General information
| Name: Peter |
| Surname: Kolb |
| E-mail: peter.kolb@uni-marburg.de |
| Cell phone number with international prefix: + |
| Country: Germany |
| Affiliation: Philipps-University Marburg |
| Gender: F □ M ⊠ |
| Year of the PhD title: 2006 |
| Personal web page: http://www.kolblab.org |
| Previous COST participation: No □ Yes ⊠ |
List of 10 selected publications within last 5 years
| 1. Structure-based discovery of β2-adrenergic receptor ligands. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 6843-6848 |
| 2. Limits of ligand selectivity from docking to models: In silico screening for A1 adenosine receptor antagonists. PLOS ONE 2012, 7, e49910. |
| 3. Identifying Modulators of CXC Receptors 3 and 4 with Tailored Selectivity using Multi-Target Docking. ACS Chem. Biol. 2015, 10, 715-724 |
| 4. Crystal structure of the human OX2 orexin receptor bound to the insomnia drug suvorexant. Nature 2015, 519, 247-250 |
| 5. Fragment-based similarity searching with infinite color space. J. Comput. Chem. 2015, 36, 1597-1608 |
| 6. SCUBIDOO: A Large yet Screenable and Easily Searchable Database of Computationally Created Chemical Compounds Optimized toward High Likelihood of Synthetic Tractability. J. Chem. Inf. Model. 2015, 55, 1824-1835 |
| 7. The mode of agonist binding to a G protein-coupled receptor switches the effect that voltage changes have on signaling. Sci. Signal. 2015, 8, ra110 |
| 8. Structure-based tailoring of compound libraries for high-throughput screening: Discovery of novel EphB4 kinase inhibitors. Proteins: Struct. Funct. Bioinf. 2008, 73, 11-18 |
| 9. Discovery of kinase inhibitors by high-throughput docking and scoring based on a transferable linear interaction energy model. J. Med. Chem. 2008, 51, 1179-1188 |
| 10. Automatic and efficient decomposition of two-dimensional structures of small molecules for fragment-based high-throughput docking. J. Med. Chem. 2006, 49, 7384-7392 |
Main skills and expertise (up to 5)
| 1. Docking |
| 2. GPCR & kinase ligands |
| 3. Chemoinformatics |
| 4. Homology modeling |
| 5. MD simulations |
Main equipment/facilities available in the participants’ lab (up to 5)
| 1. CPU cluster |
| 2. GPU cluster |
| 3. Computational chemistry software |
| 4. SCUBIDOO database (www.kolblab.org/scubidoo) |
| 5. |
Short personal activity proposal for the COST Action CA15135 (max 1000 characters)
| My lab has been working on the docking-based classification of ligand binding profiles for kinase and GPCR ligands. In order to achieve good predictive ability, multiple developments on the statistics of docking have been necessary. In parallel, we have been developing large databases of easily synthesizable compounds and are realizing some of them in multiple projects.
I am interested in developing and comparing strategies for the computational identification of multi-target ligands, specifically for protein-structure-based techniques. Moreover, I am eager to further develop screening databases and to also synthesize a number of compounds from these databases.
In terms of targets, GPCRs and kinases are my main interest, but we are currently also expanding into epigenetic target proteins, such as PRMTs. |
Work Group preference: score from 1 (preferred) to 4 (not preferred)
| Work Group of the CA15135 COST Action | Score |
| WG1: Development of new chemical entities | 1 |
| WG2: Selection of biological targets and assessment of biological data | 4 |
| WG3: Development of chemical databases | 1 |
| WG4: Development of Computational methods for multiple ligand design and discovery | 1 |
