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

Abstract: Recent advancements in 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.

• The paper introduces novel interpretable ML techniques that leverage structure-based protein representations to predict protein structures, functions, and interactions.
• It compares various representations—such as distance matrices, graphs, and molecular surfaces—to overcome invariance challenges in protein modeling.
• The study demonstrates that enhanced model interpretability supports practical applications in protein design and drug discovery.

Interpretable Machine Learning Using Structure-Based Protein Representations

The field of structural biology has seen substantial benefits from recent advancements in ML methodologies. A key example is AlphaFold2 (AF2), which has significantly improved the accuracy of protein structure predictions. This paper, authored by Vecchietti et al., examines the application of ML methods for representing protein 3D structures, with a focus on interpretability. The discussion includes how these interpretable ML methods can assist in tasks such as predicting protein structures, functions, and interactions.

Structure-Based Protein Representations

In structural biology, proteins are primarily described by their amino acid sequences and their experimental 3D structures, which can be determined using Cryo-EM and X-ray crystallography. However, in computational biology, various representations are employed to leverage ML algorithms efficiently. At the simplest level, protein structures can be depicted as point clouds of atom positions. Despite the straightforwardness, this representation lacks rotational and translational invariance, which is critical for consistent model predictions.

To address these invariance issues, the paper explores several alternative representations, including distance matrices, graphs, and molecular surfaces. For example:

• Distance Matrices: Representing pairwise distances between amino acids, enabling efficient utilization by 2D or 3D ML models.
• Graph-Based Representations: In these graphs, nodes represent amino acids, and edges denote spatial proximities or biochemical interactions.
• Surface-Based Representations: Used by methods like MaSIF, these capture geometric and chemical properties, essential for protein-protein interactions.

Interpretable Machine Learning for Protein Structural Biology

A significant challenge in ML is the black-box nature of many models. Therefore, the development of interpretable or explainable ML methods is crucial. Two primary approaches include:

1. Post-hoc Methods: Applied after model training to interpret predictions. Techniques such as Integrated Gradients (IG) and Gradient-weighted Class Activation Mapping (GradCAM) fall into this category.
2. Intrinsic Interpretability: Models designed to be interpretable by construction, such as decision trees or models incorporating attention mechanisms.

Protein Structure Prediction

AF2 and related models like RosettaFold (RF) have incorporated interpretable modules, offering confidence metrics such as pLDDT and pAE. These metrics are crucial for visualizing and assessing the model’s reliability in different protein regions. For instance, pLDDT values color-coded onto predicted structures effectively highlight areas of low and high prediction confidence. This approach enhances the utility of ML predictions in practical applications, such as de novo protein design.

Functionality Prediction

Predicting protein functionality based on sequence and structure is critical for numerous biotechnological applications. The paper illustrates two ML methods for functionality prediction:

1. GBDT-Based Models: Utilizing decision trees' inherent interpretability to reveal significant residue-residue interactions affecting functionality.
2. Graph Convolutional Networks (GCNs): Models like DeepFRI use GradCAM for post-hoc interpretability, identifying crucial regions responsible for specific functions.

Protein-Protein Interactions (PPI)

The prediction of PPI sites benefits significantly from surface-based ML methods. MaSIF leverages surface patches and geometric features to predict interaction sites, providing interpretable surface scores. Such models are vital for tasks where direct biochemical interactions on protein surfaces need elucidation, aiding in drug discovery and protein engineering.

Implications and Future Directions

The integration of interpretable ML methods into structural biology promises to accelerate advancements in drug development and protein design. By providing insights into the decision-making process of ML models, researchers can better understand and manipulate protein structures and functions. Future research may focus on enhancing these interpretable modules and refining visualization tools to handle complex biological data more effectively. Additionally, the development of inherently interpretable models may bridge gaps between ML research and practical biotechnological applications.

The move towards interpretable ML in protein structural biology is a promising direction, aimed at creating models that not only provide accurate predictions but also enhance understanding and trust among researchers. Innovations in visual representation and interaction analysis will undoubtedly continue to drive progress in this interdisciplinary field.