ESG: Extended Similarity Group method for automated protein function prediction

We present here the Extended Similarity Group (ESG) method, which annotates query sequences with Gene Ontology (GO) terms by assigning probability to each annotation computed based on iterative PSI-BLAST searches. Conventionally sequence homology based function annotation methods, such as BLAST, retrieve function information from top hits with a significant score (E-values). In contrast, the PFP method, which we have presented previously, goes one step ahead in utilizing a PSI-BLAST result by considering very weak hits even an E-value of up to 100 and also by incorporating the functional association between GO terms (FAM matrix) computed using term co-occurrence frequencies in the UniProt database. PFP is very successful which is evidenced by the top rank in the function prediction category in CASP7 competition. Our new approach, ESG method, further improves the accuracy of PFP by essentially employing PFP in an iterative fashion. An advantage of ESG is that it is built in a rigorous statistical framework: Unlike PFP method that assigns a weighted score to each GO term, ESG assigns a probability based on weights computed using the E-value of each hit sequence on the path between the original query sequence and the current hit sequence

Quantification of protein group coherence and pathway assignment using functional association

<p>Abstract</p> <p>Background</p> <p>Genomics and proteomics experiments produce a large amount of data that are awaiting functional elucidation. An important step in analyzing such data is to identify functional units, which consist of proteins that play coherent roles to carry out the function. Importantly, functional coherence is not identical with functional similarity. For example, proteins in the same pathway may not share the same Gene Ontology (GO) terms, but they work in a coordinated fashion so that the aimed function can be performed. Thus, simply applying existing functional similarity measures might not be the best solution to identify functional units in omics data.</p> <p>Results</p> <p>We have designed two scores for quantifying the functional coherence by considering association of GO terms observed in two biological contexts, co-occurrences in protein annotations and co-mentions in literature in the PubMed database. The counted co-occurrences of GO terms were normalized in a similar fashion as the statistical amino acid contact potential is computed in the protein structure prediction field. We demonstrate that the developed scores can identify functionally coherent protein sets, <it>i.e</it>. proteins in the same pathways, co-localized proteins, and protein complexes, with statistically significant score values showing a better accuracy than existing functional similarity scores. The scores are also capable of detecting protein pairs that interact with each other. It is further shown that the functional coherence scores can accurately assign proteins to their respective pathways.</p> <p>Conclusion</p> <p>We have developed two scores which quantify the functional coherence of sets of proteins. The scores reflect the actual associations of GO terms observed either in protein annotations or in literature. It has been shown that they have the ability to accurately distinguish biologically relevant groups of proteins from random ones as well as a good discriminative power for detecting interacting pairs of proteins. The scores were further successfully applied for assigning proteins to pathways.</p

Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP

<p>Abstract</p> <p>Background</p> <p>A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance.</p> <p>Results</p> <p>Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, <it>Escherichia coli</it>, <it>Saccharomyces cerevisiae</it>, and <it>Plasmodium falciparum </it>(malaria). The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO) category, <it>i.e. </it>Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the <it>funSim </it>score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the <it>funSim </it>score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted.</p> <p>Conclusion</p> <p>The analyses demonstrate that applying high confidence predictions from PFP can have a significant impact on a researchers' ability to interpret the immense biological data that are being generated today. The newly introduced functional similarity networks of the three organisms show different network properties as compared with the protein-protein interaction networks.</p

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

Computational Protein Function Prediction and its Application to the Missing Enzymes Problem

Improving the overall annotation level of genomes and completeness of biological pathways with high accuracy is the long term basic goal for this research. Large numbers of proteins are getting sequenced every year, creating a pressing need to build computational techniques for rapidly analyzing genomes to extract relevant knowledge. The purpose of this study is 1) to develop an advanced method to computationally elucidate functions of unannotated proteins, 2) to characterize the relationships between functional terms used to describe the proteins and 3) to further use these relationships to predict missing enzymes in the metabolic pathways. Here we have developed the Extended Similarity Group (ESG) method for protein annotation prediction that iteratively searches the sequence homology space around the query protein and draws consensus from the annotations of proteins in the neighborhood. In terms of prediction accuracy, ESG has been shown to outperform simple PSI-BLAST search and the PFP method previously developed in our lab. Secondly we have designed two scores, Co-occurrence Association Score (CAS) and PubMed Association Score (PAS), that capture the relationship between pairs of Gene Ontology terms used for annotating the proteins. CAS is based on co-occurrence of annotation terms in the database to annotate the same proteins, and PAS is based on co-mentions of annotation terms in the PubMed abstracts. These two scores have been successfully applied to identify functionally coherent groups of proteins that work in coordinated fashion to achieve some biological task. For newly sequenced genomes, metabolic reconstruction often leads to several missing enzymes where a known reaction is not associated with any gene product. As the next step, we use the aforementioned function association scores combined with the phylogenetic profile and microarray expression data to find the most likely matches for such missing enzymes thereby increasing the completeness of biological knowledge. Thus the principal goal achieved here is to understand and improve the computational characterization of protein annotations starting from the individual proteins and moving towards the systems level

Missing gene identification using functional coherence scores

Reconstructing metabolic and signaling pathways is an effective way of interpreting a genome sequence. A challenge in a pathway reconstruction is that often genes in a pathway cannot be easily found, reflecting current imperfect information of the target organism. In this work, we developed a new method for finding missing genes, which integrates multiple features, including gene expression, phylogenetic profile, and function association scores. Particularly, for considering function association between candidate genes and neighboring proteins to the target missing gene in the network, we used Co-occurrence Association Score (CAS) and PubMed Association Score (PAS), which are designed for capturing functional coherence of proteins. We showed that adding CAS and PAS substantially improve the accuracy of identifying missing genes in the yeast enzyme-enzyme network compared to the cases when only the conventional features, gene expression, phylogenetic profile, were used. Finally, it was also demonstrated that the accuracy improves by considering indirect neighbors to the target enzyme position in the network using a proper network-topology-based weighting scheme

Structure-and sequence-based function prediction for non-homologous proteins

Abstract The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures