A hypothetical protein (HPs) is a protein whose existence is predicted but whether or not it is expressed remains uncertain. However, many HPs in the recent past have been known to be expressed in vivo. With various methods known to identify components in cell membrane, the functional significance of thousands of proteins, especially those that that have no functional annotations, not expressed, unique or common among genomes, is least understood. Apart from this, many HPs might turn out to be pseudogenes at a later point of time and the use of these proteins remains mis-conducive. To better understand the problem many different areas have been investigated, from methods to screen functional candidates among many hypothetical proteins using feature selection algorithms, to the application of statistical methods to test the efficacy of these proteins annotations in terms of precision and accuracy. With experimental procedures for HP function prediction being low throughput by nature, there is a strong need to precisely know their functions for a better understanding of the underlying biological mechanisms associated with them. We encourage authors interested in protein annotation research to contribute in any of the following areas:
1. Computational flow for the functional annotation of uncharacterized proteins using motif discovery, sequence and structural similarity, structural homology, hypothome (interactome of hypothetical proteins) or combination of data from highly similar non-interacting proteins (Similactors).
2. Predictive approaches based on Gene Ontology (GO).
3. Feature selection and machine learning methods.
4. Diseasome approaches.
A hypothetical protein (HPs) is a protein whose existence is predicted but whether or not it is expressed remains uncertain. However, many HPs in the recent past have been known to be expressed in vivo. With various methods known to identify components in cell membrane, the functional significance of thousands of proteins, especially those that that have no functional annotations, not expressed, unique or common among genomes, is least understood. Apart from this, many HPs might turn out to be pseudogenes at a later point of time and the use of these proteins remains mis-conducive. To better understand the problem many different areas have been investigated, from methods to screen functional candidates among many hypothetical proteins using feature selection algorithms, to the application of statistical methods to test the efficacy of these proteins annotations in terms of precision and accuracy. With experimental procedures for HP function prediction being low throughput by nature, there is a strong need to precisely know their functions for a better understanding of the underlying biological mechanisms associated with them. We encourage authors interested in protein annotation research to contribute in any of the following areas:
1. Computational flow for the functional annotation of uncharacterized proteins using motif discovery, sequence and structural similarity, structural homology, hypothome (interactome of hypothetical proteins) or combination of data from highly similar non-interacting proteins (Similactors).
2. Predictive approaches based on Gene Ontology (GO).
3. Feature selection and machine learning methods.
4. Diseasome approaches.
