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APOD: accurate sequence-based predictor of disordered flexible linkers.

Zhenling Peng1,2, Qian Xing1, Lukasz Kurgan3

  • 1Center for Applied Mathematics, Tianjin University, Tianjin 300072, China.

Bioinformatics (Oxford, England)
|December 31, 2020
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Summary

Accurate computational prediction of disordered flexible linkers (DFLs) is crucial for understanding protein function. APOD, a new predictor, significantly improves DFL identification accuracy by integrating local and protein-level features.

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Disordered flexible linkers (DFLs) are intrinsically disordered regions connecting protein domains, crucial for allosteric regulation.
  • Thousands of proteins are estimated to contain DFLs, but experimental annotation is limited to fewer than 200 proteins, highlighting a significant annotation gap.
  • Existing predictors like DFLpred offer rapid but less accurate DFL identification due to limited input features and reliance on local information.

Purpose of the Study:

  • To develop a highly accurate computational predictor for Disordered Flexible Linkers (DFLs).
  • To address the annotation gap for DFLs by improving prediction accuracy over existing methods.
  • To integrate both local and global protein-level features for enhanced DFL prediction.

Main Methods:

  • Conceptualized, designed, and tested APOD (Accurate Predictor Of DFLs).
  • Utilized a comprehensive set of inputs including propensity for disorder, sequence composition, sequence conservation, and putative structural properties.
  • Employed a well-parametrized support vector machine as the predictive model.

Main Results:

  • APOD demonstrates significantly higher accuracy compared to DFLpred and other methods.
  • Achieved an Area Under the Curve (AUC) of 0.82, a 28% improvement over DFLpred.
  • Obtained a Matthews Correlation Coefficient (MCC) of 0.42, a 180% increase over DFLpred on an independent test dataset.

Conclusions:

  • APOD is the first highly accurate predictor for DFLs, effectively utilizing both local and protein-level inputs.
  • The improved accuracy of APOD makes it a suitable tool for accurate, small-scale prediction of DFLs.
  • APOD offers a substantial advancement in identifying DFLs, aiding in the understanding of protein structure and function.