CCPL: Cross-modal Contrastive Protein Learning

• URL: http://arxiv.org/abs/2303.11783v2
• Date: Wed, 4 Sep 2024 02:45:12 GMT
• Title: CCPL: Cross-modal Contrastive Protein Learning
• Authors: Jiangbin Zheng, Stan Z. Li,
• Abstract summary: 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.
• Score: 47.095862120116976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective protein representation learning is crucial for predicting protein functions. Traditional methods often pretrain protein language models on large, unlabeled amino acid sequences, followed by finetuning on labeled data. While effective, these methods underutilize the potential of protein structures, which are vital for function determination. Common structural representation techniques rely heavily on annotated data, limiting their generalizability. Moreover, structural pretraining methods, similar to natural language pretraining, can distort actual protein structures. In this work, 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, incorporating self-supervised structural constraints to maintain intrinsic structural information. We evaluated our model across various benchmarks, demonstrating the framework's superiority.

Related papers

• Long-context Protein Language Model [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. Most protein LMs are based on the Transformer architecture trained on individual proteins with short context lengths. We propose LC-PLM based on an alternative protein LM architecture, BiMamba-S, built off selective structured state-space models. We also introduce its graph-contextual variant, LC-PLM-G, which contextualizes protein-protein interaction graphs for a second stage of training.
  arXiv  Detail & Related papers  (2024-10-29T16:43:28Z)
• CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation [7.161099050722313]
  We develop a structure-sensitive supervised deep learning model, Crystal vs Predicted Evaluator for Protein Structure (CPE-Pro) CPE-Pro learns the structural information of proteins and captures inter-structural differences to achieve accurate traceability on four data classes. We utilize Foldseek to encode protein structures into "structure-sequences" and trained a protein Structural Sequence Language Model, SSLM.
  arXiv  Detail & Related papers  (2024-10-21T02:21:56Z)
• Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance? [4.7077642423577775]
  We propose ProtLOCA, a local geometry alignment method based solely on amino acid structure representation. Our method outperforms existing sequence- and structure-based representation learning methods by more quickly and accurately matching structurally consistent protein domains.
  arXiv  Detail & Related papers  (2024-06-28T08:54:37Z)
• Structure-Informed Protein Language Model [38.019425619750265]
  We introduce the integration of remote homology detection to distill structural information into protein language models. We evaluate the impact of this structure-informed training on downstream protein function prediction tasks.
  arXiv  Detail & Related papers  (2024-02-07T09:32:35Z)
• Endowing Protein Language Models with Structural Knowledge [5.587293092389789]
  We introduce a novel framework that enhances protein language models by integrating protein structural data. The refined model, termed Protein Structure Transformer (PST), is further pretrained on a small protein structure database. PST consistently outperforms the state-of-the-art foundation model for protein sequences, ESM-2, setting a new benchmark in protein function prediction.
  arXiv  Detail & Related papers  (2024-01-26T12:47:54Z)
• 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)
• 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)

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.