One of the top Open Access journals for scholarly publication, Journal of Data Mining in Genomics and Proteomics, seeks to offer the most comprehensive and trustworthy source of information on new discoveries and advancements. Proteomics data warehousing, genomic data mining, genomic and proteomics data services, data mining applications in genomics, data mining applications in proteomics, data warehousing, data mining in drug discovery, statistical data mining, data algorithms, data modelling and intelligence, data mining tools, comparative proteomics, and other topics are all quickly published in the Journal of Data Mining in Genomics & Proteomics.

The hydrolysis of nitriles into matching acids and ammonia, which are employed in chemistry to create industrially significant acids, is catalysed by nitrilelases. When used to convert nitrile or in environmental management to clean up nitrile-contaminated soil, water, and air, nitrilases typically demonstrate enantioselectivity under benign reaction circumstances. They are divided into aromatic and aliphatic nitrilases based on how they react with various substrates. In the last ten years, a considerable number of microbial, plant, and animal genomes have been sequenced, and the amount of genome sequence data has greatly increased. To identify new sources of enzymes, it will be beneficial to screen genome and proteome databases. To far, a wide variety of tools and methods have been utilised for in silico screening of genome/proteome sequence databases, including BLAST, Hidden Markov Models (HMM), and neural network categorization. Among them, motif design has been discovered to be one of the most dependable methods for effective database screening.

This is because motifs in a protein sequence indicate the unique structure and functionality of the protein, which is helpful for identifying and categorising that protein. When screening microbial isolates, nitrilase activity is measured in cultured nitrile-metabolizing bacteria that have been isolated from soil or water using the enrichment culture technique. This is a traditional approach for isolating the bacteria that break down nitrile, and the procedure of checking for nitrilase activity that follows is time- and money-consuming. Currently, gene databases and genome databases are screened for new enzymes by using bioinformatical tools and techniques as Blast, HMM (Hidden Markov Model), neural network classification, and MOTIFIND (MOtif Identification Neural Design). The manual designed motif (MDM) for aliphatic and aromatic nitrilase and their validation using Prosite, ScanProsite, BLAST, PRATT, and G-block are the subjects of the current communication. Motifs were manually created to distinguish and identify aromatic and aliphatic nitrilases on the basis of past work on in silico analysis of the amino acid sequence of the aromatic and aliphatic nitrilases. The distinctions between aromatic and aliphatic nitrilases have been discovered by computational analysis of amino acid sequences and research into the physiochemical characteristics of nitrilases.