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)

We propose that arginine side chains often play a previously unappreciated general structural role in the maintenance of tertiary structure in proteins, wherein the positively charged guanidinium group forms multiple hydrogen bonds to backbone carbonyl oxygens. Using as a criterion for a "structural" arginine one that forms 4 or more hydrogen bonds to 3 or more backbone carbonyl oxygens, we have used molecular graphics to locate arginines of interest in 4 proteins: Arg 180 in Thermus thermophilus manganese superoxide dismutase, Arg 254 in human carbonic anhydrase II, Arg 31 in Streptomyces rubiginosus xylose isomerase, and Arg 313 in Rhodospirillum rubrum ribulose-1,5-bisphosphate carboxylase/oxygenase. Arg 180 helps to mold the active site channel of superoxide dismutase, whereas in each of the other enzymes the structural arginine is buried in the "mantle" (i.e., inside, but near the surface) of the protein interior well removed from the active site, where it makes 5 hydrogen bonds to 4 backbone carbonyl oxygens. Using a more relaxed criterion of 3 or more hydrogen bonds to 2 or more backbone carbonyl oxygens, arginines that play a potentially important structural role were found in yeast enolase, Bacillus stearothermophilus glyceraldehyde-3-phosphate dehydrogenase, bacteriophage T4 and human lysozymes, Enteromorpha prolifera plastocyanin, HIV-1 protease, Trypanosoma brucei brucei and yeast triosephosphate isomerases, and Escherichia coli trp aporepressor (but not trp repressor or the trp repressor/operator complex).(ABSTRACT TRUNCATED AT 250 WORDS)
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PMID:A structural role for arginine in proteins: multiple hydrogen bonds to backbone carbonyl oxygens. 800 72

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

In this study, we developed a new pharmacophore-based interaction fingerprint (Pharm-IF) and examined its usefulness for in silico screening using machine learning techniques such as support vector machine (SVM) and random forest (RF) instead of similarity-based ranking. Using the docking results of PKA, SRC, cathepsin K, carbonic anhydrase II, and HIV-1 protease, the screening efficiencies of the Pharm-IF models were compared to GLIDE score and the residue-based IF (PLIF) models. The combination of SVM and Pharm-IF demonstrated a higher enrichment factor at 10% (5.7 on average) than those of GLIDE score (4.2) and PLIF (4.3). In terms of the size of the training sets, learning more than five crystal structures enabled the machine learning models to stably achieve better efficiencies than GLIDE score. We also employed the docking poses of known active compounds, in addition to the crystal structures, as positive samples of training sets. The enrichment factors of the RF models at 10% using the docking poses for SRC and cathepsin K showed significantly higher values (6.5 and 6.3) than those using only the crystal structures (3.9 and 3.2), respectively.
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PMID:Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening. 2003 88