ligase RNF8 suppresses Notch signaling to regulate mammary development and tumorigenesis, J Clin Invest, 128(10):4525-4542, 2018. doi: 10.1172/JCI120401
Kotlyar, M., Pastrello, C., Malik, Z., Jurisica, I., IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species. Nucleic acids research, 47(D1):D581-D589, 2019.
Endisha, H., Rockel, J., Jurisica, I., Kapoor, M., The complex landscape of microRNAs in articular cartilage: biology, pathology, and therapeutic targets, JCI Insight. 3(17):e121630, 2018.
Singh, M., Venugopal, C., Tokar, T., McFarlane, N., Subapanditha, M. K., Qazi, M., Bakhshinyan, D., Vora, P., Murty, N., Jurisica, I., Singh, S. K., Therapeutic targeting of the pre-metastatic stage in human brain metastasis, Cancer Res, 2018. ePub 2018/07/11. DOI: 10.1158/0008-5472.CAN-18-1022.
Wen, B., Tokar, T., Taibi, A., Chen, J., Jurisica, I., Comelli, E. M. Citrobacter rodentium alters the mouse colonic miRNome, Genes and Immunity, 2018. In press. ePub 2018/05/08. Doi: 10.1038/s41435-018-0026-z
Jean-Quartier C, Jeanquartier F, Jurisica I, Holzinger A, In silico cancer research towards 3R. BMC Cancer, 18(1):408, 2018
Sivade Dumousseau M, Alonso-López D, Ammari M, Bradley G, Campbell NH, Ceol A, Cesareni G, Combe C, De Las Rivas J, Del-Toro N, Heimbach J, Hermjakob H, Jurisica I, Koch M, Licata L, Lovering RC, Lynn DJ, Meldal BHM, Micklem G, Panni S, Porras P, Ricard-Blum S, Roechert B, Salwinski L, Shrivastava A, Sullivan J, Thierry-Mieg N, Yehudi Y, Van Roey K, Orchard S. Encompassing new use cases - level 3.0 of the HUPO-PSI format for molecular interactions. BMC Bioinformatics, 19(1):134, 2018.
Minatel BC, Martinez VD, Ng KW, Sage AP, Tokar T, Marshall EA, Anderson C, Enfield KSS, Stewart GL, Reis PP, Jurisica I, Lam WL., Large-scale discovery of previously undetected microRNAs specific to human liver. Hum Genomics, 12(1):16, 2018.
Tokar T, Pastrello C, Ramnarine VR, Zhu CQ, Craddock KJ, Pikor L, Vucic EA, Vary S, Shepherd FA, Tsao MS, Lam WL, Jurisica I Differentially expressed microRNAs in lung adenocarcinoma invert effects of copy number aberrations of prognostic genes. Oncotarget. 9(10):9137-9155, 2018.
Paulitti A, Corallo D, Andreuzzi E, Bizzotto D, Marastoni S, Pellicani R, Tarticchio G, Pastrello C, Jurisica I, Ligresti G, Bucciotti F, Doliana R, Colladel R, Braghetta P, Di Silvestre A, Bressan G, Colombatti A, Bonaldo P, Mongiat M. The ablation of the matricellular protein EMILIN2 causes defective vascularization due to impaired EGFR-dependent IL-8 production affecting tumor growth, Oncogene, 37(25): 3399-3414, 2018.
Tokar, T., Pastrello, C., Rossos, A., Abovsky, M., Hauschild, A.C., Tsay, M., Lu, R., Jurisica, I. mirDIP 4.1 – Integrative database of human microRNA target predictions, Nucl Acids Res, D1(46): D360-D370, 2018.
Pastrello C, Kotlyar M, Jurisica I., Informed Use of Protein-Protein Interaction Data: A Focus on the Integrated Interactions Database (IID). Methods Mol Biol., 2074:125-134, 2020. doi: 10.1007/978-1-4939-9873-9_10.
Hauschild, A-C, Pastrello, C, Kotlyar, M and Jurisica, I. Protein-protein interaction data, their quality, and major public databases. Ed. N. Przulj. Analyzing Network Data in Biology and Medicine, An Interdisciplinary Textbook for Biological, Medical and Computational Scientists, Cambridge University Press, Cambridge, UK, pp.151-192, 2019. ISBN 978-1-108-43223-8. DOI: 10.1017/978110837770
Wong, S., Pastrello, C., Kotlyar, M., Faloutsos, C., Jurisica, I. SDREGION: Fast spotting of changing communities in biological networks. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 867-875, 2018.
Kotlyar, M., Pastrello, C., Rossos, A., Jurisica, I. Protein-protein interaction databases. In: Ranganathan, S., Nakai, K., Schönbach C. and Gribskov, M. (eds.), Encyclopedia of Bioinformatics and Computational Biology, vol. 1, pp. 988–996. Oxford: Elsevier, 2018.
Rahmati, S., Pastrello, C., Rossos, A., Jurisica, I. Two Decades of Biological Pathway Databases: Results and Challenges, In: Ranganathan, S., Nakai, K., Schönbach C. and Gribskov, M. (eds.), Encyclopedia of Bioinformatics and Computational Biology, vol. 1, pp. 1071–1084. Oxford: Elsevier, 2018.
Hauschild, AC, Pastrello, C., Rossos, A., Jurisica, I. Visualization of Biomedical Networks, In: Ranganathan, S., Nakai, K., Schönbach C. and Gribskov, M. (eds.), Encyclopedia of Bioinformatics and Computational Biology, vol. 1, pp. 1016–1035. Oxford: Elsevier, 2018.


