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.

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.

Using genetic algorithms for the construction of phylogenetic trees: application to G-protein coupled receptor sequences.

Many different phylogenetic clustering techniques are used currently. One approach is to first determine the topology with a common clustering method and then calculate the branch lengths of the tree. If the resulting tree is not optimal exchanging tree branches can make some local changes in the tree topology. The whole process can be iterated until a satisfactory result has been obtained. The efficiency of this method fully depends on the initially generated tree. Although local changes are made, the optimal tree will never be found if the initial tree is poorly chosen. In this article, genetic algorithms are applied such that the optimal tree can be found even with a bad initial tree topology. This tree generating method is tested by comparing its results with the results of the FITCH program in the PHYLIP software package. Two simulated data sets and a real data set are used.

Classification and identification of proteins by means of common and specific amino acid n-tuples in unaligned sequences.

Unaligned amino acid sequences can be characterized by their composition of amino acid n-tuples (i.e. doublets, triplets, quadruplets, etc.). In this study we investigated the performance of two statistics, termed commonality and specificity, that are derived from n-tuple counts using a set of G-protein coupled receptor (GPCR) sequences. The commonality of a tuple is defined as its relative occurrence in the sequences that belong to a given GPCR subtype. The specificity of a tuple is derived from its relative occurrence in the sequences of a given GPCR subtype and from its relative non-occurrence in the sequences that do not belong to this subtype. A graphical presentation, termed 'polygram', is described for the visualization of common and specific tuples. The method can be applied to the classification of unknown GPCR sequences. It can also be applied to the identification of fragments of GPCRs, such as may occur in chimeric receptors. The method is generally applicable to other protein families and other types of coding.

The alpha and omega of G-protein coupled receptors: a novel method for classification. Part 2. Bin classification.

A novel way of classification of G-protein coupled receptors is presented that is only based on receptor sequence information by counting of amino acid residues. It involves the number of amino acid residues between the Asn residue in TM1 and the residue Cys in the loop between TM4 and TM5, the number of residues between the latter Cys residue and Pro residue in TM6, and the number of residues between the latter Pro and the last amino acid residue (called omega) in the sequence. The classification of 131 sequences, covering biogenic amine, opioid and somatostatin receptors, is visualized by means of a diagram which is referred to as a bin map. Each bin in the diagram encloses all the sequences that belong to one and only one receptor type or subtype. This so-called bin classification was obtained by means of the genetic algorithm methodology, which offers new opportunities for classifying proteins.