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RBSURFpred: Modeling protein accessible surface area in real and binary space using regularized and optimized

Sumit Tarafder1, Md Toukir Ahmed1, Sumaiya Iqbal2

  • 1Department of CSE, BUET, ECE Building, West Palasi, Dhaka 1205, Bangladesh.

Journal of Theoretical Biology
|January 7, 2018
PubMed
Summary

RBSURFpred enhances protein accessible surface area (ASA) prediction by integrating 58 diverse properties using Chou

Keywords:
Accessible surface areaMetaheuristicsPSEEProtein structureRelative solvent accessibility

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Area of Science:

  • Computational Biology and Bioinformatics
  • Machine Learning in Structural Biology

Background:

  • Accessible Surface Area (ASA) is crucial for understanding protein structure and function.
  • Accurate ASA prediction aids in protein structure prediction, binding site identification, and fold recognition.
  • Existing ASA predictors like REGAd3p offer high accuracy but can be further improved.

Purpose of the Study:

  • To develop a novel and improved predictor for protein Accessible Surface Area (ASA).
  • To enhance ASA prediction accuracy by incorporating a wider range of protein features.
  • To evaluate the new predictor against state-of-the-art methods.

Main Methods:

  • Developed RBSURFpred, a new predictor extending the REGAd3p model.
  • Incorporated 58 physicochemical, evolutionary, and structural properties.
  • Utilized Chou's general Pseudo Amino Acid Composition (PseAAC) for feature representation in 9-tuple peptides.

Main Results:

  • RBSURFpred achieved higher accuracies in predicting both real-valued and binary ASA compared to existing methods.
  • Performance was validated against state-of-the-art predictors REGAd3p and SPIDER2.
  • Rigorous analysis confirmed RBSURFpred's effectiveness across different amino acids and in biological case studies.

Conclusions:

  • RBSURFpred represents a significant advancement in ASA prediction accuracy.
  • The integration of diverse properties via PseAAC is effective for improving predictive models.
  • RBSURFpred offers a valuable tool for computational biology and structural bioinformatics research.