In-Depth Performance Evaluation of PFP and ESG Sequence-Based Function Prediction Methods in CAFA 2011 Experiment

Background Many Automatic Function Prediction (AFP) methods were developed to cope with an increasing growth of the number of gene sequences that are available from high throughput sequencing experiments. To support the development of AFP methods, it is essential to have community wide experiments for evaluating performance of existing AFP methods. Critical Assessment of Function Annotation (CAFA) is one such community experiment. The meeting of CAFA was held as a Special Interest Group (SIG) meeting at the Intelligent Systems in Molecular Biology (ISMB) conference in 2011. Here, we perform a detailed analysis of two sequence-based function prediction methods, PFP and ESG, which were developed in our lab, using the predictions submitted to CAFA. Results We evaluate PFP and ESG using four different measures in comparison with BLAST, Prior, and GOtcha. In addition to the predictions submitted to CAFA, we further investigate performance of a different scoring function to rank order predictions by PFP as well as PFP/ESG predictions enriched with Priors that simply adds frequently occurring Gene Ontology terms as a part of predictions. Prediction accuracies of each method were also evaluated separately for different functional categories. Successful and unsuccessful predictions by PFP and ESG are also discussed in comparison with BLAST. Conclusion The in-depth analysis discussed here will complement the overall assessment by the CAFA organizers. Since PFP and ESG are based on sequence database search results, our analyses are not only useful for PFP and ESG users but will also shed light on the relationship of the sequence similarity space and functions that can be inferred from the sequences

PROTEIN FUNCTION, DIVERISTY AND FUNCTIONAL INTERPLAY

Functional annotations of novel or unknown proteins is one of the central problems in post-genomics bioinformatics research. With the vast expansion of genomic and proteomic data and technologies over the last decade, development of automated function prediction (AFP) methods for large-scale identification of protein function has be-come imperative in many aspects. In this research, we address two important divergences from the “one protein – one function” concept on which all existing AFP methods are developed

Functional classification of protein domain superfamilies for protein function annotation

Proteins are made up of domains that are generally considered to be independent evolutionary and structural units having distinct functional properties. It is now well established that analysis of domains in proteins provides an effective approach to understand protein function using a `domain grammar'. Towards this end, evolutionarily-related protein domains have been classified into homologous superfamilies in CATH and SCOP databases. An ideal functional sub-classification of the domain superfamilies into `functional families' can not only help in function annotation of uncharacterised sequences but also provide a useful framework for understanding the diversity and evolution of function at the domain level. This work describes the development of a new protocol (FunFHMMer) for identifying functional families in CATH superfamilies that makes use of sequence patterns only and hence, is unaffected by the incompleteness of function annotations, annotation biases or misannotations existing in the databases. The resulting family classification was validated using known functional information and was found to generate more functionally coherent families than other domain-based protein resources. A protein function prediction pipeline was developed exploiting the functional annotations provided by the domain families which was validated by a database rollback benchmark set of proteins and an independent assessment by CAFA 2. The functional classification was found to capture the functional diversity of superfamilies well in terms of sequence, structure and the protein-context. This aided studies on evolution of protein domain function both at the superfamily level and in specific proteins of interest. The conserved positions in the functional family alignments were found to be enriched in catalytic site residues and ligand-binding site residues which led to the development of a functional site prediction tool. Lastly, the function prediction tools were assessed for annotation of moonlighting functions of proteins and a classification of moonlighting proteins was proposed based on their structure-function relationships