Learning stable latent features to better recognize protein folds and families

What is it about?

Proteins can take on many different 3D shapes, and these shapes are grouped into categories called folds and families. Recognizing which fold or family a protein belongs to helps scientists understand its function, how it evolved, and how it might interact with other molecules. This task is known as protein fold and family recognition, and it is a core challenge in structural biology. Most existing computational methods try to do this recognition using protein sequences or handcrafted features. But proteins can have very different sequences while sharing almost the same 3D structure. Because of this, many sequence-based models struggle to correctly identify folds and families, especially when proteins come from new datasets or when their structures vary slightly. Our work focuses on improving recognition by learning from 3D structural information directly. Instead of comparing full structures, we train an AI model to learn a latent representation — a simple, compressed summary of each protein’s shape. If this representation captures the right features, it becomes much easier to tell which proteins share the same fold or family. We introduce ConSOLAE, a deep learning model built to learn smooth and noise-resistant latent representations of protein structures. ConSOLAE uses a special technique called contractive regularization, which encourages the model to produce stable outputs even when the protein input changes a little. This helps the model generalize to proteins it has never seen before.

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Photo by Google DeepMind on Unsplash

Photo by Google DeepMind on Unsplash

Why is it important?

We designed and tested ConSOLAE on two versions of the SCOP protein structure database. Compared to previous methods , ConSOLAE learned latent representations that transferred better to new datasets. These findings show that ConSOLAE provides more reliable fold and family recognition, even when the model encounters proteins it was never trained on. Early results also suggest that ConSOLAE can capture subtle structural differences within closely related protein families, which is often very challenging for sequence-based models. In short, this work shows that learning smooth, stable, structure-based latent features can significantly improve protein fold and family recognition across diverse, real-world datasets. What makes this work unique and timely is its focus on representation smoothness, a property that helps models generalize as protein databases continue to grow and become more diverse. As new structural data emerges at a rapid pace, methods like ConSOLAE that emphasize robust, transferable latent features will be increasingly important for future biological discovery.

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