Bridging Phylogeny and Taxonomy with Protein-protein Interaction Networks

• URL: http://arxiv.org/abs/2310.17164v1
• Date: Thu, 26 Oct 2023 05:32:33 GMT
• Title: Bridging Phylogeny and Taxonomy with Protein-protein Interaction Networks
• Authors: Long-Huei Chen, Mohana Prasad Sathya Moorthy, and Pratyaksh Sharma
• Abstract summary: The protein-protein interaction (PPI) network provides an overview of the complex biological reactions vital to an organism's metabolism and survival. We aim to increase our understanding of the tree of life and taxonomy by gleaming information from the PPI networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The protein-protein interaction (PPI) network provides an overview of the complex biological reactions vital to an organism's metabolism and survival. Even though in the past PPI network were compared across organisms in detail, there has not been large-scale research on how individual PPI networks reflect on the species relationships. In this study we aim to increase our understanding of the tree of life and taxonomy by gleaming information from the PPI networks. We successful created (1) a predictor of network statistics based on known traits of existing species in the phylogeny, and (2) a taxonomic classifier of organism using the known protein network statistics, whether experimentally determined or predicted de novo. With the knowledge of protein interactions at its core, our two models effectively connects two field with widely diverging methodologies - the phylogeny and taxonomy of species.

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)
• Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning [78.38442423223832]
  We develop a novel codebook pre-training task, namely masked microenvironment modeling. We demonstrate superior performance and training efficiency over state-of-the-art pre-training-based methods in mutation effect prediction.
  arXiv  Detail & Related papers  (2024-05-16T03:53:21Z)
• Improved K-mer Based Prediction of Protein-Protein Interactions With Chaos Game Representation, Deep Learning and Reduced Representation Bias [0.0]
  We present a method for extracting unique pairs from an interaction dataset, generating non-redundant paired data for unbiased machine learning. We develop a convolutional neural network model capable of learning and predicting interactions from Chaos Game Representations of proteins' coding genes.
  arXiv  Detail & Related papers  (2023-10-23T10:02:23Z)
• PhyloGFN: Phylogenetic inference with generative flow networks [57.104166650526416]
  We introduce the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and phylogenetic inference. Because GFlowNets are well-suited for sampling complex structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets.
  arXiv  Detail & Related papers  (2023-10-12T23:46:08Z)
• Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks [68.90692290665648]
  We integrate knowledge learned by protein language models into several state-of-the-art geometric networks. Our findings show an overall improvement of 20% over baselines. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin.
  arXiv  Detail & Related papers  (2022-12-07T04:04:04Z)
• DeepAutoPIN: An automorphism orbits based deep neural network for characterizing the organizational diversity of protein interactomes across the tree of life [0.0]
  We study 4,738 protein interaction networks (PINs) belonging to 16 phyla to discover phyla-specific architectural features. We report that orbit usage profiles (OUPs) of networks belonging to the three domains of life are contrastingly different.
  arXiv  Detail & Related papers  (2022-03-02T10:10:20Z)
• Multimodal Pre-Training Model for Sequence-based Prediction of Protein-Protein Interaction [7.022012579173686]
  Pre-training a protein model to learn effective representation is critical for protein-protein interactions. Most pre-training models for PPIs are sequence-based, which naively adopt the language models used in natural language processing to amino acid sequences. We propose a multimodal protein pre-training model with three modalities: sequence, structure, and function.
  arXiv  Detail & Related papers  (2021-12-09T10:21:52Z)
• Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction [7.860159889216291]
  Existing methods suffer from significant performance degradation when tested in unseen dataset. We propose a graph neural network based method (GNN-PPI) for better inter-novel-protein interaction prediction.
  arXiv  Detail & Related papers  (2021-05-14T08:42:55Z)
• Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases [38.9571812880758]
  We show that Bio-JOIE can accurately identify PPIs between the SARS-CoV-2 proteins and human proteins. By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species.
  arXiv  Detail & Related papers  (2021-03-07T07:06:53Z)
• 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.