Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins.

ABSTRACT: Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divin

SUBMITTER: Wang W 

PROVIDER: S-EPMC6729729 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature