Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning
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Keywords: Protein representation learning, self-supervised learning, implicit neural representation
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TL;DR: We describe a new pre-training approach for protein representation learning using generalizable implicit neural networks on protein molecular surfaces, showing SOTA results for various tasks.
Abstract: Proteins can be represented in various ways, including their sequences, 3D structures, and surfaces. While recent studies have successfully employed sequence- or structure-based representations to address multiple tasks in protein science, there has been significant oversight in incorporating protein surface information, a critical factor for protein function. In this paper, we present a pre-training strategy that incorporates information from protein sequences, 3D structures, and surfaces to improve protein representation learning. Specifically, we utilize Implicit Neural Representations (INRs) for learning surface characteristics, and name it ProteinINR. We confirm that ProteinINR successfully reconstructs protein surfaces, and integrate this surface learning into the existing pre-training strategy of sequences and structures. Our results demonstrate that our approach can enhance performance in various downstream tasks, thereby underscoring the importance of including surface attributes in protein representation learning. These findings underline the importance of understanding protein surfaces for generating effective protein representations.
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Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
Submission Number: 6755
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