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Mutations that arise in HIV-1 protease after exposure to various HIV-1 protease inhibitors have proved to be a difficult aspect in the treatment of HIV. Mutations in the binding pocket of the protease can prevent the protease inhibitor from binding to the protein effectively. In the present study, the crystal structures of 68 HIV-1 proteases complexed with(More)
The prediction of biological activity of a chemical compound from its structural features, representing its physico-chemical properties, plays an important role in drug discovery, design and development. Since the biological data is highly non-linear, the machine-learning techniques have been widely used for modeling it. In the present work, the clustering,(More)
Using a shape analysis technique, a dataset of 83 (20 wild-type and 63 mutated) HIV-1 protease crystal structures is analyzed for the shape changes in their binding pockets. The structures were reported with different bound inhibitors (ligands) and consist of a variety of mutations. Several geometrical and topological attributes based on the volumetric(More)
A total of 355 cows were sampled (serum, n = 315; faeces, n = 355; milk, n = 209) from dairy farms located in the Punjab state of India. Faeces and serum/milk samples were screened by acid fast staining and "indigenous ELISA," respectively. IS900 PCR was used to screen faeces and milk samples. Bio-load of MAP in dairy cows was 36.9, 15.6, 16.3, and 14.4%,(More)
A comparative analysis of three important shape analysis techniques viz. real spherical harmonic (RSH) coefficient, shape signatures based on ray-tracing, and multi-resolution attributed contour tree (MACT) is performed to understand their strengths and weaknesses in terms of their shape description power and computational ability. The analysis is performed(More)
The present study develops a classification model to correlate the binding pockets of 70 HIV-1 protease crystal structures in terms of their structural descriptors to their complexed HIV-1 protease inhibitors. The Random Forest classification model is used to reduce the chemical descriptor space from 456 to the 12 most relevant descriptors based on the Gini(More)
A model for the classification of 70 HIV-1 protease crystal structure binding pockets to one of its complexed FDA approved protease inhibitors utilizing Random Forest has been developed. 456 chemical descriptors of the binding pocket of each crystal structure have been computed and are used to develop the classification model. Simulations were performed to(More)