Is World Community Grid effective? Are there successes?

Help Fight Childhood Cancer


Project Status and Findings:
In February 2014, the research team behind the Help Fight Childhood Cancer project published a groundbreaking paper on their discovery of seven promising drug candidates to treat neuroblastoma, one of the most common and dangerous forms of childhood cancer. Learn more.

From the Learn more link:

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Summary
The research team behind the Help Fight Childhood Cancer project has just published a groundbreaking paper. It reveals seven promising drug candidates - identified with the help of World Community Grid members ? for neuroblastoma, one of the most common and dangerous forms of childhood cancer.

The Help Fight Childhood Cancer research team at the Chiba Cancer Center in Japan has discovered drug candidates that show great promise as new treatments for neuroblastoma, one of the most common and dangerous forms of childhood cancer. This breakthrough marks one of the most significant scientific discoveries to date for World Community Grid.

Thanks to the contribution of over 200,000 World Community Grid members, the researchers were able to screen three million compounds and identify seven that destroy neuroblastoma tumors in mice without causing any apparent side effects.

The Chiba team plans to partner with a pharmaceutical company for further development, while also expanding their future work on World Community Grid to address other forms of childhood cancer.

In this blog post Dr. Akira Nakagawara, who leads the research team at Chiba, explains the significance of this exciting finding and its potential implications on treatments for other forms of cancer.

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All results and data derived from WCG projects must be published and freely shared.

FightAIDS@Home: Project Overview

Two Compounds Discovered that Pave the Way for New Class of AIDS Drug

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2 Mar 2010

Summary
Researchers from the FightAIDS@Home project on World Community Grid have found a potentially new way to fight drug resistant strains of AIDS.

Researchers from the FightAIDS@Home project on World Community Grid have found a potentially new way to fight drug resistant strains of AIDS. The virus which causes AIDS uses the actions of a protein called HIV protease to reproduce and spread in the body. Drugs called HIV inhibitors are currently used to keep AIDS in check by disabling this protein. These drugs attach to the HIV protease at points called binding sites which disable the protein so that it cannot perform the actions needed for the virus to replicate. However, the AIDS virus mutates or changes rather rapidly and these new strains of HIV are becoming more resistant to the current HIV inhibitor drugs. The scientists at the Scripps Research Institute have found two compounds which can attach to HIV protease at newly discovered binding sites. This may lead to a new class of drug treatments for AIDS which can address the drug resistant HIV strains. World Community Grid is being used for virtual screening experiments to develop a new class of HIV inhibitors based on the new binding sites.

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Video

Technical info on project.

The Clean Energy Project - Phase 2

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Project Status and Findings:
Researchers at Harvard have published a free database (www.molecularspace.org) cataloguing the electronic properties of over 2 million organic, carbon-based compounds and their potential for converting sunlight into electricity. These compounds were screened by volunteers on World Community Grid and this initiative is believed to be the most extensive investigation of quantum chemicals ever performed.

About 36,000 of the compounds analyzed show potential to perform at approximately double the efficiency of most current organic solar cells in production. Scientists can use this information to continue investigating the most promising candidates for use in cheaper, more efficient and more flexible solar cells. Thanks to World Community Grid volunteers, the computations for this project, which would have required 17,000 years on a single PC, were carried out in only three years, and the results are now available to stimulate research in the next generation of solar energy solutions.

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Drug Search for Leishmaniasis project update

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By: Dr. Carlos Muskus L?pez
Coordinator, Molecular Biology and Computational Unit, PECET University of Antioquia
15 Apr 2014

Summary
A project update from the Drug Search for Leishmaniasis scientists, describing the analysis of the data generated by World Community Grid volunteers.


Dear World Community Grid community,

Since we completed our work on World Community Grid, we have been running a second software program locally, which is searching for the best scoring protein-compound interactions.

