# EGG: Accuracy Estimation of Individual Multimeric Protein Models Using Deep Energy-Based Models and Graph Neural Networks

## TL;DR

This paper introduces EGG, a new method to estimate the accuracy of multimeric protein models using deep learning techniques.

## Contribution

EGG combines energy-based models and graph neural networks to improve accuracy estimation of multimeric protein models.

## Key findings

- EGG achieved fourth and third place in CASP15 for overall fold and interface accuracy estimation.
- It ranked first for identifying the top three highest-quality models in CASP15.
- The method uses message-passing and transformer layers to infer model accuracy.

## Abstract

Reliable and accurate methods of estimating the accuracy of predicted protein models are vital to understanding their respective utility. Discerning how the quaternary structure conforms can significantly improve our collective understanding of cell biology, systems biology, disease formation, and disease treatment. Accurately determining the quality of multimeric protein models is still computationally challenging, as the space of possible conformations is significantly larger when proteins form in complex with one another. Here, we present EGG (energy and graph-based architectures) to assess the accuracy of predicted multimeric protein models. We implemented message-passing and transformer layers to infer the overall fold and interface accuracy scores of predicted multimeric protein models. When evaluated with CASP15 targets, our methods achieved promising results against single model predictors: fourth and third place for determining the highest-quality model when estimating overall fold accuracy and overall interface accuracy, respectively, and first place for determining the top three highest quality models when estimating both overall fold accuracy and overall interface accuracy.