A comparison of bacterial and human prolyl oligopeptidases: 3D-QSAR analysis of amino acid and thioxo amino acid pyrrolidides and thiazolidides as inhibitors of prolyl oligopeptidases and examination of the binding sites in the homology models of prolyl oligopeptidases
The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearing in Internet chat lines. In this Letter a recently introduced source separation method,termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several blind separation algorithms for the problem considered. Complexity pursuit is a generalisation of projection pursuit to time series and it is able to use both higher-order statistical measures and temporal dependency information in separating the topics. Experimental results on chat line and newsgroup data demonstrate that the minimum complexity time series indeed do cor- respond to meaningful topics inherent in the dynamical text data,and also suggest the applic- ability of the method to query-based retrieval from a temporally changing text stream. Summary 3D-Quantitative structure-activity relationships of amino acid and thioxo amino acid pyrrolidides and thiazolidides as inhibitors of bacterial (F. meningosepticum) and human placenta prolyl oligopeptidase were investigated using CoMFA and COMSIA approaches. For the bacterial POP, the best CoMFA model obtained from 16 inhibitors is a five-component model with the following statistics: R 2 = 0.852 and RMSEcv = 0.213 for the cross-validation, and R 2 = 0.954 and RMSE = 0.119 for the fitted. The best COMSIA model obtained is a two-component model with the following statistics: R 2 = 0.826 and RMSEcv = 0.203 for the cross-validation, and R 2 = 0.915 and RMSE = 0.142 for the fitted. For the human placenta POP, the best CoMFA model obtained is again a five-component model. This model has the following statistics: R 2 = 0.885 and RMSEcv = 0.470 for the cross-validation, and R 2 = 0.966 and RMSE = 0.256 for the fitted. The best COMSIA model obtained is a two- component model with the following statistics: R 2 = 0.922 and RMSEcv = 0.339 for the cross-validation, and R 2 = 0.959 and RMSE = 0.247 for the fitted. A comparison of the homology models of the human and the bacterial POP reveals similarities and differences in the binding sites of the two proteins and provides some clues for the observed selectivity.