A PLMs based protein retrieval framework

• URL: http://arxiv.org/abs/2407.11548v1
• Date: Tue, 16 Jul 2024 09:52:42 GMT
• Title: A PLMs based protein retrieval framework
• Authors: Yuxuan Wu, Xiao Yi, Yang Tan, Huiqun Yu, Guisheng Fan,
• Abstract summary: We propose a novel protein retrieval framework that mitigates the bias towards sequence similarity. Our framework initiatively harnesses protein language models (PLMs) to embed protein sequences within a high-dimensional feature space. Extensive experiments demonstrate that our framework can equally retrieve both similar and dissimilar proteins.
• Score: 4.110243520064533
• License:
• Abstract: Protein retrieval, which targets the deconstruction of the relationship between sequences, structures and functions, empowers the advancing of biology. Basic Local Alignment Search Tool (BLAST), a sequence-similarity-based algorithm, has proved the efficiency of this field. Despite the existing tools for protein retrieval, they prioritize sequence similarity and probably overlook proteins that are dissimilar but share homology or functionality. In order to tackle this problem, we propose a novel protein retrieval framework that mitigates the bias towards sequence similarity. Our framework initiatively harnesses protein language models (PLMs) to embed protein sequences within a high-dimensional feature space, thereby enhancing the representation capacity for subsequent analysis. Subsequently, an accelerated indexed vector database is constructed to facilitate expedited access and retrieval of dense vectors. Extensive experiments demonstrate that our framework can equally retrieve both similar and dissimilar proteins. Moreover, this approach enables the identification of proteins that conventional methods fail to uncover. This framework will effectively assist in protein mining and empower the development of biology.

Related papers

• 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)
• ProtT3: Protein-to-Text Generation for Text-based Protein Understanding [88.43323947543996]
  Language Models (LMs) excel in understanding textual descriptions of proteins. Protein Language Models (PLMs) can understand and convert protein data into high-quality representations, but struggle to process texts. We introduce ProtT3, a framework for Protein-to-Text Generation for Text-based Protein Understanding.
  arXiv  Detail & Related papers  (2024-05-21T08:06:13Z)
• ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction [54.132290875513405]
  The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions. We propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time.
  arXiv  Detail & Related papers  (2024-03-30T05:32:42Z)
• ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation [38.019425619750265]
  We introduce a novel variational pseudo-likelihood framework, ProtIR, designed to improve function predictors by incorporating inter-protein similarity modeling. ProtIR showcases around 10% improvement over vanilla predictor-based methods. It achieves performance on par with protein language model-based methods, yet without the need for massive pre-training.
  arXiv  Detail & Related papers  (2024-02-10T17:31:46Z)
• Neural Embeddings for Protein Graphs [0.8258451067861933]
  We propose a novel framework for embedding protein graphs in geometric vector spaces. We learn an encoder function that preserves the structural distance between protein graphs. Our framework achieves remarkable results in the task of protein structure classification.
  arXiv  Detail & Related papers  (2023-06-07T14:50:34Z)
• 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)
• Retrieved Sequence Augmentation for Protein Representation Learning [40.13920287967866]
  We introduce Retrieved Sequence Augmentation for protein representation learning without additional alignment or pre-processing. We show that our model can transfer to new protein domains better and outperforms MSA Transformer on de novo protein prediction. Our study fills a much-encountered gap in protein prediction and brings us a step closer to demystifying the domain knowledge needed to understand protein sequences.
  arXiv  Detail & Related papers  (2023-02-24T10:31:45Z)
• 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)
• Protein Representation Learning by Geometric Structure Pretraining [27.723095456631906]
  Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences. We first present a simple yet effective encoder to learn protein geometry features. Experimental results on both function prediction and fold classification tasks show that our proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods using much less data.
  arXiv  Detail & Related papers  (2022-03-11T17:52:13Z)
• 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.