The Drug Search for Leishmaniasis (DSFL) project produced files containing data on more than one billion interactions, so the work to search and filter the best docking results will still take some time. Currently, our new software program has searched approximately 85% of the DSFL data and a preliminary analysis of the results have shown interesting results.

As you probably know, Auto-dock Vina, was the program used on World Community Grid to evaluate candidate compounds for anti-Leishmania activity. The main score ranged between 0 and -16, with the more negative being better. In the table below, you can see the top 20 compounds with docking scores around -13, which are very good in terms of predicted interaction. These 20 compounds have been shown to interact well in silico, with three Leishmania proteins with ID's 2WSA, 3MJY and 3POI.

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More info at link.

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GO Fight Against Malaria update: promising early findings for malaria & drug-resistant tuberculosis

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By: Dr. Alexander L. Perryman
14 Jul 2014

Summary
Dr. Alexander Perryman describes the analysis and initial findings from the first phase of GO Fight Against Malaria, which include the discovery of several promising hits against key drug targets for treating both malaria and drug-resistant strains of tuberculosis. They are conducting further analysis and experimentation on the massive amount of data generated by World Community Grid volunteers.

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The entire article is rather long but detailed and results look very promising. Please follow link for complete update.

Computing for Clean Water project update

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By: Dr. Francois Grey
CNMM, Tsinghua University, Beijing, China and Citizen Cyberscience Centre, Geneva, Switzerland
25 Apr 2014

Summary
Researchers announce the end of the project as they complete further studies to confirm interesting results gained from World Community Grid data and prepare to publish their exciting findings.


Over the past six months, we have been doing further analysis of the results we obtained from the Computing for Clean Water project, in order to finalize a manuscript for publication. We've now reached a level of confidence in our findings that we don't believe any further runs on World Community Grid are necessary, which marks the end of the project on World Community Grid. We aim to submit our manuscript in the coming few weeks, once all co-authors have had a chance to review it.

As we've mentioned before, the results we got from Computing for Clean Water were surprising in several ways. Since Computing for Clean Water allows us to effectively extend simulations to a new flow regime that is closer to what happens in real experiments (see the diagram below), we're being very cautious about testing our surprising results in different ways, to make sure that what we see is a genuine effect, and not some unexpected artifact of the way we are doing the calculations.

We'll be able to tell you more about exactly why our results are so surprising once the article has been submitted for publication. At that point, we'll release a so-called electronic preprint, so that colleagues ? and you, the Computing for Clean Water volunteers ? can see the results, too. For the moment, though, suffice to say that we're very excited about having discovered a new mechanism that could make water filtration by nanotubes much more efficient. This mechanism appears to have been overlooked in previous studies because they did not have the computing power to simulate the flow process in the sort of detail that we can with Computing for Clean Water.

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More info here.


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Discovering Dengue Drugs ? Together, project update March 2013

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Hello again to the members of World Community Grid. With this update, we announce that we are ready to call our projects completed. We thank World Community Grid, its member volunteers, and their computers for their help.

The major advanced from this project are as follows:

1) All calculations necessary to fully evaluate Phase 1 and Phase 2 of ?Discovering Dengue Drugs ? Together? have been completed. We are moving this project to a ?completed? status and are assembling our results for posting on our web site. At this point, our calculations, which have used a combination of World Community Grid distributed computers and supercomputers from the Texas Advanced Computer Center (TACC; Austin, TX), have identified several new dengue protease inhibitors. These inhibitors have been validated in our laboratories using rigorous biochemical and cell culture assays. Most of these lead compounds inhibited both dengue and West Nile virus proteases. A handful of analogs developed from our initial computer-discovered dengue leads have entered crucial pre-clinical pharmacokinetic and efficacy studies. Thus, our World Community Grid project has been invaluable in helping identify dengue and West Nile protease inhibitors. We will be working over the next several years to turn these inhibitors into small molecules with properties suitable for human clinical trials.

