Here is one of the projects that made it into the summer 2018 challenge:
Poonam will be using SeeSAR, CoLibri, LeadIT, FlexX, ReCore, HYDE, FlexS, FTrees, PoseView, SMARTSviewer, Mona, and DoGSiteScorer.
The following project won the 'summer 2017' scientific challenge:
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.
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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.
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.