ProteinRPN: Towards Accurate Protein Function Prediction with Graph-Based Region Proposals

• URL: http://arxiv.org/abs/2409.00610v1
• Date: Sun, 1 Sep 2024 04:40:04 GMT
• Title: ProteinRPN: Towards Accurate Protein Function Prediction with Graph-Based Region Proposals
• Authors: Shania Mitra, Lei Huang, Manolis Kellis,
• Abstract summary: We introduce the Protein Region Proposal Network (ProteinRPN) for accurate protein function prediction. ProteinRPN identifies potential functional regions (anchors) which are refined through the hierarchy-aware node drop pooling layer. The representations of the predicted functional nodes are enriched using attention mechanisms and fed into a Graph Multiset Transformer.
• Score: 4.525216077859531
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Protein function prediction is a crucial task in bioinformatics, with significant implications for understanding biological processes and disease mechanisms. While the relationship between sequence and function has been extensively explored, translating protein structure to function continues to present substantial challenges. Various models, particularly, CNN and graph-based deep learning approaches that integrate structural and functional data, have been proposed to address these challenges. However, these methods often fall short in elucidating the functional significance of key residues essential for protein functionality, as they predominantly adopt a retrospective perspective, leading to suboptimal performance. Inspired by region proposal networks in computer vision, we introduce the Protein Region Proposal Network (ProteinRPN) for accurate protein function prediction. Specifically, the region proposal module component of ProteinRPN identifies potential functional regions (anchors) which are refined through the hierarchy-aware node drop pooling layer favoring nodes with defined secondary structures and spatial proximity. The representations of the predicted functional nodes are enriched using attention mechanisms and subsequently fed into a Graph Multiset Transformer, which is trained with supervised contrastive (SupCon) and InfoNCE losses on perturbed protein structures. Our model demonstrates significant improvements in predicting Gene Ontology (GO) terms, effectively localizing functional residues within protein structures. The proposed framework provides a robust, scalable solution for protein function annotation, advancing the understanding of protein structure-function relationships in computational biology.

Related papers

• SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation [97.99658944212675]
  We introduce a novel pre-training strategy for protein foundation models. It emphasizes the interactions among amino acid residues to enhance the extraction of both short-range and long-range co-evolutionary features. Trained on a large-scale protein sequence dataset, our model demonstrates superior generalization ability.
  arXiv  Detail & Related papers  (2024-10-31T15:22:03Z)
• ProtFAD: Introducing function-aware domains as implicit modality towards protein function perception [0.3928425951824076]
  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.
  arXiv  Detail & Related papers  (2024-05-24T02:26:45Z)
• NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks [60.48306899271866]
  We propose novel semantic data augmentation methods to incorporate backbone chemical and side-chain biophysical information into protein classification tasks. Specifically, we leverage molecular biophysical, secondary structure, chemical bonds, andionic features of proteins to facilitate classification tasks.
  arXiv  Detail & Related papers  (2024-03-21T13:27:57Z)
• 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)
• Multi-level Protein Representation Learning for Blind Mutational Effect Prediction [5.207307163958806]
  This paper introduces a novel pre-training framework that cascades sequential and geometric analyzers for protein structures. It guides mutational directions toward desired traits by simulating natural selection on wild-type proteins. We assess the proposed approach using a public database and two new databases for a variety of variant effect prediction tasks.
  arXiv  Detail & Related papers  (2023-06-08T03:00:50Z)
• Learning the shape of protein micro-environments with a holographic convolutional neural network [0.0]
  We introduce Holographic Convolutional Neural Network (H-CNN) for proteins. H-CNN is a physically motivated machine learning approach to model amino acid preferences in protein structures. It accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes.
  arXiv  Detail & Related papers  (2022-11-05T16:29:15Z)
• 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)
• EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models [53.17320541056843]
  We propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network. Our EBM-Fold approach can efficiently produce high-quality decoys, compared against traditional Rosetta-based structure optimization routines.
  arXiv  Detail & Related papers  (2021-05-11T03:40:29Z)
• 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)
• 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.