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)

Comparative Molecular Field Analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) paradigm (Cramer, R.D.; et al. (1988), J. Am. Chem. Soc., 110, 5959-5967), correlates variations in the (experimental) biological activity with 3D variance in the steric and electrostatic field of modeled compounds. Of general interest to the drug design area is the interpretation of CoMFA results, in order to gain maximum benefit from an established 3D-QSAR model. CoMFA studies report results using the standard deviation (stdev) times(*) coefficient (beta) field and its contributions to make SAR statements. This field is the scalar product of the absolute stdev of the CoMFA field at a lattice point and the QSAR equation coefficient (beta) at the same point. Negative beta values yield detrimental contributions, while positive beta values are considered beneficial. The QSAR equation is based on actual field values, therefore both positive and negative field values can have beneficial effect to the target property (Y), depending on the sign of beta. The results of a CoMFA model on 59 HIV-1 protease (HIV-PR) inhibitors (Waller, C.L.; et al. (1993), J. Med. Chem., 36, 4152-4160) were compared with the HIV-PR crystal structure to analyze the correspondence between CoMFA fields and ligand binding regions in the enzyme. Local steric and electrostatic interactions were analyzed in terms of various field values and beta coefficients. While redundant for some regions, other field contours besides stdev* beta bring additional information. Using this method, we observed a unique region with negative beta values for the electrostatic field (based on a -1 charged probe) located opposite of the scissile bond, between P1 and P1', where steric stdev* beta values are positive. Four hydrophobic residues in the HIV-PR crystal delimit the region, which is suggested as a new potential hydrophobic binding site for the inhibitors. The same region was confirmed using the stdev* beta contours of a HINT (Kellogg, G.; et al. (1991), J. Comput.-Aided Mol. Design, 5, 545-552) calculation on the same model. The steric, electrostatic and lipophilic fields of the CoMFA and HINT models are presented in various forms, and the information extracted is detailed.
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PMID:3D-QSAR of human immunodeficiency virus (I) protease inhibitors. III. Interpretation of CoMFA results. 757 6

Physical organic structural properties of small molecules and macromolecules such as bond count, branching and proximity between multiple polar fragments contribute significantly to measured hydrophobicity (log P). These structural properties are encoded in the Rekker and Leo methods of calculating log P as structural-dependent factors. Regardless of the size of the atom primitive set, methods predicting log P with only atom primitives can miss subtle structural detail within series of related compounds. The HINT (Hydropathic INTeractions) model for inter- and intramolecular noncovalent interactions calculates atom-based hydrophobic constants, but uses all Leo-type factors in the calculation rather than a large set of atom primitives. Two types of applications of HINT are discussed: evaluation of the binding of an inhibitor (A74704) to HIV-1 protease, where it is shown that modeling of the protonation state (i.e., Asp25, Asp125) in the protein can strongly influence perceived substrate binding; and the use of HINT to calculate a third (hydropathic) field for CoMFA can yield a statistically enhanced and predictive model for molecular design.
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PMID:The effect of physical organic properties on hydrophobic fields. 803 11

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been developed using comparative molecular field analysis (CoMFA) on a large data set (118 compounds) of diverse cyclic urea derivatives as protease inhibitors against the human immunodeficiency virus type 1 (HIV-1). X-ray crystal structures of HIV-1 protease bound with this class of inhibitors were used to derive the most probable bioactive conformations of the inhibitors. The enzyme active site was used as a constraint to limit the number of possible conformations that are sterically accessible. The test sets have been created keeping in mind structural diversity as well as the uniform simple statistical criteria (mean, standard deviation, high and low values) of the protease inhibitory activities of the molecules compared to the training sets. Multiple predictive models have been developed with the training sets (93 compounds in each set) and validated with the corresponding test sets (25 compounds in each set). All the models yielded high predictive correlation coefficients (q2 from 0.699 to 0.727), substantially high fitted correlation coefficients (r2 from 0.965 to 0.973), and reasonably low standard errors of estimates (S from 0. 239 to 0.265). The steric and electrostatic effects have approximately equal contributions, 45% and 55% (approximately), respectively, toward explaining protease inhibitory activities. This analysis yielded models with significant information on steric and electrostatic interactions clearly discerned by the respective coefficient contour plots when overlapped on the X-ray structure of the HIV-1 protease. The HINT CoMFA study revealed significant contribution of hydrophobicity toward protease inhibitory activity. The 3D visualization technique utilizing these contour plots as well as the receptor site geometry may significantly improve our understanding of the inhibitor-protease (HIV-1) interactions and help in designing compounds with improved activity.
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PMID:Three-dimensional quantitative structure-activity relationship study on cyclic urea derivatives as HIV-1 protease inhibitors: application of comparative molecular field analysis. 992 30

"Getting it right" refers to the careful modeling of all elements in the living system, i.e. biological macromolecules, ligands and water molecules. In addition, careful attention should be paid to the protonation state of ionizable functional groups on the ligands and residues at the active site. Computational technology based on the empirical HINT program is described to: (1) calculate free energy scores for ligand binding; (2) include the implicit and explicit effects of water in and around the ligand binding site; and (3) incorporate the effects of global and local pH in molecular models. This last point argues for the simultaneous consideration of a number of molecular models, each with different protonation profiles. Data from recent studies of protein-ligand systems (trypsin, thrombin, neuraminidase, HIV-1 protease and others) are used to illustrate the concepts in the paper. Also discussed are experimental factors related to accurate free energy predictions with this and other computational technologies.
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PMID:Getting it right: modeling of pH, solvent and "nearly" everything else in virtual screening of biological targets. 1518 7

Structural water molecules within protein active sites are relevant for ligand-protein recognition because they modify the active site geometry and contribute to binding affinity. In this work an analysis of the interactions between 23 ligands and dimeric HIV-1 protease is reported. The X-ray structures of these complexes show the presence of four types of structural water molecules: water 301 (on the symmetry axis), water 313, water 313bis, and peripheral waters. Except for water 301, these are generally complemented with a symmetry-related set. The GRID program was used both for checking water locations and for placing water molecules that appear to be missing from the complexes due to crystallographic uncertainty. Hydropathic analysis of the energetic contributions using HINT indicates a significant improvement of the correlation between HINT scores and the experimentally determined binding constants when the appropriate bridging water molecules are taken into account. In the absence of water r2 = 0.30 with a standard error of +/- 1.30 kcal mol(-1) and when the energetic contributions of the constrained waters are included r2 = 0.61 with a standard error of +/- 0.98 kcal mol(-1). HINT was shown to be able to map quantitatively the contribution of individual structural waters to binding energy. The order of relevance for the various types of water is water 301 > water 313 > water 313bis > peripheral waters. Thus, to obtain the most reliable free energy predictions, the contributions of structural water molecules should be included. However, care must be taken to include the effects of water molecules that add information value and not just noise.
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PMID:Simple, intuitive calculations of free energy of binding for protein-ligand complexes. 3. The free energy contribution of structural water molecules in HIV-1 protease complexes. 1531 62

Fourteen popular scoring functions, i.e., X-Score, DrugScore, five scoring functions in the Sybyl software (D-Score, PMF-Score, G-Score, ChemScore, and F-Score), four scoring functions in the Cerius2 software (LigScore, PLP, PMF, and LUDI), two scoring functions in the GOLD program (GoldScore and ChemScore), and HINT, were tested on the refined set of the PDBbind database, a set of 800 diverse protein-ligand complexes with high-resolution crystal structures and experimentally determined Ki or Kd values. The focus of our study was to assess the ability of these scoring functions to predict binding affinities based on the experimentally determined high-resolution crystal structures of proteins in complex with their ligands. The quantitative correlation between the binding scores produced by each scoring function and the known binding constants of the 800 complexes was computed. X-Score, DrugScore, Sybyl::ChemScore, and Cerius2::PLP provided better correlations than the other scoring functions with standard deviations of 1.8-2.0 log units. These four scoring functions were also found to be robust enough to carry out computation directly on unaltered crystal structures. To examine how well scoring functions predict the binding affinities for ligands bound to the same target protein, the performance of these 14 scoring functions were evaluated on three subsets of protein-ligand complexes from the test set: HIV-1 protease complexes (82 entries), trypsin complexes (45 entries), and carbonic anhydrase II complexes (40 entries). Although the results for the HIV-1 protease subset are less than desirable, several scoring functions are able to satisfactorily predict the binding affinities for the trypsin and the carbonic anhydrase II subsets with standard deviation as low as 1.0 log unit (corresponding to 1.3-1.4 kcal/mol at room temperature). Our results demonstrate the strengths as well as the weaknesses of current scoring functions for binding affinity prediction.
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PMID:An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes. 1555 82

One of the more challenging issues in medicinal chemistry is the computation of the free energy of ligand binding to macromolecular targets. This allows for the screening of libraries of chemicals for fast and inexpensive identification of lead compounds. Many attempts have been made and several algorithms have been developed for this purpose. Whereas enthalpic contributions are evaluated using methods and equations for which there is a reasonable consensus among researchers, the entropic contribution is evaluated using very different, and, in some cases, very approximate methods, or it is entirely ignored. Entropic contributions are of primary importance in the formation of many ligand-protein complexes, as well as in protein folding. The hydrophobic interaction, associated with the release of water molecules from the protein active site and the ligand, plays a significant role in complex formation, predominantly contributing to the total entropy change and, in some cases, to the total free energy of binding. There are distinct approaches for the evaluation of the contribution of water molecules to the free energy of binding based on Newtonian mechanics force fields, multi-parameter empirical scoring functions and experimental force fields. This review describes these methods -- discussing both their advantages and limitations. Particular emphasis will be placed on HINT (Hydropatic INTeractions), a "natural" force field that takes into account in a unified way enthalpic and entropic contributions of all interacting atoms in protein-ligand complexes, including released and structured water molecules. As a case-study, the contribution of water molecules to the binding free energy of HIV-1 protease inhibitors is evaluated.
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PMID:Free energy of ligand binding to protein: evaluation of the contribution of water molecules by computational methods. 1557 3

Algorithms and protocols are described for the optimization for H-bonding of isolated singular H2O molecules and entire networks of H2O molecules. Unlike other approaches that are prone to being trapped in local energy minima, these methods rely on exhaustive searches of orientation space for the H2O molecules. The results are scored with the HINT hydropathic interaction model, but the algorithms should be general for any energy-scoring computation. Two examples are provided: 1) the tightly-bound H2O molecule 301 of HIV-1 protease is shown to be more reasonably oriented in terms of forming H-bonds with this method than with a molecular mechanics energy minimization method; and 2) the H2O network surrounding carbonmonoxymyoglobin is constructed and analyzed for a 1.80-A neutron-diffraction structure. The H-atom positions calculated with this method show a somewhat better agreement with the experimental results than do the H-atom positions calculated with molecular mechanics, and both are considerably better than random.
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PMID:The importance of being exhaustive. Optimization of bridging structural water molecules and water networks in models of biological systems. 1719 77