Enhanced Protein Intrinsic Disorder Prediction Through Dual-View Multiscale Features and Multi-objective Evolutionary Algorithm

TL;DR

This paper introduces D2MOE, a novel method combining dual-view multiscale feature extraction with multi-objective evolutionary algorithms to improve residue-level prediction of intrinsically disordered protein regions, outperforming existing methods.

Contribution

The paper presents a new dual-view multiscale feature extraction technique and an adaptive evolutionary algorithm for optimal feature fusion in protein disorder prediction.

Findings

D2MOE outperforms state-of-the-art methods on benchmark datasets.

The method effectively balances prediction accuracy and model compactness.

Dual-view multiscale features capture diverse structural information.

Abstract

Intrinsically disordered regions of proteins play a crucial role in cell signaling and drug discovery. However, their high structural flexibility makes accurate residue-level prediction challenging. Existing methods often rely on single-view representations or rigid manual fusion strategies, which fail to effectively balance the complex interplay between local amino acid preferences and long-range sequence patterns. To address these limitations, we propose D2MOE, a Dual-View Multiscale Features and Multi-objective Evolutionary Algorithm, which consists of two stages. First, a dual-view multiscale feature extraction method is introduced. This method integrates evolutionary views with deep semantic views and employs multiscale extractors to capture structural information across diverse receptive fields. Second, a multi-objective evolutionary algorithm is designed to adaptively discover optimal fusion architectures. By co-evolving discrete feature selection and continuous fusion weights, the algorithm adaptively explores optimal cross-feature architectures to enhance predictive accuracy while maintaining model compactness. Experimental results across three benchmark datasets demonstrate that D2MOE consistently outperforms state-of-the-art methods. D2MOE combines the feature extraction capabilities of deep learning with the global search advantages of evolutionary algorithms, enabling efficient feature integration without manual design, and providing a robust computational tool for protein disorder prediction.