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Effusion: prediction of protein function from sequence similarity networks.

ABSTRACT: Motivation:Critical evaluation of methods for protein function prediction shows that data integration improves the performance of methods that predict protein function, but a basic BLAST-based method is still a top contender. We sought to engineer a method that modernizes the classical approach while avoiding pitfalls common to state-of-the-art methods. Results:We present a method for predicting protein function, Effusion, which uses a sequence similarity network to add context for homology transfer, a probabilistic model to account for the uncertainty in labels and function propagation, and the structure of the Gene Ontology (GO) to best utilize sparse input labels and make consistent output predictions. Effusion's model makes it practical to integrate rare experimental data and abundant primary sequence and sequence similarity. We demonstrate Effusion's performance using a critical evaluation method and provide an in-depth analysis. We also dissect the design decisions we used to address challenges for predicting protein function. Finally, we propose directions in which the framework of the method can be modified for additional predictive power. Availability and implementation:The source code for an implementation of Effusion is freely available at https://github.com/babbittlab/effusion. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Yunes JM

PROVIDER: S-EPMC6361244 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Project description:BackgroundPredicting protein function from primary sequence is an important open problem in modern biology. Not only are there many thousands of proteins of unknown function, current approaches for predicting function must be improved upon. One problem in particular is overly-specific function predictions which we address here with a new statistical model of the relationship between protein sequence similarity and protein function similarity.MethodologyOur statistical model is based on sets of proteins with experimentally validated functions and numeric measures of function specificity and function similarity derived from the Gene Ontology. The model predicts the similarity of function between two proteins given their amino acid sequence similarity measured by statistics from the BLAST sequence alignment algorithm. A novel aspect of our model is that it predicts the degree of function similarity shared between two proteins over a continuous range of sequence similarity, facilitating prediction of function with an appropriate level of specificity.SignificanceOur model shows nearly exact function similarity for proteins with high sequence similarity (bit score >244.7, e-value >1e(-62), non-redundant NCBI protein database (NRDB)) and only small likelihood of specific function match for proteins with low sequence similarity (bit score <54.6, e-value <1e(-05), NRDB). For sequence similarity ranges in between our annotation model shows an increasing relationship between function similarity and sequence similarity, but with considerable variability. We applied the model to a large set of proteins of unknown function, and predicted functions for thousands of these proteins ranging from general to very specific. We also applied the model to a data set of proteins with previously assigned, specific functions that were electronically based. We show that, on average, these prior function predictions are more specific (quite possibly overly-specific) compared to predictions from our model that is based on proteins with experimentally determined function.

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Effusion: prediction of protein function from sequence similarity networks.

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