Ligand conformational space

The study of receptor-ligand binding is one of the most important applications of free energy simulations [1]. To approach this problem theoretically, one must first partition the conformational space into bound and unbound states. There is no unique way to do this, but in practical situations there is often a natural choice. The equilibrium binding constant is... 

Virtual screening applications based on superposition or docking usually contain difficult-to-solve optimization problems with a mixed combinatorial and numerical flavor. The combinatorial aspect results from discrete models of conformational flexibility and molecular interactions. The numerical aspect results from describing the relative orientation of two objects, either two superimposed molecules or a ligand with respect to a protein in docking calculations. Problems of this kind are in most cases hard to solve optimally with reasonable compute resources. Sometimes, the combinatorial and the numerical part of such a problem can be separated and independently solved. For example, several virtual screening tools enumerate the conformational space of a molecule in order to address a major combinatorial part of the problem independently (see for example [199]). Alternatively, heuristic search techniques are used to tackle the problem as a whole. Some of them will be covered in this section. 

One way of addressing the issue of a lack of precision in the locations of functionally important side chains is to determine the range of conformational space available to them in different ligands. To this end, we undertook a detailed comparison of the structures of AP-A and AP-B in solution [47]. This proved to be a useful exercise both in terms of defining the positions of side chains known to be important for cardiotonic activity and identifying neighboring residues which might also be involved [47]. 

In principle, every conformer of a set of flexible ligands could be stored in a database, and then each conformation could be evaluated with rigid-body docking algorithms. The size of the ensemble is critical since the computing time increases linearly with the number of conformations and the quality of the result drops with larger differences between the most similar conformation of the ensemble and the actual complex conformation. Thus a balance must be struck between computing time requirements and the desire to cover all of conformational space. 

---