$E^3$former: An Adaptive Energy-Aware Elastic Equivariant Transformer Model For Protein Representation Learning
Keywords: protein representation learning, geometric deep learning, equivariant neural networks, proteins
TL;DR: We present an adaptive equivariant Transformer-SSM mixed framework for structure-informed protein representation learning, achieving state-of-the-art performance on predicted and experimental data in structure-intensive tasks.
Abstract: Structure-informed protein representation learning is essential for effective protein function annotation and \textit{de novo} design. However, the presence of inherent noise in both crystal and AlphaFold-predicted structures poses significant challenges for existing methods in learning robust protein representations. To address these issues, we propose a novel equivariant Transformer-State Space Model(SSM) hybrid framework, termed $E^3$former, designed for efficient protein representation. Our approach leverages energy function-based receptive fields to construct proximity graphs and incorporates an equivariant high-tensor-elastic selective SSM within the transformer architecture. These components enable the model to adapt to complex atom interactions and extract geometric features with higher signal-to-noise ratios. Empirical results demonstrate that our model outperforms existing methods in structure-intensive tasks, such as inverse folding and binding site prediction, particularly when using predicted structures, owing to its enhanced tolerance to data deviation and noise. Our approach offers a novel perspective for conducting biological function research and drug discovery using noisy protein structure data. Our code is available on https://anonymous.4open.science/r/E3former-207E
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 5996
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