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Intrinsically Disordered Proteins02:18

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Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
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IDP-Seq2Seq: identification of intrinsically disordered regions based on sequence to sequence learning.

Yi-Jun Tang1, Yi-He Pang1, Bin Liu1,2

  • 1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

Bioinformatics (Oxford, England)
|July 24, 2020
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Summary

This study introduces IDP-Seq2Seq, a novel method using natural language processing to predict intrinsically disordered regions (IDRs) in proteins by mapping sequences to a semantic space. The new predictor outperforms existing methods for accurate protein structure and function analysis.

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Area of Science:

  • * Bioinformatics
  • * Computational Biology
  • * Structural Biology

Background:

  • * Intrinsically disordered regions (IDRs) are crucial for various biological functions but challenging to predict accurately.
  • * Existing computational methods for IDR prediction often lack the ability to capture protein structure characteristics by solely operating in the sequence space.
  • * There is a need for improved prediction strategies, particularly fusion methods, to enhance the performance and generalization of intrinsically disordered region predictors.

Purpose of the Study:

  • * To develop a novel computational method for predicting intrinsically disordered regions (IDRs) in proteins.
  • * To enhance the prediction of protein structure and function by converting sequence information into a 'semantic space' that reflects structural patterns.
  • * To improve the discriminative power and generalization capabilities of IDR prediction models through fusion strategies.

Main Methods:

  • * Applied Sequence to Sequence Learning (Seq2Seq), a natural language processing technique, to map protein sequences into a 'semantic space'.
  • * Incorporated predicted residue-residue contacts (CCMs) and other sequence-based features to represent structural patterns.
  • * Utilized an Attention mechanism to capture global associations between residue pairs within protein sequences.
  • * Developed three length-dependent predictors (IDP-Seq2Seq-L, IDP-Seq2Seq-S, IDP-Seq2Seq-G) and fused them into a single predictor, IDP-Seq2Seq.

Main Results:

  • * The IDP-Seq2Seq predictor demonstrated superior performance compared to existing methods across four independent test datasets and the CASP test dataset.
  • * The predictor proved insensitive to the ratios of long and short intrinsically disordered regions.
  • * Experimental results confirmed the enhanced discriminative power and generalization of the fused IDP-Seq2Seq model.

Conclusions:

  • * The developed IDP-Seq2Seq predictor effectively maps protein sequences to a 'semantic space' for improved intrinsically disordered region (IDR) identification.
  • * IDP-Seq2Seq offers enhanced prediction accuracy and generalization, outperforming current state-of-the-art methods.
  • * A user-friendly web server for IDP-Seq2Seq is publicly available, facilitating its use by experimental scientists for IDR identification.