A Look inside HIV Resistance through Retroviral Protease Interaction Maps

Abstract

Retroviruses affect a large number of species, from fish and birds to mammals and humans, with global socioeconomic negative impacts. Here the authors report and experimentally validate a novel approach for the analysis of the molecular networks that are involved in the recognition of substrates by retroviral proteases. Using multivariate analysis of the sequence-based physiochemical descriptions of 61 retroviral proteases comprising wild-type proteases, natural mutants, and drug-resistant forms of proteases from nine different viral species in relation to their ability to cleave 299 substrates, the authors mapped the physicochemical properties and cross-dependencies of the amino acids of the proteases and their substrates, which revealed a complex molecular interaction network of substrate recognition and cleavage. The approach allowed a detailed analysis of the molecular-chemical mechanisms involved in substrate cleavage by retroviral proteases.Citation: Kontijevskis A, Prusis P, Petrovska R, Yahorava S, Mutulis F, et al. (2007) A look inside HIV resistance through retroviral protease interaction maps. PLoS Comput Biol 3(3): e48.

Identification of Structural Mechanisms of HIV-1 Protease Specificity Using Computational Peptide Docking: Implications for Drug Resistance

“…Hydrogen bonding and solvation energies played a major role as well, particularly with D30' and G48. These results are in broad agreement with bioinformatics-based identification of specificity-determining residues over a data set of nine retroviral proteases and corresponding cleavage data carried out by Kontijevskis et al (Kontijevskis et al, 2007a). Using multivariate analysis using physiochemical descriptors based on the protease sequences, they also found D30, V82, and I84 to play the major role in determining cleavage specificity.…”

“…With the exception of two relatively common sequence motifs at P1-P1’, aromatic-proline (Aro-P) and hydrophobic-hydrophobic (Hφ-Hφ)(Beck et al, 2002), there are few salient features in the sequences of cleavable peptides and a high degree of interdependence of various peptide residues (Kontijevskis et al, 2007b). In an impressive study by Kontijevskis et al (Kontijevskis et al, 2007a), a statistical model developed from a large database of cleavable and non-cleavable peptides for nine different retroviral proteases identified a number of physico-chemical relationships between peptide and protease residues that accurately define and predict cleavability. Ultimately, purely sequence-based methods, can, at best, implicate, but not explicitly model, the underlying structural and energetic mechanisms of substrate selectivity that are essential for drug design.…”

Proteochemometric modeling of HIV protease susceptibility

“…Proteochemometrics utilizes the physico-chemical and structural properties of series of ligands and proteins to predict their interaction [ 10 ]. Proteochemometrics has been successfully used to model various classes of G-protein coupled receptors [ 9 , 11 - 17 ], antibodies [ 18 ], as well as aspartate proteases' ability to cleave their substrates [ 19 ]. Here, we show that proteochemometrics can be used to model HIV protease resistance.…”

The C1C2: A framework for simultaneous model selection and assessment

“…Typical examples include analyzing potential drug activity through proteochemometrics and quantitative structure-activity relationship (QSAR) modeling [1-3], discovering gene regulatory binding-site modules [4], and predicting clinical outcomes of cancer from gene expression data [5]. However, recent articles have indicated that predictive modeling approaches have not fully fulfilled expectations for solving real problems.…”