Development of Computational Tools for Protein-ligands Analysis
Loïc Dreano, Mael Briand, Ashenafi Legehar, Evgeni Grazhdankin and Henri Xhaard
Faculty of Pharmacy, Division of Pharmaceutical Chemistry and Technology, Computational Drug Discovery group, University of Helsinki, Viikinkaari 5E, P.O. Box 56, FI-00014 Helsinki, Finland
There are around 190,000 biological macromolecular structures in the Protein Data Bank (PDB)[1] that provide a wealth of information about molecular interactions. We built and stored into a PostgreSQL database [2] two datasets of PDB structures that aim to minimize the redundancy and maximize the coverage of this information. Using those datasets, we developed a knowledge-based scoring function able to assess the fitness of the 3D environment around an atom in a protein structure. Scoring a set of 359 high-resolution PDB structures, highlighted the importance of the solvation of protein structure. Hence, using a grid-based score attribution approach, we developed two tools (1) to automatically solvate a structure (2) to identify ligand hotspots.
References
[1] H.M. Berman; J. Westbrook, Z. Feng; G. Gilliland; T.N. Bhat; H. Weissig; I.N. Shindyalov; P.E. Bourne. (2000) The Protein Data Bank Nucleic Acids Research, 28: 235-242.
[2] M. Briand, L. Dreano, A. Legehar, E. Grazhdankin, L. Ghemtio, H. Xhaard,; ] Exploring cooperative molecular contacts using a PostgreSQL database system, (accepted 18 January 2023) Molecular Informatics.
