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scientific challenge

fall 2018 challenge launched
submit your proposal until August 24th

news of the week

Here is one of the projects that made it into the summer 2018 challenge:

Inflammatory diseases diabetes, cancer, rheumatoid arthritis

Discovery of novel inhibitors to TNF-alpha and amyloidosis, as potential future pharma
Inflammatory diseases like diabetes, cancer, rheumatoid arthritis, infectious diseases are currently severe illnesses suffered by almost every country. Unknown mechanisms are responsible in multicellular organisms to acquire multidrug resistance for existing therapies. To address this, we propose to use combination therapy to treat the problems of multidrug resistance, inflammatory disorders and autoimmune disorders. Studies reveal Amyloidosis and TNF-alpha are two different and critical pathways involved in immune-related disorders and inflammatory diseases. Computational methods like structure-based and ligand-based virtual screening methods will be used in this study to identify the new chemical entities as inhibitors to TNF-alpha and amyloidosis signaling pathways. LeadIT, FlexS and Recore tools will be utilized to apply to discover new inhibitors to TNF-alpha and amyloidosis. Finally, detected chemicals as inhibitors will be validated experimentally using in-vitro assays.
"We intend to achieve the following milestone(s):
  1. Understand SAR and Structure- based virtual screening to discover inhibitors to TNF-alpha.
  2. Understand SAR and Ligand-based virtual screening to discover inhibitors of amyloidosis.
  3. Experimental validation of in-silico based identified inhibitors of TNF-alpha and amyloidosis."
— Poonam Kalhotra, Escuela Nacional De Ciencias Biológicas - Instituto Politecnico Nacional, Mexico

Poonam will be using SeeSAR, CoLibri, LeadIT, FlexX, ReCore, HYDE, FlexS, FTrees, PoseView, SMARTSviewer, Mona, and DoGSiteScorer.

current champion

The following project won the 'summer 2017' scientific challenge:

 
Computer-assisted selective optimization of side-activities
Julius Pollinger
Goethe-University, Frankfurt am Main, Germany
Julius Pollinger
Goethe-University
Frankfurt am Main, Germany