Other news
In other news, we have been able to secure several grants to enable funding for the project, including:

Novel methods for integrative computational biology from Natural Sciences and Engineering Council of Canada,
Interactome mapping of disease-related proteins using split intein-mediated protein ligation (SIMPL) from Genome Canada,
The Next Generation Signalling Biology Platform from Ontario Research Funds
Canadian Institutes of Health in collaboration with European funding agencies
Importantly, we also had a chance to host two World Community Grid and MCM supporters at our institute[3]. Dylan Bucci, a Sisler High School student, and network and cybersecurity teacher Robert Esposito visited our research institution to meet with us and our collaborators, the scientists who use the results from the analysis. It was interesting for us to learn about their motivation, and for them to experience direct and indirect research links to MCM.

Thank you for all the contributed computing power that makes this research possible.

The MCM Team


[1] Rahmati, S., Abovsky, M., Pastrello, C., Jurisica, I. pathDIP: An annotated resource for known and predicted human gene-pathway associations and pathway enrichment analysis. Nucl Acids Res 45(D1): D419-D426, 2017.

[2] Rahmati, S., Abovsky, M., Pastrello, C., Kotlyar, M., Lu, R., Cumbaa, C.A., Rahman, P., Chandran, V. and Jurisica, I. pathDIP 4: An extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species, Nucl Acids Res, In press. 2019. https://doi.org/10.1093/nar/gkz989

[3] CTV News  Interview on IBM World Community Grid, Mapping Cancer Markers, May 3, 2019
大意:
MCM分三个子项目:肺癌、卵巢癌、肉瘤,目前肺癌早已经算完了正在进行结果数据分析处理,卵巢癌也快算完了,肉瘤的任务正在准备中(由于任务比较复杂,我们还在想办法优化算法)。
通过对肺癌结果的初步分析,我们选出了5个标记:环磷酰胺通路、异环磷酰胺通路、乙醇氧化、脂肪酸氧化、氧化应激的监管通路(红细胞)。这5个标记都与体内化学物质崩溃的新陈代谢有关。
接下来我们将继续对肺癌结果数据进行二次分析,并准备对即将完成的肉瘤数据进行初步分析。

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发表于 2020-3-10 12:26:18 | 显示全部楼层
本帖最后由 0xCAFEBABE 于 2020-3-13 13:13 编辑

By: The Smash Childhood Cancer research team
9 三月 2020      

摘要
The Smash Childhood Cancer researchers are pleased to announce that Dr. Godfrey Chan, a founding member of the team, will be the new Principal Investigator for the project. The project is re-starting with two new targets to investigate in the continuing search for better childhood cancer treatments.

Thank You to Dr. Akira Nakagawara

Dr. Akira Nakagawara

The Smash Childhood Cancer team and World Community Grid thank Dr. Akira Nakagawara for his many years of leadership and service as Principal Investigator of the Help Fight Childhood Cancer and Smash Childhood Cancer research teams. He is stepping down as Principal Investigator to spend more time with his family, but will remain part of the research team and will be involved in future work.

