# EC-Bench: a benchmark for enzyme commission number prediction

## TL;DR

EC-Bench is a new benchmark for evaluating enzyme classification methods, enabling fair comparisons and insights into their performance.

## Contribution

EC-Bench introduces a unified framework for evaluating enzyme classification methods with diverse metrics and datasets.

## Key findings

- EC-Bench reveals significant performance variation among enzyme classification methods.
- Different methods show distinct advantages in specific tasks like EC number completion and recommendation.
- The benchmark supports objective comparison and evaluation of new enzyme classification approaches.

## Abstract

Enzymes are proteins that catalyze specific biochemical reactions in cells. Enzyme Commission (EC) numbers are used to annotate enzymes in a four-level hierarchy that classifies enzymes based on the specific chemical reactions they catalyze. Accurate EC number prediction is essential for understanding enzyme functions. Despite the availability of numerous methods for predicting EC numbers from protein sequences, there is no unified framework for evaluating and studying such methods systematically. This gap limits the ability of the community to identify the most effective approaches for enzyme annotation.

We introduce EC-Bench, a benchmark for EC number prediction, consisting of (i) an initial representative set of existing methods (including homology-based, deep learning, contrastive learning, and language model methods), (ii) existing and novel accuracy and efficiency performance metrics, and (iii) selected datasets to allow for comprehensive comparative study. EC-Bench is open-source and provides a framework for researchers to not only compare among existing methods objectively under uniform conditions, but also to introduce and effectively evaluate performance of new methods in a comparative framework. To demonstrate the utility of EC-Bench, we perform extensive experimentation to compare the existing EC number prediction methods and establish their advantages and disadvantages in a variety of prediction tasks, namely “exact EC number prediction,” “EC number completion,” and (partial or additional) “EC number recommendation.” We find wide variation in the performance of different methods, but also subtle but potentially useful differences in the performance of different methods across tasks and for different parts of the EC hierarchy.

The benchmarking pipeline is available at https://github.com/dsaeedeh/EC-Bench.