Transfer Learning for Protein Structure Classification at Low Resolution

• URL: http://arxiv.org/abs/2008.04757v4
• Date: Mon, 31 Aug 2020 17:02:33 GMT
• Title: Transfer Learning for Protein Structure Classification at Low Resolution
• Authors: Alexander Hudson and Shaogang Gong
• Abstract summary: We show that it is possible to make accurate ($geq$80%) predictions of protein class and architecture from structures determined at low ($leq$3A) resolution. We provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function.
• Score: 124.5573289131546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate ($\geq$80%) predictions of protein class and architecture from structures determined at low ($>$3A) resolution, using a deep convolutional neural network trained on high-resolution ($\leq$3A) structures represented as 2D matrices. Thus, 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. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high-resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Related papers

• 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)
• 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)
• A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration [4.909112037834705]
  This paper adopts a two-dimensional fusion deep neural network model, DstruCCN, which uses Convolutional Neural Networks (CCN) and a supervised Transformer protein language model for single-sequence protein structure prediction. The training features of the two are combined to predict the protein Transformer binding site matrix, and then the three-dimensional structure is reconstructed using energy minimization.
  arXiv  Detail & Related papers  (2024-02-29T12:24:20Z)
• Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction [43.46012602267272]
  Protein structure-based property prediction has emerged as a promising approach for various biological tasks. Current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. Our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures.
  arXiv  Detail & Related papers  (2023-10-14T08:43:42Z)
• CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attention [7.716601082662128]
  A protein's three-dimensional structure often poses nontrivial computation costs. We propose the first transformer-based model that directly utilizes protein crystallography and partial structure information. We demonstrate our method, dubbed textttCrysFormer, can achieve accurate predictions, based on a much smaller dataset size and with reduced computation costs.
  arXiv  Detail & Related papers  (2023-10-05T21:10:22Z)
• 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)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• 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)
• 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)

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.