Under Dr. Nakagawara’s leadership, the Help Fight Childhood Cancer project discovered seven drug candidates that show great promise as new treatments for neuroblastoma, one of the most common and dangerous forms of childhood cancer. To help build on these discoveries, he expanded the original project into the international collaboration of the Smash Childhood Cancer project, which is searching for potential treatments for brain tumors, Wilms' tumors (malignancies in the kidneys), hepatoblastoma (liver cancer), germ cell tumors, and osteosarcoma (bone cancer).

Welcome to Dr. Godfrey Chan

Dr. Godfrey Chan
Dr. Godfrey Chan, one of the original members of the Smash Childhood Cancer team, will be the project’s new Principal Investigator. As a pediatric oncologist and researcher focused on translational medical research and clinical trials, his specialty is the screening and development of new drugs targeting known or newly defined molecules.

He is Head & Chief of Service of the Department of Pediatrics & Adolescent Medicine at The University of Hong Kong. He is also the Director of Molecular Laboratory for Traditional Chinese Medicine (New Drug Screening for Immunology & Cancer), and was the Deputy Director of the Stem Cells & Regenerative Medicine Consortium (Clinical Application of Mesenchymal Stem Cells) at The University of Hong Kong.

Dr. Chan serves as the Continental Chairman (Africa, Asia and Australia) of Advance Neuroblastoma Research and Executive Committee Member of SIOPEN (European Neuroblastoma) group. He has earned several international awards (ANR, SIOP, ASPR, Endeavor Executive Award-Australian Government, Outstanding Pediatrician of APPA) for his clinical and laboratory research works on childhood neurogenic tumors and stem cells biology.

Thank you to both outstanding researchers for their ongoing work in the fight against childhood cancer.

New Targets

The newest Smash Childhood Cancer work units will look at two potentially important targets, PRDM14 and Fox01.

PRDM14 is involved in intracranial germ cell tumors (IGCTs) that primarily affect adolescents and young adults. These are very rare brain tumors that have a much higher incidence in Japan and East Asia. In addition to IGCTs, PRDM14 also affects non-small cell lung cancer, breast cancer, leukemia (both pre-B cell and T-cell) as well as prostate cancer.

Fox01 is believed to play a role in the development of a number of cancers in addition to childhood cancers, including prostate, endometrial, pancreatic, and others.

After World Community Grid’s work on these two targets is finished, we anticipate having a few more for volunteers to crunch. Thank you to everyone for supporting the Smash Childhood Cancer project.  

-------------------------------------
机翻:
Smash儿童癌症研究小组和世界社区网格感谢Akira Nakagawara博士多年来作为主要研究者的领导和服务,帮助抗击儿童癌症和Smash儿童癌症研究小组。他将辞去首席调查员的职务,以便有更多时间陪伴家人,但仍将是研究团队的一员,并将参与未来的工作。

在Nakagawara博士的领导下,帮助对抗儿童癌症项目发现了7种候选药物,作为治疗神经母细胞瘤(儿童癌症最常见和最危险的形式之一)的新疗法,显示出巨大的希望。为了帮助建立在这些发现的基础上,他将最初的项目扩展到Smash儿童癌症项目的国际合作中,该项目正在寻找脑肿瘤、肾母细胞瘤(肾脏恶性肿瘤)、肝母细胞瘤(肝癌)、生殖细胞瘤和骨肉瘤(骨癌)的潜在治疗方法。

陈戈弗雷博士,儿童癌症小组的原成员之一,将成为该项目的新首席研究员。作为一名专注于转化医学研究和临床试验的儿科肿瘤学家和研究员,他的专业是筛选和开发针对已知或新定义分子的新药。

他是香港大学儿科和青少年医学系的主任和服务主任。现任中医药分子实验室(新药免疫学与癌症筛查)主任,香港大学干细胞与再生医学联合会(骨髓间充质干细胞临床应用)副主任。

陈博士是高级神经母细胞瘤研究的大陆主席(非洲、亚洲和澳大利亚)和欧洲神经母细胞瘤集团执行委员会成员。他因在儿童神经源性肿瘤和干细胞生物学方面的临床和实验室研究工作获得了多个国际奖项(ANR、SIOP、ASPR、澳大利亚政府努力执行奖、APPA杰出儿科医生)。

