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.

Evolution of anti-HIV drug candidates. Part 2: Diaryltriazine (DATA) analogues.

A synthesis program directed toward improving the stability of imidoyl thiourea based non-nucleoside reverse transcriptase inhibitors (NNRTIs) led to the discovery of diaryltriazines (DATAs), a new class of potent NNRTIs. The synthesis and anti-HIV structure-activity relationship (SAR) studies of a series of DATA derivatives are described.

Evolution of anti-HIV drug candidates. Part 3: Diarylpyrimidine (DAPY) analogues.

The synthesis and anti-HIV-1 activity of a series of diarylpyrimidines (DAPYs) are described. Several members of this novel class of non-nucleoside reverse transcriptase inhibitors (NNRTIs) are extremely potent against both wild-type and a panel of clinically significant single- and double-mutant strains of HIV-1.

The alpha and omega of G-protein coupled receptors: a novel method for classification. Part 1.

In this paper, a novel method is presented for classification of 113 neurotransmitter and opioid receptors that is based on the counting of amino acid residues. With the use of sequence alignment, ten conserved key residues were identified: alpha (the first Met residue of the sequence), Asn in the tentative first transmembrane domain (TM1), Asp (TM2), Cys (top of TM3), Arg (bottom of TM3), Trp (TM4), Cys (in the loop between TM4 and TM5), Pro (TM5), Pro (TM6), Pro (TM7) and omega (the last residue of the sequence). The number of residues between these key residues is unique for each receptor or receptor subtype and is used for classification. The number of residues between two key residues defines a segment. The sum of the segments before or after a key residue is defined as a partition. In total, 73 possible classification schemes were found using two or three segments, partitions or a combination of segments and partitions. The surprising and striking results is that each of the sequences examined can be characterized by a code consisting of two or three figures. Each figure represents a number of amino acid residues.

A molecular model for the interaction between vorozole and other non-steroidal inhibitors and human cytochrome P450 19 (P450 aromatase).

In a previous study (Vanden Bossche et al., Breast Cancer Res. Treat. 30 (1994) 43) the interaction between (+)-S-vorozole and the I-helix of cytochrome P450 19 (P450 aromatase) has been reported. In the present study we extended the "I-helix model" by incorporating the C-terminus of P450 aromatase. The crystal structures of P450 101 (P450 cam), 102 (P450 BM-3) and 108 (P450 terp) reveal that the C-terminus is structurally conserved and forms part of their respective substrate binding pocket. Furthermore, the present study is extended to the interaction between P450 aromatase and its natural substrate androstenedione and the non-steroidal inhibitors (-)-R-vorozole, (-)-S-fadrozole, R-liarozole and (-)-R-aminoglutethimide. It is found that (+)-S-vorozole, (-)-S-fadrozole and R-liarozole bind in a comparable way to P450 aromatase and interact with both the I-helix (Glu302 and Asp309) and C-terminus (Ser478 and His480). The weak activity of (-)-R-aminoglutethimide might be attributed to a lack of interaction with the C-terminus.

Aromatase inhibitors--mechanisms for non-steroidal inhibitors.

The conversion of androgens to estrogens occurs in a variety of cells and tissues, such as ovarian granulosa and testicular cells, placenta, adipose tissue, and various sites of the brain. The extragonadal synthesis of estrogens has great pathophysiological importance. Estrogens produced by, for example, adipose tissue have a role in the pathogenesis of certain forms of breast cancer and endometrial adenocarcinoma. The biosynthesis of estrogens is catalyzed by the aromatase, an enzyme localized in the endoplasmic reticulum that consists of two components: a cytochrome P450 (P450 Arom, P450 19 product of the CYP19 gene) and the NADPH cytochrome P450 reductase. The alignment of the amino acid sequences of human P450 19 with other mammalian P450s shows little sequence similarity, which indicates not only that P450 19 is a unique form of the P450 superfamily but also that the aromatase may be a good target for the development of selective P450 inhibitors. Aminoglutethimide (AG) is the pioneer drug of the reversible competitive nonsteroidal aromatase inhibitors. Since AG is a nonspecific aromatase inhibitor and presents some problems with tolerability, a number of structural analogues have been synthesized. For example, rogletimide is slightly less potent than AG but has the advantage of not inhibiting the cholesterol side-chain cleavage and is devoid of sedative action. Elongation of the ethyl substituent of AG and rogletimide leads to an increase in aromatase inhibition. Further studies led to the discovery of a new generation of much more potent aromatase inhibitors. An example is fadrozole. However, although fadrozole is a poor inhibitor of the cholesterol side-chain cleavage, it suppresses aldosterone release by ACTH-stimulated human adrenocortical cells. More selective aromatase inhibitors are the triazole derivatives. Examples are CGS 20267, CGS 47645, R 76 713, and ICI D1033. R 76 713's aromatase inhibitory effect is largely due to its (+)-S-enantiomer, vorozole. Computer modeling studies of the interaction of vorozole with part of the "I-helix" of P450 19 suggest that the chlorine-substituted phenyl ring of vorozole interacts with the gamma-carbonyl group of Glu-302. Thr-310, which corresponds to the highly conserved Thr-252 in P450 101, interacts with vorozole's triazole ring, and the 1-methyl-benzotriazole moiety binds near Asp-309.

Molecular pharmacology of vitamin E: structural aspects of antioxidant activity.

In this review, the involvement of vitamin E in free radical physiology and antioxidant mechanisms is discussed. Moreover, structure-activity relationship (SAR) studies on vitamin E analogues are presented. A molecular explanation for the antioxidant activity often is based on molecular parameters, such as Hammett sigma and Brown sigma +. These parameters correlate with the activity. Using semiempirical calculations, we have found other molecular parameters related to electron distribution and structure (such as the difference in heat of formation between the compound and its radical or the energy of the highest occupied molecular orbital, HOMO) which correlate with the antioxidant action of vitamin E and its derivatives.

A preliminary 3D model for cytochrome P450 2D6 constructed by homology model building.

A homology model building study of cytochrome P450 2D6 has been carried out based on the crystal structure of cytochrome P450 101. The primary sequences of P450 101 and P450 2D6 were aligned by making use of an automated alignment procedure. This alignment was adjusted manually by matching alpha-helices (C, D, G, I, J, K and L) and beta-sheets (beta 3/beta 4) of P450 101 that are proposed to be conserved in membrane-bound P450s (Ouzounis and Melvin [Eur. J. Biochem., 198 (1991) 307]) to the corresponding regions in the primary amino acid sequence of P450 2D6. Furthermore, alpha-helices B, B' and F were found to be conserved in P450 2D6. No significant homology between the remaining regions of P450 101 and P450 2D6 could be found and these regions were therefore deleted. A 3D model of P450 2D6 was constructed by copying the coordinates of the residues from the crystal structure of P450 101 to the corresponding residues in P450 2D6. The regions without a significant homology with P450 101 were not incorporated into the model. After energy-minimization of the resulting 3D model of P450 2D6, possible active site residues were identified by fitting the substrates debrisoquine and dextrometorphan into the proposed active site. Both substrates could be positioned into a planar pocket near the heme region formed by residues Val370, Pro371, Leu372, Trp316, and part of the oxygen binding site of P450 2D6. Furthermore, the carboxylate group of either Asp100 or Asp301 was identified as a possible candidate for the proposed interaction with basic nitrogen atom(s) of the substrates.(ABSTRACT TRUNCATED AT 250 WORDS)