ProtFAD: Introducing function-aware domains as implicit modality towards protein function perception
- URL: http://arxiv.org/abs/2405.15158v1
- Date: Fri, 24 May 2024 02:26:45 GMT
- Title: ProtFAD: Introducing function-aware domains as implicit modality towards protein function perception
- Authors: Mingqing Wang, Zhiwei Nie, Yonghong He, Zhixiang Ren,
- Abstract summary: We propose a function-aware domain representation and a domain-joint contrastive learning strategy to distinguish different protein functions.
Our approach significantly and comprehensively outperforms the state-of-the-art methods on various benchmarks.
- Score: 0.3928425951824076
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Protein function prediction is currently achieved by encoding its sequence or structure, where the sequence-to-function transcendence and high-quality structural data scarcity lead to obvious performance bottlenecks. Protein domains are "building blocks" of proteins that are functionally independent, and their combinations determine the diverse biological functions. However, most existing studies have yet to thoroughly explore the intricate functional information contained in the protein domains. To fill this gap, we propose a synergistic integration approach for a function-aware domain representation, and a domain-joint contrastive learning strategy to distinguish different protein functions while aligning the modalities. Specifically, we associate domains with the GO terms as function priors to pre-train domain embeddings. Furthermore, we partition proteins into multiple sub-views based on continuous joint domains for contrastive training under the supervision of a novel triplet InfoNCE loss. Our approach significantly and comprehensively outperforms the state-of-the-art methods on various benchmarks, and clearly differentiates proteins carrying distinct functions compared to the competitor.
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) - Clustering for Protein Representation Learning [72.72957540484664]
We propose a neural clustering framework that can automatically discover the critical components of a protein.
Our framework treats a protein as a graph, where each node represents an amino acid and each edge represents a spatial or sequential connection between amino acids.
We evaluate on four protein-related tasks: protein fold classification, enzyme reaction classification, gene term prediction, and enzyme commission number prediction.
arXiv Detail & Related papers (2024-03-30T05:51:09Z) - 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) - PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for
Efficient and Generalizable Compound-Protein Interaction Prediction [63.50967073653953]
Compound-Protein Interaction prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery.
Existing deep learning-based methods utilize only the single modality of protein sequences or structures.
We propose a novel multi-scale Protein Sequence-structure Contrasting framework for CPI prediction.
arXiv Detail & Related papers (2024-02-13T03:51:10Z) - Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion
Trajectory Prediction [29.375830561817047]
Self-supervised pre-training methods on proteins have recently gained attention, with most approaches focusing on either protein sequences or structures.
We propose the DiffPreT approach to pre-train a protein encoder by sequence-structure joint diffusion modeling.
We enhance DiffPreT by a method called Siamese Diffusion Trajectory Prediction (SiamDiff) to capture the correlation between different conformers of a protein.
arXiv Detail & Related papers (2023-01-28T02:48:20Z) - 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) - Multi-Scale Representation Learning on Proteins [78.31410227443102]
This paper introduces a multi-scale graph construction of a protein -- HoloProt.
The surface captures coarser details of the protein, while sequence as primary component and structure captures finer details.
Our graph encoder then learns a multi-scale representation by allowing each level to integrate the encoding from level(s) below with the graph at that level.
arXiv Detail & Related papers (2022-04-04T08:29:17Z) - Leveraging Sequence Embedding and Convolutional Neural Network for
Protein Function Prediction [27.212743275697825]
Main challenges of protein function prediction are the large label space and the lack of labeled training data.
Our method leverages unsupervised sequence embedding and the success of deep convolutional neural network to overcome these challenges.
arXiv Detail & Related papers (2021-12-01T08:31:01Z) - PersGNN: Applying Topological Data Analysis and Geometric Deep Learning
to Structure-Based Protein Function Prediction [0.07340017786387766]
In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank.
We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis.
arXiv Detail & Related papers (2020-10-30T02:24:35Z) - Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein
Structures [18.961218808251076]
We propose two new learning operations enabling deep 3D analysis of large-scale protein data.
First, we introduce a novel convolution operator which considers both, the intrinsic (invariant under protein folding) as well as extrinsic (invariant under bonding) structure.
Second, we enable a multi-scale protein analysis by introducing hierarchical pooling operators, exploiting the fact that proteins are a recombination of a finite set of amino acids.
arXiv Detail & Related papers (2020-07-13T09:02:40Z) - 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.