感谢这两位杰出的研究人员为防治儿童癌症所做的不懈努力。


新目标


最新的Smash儿童癌症工作小组将研究两个潜在的重要目标,PRDM14和Fox01。

PRDM14与主要影响青少年和年轻人的颅内生殖细胞肿瘤(IGCT)有关。这些是非常罕见的脑肿瘤,在日本和东亚的发病率要高得多。除IGCTs外,PRDM14还影响非小细胞肺癌、乳腺癌、白血病(前B细胞和T细胞)以及前列腺癌。

据信,Fox01除了儿童癌症外,还参与了许多癌症的发展,包括前列腺癌、子宫内膜癌、胰腺癌和其他癌症。

在世界社区网格完成这两个目标的工作后,我们预计还会有一些志愿者需要处理的问题。感谢大家支持儿童癌症项目。

-------------------------------------
大意:SCC 又复活啦!

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金鹏 + 20 辛苦了!

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发表于 2020-7-2 10:15:06 | 显示全部楼层
FightAIDS@home – Phase 1 researchers identified new potential targets for antivirals
By: The FightAIDS@Home research team
1 七月 2020          

摘要
A protein called HIV-1 capsid (CA), which is crucial to the replication of HIV, may have some recently discovered vulnerabilities.

Background on FightAIDS@Home – Phase 1

Many people live with Human Immunodeficiency Virus (HIV), and do not get sick for many years. However, because the human body cannot generate antibodies that can eradicate HIV, it can slowly infect key cells of the immune system and impair their function or even destroy them. Eventually, HIV infection results in progressive depletion of a person’s immune system, leading to Acquired Immune Deficiency Syndrome (AIDS). The immune system is said to be deficient when it can no longer fulfill its role of fighting off infections and cancers.

HIV is difficult to stop because when it replicates, it does not do so perfectly and is thus continually changing. Scientists are studying HIV intensively to find ways to stop the onset of AIDS. The FightAIDS@Home project was created to search for drugs that can disable a key step in HIV's life cycle — specifically by blocking HIV-1 protease.

Blocking HIV Protease

Proteins are the basic building blocks in all of life's functions. Proteins are long chains of smaller molecules called amino acids. Enzymes are particular kinds of proteins that accelerate biochemical reactions. A protease (pronounced "pro-tee-ace") is an enzyme that is able to cut proteins apart at some point along the amino acid chain. While only a small percent of all of the proteins in an organism are proteases, they are very important in the proper functioning of its life processes.

Not all proteases are good. HIV makes and uses a particular protease, HIV-1 protease, which it uses to make the virus's different proteins.

This is where ligands play an important role. Ligands are small molecules that come from outside the cell that attach, or "bind," to pockets in proteins. You can think of a ligand binding to its receptor like a key fitting into a lock. The FightAIDS@Home project specifically searched for ligands (drugs), which can attach to the HIV-1 protease receptor in a way that blocks its ability to function as an enzyme. This prevents the virus from spreading further in the body and developing into AIDS. Molecules that block HIV protease are called "protease inhibitors."

Potential New Targets for Antivirals

A protein called HIV-1 capsid (CA) is a structural protein crucial to the viral replication cycle as it encloses the viral genome. Its involvement in both early and the late stage of the infection led to efforts in developing antivirals targeting CA.


Structure and assembly of the HIV-1 capsid core. Capsid protein folds to form two domains connected by a flexible linker (A) and forms hexamers (B) and pentamers (red) in the mature core (C), which encloses the viral RNA and ultimately houses reverse transcription (D).

In a recent paper, the FightAIDS@Home research team detailed their findings on new, interesting, potentially druggable regions on the surface of HIV-1 capsid protein, which were then targeted with virtual screenings on World Community Grid to search for compounds capable of binding on these regions.

The structural characterization, biochemical and virological assays discussed in the paper show that the identified site could be amenable to antiviral targeting.


Top 5 compounds from a virtual screen of the NDI pocket. Docking results are shown as the superposition of five different molecules (yellow sticks) binding in the NDI pocket from the X-ray crystal structure of the native CA (PDB ID: 4XFX).

The paper, which you can read here, presents the data analysis and the details of the researchers’ findings.