2) Phase 2 of our project combined the computer resources of World Community Grid and TACC to provide unparalleled computing power to calculate the free energies of binding for several diverse proteins and a large number of potential ligands. We hypothesized that these extensive molecular dynamics calculations would provide statistical mechanical conformational ensembles from which we could accurately calculate free energies of binding. This was developed as a robust method to identify false-positive hits and more accurately discriminate between inhibitors and non-inhibitors. Unfortunately, calculations of the free energy of binding for a number of different protein:ligand systems did not match experimental measurements. Moreover, as we tested greater numbers of systems (facilitated by the combined computer resources of World Community Grid and TACC), we observed that an unacceptably large number of free energy calculations incorrectly predicted that known non-inhibitors would bind to a protein target. Thus, we conclude that the current state of free energy of binding calculations are not yet able to discriminate between binders and non-binders for every protein system However, as we reported earlier, there are some systems (e.g., lysozyme) where the free energy of binding calculations provided excellent discrimination between binders and non-binders. This work has shown that our current Phase 2 calculations are valuable at discriminating between true and false-positive hits for some target proteins, but not for all systems. We will continue our efforts to develop robust free energy of binding calculations that can be applied to a larger set of target proteins. This basic research project will not require the massive computing resources of World Community Grid at this time.

As always, we appreciate the time, effort, and dedication of World Community Grid members. The advances we are making in better understanding drug discovery software and its current limitations could not have been made without your efforts and your computer cycles.

Sincerely, Stan

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Help Conquer Cancer project update

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9 Jul 2014

Summary
The research team expands to advance their analysis of the millions of protein-crystallization images processed by World Community Grid volunteers. This will help scientists understand how protein structure can lead to better cancer drug design.


Dear World Community Grid volunteers,

Since you completed your calculations for Help Conquer Cancer (HCC) in 2013, we have begun analyzing the results you generated. Here, we provide an update on that analysis work as next steps to publish our findings and make the data publicly available.

Analyzing Results

Biologists and medical researchers use the three-dimensional (3D) structure of proteins to design drugs and understand protein function. Solving a protein's 3D structure requires a long and difficult sequence of steps. The protein needs to be made into a pure crystal (like you might do to crystalize sugar by slowly evaporating sugar water.) Then X-rays are shown through the crystal, and the neat array of protein molecules in the crystal creates a pattern on the film which can be analyzed mathematically to ascertain the structure of each protein molecule. Unlike sugar, protein is notoriously difficult to crystallize. HCC addressed this bottleneck in the pipeline: with a method for recognizing successfully formed protein crystals in images taken from a very large number of automated experimental attempts. For HCC, World Community Grid volunteers analyzed hundreds of millions of these images, but these results need to be processed further in order to generate reliable automatic image classifiers, discover trends in data, and ultimately improve our understanding how proteins form crystals. Our analysis work is in progress, and there are some exciting results we will be reporting on next time.

Additionally, over the last year we have devoted considerable energy and resources to our new project on World Community Grid - Mapping Cancer Markers (MCM), and other cancer-gene-signature projects that our research group is involved in. To help with both priorities and directions, our team expanded and we have a new Post-Doctoral Fellow (Dr. Lisa Yan) helping us with advancing our HCC research.

Publishing our results and findings

We have not yet decided the time-frame or the exact form of how we will make the HCC data you generated available to the public. Thanks to World Community Grid volunteers, our project's terabytes of raw image data have been transformed into terabytes of computed image features (morphological image properties used in automated image classification). The identity of proteins in the crystallization trials is largely unknown to us and partially unknown even to the Hauptman Woodward Institute (HWI), the source of the images. The features we have computed do not directly relate to crystallization outcomes or human-understandable image labels. A classifier is required to translate computed features to meaningful human labels or experimental outcomes. We have trained multiple image classifiers so far, but are confident that we can improve them. It is essential (and practical) that we finish this part of research, and publish our findings before releasing the useful data.

