Dataset Information

Structure-based prediction of potential binding and nonbinding peptides to HIV-1 protease.

ABSTRACT: HIV-1 protease is a major drug target against AIDS as it permits viral maturation by processing the gag and pol polyproteins of the virus. The cleavage sites in these polyproteins do not have obvious sequence homology or a binding motif and the specificity of the protease is not easily determined. We used various threading approaches, together with the crystal structures of substrate complexes which served as template structures, to study the substrate specificity of HIV-1 protease with the aim of obtaining a better differentiation between binding and nonbinding sequences. The predictions from threading improved when distance-dependent interaction energy functions were used instead of contact matrices. To rank the peptides and properly account for the peptide's conformation in the total energy, the results from using short-range potentials on multiple template structures were averaged. Finally, a dynamic threading approach is introduced which is potentially useful for cases when there is only one template structure available. The conformational energy of the peptide-especially the term accounting for the side chains-was found to be important in differentiating between binding and nonbinding sequences. Hence, the substrate specificity, and thus the ability of the virus to mature, is affected by the compatibility of the substrate peptide to fit within the limited conformational space of the active site groove.

SUBMITTER: Kurt N

PROVIDER: S-EPMC1303207 | biostudies-literature | 2003 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Structure-based prediction of potential binding and nonbinding peptides to HIV-1 protease.

Kurt Nese N Haliloglu Turkan T Schiffer Celia A CA

Biophysical journal 20030801 2

HIV-1 protease is a major drug target against AIDS as it permits viral maturation by processing the gag and pol polyproteins of the virus. The cleavage sites in these polyproteins do not have obvious sequence homology or a binding motif and the specificity of the protease is not easily determined. We used various threading approaches, together with the crystal structures of substrate complexes which served as template structures, to study the substrate specificity of HIV-1 protease with the aim ...[more]

PMID: 12885633

Similar Datasets

Project description:The prediction of binding energies from the three-dimensional (3D) structure of a protein-ligand complex is an important goal of biophysics and structural biology. Here, we critically assess the use of empirical, solvent-accessible surface area-based calculations for the prediction of the binding of Src-SH2 domain with a series of tyrosyl phosphopeptides based on the high-affinity ligand from the hamster middle T antigen (hmT), where the residue in the pY+ 3 position has been changed. Two other peptides based on the C-terminal regulatory site of the Src protein and the platelet-derived growth factor receptor (PDGFR) are also investigated. Here, we take into account the effects of proton linkage on binding, and test five different surface area-based models that include different treatments for the contributions to conformational change and protein solvation. These differences relate to the treatment of conformational flexibility in the peptide ligand and the inclusion of proximal ordered solvent molecules in the surface area calculations. This allowed the calculation of a range of thermodynamic state functions (deltaCp, deltaS, deltaH, and deltaG) directly from structure. Comparison with the experimentally derived data shows little agreement for the interaction of SrcSH2 domain and the range of tyrosyl phosphopeptides. Furthermore, the adoption of the different models to treat conformational change and solvation has a dramatic effect on the calculated thermodynamic functions, making the predicted binding energies highly model dependent. While empirical, solvent-accessible surface area based calculations are becoming widely adopted to interpret thermodynamic data, this study highlights potential problems with application and interpretation of this type of approach. There is undoubtedly some agreement between predicted and experimentally determined thermodynamic parameters: however, the tolerance of this approach is not sufficient to make it ubiquitously applicable.

Project description:In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.

Dataset Information

Structure-based prediction of potential binding and nonbinding peptides to HIV-1 protease.

Publications

Structure-based prediction of potential binding and nonbinding peptides to HIV-1 protease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets