Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:3.4.23.16 (HIV-1 protease)
 2,107 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

A comparative binding energy (COMBINE) analysis (Ortiz et al. J. Med. Chem. 1995, 38, 2681-2691) has been performed on a training set of 33 HIV-1 protease inhibitors, and the resulting regression models have been validated using an additional external set of 16 inhibitors. This data set was originally reported by Holloway et al. (J. Med. Chem. 1995, 38, 305-317), who showed the usefulness of molecular mechanics interaction energies for predicting the activity of novel HIV-1 protease inhibitors within the framework of the MM2X force field and linear regression techniques. We first used the AMBER force field on the same set of three-dimensional structures to check up on any possible force-field dependencies. In agreement with the previous findings, the calculated raw ligand-receptor interaction energies were highly correlated with the inhibitory activities (r2 = 0.81), and the linear regression model relating both magnitudes had an acceptable predictive ability both in internal validation tests (q2 = 0.79, SDEPcv = 0.61) and when applied to the external set of 16 different inhibitors (SDEPex = 1.08). When the interaction energies were further analyzed using the COMBINE formalism, the resulting PLS model showed improved fitting properties (r2 = 0.89) and provided better estimations for the activity of the compounds in the external data set (SDEPex = 0.83). Computation of the electrostatic part of the ligand-receptor interactions by numerically solving the Poisson-Boltzmann equation did not improve the quality of the linear regression model. On the contrary, incorporation of the solvent-screened residue-based electrostatic interactions and two additional descriptors representing the electrostatic energy contributions to the partial desolvation of both the ligands and the receptor resulted in a COMBINE model that achieved a remarkable predictive ability, as assessed by both internal (q2 = 0.73, SDEPcv = 0.69) and external validation tests (SDEPex = 0.59). Finally, when all the inhibitors studied were merged into a single expanded set, a new model was obtained that explained 91% of the variance in biological activity (r2 = 0.91), with very high predictive ability (q2 = 0.81, SDEPcv = 0.66). In addition, the COMBINE analysis provided valuable information about the relative importance of the contributions to the activity of individual residues that can be fruitfully used to design better inhibitors. All in all, COMBINE analysis is validated as a powerful methodology for predicting binding affinities and pharmacological activities of congeneric ligands that bind to a common receptor.
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PMID:Comparative binding energy analysis of HIV-1 protease inhibitors: incorporation of solvent effects and validation as a powerful tool in receptor-based drug design. 952 59

The Fisher's discriminant ratio has been used as a class separability criterion and implemented in a k-means clustering algorithm for performing simultaneous feature selection and data set trimming on a set of 221 HIV-1 protease inhibitors. The total number of molecular descriptors computed for each inhibitor is 43, and they are scaled to lie between 1 and 0 before being subjected to the feature selection process. Since the purpose is to select some of the most class sensitive descriptors, several feature evaluation indices such as the Shannon entropy, the linear regression of selected descriptors on the pKi of selected inhibitors, and a stepwise variable selection program are used to filter them. While the Shannon entropy provides the information content for each descriptor computed, more class sensitive descriptors are searched by both the linear regression and stepwise variable selection procedures. The inhibitors are divided into several different numbers of classes. They are subsequently divided into five classes due to the fact that the best feature selection result is obtained by the division. Most of the good features selected are the topological descriptors, and they are correlated well with the pKi values. The outliers or the inhibitors with less class-sensitive descriptor values computed for each selected descriptor are identified and gathered by the k-means clustering algorithm. These are the trimmed inhibitors, while the remaining ones are retained or selected. We find that 44% or 98 inhibitors can be retained when the number of good descriptors selected for clustering is three. The descriptor values of these selected inhibitors are far more class sensitive than the original ones as evidenced by substantial increasing in statistical significance when they are subjected to both the SYBYL CoMFA PLS and Cerius2 PLS regression analyses.
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PMID:Implementing the Fisher's discriminant ratio in a k-means clustering algorithm for feature selection and data set trimming. 1474 Oct 13

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for chromone derivatives against HIV-1 protease using molecular field analysis (MFA) with genetic partial least square algorithms (G/PLS). Three different alignment methods: field fit, pharmacophore-based, and receptor-based were used to derive three MFA models. All models produced good predictive ability with high cross-validated r(2) (r(2) (cv)), conventional r(2), and predictive r(2)(r(2)(pred)) values. The receptor-based MFA showed the best statistical results with r(2) (cv) = 0.789, r(2)= 0.886, and r(2)(pred) = 0.995. The result obtained from the receptor-based model was compared with the docking simulation of the most active compound 21 in this chromone series to the binding pocket of HIV-1 protease (PDB entry 1AJX). It was shown that the MFA model related well with the binding structure of the complex and can provide guidelines to design more potent HIV-1 protease inhibitors.
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PMID:3D-QSAR studies on chromone derivatives as HIV-1 protease inhibitors: application of molecular field analysis. 1844 18