Protein quality assessment with a loss function designed for high-quality decoys

Frontiers in Bioinformatics

Bioinformatics

Soumyadip Roy¹  Asa Ben-Hur² 

[1] Department of Computer Science, Colorado State University, Fort Collins, CO, United States;null;

关键词: protein structure quality assessment;    deep learning;    graph convolutional networks;    epsilon-insensitive loss function;    critical assessment of structure prediction;   

DOI  :  10.3389/fbinf.2023.1198218

 received in 2023-03-31, accepted in 2023-09-29,  发布年份 2023

来源: Frontiers

【 摘 要 】

Motivation: The prediction of a protein 3D structure is essential for understanding protein function, drug discovery, and disease mechanisms; with the advent of methods like AlphaFold that are capable of producing very high-quality decoys, ensuring the quality of those decoys can provide further confidence in the accuracy of their predictions.Results: In this work, we describe Qϵ, a graph convolutional network (GCN) that utilizes a minimal set of atom and residue features as inputs to predict the global distance test total score (GDTTS) and local distance difference test (lDDT) score of a decoy. To improve the model’s performance, we introduce a novel loss function based on the ϵ-insensitive loss function used for SVM regression. This loss function is specifically designed for evaluating the characteristics of the quality assessment problem and provides predictions with improved accuracy over standard loss functions used for this task. Despite using only a minimal set of features, it matches the performance of recent state-of-the-art methods like DeepUMQA.Availability: The code for Qϵ is available at https://github.com/soumyadip1997/qepsilon.

【 授权许可】

Unknown   
Copyright © 2023 Roy and Ben-Hur.