Thanks to everyone who has supported this project.
大意:
我们在faah-1段中找到了一个hiv-1衣壳(用于包裹病毒RNA)靶点,随后我们针对这个靶点进行了药物筛选,目前找出来TOP5的潜在药物。详见论文

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发表于 2020-8-15 23:05:28 | 显示全部楼层
August Update: OpenPandemics - COVID-19
13 八月 2020          

摘要
OpenPandemics - COVID-19 launched in May, and the work has just begun. Learn more about the scientists' progress so far, as well as the collaborations that will help move the research forward in the coming weeks and months.


The OpenPandemics research team
Top: Stefano Forli, Paolo Governa, Andreas Tillack, Jérôme Eberhardt
Middle: Giulia Bianco, Batuujin Burendei, Diogo Santos-Martins, Martina Maritan
Bottom: Matthew Holcomb, Christina Garza
Click here to learn more about the team.

Background

OpenPandemics - COVID-19 was created to help accelerate the search for COVID-19 treatments. You can learn more about the details of the work on the research team's website.

Data analysis

Since OpenPandemics - COVID-19 launched in May, the project has screened millions of chemical compounds that may be potential treatments for the disease. The screening is still ongoing, but the research team has analyzed the results they've gotten so far (45.7 million runs and 2.3 billion poses), and have narrowed the initial group down to approximately 1,500 compounds (0.00003 % of the total ligands docked!) that have been selected for their interactions with the target proteins and that warrant further analysis.

From the current group of 1,500 compounds, the researchers will do manual analysis to identify approximately 100 of the most promising, which will then go to their collaborators for further laboratory testing. (You can see a list of collaborators toward the bottom of the Research Participants page.)

While this testing goes on, they'll continue analyzing the data they receive from us and will continue to send new work to us.

Additional collaborations

The principal investigator is in touch with the team of European scientists of the Coronavirus Structural Taskforce to help improve the quality of results for all basic research on SARS-CoV-2. The team is collecting and curating protein structural data available on the SARS-CoV and SARS-CoV2 viruses that is getting constantly produced by researchers all over the world. They refine the published data, and perform statistical validation and diagnostics on the structures to correct for experimental errors or fluctuations.

Grant award

The Forli lab recently received a Baxter Foundation Young Investigator Award for the identification of new chemical compounds as promising drug development candidates.

GPU version

The World Community Grid development team is starting to create work units for initial alpha testing. Their current issue is creating work units that can run on both CPU and GPU, rather than needing to create two different versions, which would add complexity to the project. The next step within IBM will be a security review of the new version. The work is ongoing and there's currently no estimate for when it will be completed.

Potential publications

The researchers are working on several papers. One is a paper about the history of AutoDock (the software that runs OpenPandemics, which was created at Scripps Research). If the paper is accepted, it will be published early in 2021.

They also submitted a paper describing the development and application of protocols for the study of covalent inhibitors (including the reactive docking protocol used in the OpenPandemics).

Another paper has been submitted describing their collaborative work with the Oak Ridge National Laboratory team and NVIDIA to run simulations to identify new molecules against the SARS-CoV2 virus.

Current status of work units

Available for download:  4,882 batches
In progress:  2,052 batches (16,101,394 work units)
Completed:  5,459 batches (2,587 batches in the last 30 days,
an average of 86 batches per day)
Estimated backlog: 56 days

Click here to learn more about World Community Grid's monthly project updates.
大意:
新冠项目进展报告
目前已经筛选出了大约1500个潜在分子(命中率约0.00003%),我们选了最好的100个送合作实验室进行检测。
我们开发了gpu版,不过还在内测,具体上线日期暂时无法确定。
目前的任务还够算56天,大家且算且珍惜。

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发表于 2020-9-9 09:34:10 | 显示全部楼层
Planned Maintenance on Thursday, September 10, 2020
8 九月 2020          

摘要
We are doing a file system check on our servers on Thursday, September 10 beginning at 14:00 UTC.

We will be doing a file system check on our servers on Thursday, September 10 beginning at 14:00 UTC. We anticipate that the work will take approximately 24 hours.

During this time, volunteers will not be able to upload or download new work. The website and forums will still be accessible.

Volunteers will not need to take any particular action, as your devices will automatically retry their connections after the maintenance work is completed.  

