Computer-aided Drug Design

Computer-aided drug design, hit finding and optimisation (WP1)

Prof. Antti Poso
University of Eastern Finland

WP1 has the objective of carrying out ‘in silico’ virtual screening against selected targets in order to find structurally diverse hit compounds. A second aim of WP1 is to support individual hit optimisation projects developed under WP2. The specific goals of WP1 are:

  1. Use structure-based or ligand-based virtual screening to prioritize compounds to be purchased or synthesized for biological screening.
  2. Perform cycles of in silico optimization of selected structures in terms of binding affinity; selectivity; and absorption, distribution, metabolism, and excretion (ADME) properties.

Within WP1 most of the modeling work will be carried out at University of Eastern Finland, which works as a design hub. Other INTEGRATE Consortium members will also participate in selected areas based on the protein target and available capacities at the moment of executions. This will allow efficient data flow between WP1, WP2 and WP3 and allows high-quality supervision of work.

Protein models — Protein modeling is based on standard approach of sequence and structural conservation and carried out using available software packages like Modeller and Discovery Studio. Sequence alignments are based on several methods, including ClustalW and BLAST, and information concerning protein environment and known mutations is also used.

Docking models — Preliminary docking and pharmacophore models for virtual screening and hit optimisation will be created out using both ligand-based and structural-based approaches. In those cases where target protein structure is available, molecular docking algorithms (like but not limited to Glide, Gold and Autodock) will be used. Preliminary validation of docking models is based on usage of known active compounds and decoy sets. Work is carried out in close collaboration with WP2 and WP3 to enable constant data flow between WPs and to enable successful hit-to-lead optimization of compounds. Target libraries to be used for screening are those with good availability/known high quality of compounds, including Enamine, Asinex and Chembridge, internal libraries and the natural compound libraries received from 3rd parties (UT and SIRC/Shanghai). In addition, compound libraries designed by WP2 are used. The selection of libraries will also be affected by the nature of the target protein and known active inhibitors.

QSAR models — In suitable cases pharmacophores, shape-based screening, 2D/3D fingerprints and 3D-QSAR methods are also used to support both screening and optimisation. At later stages, ADMET properties relevant for antimicrobial compounds are also modelled (QSAR approach) to support the optimization of hit compounds. All the models are statistically validated using both internal and external test sets and pre-fixed evaluation criteria.

Final models — All the created docking/QSAR models are constantly and iterative updated and used both in virtual screening and hit optimisation work, so to support WP2 and WP3. Once new bioactive data is available from WP3 it will be implemented in the corresponding docking/QSAR models to minimize the errors of prediction. This is especially true with traditional and 3D-QSAR work where the size of the dataset has enable larger applicable area for prediction. In normal cases the models do not change very much after certain number of compounds/chemical classes have been added into the training set. The quality of the model is always evaluated using test set (both internal and external).

Objectives: To develop biosensor-based HTS platform and to show it’s functionality in antibacterial screening. To evaluate in vitro antibacterial properties of compounds provided by WP1-2 by and to further validate the HTS hits through a set of in vitro follow-up studies.

Expected Results: Proven functionality of biosensor-based HTS platform in detecting antibacterial properties. Identification of novel antibacterial compounds against Gram-negative bacteria, which show selectivity and promising therapeutic potential.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska Curie grant agreement No  642620
European Union