FFPred 2.0: Improved Homology-Independent Prediction of Gene Ontology Terms for Eukaryotic Protein Sequences

<div><p>To understand fully cell behaviour, biologists are making progress towards cataloguing the functional elements in the human genome and characterising their roles across a variety of tissues and conditions. Yet, functional information – either experimentally validated or computationally inferred by similarity – remains completely missing for approximately 30% of human proteins. FFPred was initially developed to bridge this gap by targeting sequences with distant or no homologues of known function and by exploiting clear patterns of intrinsic disorder associated with particular molecular activities and biological processes. Here, we present an updated and improved version, which builds on larger datasets of protein sequences and annotations, and uses updated component feature predictors as well as revised training procedures. FFPred 2.0 includes support vector regression models for the prediction of 442 Gene Ontology (GO) terms, which largely expand the coverage of the ontology and of the biological process category in particular. The GO term list mainly revolves around macromolecular interactions and their role in regulatory, signalling, developmental and metabolic processes. Benchmarking experiments on newly annotated proteins show that FFPred 2.0 provides more accurate functional assignments than its predecessor and the ProtFun server do; also, its assignments can complement information obtained using BLAST-based transfer of annotations, improving especially prediction in the biological process category. Furthermore, FFPred 2.0 can be used to annotate proteins belonging to several eukaryotic organisms with a limited decrease in prediction quality. We illustrate all these points through the use of both precision-recall plots and of the COGIC scores, which we recently proposed as an alternative numerical evaluation measure of function prediction accuracy.</p></div

Combining expression-based features with sequence-based features boosts the predictive accuracy when only adopting sequence-based features for predicting all three domains of protein function.

<p>(a,d) the MCC and AUROC values obtained by different features for predicting biological process domain of GO terms over cross validation. (b,e) the MCC and AUROC values obtained by different features for predicting molecular function domain of GO terms over cross validation. (c,f) the MCC and AUROC values obtained by different features for predicting cellular component domain of GO terms over cross validation.</p

Mean AUROC values obtained by different expression-based feature groups and the sequence-based feature group over cross validation.

<p>Mean AUROC values obtained by different expression-based feature groups and the sequence-based feature group over cross validation.</p

Mean MCC values obtained by different combinations of expression-based and the sequence-based feature groups over cross validation.

<p>Mean MCC values obtained by different combinations of expression-based and the sequence-based feature groups over cross validation.</p

Distribution of related feature importance value for features denoting each of main developmental stages for <i>Drosophila</i>.

<p>(a) the distribution of RFI values for predicting biological process domain of GO terms; (b) the distribution of RFI values for predicting moleulcar function domain of GO terms; (c) the distribution of RFI values for predicting cellular component domain of GO terms.</p

Validated proteins predicted by FFPred-fly+.

<p>Validated proteins predicted by FFPred-fly+.</p

Expression-based features show competitive performance against the sequence-based features for predicting the biological process domain of protein function.

<p>(a,d) the MCC and AUROC values obtained by different features for predicting the biological process domain of GO terms over cross validation. (b,e) the MCC and AUROC values obtained by different features for predicting the molecular function domain of GO terms over cross validation. (c,f) the MCC and AUROC values obtained by different features for predicting the cellular component domain of GO terms over cross validation.</p