ISSN: 2169-0138
Markus Lill
Accepted Abstracts: Drug Design
Molecular recognition between receptors and ligands through non-covalent association plays a fundamental role in virtually all biochemical processes in living organisms. Several computational concepts have been devised to study protein-ligand binding. These techniques are routinely used in academia and industry for identifying and optimizing potential drug candidates. While those methods have been widely used to attain a qualitative understanding of ligand binding to proteins, a current challenge is to quantify their interaction in a reasonable amount of time. One major issue is that the protein in reality can adapt its shape and properties upon each individual ligand binding to it (induced protein fit). In this context, I will present our development of new concepts (Software: Limoc and CorLps) incorporating protein flexibility and protein dynamics into docking. Limoc generates an ensemble of holo-like protein structures relevant for binding of structurally diverse ligands starting from a single apo or holo X-ray structure. Docking to an ensemble of protein structures has proven its utility for docking, but using a large ensemble of structures can reduce the efficiency of docking and can increase the number of false positives in virtual screening. I will describe different schemes to reduce the ensemble of protein structures to increase efficiency and enrichment quality. Utilizing experimental knowledge about actives for a target protein allows the reduction of ensemble members to a minimum of three protein structures, increasing enrichment quality and efficiency simultaneously.