Structure-Aligned Protein Language Model

• URL: http://arxiv.org/abs/2505.16896v1
• Date: Thu, 22 May 2025 16:56:12 GMT
• Title: Structure-Aligned Protein Language Model
• Authors: Can Chen, David Heurtel-Depeiges, Robert M. Vernon, Christopher James Langmead, Yoshua Bengio, Quentin Fournier,
• Abstract summary: Protein language models (pLMs) pre-trained on vast protein sequence databases excel at various downstream tasks but lack structural knowledge essential for many biological applications.<n>We integrate structural insights from pre-trained protein graph neural networks (pGNNs) into pLMs through a latent-level contrastive learning task.<n>This task aligns residue representations from pLMs with those from pGNNs across multiple proteins, enriching pLMs with inter-protein structural knowledge.
• Score: 42.03167740260325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Protein language models (pLMs) pre-trained on vast protein sequence databases excel at various downstream tasks but lack the structural knowledge essential for many biological applications. To address this, we integrate structural insights from pre-trained protein graph neural networks (pGNNs) into pLMs through a latent-level contrastive learning task. This task aligns residue representations from pLMs with those from pGNNs across multiple proteins, enriching pLMs with inter-protein structural knowledge. Additionally, we incorporate a physical-level task that infuses intra-protein structural knowledge by optimizing pLMs to predict structural tokens. The proposed dual-task framework effectively incorporates both inter-protein and intra-protein structural knowledge into pLMs. Given the variability in the quality of protein structures in PDB, we further introduce a residue loss selection module, which uses a small model trained on high-quality structures to select reliable yet challenging residue losses for the pLM to learn. Applying our structure alignment method to the state-of-the-art ESM2 and AMPLIFY results in notable performance gains across a wide range of tasks, including a 12.7% increase in ESM2 contact prediction. The data, code, and resulting SaESM2 and SaAMPLIFY models will be released on Hugging Face.

Related papers

• PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs [80.08310253195144]
  PRING is the first benchmark that evaluates protein-protein interaction prediction from a graph-level perspective.<n> PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions.
  arXiv  Detail & Related papers  (2025-07-07T15:21:05Z)
• DISPROTBENCH: A Disorder-Aware, Task-Rich Benchmark for Evaluating Protein Structure Prediction in Realistic Biological Contexts [76.59606029593085]
  DisProtBench is a benchmark for evaluating protein structure prediction models (PSPMs) under structural disorder and complex biological conditions.<n>DisProtBench spans three key axes: data complexity, task diversity, and Interpretability.<n>Results reveal significant variability in model robustness under disorder, with low-confidence regions linked to functional prediction failures.
  arXiv  Detail & Related papers  (2025-06-18T23:58:22Z)
• Beyond Simple Concatenation: Fairly Assessing PLM Architectures for Multi-Chain Protein-Protein Interactions Prediction [0.2509487459755192]
  Protein-protein interactions (PPIs) are fundamental to numerous cellular processes.<n>PLMs have demonstrated remarkable success in predicting protein structure and function.<n>Their application to sequence-based PPI binding affinity prediction remains relatively underexplored.
  arXiv  Detail & Related papers  (2025-05-26T14:23:08Z)
• 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>Our framework employs attention and gating mechanisms to enable effective interaction between pLMs-generated sequential representations and GNN-extracted structural features.
  arXiv  Detail & Related papers  (2025-04-07T06:47:49Z)
• Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers [76.95505296417866]
  Self-supervised training of language models (LMs) has seen great success for protein sequences in learning meaningful representations and for generative drug design.<n>Most protein LMs are based on the Transformer architecture trained on individual proteins with short context lengths.<n>In this work, we propose LC-PLM based on an alternative protein LM architecture, BiMamba-S, built upon selective structured state-space models.
  arXiv  Detail & Related papers  (2024-10-29T16:43:28Z)
• Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding [43.811432723460534]
  We introduce Structure-Enhanced Protein Instruction Tuning (SEPIT) framework to bridge this gap. Our approach integrates a noval structure-aware module into pLMs to inform them with structural knowledge, and then connects these enhanced pLMs to large language models (LLMs) to generate understanding of proteins. We construct the largest and most comprehensive protein instruction dataset to date, which allows us to train and evaluate the general-purpose protein understanding model.
  arXiv  Detail & Related papers  (2024-10-04T16:02:50Z)
• Evaluating representation learning on the protein structure universe [19.856785982914243]
  ProteinWorkshop is a benchmark suite for representation learning on protein structures with Graph Neural Networks. We consider large-scale pre-training and downstream tasks on both experimental and predicted structures. We find that: (1) large-scale pretraining on AlphaFold structures and auxiliary tasks consistently improve the performance of both rotation-invariant and equivariant GNNs.
  arXiv  Detail & Related papers  (2024-06-19T21:48:34Z)
• A Systematic Study of Joint Representation Learning on Protein Sequences and Structures [38.94729758958265]
  Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge. Our study undertakes a comprehensive exploration of joint protein representation learning by integrating a state-of-the-art PLM with distinct structure encoders.
  arXiv  Detail & Related papers  (2023-03-11T01:24:10Z)
• Structure-informed Language Models Are Protein Designers [69.70134899296912]
  We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs) We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. Experiments show that our approach outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-02-03T10:49:52Z)
• Learning Geometrically Disentangled Representations of Protein Folding Simulations [72.03095377508856]
  This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein. Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules. Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
  arXiv  Detail & Related papers  (2022-05-20T19:38:00Z)

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.