BionoiNet: ligand-binding site classification with off-the-shelf deep neural network.

Motivation: Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods.

Effect of complement component 5 polymorphisms on mastitis resistance in Egyptian buffalo and cattle.

Mastitis is one of the costliest diseases affecting the world's dairy industry. The important contribution of complement Component 5 (C5) to phagocytosis, which plays a major role in the defence of the bovine mammary gland against infection, makes this component of innate immunity a potential contributor in defending udder against mastitis. The objectives of this study were to sequence and analyse the whole coding region of the C5 gene in Egyptian buffalo and cattle, to detect any nucleotide variations (polymorphisms) and to investigate their associations with milk somatic cell score (SCS) as an indicator of mastitis in dairy animals.