Paper publications

The Grid-computed results of Help Conquer Cancer have yet to be fully analyzed. Once complete, we intend to publish one or more papers based on the analysis, but cannot currently estimate a time-frame.

Collaborations

The High-Throughput Screening Lab at HWI supplied the original protein-crystallization image data, and indeed continues to generate more. Both HWI and the scientists who send them protein samples will benefit from the HCC research in two ways: better systems for automatically classifying protein-crystallization images (saving time and manual labour), and better understanding of the protein crystallization process.

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Information out there seems to be pretty sparse especially when mainstream media is concentrating on the bad news.

Put it all together, like you have done here PG, and we get to realise that our efforts are helping.

Thanks for this PG :up:

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Originally Posted by OldChap

Information out there seems to be pretty sparse especially when mainstream media is concentrating on the bad news.

Put it all together, like you have done here PG, and we get to realise that our efforts are helping.

Thanks for this PG :up:

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I knew we had some accomplishments but even I was a surprised at how well the projects have done. It is inspiring to keep-on-keeping on.

Since some of this takes years and years all the results are not even in yet. Some of this will not even be realized for years to come.

:toast:

Some WCG statistics.

http://img.techpowerup.org/140927/Stats_global.jpg

Maybe this isn't the spot but I have been trying to puzzle out what projects I can do with AMD Video cards? My computer time isn't anywhere where it used to be and concentrating on things for extend times is getting to be a incredibly difficult feat for me. So if anyone can point me in the right direction I would be thankful :yepp:

Isn't GPUGrid working with AMD cards now?

Never mind, appears not.

Awesome work compiling all of these into one thread! Come across these every once in a while, but when it's all in one place, it's pretty impressive! :clap:

Hope to continue reading about their discoveries!

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Originally Posted by MaxCFM

Maybe this isn't the spot but I have been trying to puzzle out what projects I can do with AMD Video cards? My computer time isn't anywhere where it used to be and concentrating on things for extend times is getting to be a incredibly difficult feat for me. So if anyone can point me in the right direction I would be thankful :yepp:

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It depends what you're interested in:

I don't have any AMD cards, so someone else can probably expand, but I know Seti, PrimeGrid, and Collatz run AMD...

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Originally Posted by MaxCFM

Maybe this isn't the spot but I have been trying to puzzle out what projects I can do with AMD Video cards? My computer time isn't anywhere where it used to be and concentrating on things for extend times is getting to be a incredibly difficult feat for me. So if anyone can point me in the right direction I would be thankful :yepp:

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Einstein@home uses AMD. Right now we are storming up the charts with Stoneageman leading the way. Join us.

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Originally Posted by PoppaGeek

Einstein@home uses AMD. Right now we are storming up the charts with Stoneageman leading the way. Join us.

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Thanks! I've joined with my finicky video card :rolleyes:

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Originally Posted by Otis11

It depends what you're interested in:

I don't have any AMD cards, so someone else can probably expand, but I know Seti, PrimeGrid, and Collatz run AMD...

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Thanks for the info!

Some interesting info on the Mapping Cancer Markers project.

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Approach

The Mapping Cancer Markers (MCM) project focuses on clinical application - discovering specific groups of markers that can be used to improve detection, diagnosis, prognosis and treatment of cancer. As a second goal, the comprehensive analysis of existing molecular profiles of cancer samples will lead to unraveling characteristics of such groups of markers - and in turn improving our understanding how to find them more efficiently.

