Ligand-based Computation of HIV-1 Integrase Inhibition Strength within a Series of-ketoamide Derivatives
Derivatives Internet Electronic Journal of Molecular Design
Motivation. A continuous demand exists for novel bioactive molecules. When a lead structure has been discovered and looks promising for further development, series of analogues will be made. Normally, the synthesis of many compounds is required to improve on the activity, or to keep good activity while optimizing other properties of relevance. A computational model that accurately predicts the activity of derivatives before their synthesis is beneficial to the speed and cost of lead
... of lead optimization. It can be advantageous when such a model does not require geometrical information on the target protein structure. Method. A conformational analysis was performed on 201 ketoamide ester derivatives that inhibit HIV integrase. The derivatives were aligned to the lowest energy conformer of the most potent inhibitor with the SEAL method. Five CoMSIA fields were computed for each compound taking into account steric, polarizability, charge, H-bond acceptor, and H-bond donor properties. A model for integrase-inhibitor interaction was derived by PLS regression. The predictivity of the model was tested by scrambling the data, leave-n-out experiments and applying the model to a ketoamide acid series of integrase inhibitors. In order to elucidate the binding mode of the inhibitors, the model was mapped on a crystal structure of the integrase enzyme. Results. The CoMSIA model derived from the 201 ketoamide ester derivatives has an R 2 of 0.75. The resulting fields of the molecular properties required for strong inhibition can be qualitatively understood. Scrambling the data prohibited the derivation of a predictive model. The models derived from 100 derivatives when applied to the remaining 101 compounds, resulted in a prediction with an absolute deviation of 0.28 log 10 unit/compound. The accuracy of prediction when the model was applied to 74 ketoamide acids was 0.42 log 10 unit/compound. Mapping the model onto the integrase enzyme did not lead to an obvious binding mode. Conclusions. The predictivity of our model allows for guiding the synthesis of novel analogues. Our approach holds its predictive value when applied to a different series of inhibitors. The geometry of integrase-inhibitor binding is not very well understood at the present time, which emphasizes the advantages of an approach that does not require this knowledge for the design of novel active compounds.