Metabolic disorders

Computer-assisted selective optimization of side-activities
Julius summarizes:
The discovery of bioactive small molecules is an expensive and time consuming yet central task in drug discovery. A potentially superior way to identify new drug candidates is the selective optimization of side activities (SOSA), which employs known drugs for their side-activities as lead compounds. Virtually every small molecule drug interacts with more than a single molecular target and, thus, has side-activities. Sometimes, these side-activities may be of therapeutic value and structural optimization to turn the side-activity into the main activity can generate a new drug. As a key advantage of this strategy, analogues of approved drugs inherit their previously optimized favourable characteristics concerning toxicity, solubility, bioavailability and metabolic stability, and are drug-like by definition. We intended to apply the SOSA strategy to the CysLT1 inhibitor cinalukast for which we have observed a previously unknown side-activity on the peroxisome-proliferator activated receptors (PPAR) alpha and gamma. These ligand-activated transcription factors play a crucial role in metabolic disorders. To speed up the SOSA-focussed structural variation of cinalukast towards potent PPAR modulators, we intended to employ in silico techniques to support the SOSA concept. We started this endeavour using SeeSAR and its editing mode to visualize and identify key ligand-receptor interactions. Moreover, FlexX and Hyde served to analyse the binding mode of a small combinatorial set of cinalukast derivatives and allowed an in silico estimation of its structure-activity relationship (SAR). This enabled us to select structural elements whose variation promised to improve potency on PPARs. As a proof-of-principle, two cinalukast analogues were prepared and tested in vitro. Their experimentally determined modulatory activity on PPARs nicely correlated with the predictions of Hyde confirming the suitability of our computational approach to support SOSA. Moreover, these cinalukast analogues revealed remarkable improvements in toxicity over the lead compound. Next, we generated a combinatorial library of approx. 8000 cinalukast derivatives using adaptions of the KNIME workflows STORM and MedChemWizard. The BioSolveIT KNIME nodes were then employed for automated docking and screening to identify the most promising candidate compounds in the library. With the implementation of this automated and time-saving workflow for a computationally guided SOSA approach, we are speeding up the structural optimization of cinalukast towards potent PPAR modulators and several predicted analogues are in preparation and characterization. This successful combination of computational tools in compound optimization highlights the potential of computer-assisted SOSA for future drug discovery.
The following goals have been achieved:
  1. First, we intended to identify variations of our lead structure cinalukast to counter the compound’s high toxicity and improve key characteristics such as selectivity or solubility. We employed SeeSAR to study a manually defined library of 40 analogues comprising variations which we chose as a starting SAR. We observed that the core scaffold of cinalukast provides an ideal angle for the ligand-receptor interaction and therefore our initially intended changes in this part were discarded. Thereby, the computational approach significantly reduced the number of analogues that had to be prepared. The most promising analogues according to SeeSAR were further investigated with FlexX and Hyde scoring to analyse ligand-target interactions and binding contributions. Two analogues were selected for synthesis and in vitro characterization taking into account all computational results to confirm a correlation between predicted and experimentally determined values.
  2. As a second goal, we aimed for broader variations of the lead structure to improve potency on the new target. We tried to employ the KNIME workflows MedChemWizard and STORM and successfully adapted them to our needs. With the basic concept of these workflows and using KNIME nodes provided by BioSolveIT, we built and screened a large combinatorial library of approx. 8000 analogues of the lead compound. Predicted information on compound binding obtained from SeeSAR helped the library design. After the automated library generation and screening workflow, we manually analysed top-ranking compounds by docking with the SeeSAR and using the “Interactive BioSolveIT table” for various features. The three most potent derivatives from the automated screening workflow turned out very favourable and were selected for synthesis and in vitro characterization.
  3. As a third objective, we aimed to confirm the predicted data (goal 1 and 2) in vitro by synthesizing and testing the selected analogues. To confirm the suitability of our computational approach, we selected two compounds for synthesis to study the correlation between predicted and experimentally determined value. The in vitro activities of these analogues nicely correlated with their predicted Hyde scores thus characterizing this ranking approach as suitable for our optimization strategy. Moreover, we selected compounds for synthesis to reduce the problematic toxicity of the compound class which was achieved by minor structural changes deduced from SeeSAR calculations. Finally, we employed the previously validated Hyde scoring as decision method for selecting compounds from a large combinatorial library to optimize potency of the compound class with as few analogues to be prepared as possible and the three top-ranking candidates are in synthesis/in vitro characterization.

For more information please visit the hall of fame.

invitation

BioSolveIT is inviting academic teams, non-profit organizations and individuals to participate in an exciting Scientific Challenge: if you are working on a drug discovery problem, take advantage of BioSolveIT's wide array of software tools to meet your goals. How to participate? Just send us a proposal for the project you'd like to advance using BioSolveIT software. We will review every proposal very carefully and award the most attractive ones. A new contest starts every three months.

motivation

In a first phase, the most promising proposals will receive free BioSolveIT licenses for 3 months to con­duct the desired research. For phase II, the most interesting results are granted a free license extension by 9 months and we will sponsor the presentation of the overall best achievement with a travel grant of 1000€. For more details please read the terms of challenge.

procedure

  1. To enter the fall 2018 contest, please
    submit your proposal until August 24th 2018
  2. Based on scientific novelty, interest of target, and approach sought, we will select from all submissions the best, maximum 5 to enter the contest. Every participant will be informed of our decision by September 1st. These most promising projects will receive free fully functional licenses and support to all relevant BioSolveIT tools, valid for 3 months.
  3. After the initial 3 months the best, maximum 3 projects will receive another 9 months of free software access to BioSolveIT's entire software suite and premium support. And after 9 months, the overall best project will be rewarded with a travel grant of 1000€ to a high impact conference for a presentation of the results.