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Bixin is a natural dye and a high commercial important compound, produced from Bixin synthetic pathway in case of Bixa orellana plant. The particular enzyme Lycopene cleavage Oxygenase catalyzes the fi rst step of reaction pathways from Trans-lycopene to Bixin synthesis. The 3D structure of the enzyme was predicted by MODELLER program and the missing side chains were verifi ed by SCRWL4 tool. Model validation was done by using the output of PROCHECK and DOPE score. The Ramachandran plot for the model was observed as 87.3 percentages of residues is in favourable regions that indicate the model is reliable. Molecular dynamics simulation of the model protein was performed in water 5 lakh times for 1000 Pico second and at 300K by GROMACS software in a high performance computing environment. The energy value and RMSD (Root men square deviation) fl uctuation of Carbon alpha back bone of the model was computed that confi rms the stability of the model protein.

Currently data mining is an essential tool to discover the hidden data and important patterns from a large data set. The present work is a pilot study that compares the result of sequence based phylogenetic study and some of the physicochemical and structural feature based clustering of Laccase enzyme sequences. Total of 50 homologous sequences were obtained specific to each of the organism like plant, fungi and bacteria. Multiple sequences alignment of sequences was performed followed by phylogenetic tree construction and consistency study also to observe the major clusters. Again the major domain and motif analysis was done to support the study in the divergence pattern of Laccase enzyme sequences. There after 13 numbers of physicochemical and structural features were computed for each enzyme sequences. Then data normalisation and k-means clustering technique revealed that the fungi, bacteria and plant were obtained in three distinct clusters. The analysis indicates that the result of sequence based classification is in a good agreement with physicochemical basis of classification of proteins. The methods can be further optimised for different clustering algorithm to obtain specific physicochemical features that would help to classification of proteins.

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Context:Boswellic acid consists of a series of pentacyclic triterpene molecules that are produced by the plant Boswellia serrata. The potential applications of Bowsellic acid for treatment of cancer have been focused here.Aims:To predict the property of the bowsellic acid derivatives as anticancer compounds by various computational approaches.Materials and Methods:In this work, all total 65 derivatives of bowsellic acids from the PubChem database were considered for the study. After energy minimization of the ligands various types of molecular descriptors were computed and corresponding two-dimensional quantitative structure activity relationship (QSAR) models were obtained by taking Andrews coefficient as the dependent variable.Statistical Analysis Used:Different types of comparative analysis were used for QSAR study are multiple linear regression, partial least squares, support vector machines and artificial neural network.Results:From the study geometrical descriptors shows the highest correlation coefficient, which indicates the binding factor of the compound. To evaluate the anticancer property molecular docking study of six selected ligands based on Andrews affinity were performed with nuclear factor-kappa protein kinase (Protein Data Bank ID 4G3D), which is an established therapeutic target for cancers. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound.Conclusions:Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound.