Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs

• URL: http://arxiv.org/abs/2606.19374v1
• Date: Fri, 12 Jun 2026 07:33:44 GMT
• Title: Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs
• Abstract summary: We introduce a secondary-structure-aware graph neural network for protein representation learning.<n>We evaluate the proposed approach on commonly used protein benchmarks.<n>The resulting graph representations offer enhanced biological interpretability.
• Score: 15.470397852131677
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Graph-based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially reflect the principles governing protein folding. Proteins instead adopt complex three-dimensional conformations organized around secondary structure elements, such as $α$-helices and $β$-sheets, which encode recurring local motifs and stabilizing hydrogen-bond interactions. In this work, we introduce a secondary-structure-aware graph neural network for protein representation learning. Residue-level node representations are augmented with secondary structure assignments, and graph edges are constructed from hydrogen-bond interactions filtered by their energetic strength. This design enables the model to capture both local structural context and long-range couplings that are central to protein stability and function. We evaluate the proposed approach on commonly used protein benchmarks and observe consistent improvements over existing graph-based methods. In addition, the resulting graph representations offer enhanced biological interpretability, as the learned connectivity aligns with established structural motifs. These findings suggest that incorporating secondary structure and energy-filtered hydrogen-bond topology provides an effective inductive bias for protein representation learning. The code is released at https://github.com/mohamedmohamed2021/SSProNet

Related papers

• Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction [30.613275405893265]
  We propose a curvature-informed potential energy surface (CPES) graph neural network for protein-ligand binding affinity prediction.<n>CPES incorporates physics-informed curvature representations to model conformational flexibility.<n>CPES achieves improved predictive performance and offers physical interpretability.
  arXiv  Detail & Related papers  (2026-06-12T07:53:26Z)
• Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute [64.59043351161004]
  We propose Proteina-Complexa, a novel fully atomistic binder generation method unifying both paradigms.<n>Proteina-Complexa sets a new state of the art in computational binder design benchmarks.<n>We also demonstrate interface hydrogen bond optimization, fold class-guided binder generation, and extensions to small molecule targets and enzyme design tasks.
  arXiv  Detail & Related papers  (2026-03-30T01:54:03Z)
• Protein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers [26.044929670768]
  This study tackles the challenging task of predicting secondary structures from protein primary sequences.<n>We utilize protein residue graphs and introduce various forms of sequential or structural connections to capture enhanced spatial information.<n>We stack multiple convolutional layers to efficiently learn combined insights from the protein's spatial graph.
  arXiv  Detail & Related papers  (2025-11-17T18:39:13Z)
• S$^2$Drug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening [72.89086338778098]
  We propose a two-stage framework for protein-ligand contrastive representation learning.<n>In the first stage, we perform protein sequence pretraining on ChemBL using an ESM2-based backbone.<n>In the second stage, we fine-tune on PDBBind by fusing sequence and structure information through a residue-level gating module.<n>This auxiliary task guides the model to accurately localize binding residues within the protein sequence and capture their 3D spatial arrangement.
  arXiv  Detail & Related papers  (2025-11-10T11:57:47Z)
• Bidirectional Hierarchical Protein Multi-Modal Representation Learning [4.682021474006426]
  Protein language models (pLMs) pretrained on large scale protein sequences have demonstrated significant success in sequence-based tasks.<n> graph neural networks (GNNs) designed to leverage 3D structural information have shown promising generalization in protein-related prediction tasks.<n>We propose a bidirectional and hierarchical (Bi-Hierarchical) fusion approach to capture richer and more comprehensive protein representations.
  arXiv  Detail & Related papers  (2025-04-07T06:47:49Z)
• 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 Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity [52.67037774136973]
  Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. We propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool)
  arXiv  Detail & Related papers  (2021-07-21T03:34:09Z)
• 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.