Interpretation protein-ligand interaction

Fisher, H. F, and Singh, N. (1995). Calorimetric methods for interpreting protein-ligand interactions. Methods Enzymol. 259, 194-221. 

As stated earlier, a commercially useful computational method must be fast, accurate, transferable, and interpretable. Logical steps one can take towards this goal are now presented. It is important to point out that under current resources, these four criteria cannot be simultaneously optimized. With current computational capabilities, the most complete theoretical description of protein-ligand interactions (which may involve many-body terms) cannot... 

The first part of this section describes how this interpretation is done and which assumptions have been made to retrieve plausible results. The second part will then describe the algorithms used to compute bond characteristics from geometric information, because the PDB file format and its successors include no means to specify hybridization states or bond orders, which are essential for the characterization of properties of small organic ligands. Ligand bond characteristics interpretation is a prerequisite for the step to follow the detailed description of protein-ligand interactions by pharmacophore models. 

Extrinsic Cotton effects are due to the inherent dissymmetry of the enzyme-bound chromophore (an inherent effect) and/or to the interactions of the chromophore with the encompassing dissymmetric environment (interactive effects). The inherent effects are those which the free chromophore would exhibit if its conformation were identical with that of the enzyme-bound form. The interactive effects result from protein-ligand interactions or ligand-ligand interactions. The main problem in interpretation of die CD of enzyme-bound chromophores is distinguishing between the inherent and the interactive effects. 

In spite of many inconsistencies and difficulties in interpretation, most of the experimental data suggest that simple additive models for the protein-ligand interactions might be a good starting point for the development of empirical scoring functions. Indeed, the first scoring functions actually built upon experimental work pubhshed in 1994 by Bbhm [29]. 

Another approach based on statistical analysis of residue-specific protein-ligand interaction terms is named TScore [138[. Here, individual protein-ligand interactions are evaluated for each residue within 8 A around the ligand. Force field-based descriptors are replaced by Chemscore [96] and PLP [139] terms for hydrogen bonds, lipophilicity, and steric contact. Its application to a series of factor Xa inhibitors resulted in a predictive tailored scoring function vdth 52 selected descriptors after PLS analysis q = 0.(>4, r = 0.83). This concept allows a visual interpretation of... 

---