Recent advances in interpretable machine learning using structure-based protein representations

• URL: http://arxiv.org/abs/2409.17726v1
• Date: Thu, 26 Sep 2024 10:56:27 GMT
• Title: Recent advances in interpretable machine learning using structure-based protein representations
• Authors: Luiz Felipe Vecchietti, Minji Lee, Begench Hangeldiyev, Hyunkyu Jung, Hahnbeom Park, Tae-Kyun Kim, Meeyoung Cha, Ho Min Kim,
• Abstract summary: Recent advancements in machine learning (ML) are transforming the field of structural biology. We present various methods for representing protein 3D structures from low to high-resolution. We show how interpretable ML methods can support tasks such as predicting protein structures, protein function, and protein-protein interactions.
• Score: 30.907048279915312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-to-use interfaces and interpretable outcomes from the neural network architecture, such as the confidence scores used to color the predicted structures, have made AlphaFold accessible even to non-ML experts. In this paper, we present various methods for representing protein 3D structures from low- to high-resolution, and show how interpretable ML methods can support tasks such as predicting protein structures, protein function, and protein-protein interactions. This survey also emphasizes the significance of interpreting and visualizing ML-based inference for structure-based protein representations that enhance interpretability and knowledge discovery. Developing such interpretable approaches promises to further accelerate fields including drug development and protein design.

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