Hypothetical proteins (HP) are those that are not characterized in the laboratory and so remain “orphaned” in genomic databases. In recent times there has been a lot of progress in characterizing HPs in the laboratory. Various methods, such as sequence capture and Next Generation Sequencing (NGS), have been used to rapidly identify HP functions and their encoded genes. Applications and methods, such as the isolation of single genes, are greatly facilitated by pull-down assays to characterize proteins. Furthermore, there are methods to extract proteins from either the whole cell or a subcellular fraction. But the weakness is that some assays are fairly expensive and laborious, and characterizing HP function is always imperfect. In the recent past, statistical interpretations of the in silico selection strategies have improved the identification of the most promising candidates, including those from various annotation methods, such as protein interaction networks (PIN). Given the improvements in technology that have permitted a substantial increase in computational annotation, we ask if the prediction of HP function in silico (validation of models through algorithms and data subsets) could likewise be improved. In this work, we apply a bioinformatics analogy to each step of a wet lab experiment performed to predict aspects confirming protein function. Although it may be a less bona fide approach, assigning a putative function from conservation observed in homologous protein sequences might be worthwhile to consider prior to a wet lab experiment.

Hypothetical proteins (假设蛋白质)

Omics (组学)

Systems biology (系统生物学)

Functional genomics (功能基因组学)

Annotation (注释)