Docking of small flexible molecules to protein binding sites is a crucial part of drug discovery projects. State-of-the-art docking algorithms successfully predict the experimental structures only in 30 to 60 percent. This can be attributed to two deficiencies of today’s algorithms: failure of the scoring functions and insufficient sampling. In the project proposed here, we will deal with the second problem. Therefore, a new sampling algorithm based on Ant Colony Optimization (ACO) will be applied to the docking problem. ACO is inspired by the behavior of some ant species, which are remarkably successful in finding the shortest path between their nest and a food source, and has shown very efficiently in solving hard optimization problems. The ACO docking algorithm will then be compared to an implementation of the docking problem using a genetic algorithm as well as to published results of other state-of-the-art docking programs. To enable large-scale virtual screening experiments, options to reduce the consumption of computer time will be explored.
|(2009): Molecular Visualization in the Rational Drug Design Process Frontiers in Bioscience. 2009, 14(14), pp. 2559-2583. ISSN 1093-9946. eISSN 1093-4715. Available under: doi: 10.2741/3398
The visualization of molecular scenarios on an atomic level can help to interpret experimental and theoretical findings. This is demonstrated in this review article with the specific field of drug design. State-of-the-art visualization techniques are described and applied to the different stages of the rational design process. Numerous examples from the literature, in which visualization was used as a major tool in the data analysis and interpretation, are provided to show that images are not only useful for
|(2009): Empirical Scoring Functions for Advanced Protein-Ligand Docking with PLANTS Journal of Chemical Information and Modelling. 2009, 49(1), pp. 84-96. ISSN 1549-9596. eISSN 1549-960X. Available under: doi: 10.1021/ci800298z
In this paper we present two empirical scoring functions, PLANTS(CHEMPLP) and PLANTS(PLP), designed for our docking algorithm PLANTS (Protein-Ligand ANT System), which is based on ant colony optimization (ACO). They are related, regarding their functional form, to parts of already published scoring functions and force fields. The parametrization procedure described here was able to identify several parameter settings showing an excellent performance for the task of pose prediction on two test sets comprising 298 complexes in total. Up to 87% of the complexes of the Astex diverse set and 77% of the CCDC/Astex clean listnc (noncovalently bound complexes of the clean list) could be reproduced with root-mean-square deviations of less than 2 A with respect to the experimentally determined structures. A comparison with the state-of-the-art docking tool GOLD clearly shows that this is, especially for the druglike Astex diverse set, an improvement in pose prediction performance. Additionally, optimized parameter settings for the search algorithm were identified, which can be used to balance pose prediction reliability and search speed.
|01.03.2007 – 28.02.2008