We are planning to extend the deadlines for work units by at least 24 hours during this outage.

We appreciate your patience and participation.
大意:
计划于北京时间9月10日晚(明晚)22点开始对服务器文件系统进行自检,届时不能上传/下载任务,网站/论坛可正常访问。预计耗时24小时。为此当前所有未回传任务自动延期24小时。
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发表于 2020-9-18 09:58:01 | 显示全部楼层
September Update: OpenPandemics - COVID-19
17 九月 2020          

摘要
The researchers and the World Community Grid tech team are continuing their work to get the project working on GPU.

Background

OpenPandemics - COVID-19 was created to help accelerate the search for COVID-19 treatments. You can learn more about the details of the work on the research team's website.

GPU version of OpenPandemics

Both the research team and World Community Grid tech team are continuing to make progress on porting the software that powers OpenPandemics to GPU.

The researchers are working on performance improvements for an OpenCL version. Meanwhile, World Community Grid has submitted the code for IBM's Open Source review and a security review. We don't currently know exactly when the IBM reviews will be done.

AutoDock Suite at 30

The research team recently published a paper on the history of AutoDock, which is the software that powers OpenPandemics, FightAIDS@Home, and other projects that have searched for potential treatments against various diseases. You can read the paper here.

Current status of work units

Available for download: 3,452 batches
In progress: 2,259 batches (18,949,527 work units)
Completed:  9,479 batches
                     2,991 batches in the last 30 days
                     Average of 99.7 batches per day)
Estimated backlog: 34.6 days


Click here to learn more about World Community Grid's monthly project updates.
大意:
新冠项目的OpenCL-gpu版计算内核已经提交给IBM做代码/安全审查,具体完成日期未知。
目前任务够算34.6天
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发表于 2021-4-7 09:46:55 | 显示全部楼层
本帖最后由 0xCAFEBABE 于 2021-4-7 09:49 编辑

OpenPandemics - COVID-19 Now Running on Machines with Graphics Processing Units
6 四月 2021           

摘要

The software that powers OpenPandemics - COVID-19 has been adapted to use GPU power. This will not only help the researchers to screen more molecules, but also could help them study molecules that are more complex.

For several months, the OpenPandemics - COVID-19 researchers in the Forli Lab at Scripps Research and World Community Grid's tech team have been working behind the scenes to adapt AutoDock 4, the software that powers the project, for use on devices with graphics processing units (GPUs).

During beta testing, we found that work units running on volunteers' GPUs finished an average of 500 times faster (GPU elapsed time vs CPU time) than the same amount of work when it was run using the CPU version of AutoDock.

What is a GPU?

A graphics processing unit (GPU) is another name for the graphics card inside a computer originally used to display text and images on a monitor. As GPUs evolved they gained vast amounts of parallel processing power. Initially, this was a by-product of accelerating specialized graphics related calculations but eventually manufacturers extended these capabilities to allow general purpose computations. As a consequence, modern GPUs can perform certain types of computations significantly faster than the central processing units (CPUs) that orchestrate all work in a computer. In order to utilize a GPU for scientific calculations, however, applications have to be modified to be able to utilize high levels of parallelism to leverage the GPUs’ computational capabilities efficiently.

If a volunteer runs OpenPandemics - COVID-19 on a device that has a suitable GPU, and adjusts their settings accordingly, that device can complete work units much more quickly than a device using just a CPU.

Why is it important to increase the speed of this project?

AutoDock-GPU (AD-GPU) could increase the project's chances of finding a molecule with anti-viral properties even further.

Compared to AutoDock 4 (the current version of the software that is used for OpenPandemics - COVID-19), AD-GPU is much faster which will provide a nice boost to the already amazing rate of docked results.

Furthermore, AD-GPU has an improved search algorithm that exhibits a greater probability of finding strong interactions between the molecules and viral proteins, and is well suited to dock larger or more complex molecules. This means that the researchers can use AD-GPU to not only screen more molecules, but also to enable the search of more complex molecules.


1.jpg




Comparison of OpenPandemics GPU to CPU for the originally CPU-targeted batches 30010-30019 run during beta testing. Average Days to complete one entire batch (blue bars) are shown with Average Points per batch (orange bars). Overall average speedup was 334 times (maximum observed was 516x). The overall average points per batch highlight a 1.6x increase in the algorithmic efficiency of the GPU. This increased efficiency will be leveraged in GPU-targeted packages and lead to much higher points per GPU-batch.

