Hypothetical proteins (HPs) are the proteins predicted to be expressed from an open reading frame, making a substantial fraction of proteomes in both prokaryotes and eukaryotes. Genome projects have led to the identification of many therapeutic targets, the putative function of the protein, and their interactions. In this review we enlist various methods linking annotation to structural and functional prediction of HPs that assist in the discovery of new structures and functions serving as markers and pharmacological targets for drug designing, discovery, and screening. Further we give an overview of how mass spectrometry as an analytical technique is used to validate protein characterisation. We discuss how microarrays and protein expression profiles help understanding the biological systems through a systems-wide study of proteins and their interactions with other proteins and non-proteinaceous molecules to control complex processes in cells. Finally, we articulate challenges on how next generation sequencing methods have accelerated multiple areas of genomics with special focus on uncharacterized proteins.
BackgroundHypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted. With the rise in number of annotation strategies, next-generation sequencing methods have provided further understanding the functions of HPs.ResultsIn our previous work, we developed a six-point classification scoring schema with annotation pertaining to protein family scores, orthology, protein interaction/association studies, bidirectional best BLAST hits, sorting signals, known databases and visualizers which were used to validate protein interactions. In this study, we introduced three more classifiers to our annotation system, viz. pseudogenes linked to HPs, homology modelling and non-coding RNAs associated to HPs. We discuss the challenges and performance of these classifiers using machine learning heuristics with an improved accuracy from Perceptron (81.08 to 97.67), Naive Bayes (54.05 to 96.67), Decision tree J48 (67.57 to 97.00), and SMO_npolyk (59.46 to 96.67).ConclusionWith the introduction of three new classification features, the performance of the nine-point classification scoring schema has an improved accuracy to functionally annotate the HPs.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2554-y) contains supplementary material, which is available to authorized users.
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