Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts: Gene/Protein Disease Symptom Drug Enzyme Compound   Target Concepts: Gene/Protein Disease Symptom Drug Enzyme Compound

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Query: EC:3.4.21.4 (trypsin)
42,187 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

SPROUT is a new computer program for constrained structure generation that is designed to generate molecules for a range of applications in molecular recognition. It uses artificial intelligence techniques to moderate the combinatorial explosion that is inherent in structure generation. The program is presented here for the design of enzyme inhibitors. Structure generation is divided into two phases: (i) primary structure generation to produce molecular graphs to fit the steric constraints; and (ii) secondary structure generation which is the process of introducing appropriate functionality to the graphs to produce molecules that satisfy the secondary constraints, e.g., electrostatics and hydrophobicity. Primary structure generation has been tested on two enzyme receptor sites; the p-amidino-phenyl-pyruvate binding site of trypsin and the acetyl pepstatin binding site of HIV-1 protease. The program successfully generates structures that resemble known substrates and, more importantly, the predictive power of the program has been demonstrated by its ability to suggest novel structures.
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PMID:SPROUT: a program for structure generation. 832 May 53

The interaction of novel series of synthetic inhibitors with various serine proteases (leukocyte elastase, thrombin, cathepsin G, chymotrypsin, plasminogen activators and plasmin) and an aspartic protease (HIV-1 protease) were studied. Various aspects were analyzed: mechanism of action, structure-activity relationships, and in some cases, molecular modelling and biological evaluation. Functionalized cyclopeptides and N-aryl azetidin-2-ones behaved as suicide substrates acting specifically on trypsin-like proteases (thrombin or urokinase) and elastases, respectively. Novel hydrazinopeptides acted as reversible inhibitors of elastases. Coumarin derivatives inactivated very efficiently chymotrypsin-like proteases (k(inact)/K(I) = 760,000 M(-1) .s(-1)). Inhibitors of HIV-1 protease acting either as inactivators or dimerization inhibitors are under investigation. The inhibitors described above are useful for elucidating the biological roles of the target enzymes and constitute potential drugs.
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PMID:[Synthetic inhibitors targeting serine and aspartic acid proteases]. 877 49

Two three-dimensional (3D) molecular descriptors are used to classify 73 protease inhibitors against the human immunodeficiency virus type 1 (HIV-1). X-ray structures of these HIV-1 protease bound inhibitors are used as templates to generate the most probable bioactive conformations of the inhibitors. A convex hull computation algorithm is applied to each structure generated. The frequency of atoms lying on the vertexes of each hull is counted. Vertexes of the same atomic charge state are then gathered together as a set of commonly exposed groups for all the structures generated. The first 3D descriptor is computed as the maximum molecular path length among any three distinct commonly exposed groups, while the second 3D one is computed as the maximum molecular path length among any three atoms of nonconvex hull vertexes. We find that the 73 HIV-1 protease inhibitors can be classified by the first 3D descriptor into two groups, which agrees with the result of visual classification using the activity data as a criterion for these compounds. The classification scheme is then used to classify a database of 427 active trypsin inhibitors and their inactive analogues. The structures of these compounds are generated theoretically from steps of energy minimization and molecular dynamics. Classification for all these compounds is performed using the SYBYL hierarchical clustering method on the first 3D descriptor and then the second 3D one computed. It is found that some inactive analogues are completely separated from the active inhibitors at the first stage of classification using the first 3D descriptor. Most of the highly active inhibitors are classified into a cluster at the second stage of classification using the second 3D descriptor. Finally, most of these highly active inhibitors are separated from all the accompanying inactive analogues in the cluster through a structural alignment process using a set of commonly exposed groups determined for them.
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PMID:Classification of some active compounds and their inactive analogues using two three-dimensional molecular descriptors derived from computation of three-dimensional convex hulls for structures theoretically generated for them. 1104 16

The capacity of interferon beta to alter the course of multiple sclerosis has promoted a new therapeutic concept, based upon the modulation of the immune response rather than its suppression. As the proteasome plays a crucial role in the control of the inflammatory process and immune cell survival, targeting the proteasome appears as a novel approach for the prevention and treatment of inflammatory autoimmune diseases. We have previously shown that ritonavir, an HIV-1 protease inhibitor used in AIDS therapy, can modulate the proteasome function by inhibiting the chymotrypsin-like activity and enhancing the trypsin-like activity. We have, therefore, explored its therapeutic potential on experimental autoimmune encephalomyelitis (EAE), an experimental model of multiple sclerosis, in Lewis rats and SJL mice. Daily administration of ritonavir during autoimmune antigen stimulation prevented clinical symptoms of EAE in a dose- and time-dependent manner. This protection was accompanied by an inhibition of the mononuclear cell infiltration into the central nervous system usually observed in EAE. Despite a complete absence of clinical symptoms during first EAE induction, ritonavir-treated animals became resistant to further induction of EAE, suggesting an immune mechanism of protection. These results suggest that proteasome modulation using ritonavir or analogues may be of interest for patients with multiple sclerosis.
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PMID:Protection against experimental autoimmune encephalomyelitis by a proteasome modulator. 1149 58

Two techniques for determining enzyme kinetic constants using isothermal titration microcalorimetry are presented. The methods are based on the proportionality between the rate of a reaction and the thermal power (heat/time) generated. (i) An enzyme can be titrated with increasing amounts of substrate, while pseudo-first-order conditions are maintained. (ii) Following a single injection, the change in thermal power as substrate is depleted can be continuously monitored. Both methods allow highly precise kinetic characterization in a single experiment and can be used to measure enzyme inhibition. Applicability is demonstrated using a representative enzyme from each EC classification, including (i) oxidation-reduction activity of DHFR (EC 1.5.1.3); (ii) transferase activity of creatine phosphokinase (EC 2.7.3.2) and hexokinase (EC 2.7.1.1); (iii) hydrolytic activity of Helicobacter pylori urease (EC 3.5.1.5), trypsin (EC 3.4.21.4), and the HIV-1 protease (EC 3.4.21.16); (iv) lyase activity of heparinase (EC 4.1.1.7); and (v) ligase activity of pyruvate carboxylate (EC 6.4.1.1). This nondestructive method is completely general, enabling precise analysis of reactions in spectroscopically opaque solutions, using physiological substrates. Such a universal assay may have wide applicability in functional genomics.
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PMID:Enzyme kinetics determined using calorimetry: a general assay for enzyme activity? 1155 13

A major goal in ligand and drug design is the optimization of the binding affinity of selected lead molecules. However, the binding affinity is defined by the free energy of binding, which, in turn, is determined by the enthalpy and entropy changes. Because the binding enthalpy is the term that predominantly reflects the strength of the interactions of the ligand with its target relative to those with the solvent, it is desirable to develop ways of predicting enthalpy changes from structural considerations. The application of structure/enthalpy correlations derived from protein stability data has yielded inconsistent results when applied to small ligands of pharmaceutical interest (MW < 800). Here we present a first attempt at an empirical parameterization of the binding enthalpy for small ligands in terms of structural information. We find that at least three terms need to be considered: (1) the intrinsic enthalpy change that reflects the nature of the interactions between ligand, target, and solvent; (2) the enthalpy associated with any possible conformational change in the protein or ligand upon binding; and, (3) the enthalpy associated with protonation/deprotonation events, if present. As in the case of protein stability, the intrinsic binding enthalpy scales with changes in solvent accessible surface areas. However, an accurate estimation of the intrinsic binding enthalpy requires explicit consideration of long-lived water molecules at the binding interface. The best statistical structure/enthalpy correlation is obtained when buried water molecules within 5-7 A of the ligand are included in the calculations. For all seven protein systems considered (HIV-1 protease, dihydrodipicolinate reductase, Rnase T1, streptavidin, pp60c-Src SH2 domain, Hsp90 molecular chaperone, and bovine beta-trypsin) the binding enthalpy of 25 small molecular weight peptide and nonpeptide ligands can be accounted for with a standard error of +/-0.3 kcal x mol(-1).
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PMID:Structural parameterization of the binding enthalpy of small ligands. 1221 Sep 99

An assessment of nine scoring functions commonly applied in docking using a set of 189 protein-ligand complexes is presented. The scoring functions include the CHARMm potential, the scoring function DrugScore, the scoring function used in AutoDock, the three scoring functions implemented in DOCK, as well as three scoring functions implemented in the CScore module in SYBYL (PMF, Gold, ChemScore). We evaluated the abilities of these scoring functions to recognize near-native configurations among a set of decoys and to rank binding affinities. Binding site decoys were generated by molecular dynamics with restraints. To investigate whether the scoring functions can also be applied for binding site detection, decoys on the protein surface were generated. The influence of the assignment of protonation states was probed by either assigning "standard" protonation states to binding site residues or adjusting protonation states according to experimental evidence. The role of solvation models in conjunction with CHARMm was explored in detail. These include a distance-dependent dielectric function, a generalized Born model, and the Poisson equation. We evaluated the effect of using a rigid receptor on the outcome of docking by generating all-pairs decoys ("cross-decoys") for six trypsin and seven HIV-1 protease complexes. The scoring functions perform well to discriminate near-native from misdocked conformations, with CHARMm, DOCK-energy, DrugScore, ChemScore, and AutoDock yielding recognition rates of around 80%. Significant degradation in performance is observed in going from decoy to cross-decoy recognition for CHARMm in the case of HIV-1 protease, whereas DrugScore and ChemScore, as well as CHARMm in the case of trypsin, show only small deterioration. In contrast, the prediction of binding affinities remains problematic for all of the scoring functions. ChemScore gives the highest correlation value with R(2) = 0.51 for the set of 189 complexes and R(2) = 0.43 for the set of 116 complexes that does not contain any of the complexes used to calibrate this scoring function. Neither a more accurate treatment of solvation nor a more sophisticated charge model for zinc improves the quality of the results. Improved modeling of the protonation states, however, leads to a better prediction of binding affinities in the case of the generalized Born and the Poisson continuum models used in conjunction with the CHARMm force field.
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PMID:Assessing scoring functions for protein-ligand interactions. 1516 85

"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

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

New 2-bromomethyl-8-substituted-benzo[c]chromen-6-ones have been synthesized and their bioactive properties have been evaluated on different enzymatic models: serine proteases (trypsin and alpha-chymotrypsin), HIV aspartyl protease, nitric oxide synthase and a panel of protein kinases. These new derivatives can provide upon chemical or enzymatic attack, very reactive quinonimine methide intermediates, which could be utilized for the design of enzyme inhibitors. We found that some of these new derivatives exhibit modest inhibitory activities on the studied enzyme models, but it could be improved after structure optimization.
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PMID:New 2-bromomethyl-8-substituted-benzo[c]chromen-6-ones. Synthesis and biological properties. 1558 26


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