Our strategy to reduce mortality includes three steps:

Increase number of cases diagnosed at earlier stage
We need to identify biomarkers for early cancer detection

Individualized treatment
We need to find biomarkers for treatment selection & response monitoring

Improved treatment
We need to improve our understanding of disease mechanism and drug mechanism_of_action
We need to identify useful drug combinations & design new medicines

To address these challenges, we propose to conduct an integrative analysis and comprehensive computational and biological validation of putative markers. To find all good markers, requires to use large patient cohorts (thousands of patient samples) and test billions of marker combinations, which would be intractable even on the World Community Grid. Thus, we have developed software which uses heuristics (clever steps that reduce the enormous search space by focusing the search on the most relevant subsets of combinations) which can greatly reduce the computational effort required to look for significant marker patterns. Even these software methods require a very large amount of computer processing power. By using World Community Grid, the Mapping Cancer Markers researchers are able to break down this overwhelming process into smaller, manageable tasks which can be performed by our volunteers' computing devices. World Community Grid therefore allows researchers to undertake this ambitious research. Thus, MCM focuses on three goals:

Identify sets of markers that may be able to predict if a person is at high risk of developing a particular cancer and increase the possibility of early detection.

Identify combinations of markers, which may predict a patient?s response to specific treatments. This would help make the treatment more personalized and could guide the development of customized therapeutic treatments for that patient.
Develop more efficient computational methods for discovering relevant patterns of markers.

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Yes yes yes yes yes!

I tried to tell this exact thing to a family friend who's an oncologist with sway at his hospital's research arm - unfortunately he wasn't techy enough to understand the practicality of the approach... Glad someone is doing this!

(This sort of intelligent, semi-near-term solution makes me want to crunch for them that much more!)

Go MCM! :woot:

The need:

14.1 million adults in the world were diagnosed with cancer in 2012.

There were 8.2 million deaths from cancer in the world in 2012.



http://img.techpowerup.org/141009/cancer.jpg
source: http://www.cancer.org/acs/groups/con...spc-042151.pdf


http://img.techpowerup.org/141009/cancer2.png

http://img.techpowerup.org/141009/cancer3.png

Paper published about processing the results from the Nutritious Rice for the World project

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Summary
A paper about innovations and performance improvements that were used to process the results data from the Nutritious Rice for the World project. The paper was published online on March 17, 2014 in the journal Bioinformatics.

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Paper Title:

"fast_protein_cluster: parallel and optimized clustering of large-scale protein modeling data"

Lay Person Abstract:

The Nutritious Rice for the World project used World Community Grid to predict the structures of proteins in a number of genomes of rice. This is the first step in understanding the functions of those proteins and how they play a role in better nutrition and resistance to environmental stresses such as drought, pests and disease. The project has been processing the results computed using World Community Grid. To obtain more accurate structure predictions, various methods were used to combine information from groups of similar protein models to identify the most accurate models.
The Nutritious rice for the World project generated sets of protein models that were too large to be clustered into subsets by existing techniques which were too slow and used too much memory. New algorithms were devised that were orders of magnitude faster. The new software also exploits GPUs, multi-core CPUs, and on-chip co-processors to make the structural comparisons faster. The new methods use less memory and result in significantly more accurate structure predictions. The software and code has been made available to be used by protein folding community.
Using the new software, the entire Nutritious Rice for the World dataset was analyzed in 6 weeks.

Technical Abstract:

Motivation: fast_protein_cluster is a fast, parallel and memory efficient package used to cluster 60 000 sets of protein models (with up to 550 000 models per set) generated by the Nutritious Rice for the World project.
Results: fast_protein_cluster is an optimized and extensible toolkit that supports Root Mean Square Deviation after optimal superposition (RMSD) and Template Modeling score (TM-score) as metrics. RMSD calculations using a laptop CPU are 60? faster than qcprot and 3? faster than current graphics processing unit (GPU) implementations. New GPU code further increases the speed of RMSD and TM-score calculations. fast_protein_cluster provides novel k-means and hierarchical clustering methods that are up to 250? and 2000? faster, respectively, than Clusco, and identify significantly more accurate models than Spicker and Clusco.
Availability and implementation: fast_protein_cluster is written in C++ using OpenMP for multi-threading support. Custom streaming Single Instruction Multiple Data (SIMD) extensions and advanced vector extension intrinsics code accelerate CPU calculations, and OpenCL kernels support AMD and Nvidia GPUs. fast_protein_cluster is available under the M.I.T. license.

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To view the paper, please click here.

A new drug lead to combat dengue fever

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We have recently made an exciting discovery using insights from Discovering Dengue Drugs - Together to guide additional calculations on our web portal for advanced computer-based drug discovery, DrugDiscovery@TACC. A molecule has demonstrated success in binding to and disabling a key dengue enzyme that is necessary for the virus to replicate.

Furthermore, it also shows signs of being able to effectively disable related flaviviruses, such as the West Nile virus. Importantly, our newly discovered drug lead also demonstrates no negative side effects such as adverse toxicity, carcinogenicity or mutagenicity risks, making it a promising antiviral drug candidate for dengue and potentially other flavivirues. We are working with medicinal chemists to synthesize variants of this exciting candidate molecule with the goal of improving its activity for planned pre-clinical and clinical trials.

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A new lead in the fight against dengue fever

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12 Nov 2014
Summary

The Discovering Dengue Drugs research team has published a paper describing a new drug candidate that was discovered with the help of World Community Grid volunteers. This drug candidate has no known adverse risks, and seems to be an attractive candidate for preclinical studies. A new antiviral treatment would be hugely beneficial for the roughly half of the world?s population that is at risk of contracting dengue or other related viruses such as hepatitis C, yellow fever and West Nile.

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Paper Title:

"Identification of a Novel Inhibitor of Dengue Virus Protease through Use of a Virtual Screening Drug Discovery Web Portal"

Lay Person Abstract:

We report the discovery of a small molecule that inhibited the function of the dengue virus protease (NS2B-NS3pro). This molecule was discovered using the web portal DrugDiscovery@TACC, which we developed to facilitate access to supercomputer resources for structure-based virtual screening. This site extended drug discovery studies originally performed on IBM's World Community Grid and enabled drug-like commercially available small molecule libraries to be rapidly screened against several dengue virus protease structures. As proof-of-concept, we show that a small molecule identified using DrugDiscovery@TACC was a strong inhibitor of the dengue virus protease. In addition, this small molecule also inhibited the functioning of the protease of the related West Nile virus. This newly discovered dengue virus drug candidate has no chemical features associated with adverse toxicity, carcinogenicity, or mutagenicity risks and thus is attractive for additional preclinical drug studies.

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Technical Abstract:

We report the discovery of a novel small-molecule inhibitor of the dengue virus (DENV) protease (NS2B-NS3pro) using a newly constructed Web-based portal (DrugDiscovery@TACC) for structure-based virtual screening. Our drug discovery portal, an extension of virtual screening studies performed using IBM's World Community Grid, facilitated access to supercomputer resources managed by the Texas Advanced Computing Center (TACC) and enabled druglike commercially available small-molecule libraries to be rapidly screened against several high-resolution DENV NS2B-NS3pro crystallographic structures. Detailed analysis of virtual screening docking scores and hydrogen-bonding interactions between each docked ligand and the NS2B-NS3pro Ser135 side chain were used to select molecules for experimental validation. Compounds were ordered from established chemical companies, and compounds with established aqueous solubility were tested for their ability to inhibit DENV NS2B-NS3pro cleavage of a model substrate in kinetic studies. As a proof-of-concept, we validated a small-molecule dihydronaphthalenone hit as a single-digit-micromolar mixed noncompetitive inhibitor of the DENV protease. Since the dihydronaphthalenone was predicted to interact with NS2B-NS3pro residues that are largely conserved between DENV and the related West Nile virus (WNV), we tested this inhibitor against WNV NS2B-NS3pro and observed a similar mixed noncompetitive inhibition mechanism. However, the inhibition constants were ∼10-fold larger against the WNV protease relative to the DENV protease. This novel validated lead had no chemical features or pharmacophores associated with adverse toxicity, carcinogenicity, or mutagenicity risks and thus is attractive for additional characterization and optimization.

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Access to Paper:

To view the paper, please click here.