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Updated: Feb 10, 2026
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Characterization of Glycoproteins with the Immunoglobulin Fold by X-Ray Crystallography and Biophysical Techniques
Published on: July 5, 2018
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Sequentially distant but structurally similar proteins exhibit fold specific patterns based on their biophysical
Senthilnathan Rajendran1, Arunachalam Jothi1
1Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, Tamil Nadu, 613401, India.
Computational Biology and Chemistry
|May 22, 2018
Summary
Protein structure and function are dictated by amino acid biophysical properties, not just sequence. This study reveals fold-specific patterns in these properties, enabling accurate protein classification.
Area of Science:
- Biochemistry
- Structural Biology
- Bioinformatics
Background:
- Protein three-dimensional structure arises from amino acid residue interactions.
- These interactions are governed by the biophysical properties of amino acids.
- Proteins can share similar structures (folds) despite low sequence similarity.
Purpose of the Study:
- To analyze and characterize proteins with low sequence similarity (<40%) based on amino acid biophysical properties.
- To identify fold-specific patterns in these properties.
- To develop classification models for predicting protein folds using biophysical properties.
Main Methods:
- Selected protein structures from ten subfolds across three mainfolds (CATH classification) with <40% sequence similarity.
- Utilized normalized values of 49 amino acid physio-chemical, energetic, and conformational properties.
- Trained Naive Bayes (NB), Support Vector Machines (SVM), and Bayesian Generalized Linear Model (BGLM) classifiers.
Main Results:
- Characterized protein folds using average biophysical property values.
- Observed fold-specific correlations among biophysical properties, even with low sequence similarity.
- The BGLM classifier achieved 81.43% accuracy for all-beta proteins and 83.37% for all-alpha and alpha-beta proteins.
Conclusions:
- Amino acid biophysical properties provide a basis for classifying protein folds, independent of sequence similarity.
- The Bayesian Generalized Linear Model (BGLM) demonstrates effectiveness in discriminating protein structural categories based on these properties.
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