Identification of functionally related enzymes by learning-to-rank methods

TL;DR

This paper demonstrates that kernel-based learning-to-rank algorithms significantly improve the ranking of enzymes by their biological function, using active site similarities and enzyme annotations to outperform traditional similarity measures.

Contribution

The study introduces a novel application of learning-to-rank methods that incorporate enzyme annotations and active site similarities, enhancing enzyme function prediction accuracy.

Findings

Kernel-based learning algorithms improve enzyme ranking accuracy.

Active site similarity measures outperform sequence-based measures.

Significant improvements observed in enzyme function prediction experiments.

Abstract

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes.