PreDSLpmo: A neural network-based prediction tool for functional annotation of lytic polysaccharide monooxygenases.

Lytic polysaccharide monooxygenases (LPMOs), a family of copper-dependent oxidative enzymes, boost the degradation of polysaccharides such as cellulose, chitin, and others. While experimental methods are used to validate LPMO function, a computational method that can aid experimental methods and provide fast and accurate classification of sequences into LPMOs and its families would be an important step towards understanding the breadth of contributions these enzymes make in deconstruction of recalcitrant polysaccharides. In this study, we developed a machine learning-based tool called PreDSLpmo that employs two different approaches to functionally classify protein sequences into the major LPMO families (AA9 and AA10).

Amino Acid-Dependent Alterations in Cell Wall and Cell Morphology of DR1.

exhibits growth medium-dependent morphological variation in cell shape, but there is no evidence whether this phenomenon is observed in other members of the Deinococcaceae family. In this study, we isolated a red-pigmented, aerobic, strain DR1 from Dadri wetland, India. This strain exhibited cell-morphology transition from rod-shaped cells to multi-cell chains in a growth-medium-dependent fashion.

An integrative approach to develop computational pipeline for drug-target interaction network analysis.

Understanding the general principles governing the functioning of biological networks is a major challenge of the current era. Functionality of biological networks can be observed from drug and target interaction perspective. All possible modes of operations of biological networks are confined by the interaction analysis.

Draft Genome Sequence of DR1, a Novel Strain Isolated from a Freshwater Wetland.

strain DR1, a red-pigmented, arsenic- and radiation-resistant bacterium, was isolated from a water sample of the Dadri wetland, Uttar Pradesh, India. Here, we report a draft genome sequence of this strain, which may provide useful information regarding the genes and pathways involved in heavy-metal bioremediation.