Gas phase derivatization of ammonia with 4-fluoro-7-nitrobenzo-2-oxa-1,3-diazole and its application to urease assay.

An ammonia-specific and rapid fluorometric method for determination of ammonia and urease activity was developed. The method is designed to assay ammonia levels or urease activity for the rapid diagnosis of Helicobacter pylori infection. 4-Fluoro-7-nitrobenzo-2-oxa-1,3-diazole was used to derivatize ammonia and 4-amino-7-nitrobenzo-2-oxa-1,3-diazole was analysed by high performance liquid chromatography at an excitation wavelength of 455 nm and an emission wavelength of 520 nm. Derivatization was designed to react with ammonia gas produced in a strong alkaline pH sample. The fluorescent intensity was linear in the range of 0.1-10 mM ammonia per tube when the reaction was carried out for 15 min at 37 degrees C. Urease activity, judged as the amount of ammonia production from urea, could be measured at 25 ng per tube (S/N = 1.5) with Jack bean meal urease. Because of its rapidity, this assay is potentially superior to the current standard method in use in clinical settings.     

Bootstrap-based consensus scoring method for protein-ligand docking

To improve the performance of a single scoring function used in a protein-ligand docking program, we developed a bootstrap-based consensus scoring (BBCS) method, which is based on ensemble learning. BBCS combines multiple scorings, each of which has the same function form but different energy-parameter sets. These multiple energy-parameter sets are generated in two steps: (1) generation of training sets by a bootstrap method and (2) optimization of energy-parameter set by a Z-score approach, which is based on energy landscape theory as used in protein folding, against each training set. In this study, we applied BBCS to the FlexX scoring function. Using given 50 complexes, we generated 100 training sets and obtained 100 optimized energy-parameter sets. These parameter sets were tested against 48 complexes different from the training sets. BBCS was shown to be an improvement over single scoring when using a parameter set optimized by the same Z-score approach. Comparing BBCS with the original FlexX scoring function, we found that (1) the success rate of recognizing the crystal structure at the top relative to decoys increased from 33.3% to 52.1% and that (2) the rank of the crystal structure improved for 54.2% of the complexes and worsened for none. We also found that BBCS performed better than conventional consensus scoring (CS).     

Hidden active information in a random compound library: extraction using a pseudo-structure-activity relationship model

We propose a hypothesis that "a model of active compound can be provided by integrating information of compounds high-ranked by docking simulation of a random compound library". In our hypothesis, the inclusion of true active compounds in the high-ranked compound is not necessary. We regard the high-ranked compounds as being pseudo-active compounds. As a method to embody our hypothesis, we introduce a pseudo-structure-activity relationship (PSAR) model. Although the PSAR model is the same as a quantitative structure activity relationship (QSAR) model, in terms of statistical methodology, the implications of the training data are different. Known active compounds (ligands) are used as training data in the QSAR model, whereas the pseudo-active compounds are used in the PSAR model. In this study, Random Forest was used as a machine-learning algorithm. From tests for four functionally different targets, estrogen receptor antagonist (ER), thymidine kinase (TK), thrombin, and acetylcholine esterase (AChE), using five scoring functions, we obtained three conclusions: (1) the PSAR models significantly gave higher percentages of known ligands found than random sampling, and these results are sufficient to support our hypothesis; (2) the PSAR models gave higher percentages of known ligands found than normal scoring by scoring function, and these results demonstrate the practical usefulness of the PSAR model; and (3) the PSAR model can assess compounds failed in the docking simulation. Note that PSAR and QSAR models are used in different situations; the advantage of the PSAR model emerges when no ligand is available as training data or when one wants to find novel types of ligands, whereas the QSAR model is effective for finding compounds similar to known ligands when the ligands are already known.