More molecules, more complex molecules, and in less time...this is all crucial to help find potential treatments for a virus that is not only still spreading in most of the world but is also continuing to mutate.

So are devices without GPUs still important for this project?

Yes! Currently, only about 20 percent of World Community Grid power comes from devices with GPU, so participation from every willing CPU-enabled computer, Android device, and Raspberry Pi remains crucial.

Thank you to everyone who is supporting OpenPandemics - COVID-19!

To learn more about GPU power for this project--including how to make your GPU-enabled device can participate--visit our GPU FAQs.



==机翻===================

openpandiseases-COVID-19现在可以运行在带有图形处理单元的机器上
6 四月 2021           

摘要
为openpandiseases-COVID-19提供动力的软件已经被修改为使用GPU动力。这不仅有助于研究人员筛选更多的分子,还可以帮助他们研究更复杂的分子。


几个月来,斯克里普斯研究公司Forli实验室的openpodiseases-COVID-19研究人员和世界社区网格公司的技术团队一直在幕后工作,以适应AutoDock 4,该项目的驱动软件,用于带有图形处理单元(gpu)的设备。

在beta测试期间,我们发现在志愿者的GPU上运行的工作单元比使用AutoDock的CPU版本运行的工作单元平均快500倍(GPU运行时间vs CPU时间)。

什么是GPU?
图形处理单元(GPU)是计算机内部图形卡的另一个名称,最初用于在显示器上显示文本和图像。随着gpu的发展,它们获得了大量的并行处理能力。最初,这是加速专业图形相关计算的副产品,但最终制造商将这些功能扩展到允许通用计算。因此,现代gpu可以执行某些类型的计算,其速度明显快于在计算机中协调所有工作的中央处理器(cpu)。然而,为了利用GPU进行科学计算,必须对应用程序进行修改,以便能够利用高水平的并行性来有效地利用GPU的计算能力。

如果一个志愿者在一个有合适GPU的设备上运行openpandiseases-COVID-19,并相应地调整他们的设置,那么这个设备可以比仅仅使用CPU的设备更快地完成工作单元。

为什么提高这个项目的速度很重要?
AutoDock GPU(AD-GPU)可以进一步增加该项目找到具有抗病毒特性的分子的机会。
与AutoDock 4(当前用于OpenPandiseases-COVID-19的软件版本)相比,AD-GPU的速度要快得多,这将很好地提高已经惊人的停靠速度。
此外,AD-GPU有一个改进的搜索算法,显示出更大的概率发现分子和病毒蛋白之间的强相互作用,并非常适合停靠更大或更复杂的分子。这意味着研究人员不仅可以使用AD-GPU筛选更多的分子,还可以搜索更复杂的分子。


在beta测试期间运行的最初以CPU为目标的批30010-30019的openpopu与CPU的比较。完成一整批的平均天数(蓝色条)显示为每批的平均分数(橙色条)。总体平均加速比为334倍(观察到的最大加速比为516倍)。每批的总体平均点数突出显示GPU的算法效率提高了1.6倍。这一提高的效率将在GPU目标包中得到利用,并导致每个GPU批的分数更高。

更多的分子,更复杂的分子,在更短的时间内……这一切对于帮助找到一种病毒的潜在治疗方法至关重要,这种病毒不仅仍在世界大部分地区传播,而且还在继续变异。

那么,没有gpu的设备对这个项目仍然重要吗?

对!目前,世界上只有20%的电网电力来自带有GPU的设备,因此每一台愿意使用CPU的计算机、Android设备和Raspberry-Pi的参与仍然至关重要。

感谢所有支持openpandiseases-COVID-19的人!

要了解有关此项目的GPU电源的更多信息(包括如何使支持GPU的设备可以参与),请访问我们的GPU常见问题解答。


==大意===================
COVID-19 项目现在支持使用 GPU 计算啦,并且算力相比 CPU 会大幅提升!
但是由于现在全网算力的 80% 仍然来自于 CPU 计算,所以不能参与 GPU 计算的小伙伴们也不要沮丧,CPU 计算对项目来说也是至关重要的。

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