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.

Classification and regression trees--studies of HIV reverse transcriptase inhibitors.

In this paper, the application of Classification And Regression Trees (CART) is presented for the analysis of biological activity of Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs). The data consist of the biological activities, expressed as pIC50, of 208 NNRTIs against wild-type HIV virus (HIV-1) and four mutant strains (181C, 103N, 100I, 188L) and the computed interaction energies with the Reverse Transcriptase (RT) binding pocket. CART explains the observed biological activity of NNRTIs in terms of interactions with individual amino acids in the RT binding pocket, i.e., the original data variables.

Inhibition and substrate recognition--a computational approach applied to HIV protease.

We have developed a computational approach in which an inhibitor's 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.

Ab initio calculations on iron-porphyrin model systems for intermediates in the oxidative cycle of cytochrome P450s.

Geometry optimizations for several spin states of the iron(III)-S-methyl- porphyrin complex, the iron (III)-oxo-S-methyl-porphyrin complex and the respective anions were performed in order to examine models for intermediates in the oxidative cycle of cytochrome P450. The aim of this study was to obtain insights into the ground states of the intermediates of this catalytic cycle and to use the ab initio calculated geometries and charge distributions to suggest better and more realistic parameters for forcefields which are generally used for modeling P450s. The results indicate that the ground states of both the iron(III)-S-methyl-porphyrin complex and the iron(III)-oxo-S-methyl-porphyrin complex are sextet spin states (high spin). The ground states of the anions of both complexes are probably quintet spin states. The fact that experimentally a shift from low spin to high spin is observed upon binding of the substrate suggests that the ab initio calculations for the iron(III)-S-methyl-porphyrin complex in vacuum give a correct representation of the (hydrophobic) substrate-bound state of the active site of P450. The ab initio geometries of the iron-porphyrin complexes are very similar to the experimentally observed geometries, except for the longer iron-sulfur bond in ab initio calculations, which is probably caused by the omission of polarization functions on the sulfur atom during the geometry optimization. The charge distribution in all ab initio calculated complexes can be described by a series of concentric rings of alternating charge, thus allowing a relatively large positive charge on the iron atom. The commonly used forcefields generally underestimate the charge differences between the iron atom and the different parts of the porphyrin moiety or ignore the charges completely. Although forcefield calculations can reproduce the experimental geometry of iron-porphyrin moieties, extension of the forcefields with charges obtained from ab initio calculations should give a better description of the heme moiety in protein modeling and docking experiments.