Paulus Joannes Lewi

Improving QSAR models for the biological activity of HIV Reverse Transcriptase inhibitors: Aspects of outlier detection and uninformative variable elimination

The goal of this study is to derive a methodology for modeling the biological activity of non-nucleoside HIV Reverse Transcriptase (RT) inhibitors. The difficulties that were encountered during the modeling attempts are discussed, together with their origin and solutions. With the selected multivariate techniques: robust principal component analysis, partial least squares, robust partial least squares and uninformative variable elimination partial least squares, it is possible to explore and to model the contaminated data satisfactory. It is shown that these techniques are versatile and valuable tools in modeling and exploring biochemical data.

Inhibition and substrate recognition: a computational approach applied to HIV protease

We have developed a computational approach in which an inhibitors strength is determined from its interaction energy with a limited set of amino acid residues of the inhibited protein. We applied this method to HIV protease. The method uses a consensus structure built from X-ray crystallographic data. All inhibitors are docked into the consensus structure. Given that not every ligand–protein interaction causes inhibition, we implemented a genetic algorithm to determine the relevant set of residues. The algorithm optimizes the q2 between the sum of interaction energies and the observed inhibition constants. The best possible predictive model resulting has a q2 of 0.63. External validation by examining the predictivity for compounds not used in derivation of the model leads to a prediction accuracy between 0.9 and 1.5 log10 unit. Out of 198 residues in the whole protein, the best internally predictive model defines a subset of 20 residues and the best externally predictive model one of 9 residues. These residues are distributed over the subsites of the enzyme. This approach provides insight in which interactions are important for inhibiting HIV protease and it allows for quantitative prediction of inhibitor strength.

Combination of dendrograms with plots of latent variables: Application to G-protein coupled receptor sequences

Abstract Dendrograms provide the logical connectedness of the elements of a set, such as proteins that have evolved from common ancestors. The arrangement of the branches of a dendrogram is not unique and many equivalent dendrograms may represent the same logical structure. Plots of latent variables (LV) reproduce the geometrical structure of the elements in low-dimensional space. The reliability of such a low-dimensional plot is determined by how well spatial structure is preserved by the first two or three latent variables. In cases where the number of relevant latent variables is larger, the reliability of an LV plot may be low. We show that LV plots can be used for the display of logical structure even when the spatial structure is poorly represented. Applications are found in the comparison of amino acid sequences of proteins that have evolved through random mutations and natural selection. More generally, the approach is feasible when elements have evolved from one another or from unknown common ancestors. In that case, dendrograms and low-dimensional LV plots can be combined into a single display which preserves as much as possible of the logical and of the spatial structure.