PUFFIN: Protein Unit Discovery with Functional Supervision

• URL: http://arxiv.org/abs/2604.14796v1
• Date: Thu, 16 Apr 2026 09:10:15 GMT
• Title: PUFFIN: Protein Unit Discovery with Functional Supervision
• Abstract summary: We introduce PUFFIN, a data-driven framework for discovering protein units.<n>PUFFIN represents proteins as residue-level structure graphs and applies a graph neural network with a structure-aware pooling mechanism.<n>We show that the learned units are structurally coherent, exhibit organized associations with molecular function, and show meaningful correspondence with curated InterPro annotations.
• Score: 2.0544239209511983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Proteins carry out biological functions through the coordinated action of groups of residues organized into structural arrangements. These arrangements, which we refer to as protein units, exist at an intermediate scale, being larger than individual residues yet smaller than entire proteins. A deeper understanding of protein function can be achieved by identifying these units and their associations with function. However, existing approaches either focus on residue-level signals, rely on curated annotations, or segment protein structures without incorporating functional information, thereby limiting interpretable analysis of structure-function relationships. We introduce PUFFIN, a data-driven framework for discovering protein units by jointly learning structural partitioning and functional supervision. PUFFIN represents proteins as residue-level structure graphs and applies a graph neural network with a structure-aware pooling mechanism that partitions each protein into multi-residue units, with functional supervision that shapes the partition. We show that the learned units are structurally coherent, exhibit organized associations with molecular function, and show meaningful correspondence with curated InterPro annotations. Together, these results demonstrate that PUFFIN provides an interpretable framework for analyzing structure-function relationships using learned protein units and their statistical function associations. We made our source code available at https://github.com/boun-tabi-lifelu/puffin.

Related papers

• Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition [13.468392186646382]
  ProtDiS is a knowledge-guided framework that decomposes pretrained protein micro-environment embeddings into biologically grounded dimensions.<n>Inspired by the information bottleneck principle, ProtDiS learns representations that balance informativeness and compression.<n>Protein- and residue-level analyses show that ProtDiS differentiates proteins with similar folds but divergent functions and captures fine-grained biophysical signals critical.
  arXiv  Detail & Related papers  (2026-05-12T07:12:12Z)
• Fast and Interpretable Protein Substructure Alignment via Optimal Transport [24.342562385561134]
  This study presents PLASMA, the first deep learning framework for efficient and interpretable residue-level protein substructure alignment.<n>We demonstrate that PLASMA achieves accurate, lightweight, and interpretable residue-level alignment.<n>Our method addresses a critical gap in protein structure analysis tools and offers new opportunities for functional annotation, evolutionary studies, and structure-based drug design.
  arXiv  Detail & Related papers  (2025-10-12T10:47:29Z)
• BioBlobs: Differentiable Graph Partitioning for Protein Representation Learning [3.6641231031729173]
  We introduce BioBlobs, a plug-and-play module that represents proteins by dynamically partitioning structures into flexibly-sized substructures ("blobs")<n>The resulting blobs are quantized into a shared and interpretable codebook, yielding a discrete vocabulary of function-relevant protein substructures used to compute protein embeddings.<n>We show that BioBlobs representations improve the performance of widely used protein encoders such as GVP-GNN across various PRL tasks.
  arXiv  Detail & Related papers  (2025-10-02T03:25:02Z)
• ProteinRPN: Towards Accurate Protein Function Prediction with Graph-Based Region Proposals [4.525216077859531]
  We introduce the Protein Region Proposal Network (ProteinRPN) for accurate protein function prediction. ProteinRPN identifies potential functional regions (anchors) which are refined through the hierarchy-aware node drop pooling layer. The representations of the predicted functional nodes are enriched using attention mechanisms and fed into a Graph Multiset Transformer.
  arXiv  Detail & Related papers  (2024-09-01T04:40:04Z)
• ProtFAD: Introducing function-aware domains as implicit modality towards protein function prediction [4.299777426056576]
  We propose a function-aware domain representation and a domain-joint contrastive learning strategy to distinguish different protein functions.<n>Our approach significantly and comprehensively outperforms the state-of-the-art methods on various benchmarks.
  arXiv  Detail & Related papers  (2024-05-24T02:26:45Z)
• A Latent Diffusion Model for Protein Structure Generation [50.74232632854264]
  We propose a latent diffusion model that can reduce the complexity of protein modeling. We show that our method can effectively generate novel protein backbone structures with high designability and efficiency.
  arXiv  Detail & Related papers  (2023-05-06T19:10:19Z)
• CCPL: Cross-modal Contrastive Protein Learning [47.095862120116976]
  We introduce a novel unsupervised protein structure representation pretraining method, cross-modal contrastive protein learning (CCPL) CCPL leverages a robust protein language model and uses unsupervised contrastive alignment to enhance structure learning. We evaluated our model across various benchmarks, demonstrating the framework's superiority.
  arXiv  Detail & Related papers  (2023-03-19T08:19:10Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Structure-aware Protein Self-supervised Learning [50.04673179816619]
  We propose a novel structure-aware protein self-supervised learning method to capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information. We identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme.
  arXiv  Detail & Related papers  (2022-04-06T02:18:41Z)
• Transfer Learning for Protein Structure Classification at Low Resolution [124.5573289131546]
  We show that it is possible to make accurate ($geq$80%) predictions of protein class and architecture from structures determined at low ($leq$3A) resolution. We provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function.
  arXiv  Detail & Related papers  (2020-08-11T15:01:32Z)
• BERTology Meets Biology: Interpreting Attention in Protein Language Models [124.8966298974842]
  We demonstrate methods for analyzing protein Transformer models through the lens of attention. We show that attention captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure. We also present a three-dimensional visualization of the interaction between attention and protein structure.
  arXiv  Detail & Related papers  (2020-06-26T21:50:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.