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[2502.16189] Co-Evolution-Based Metal-Binding Residue Prediction with Graph Neural Networks
Summary
This paper presents a novel approach using Graph Neural Networks (GNNs) to predict metal-binding residues in proteins, significantly improving accuracy over existing methods.
Why It Matters
Understanding protein-metal interactions is crucial for advancements in structural biology and biochemistry. This research enhances predictive capabilities, which can lead to better metalloprotein design and functional annotations, impacting drug development and synthetic biology.
Key Takeaways
- Introduces the Metal-Binding Graph Neural Network (MBGNN) for improved residue prediction.
- Achieves notable F1 score improvements over the previous state-of-the-art method, MetalNet2.
- Demonstrates superior performance on multiple datasets for binding residue identification and metal type classification.
- Highlights the importance of co-evolutionary networks in understanding protein functions.
- Provides open access to code and data, promoting further research in the field.
Computer Science > Machine Learning arXiv:2502.16189 (cs) [Submitted on 22 Feb 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:Co-Evolution-Based Metal-Binding Residue Prediction with Graph Neural Networks Authors:Sayedmohammadreza Rastegari, Sina Tabakhi, Xianyuan Liu, Tianyi Jiang, Wei Sang, Haiping Lu View a PDF of the paper titled Co-Evolution-Based Metal-Binding Residue Prediction with Graph Neural Networks, by Sayedmohammadreza Rastegari and 5 other authors View PDF HTML (experimental) Abstract:Understanding protein-metal interactions is central to structural biology, with metal ions being vital for catalysis, stability, and signal transduction. Predicting metal-binding residues and metal types remains challenging due to the structural and evolutionary complexity of proteins. Conventional sequence- and structure-based methods often fail to capture co-evolutionary constraints that reflect how residues evolve together to maintain metal-binding functionality. Recent co-evolution-based methods capture part of this information, but still underutilize the complete co-evolved residue network. To address this limitation, we introduce the Metal-Binding Graph Neural Network (MBGNN), which leverages the complete co-evolved residue network to better capture complex dependencies within protein structures. Experimental results show that MBGNN substantially outperforms the state-of-the-art co-evolution-based method MetalNet2, achieving F1 score improvements of 2.5% fo...