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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
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Computational Analysis Predicts Correlations among Amino Acids in SARS-CoV-2 Proteomes
Emmanuel Broni1, Whelton A Miller1,2
1Department of Medicine, Loyola University Medical Center, Loyola University Chicago, Maywood, IL 60153, USA.
Biomedicines
|February 25, 2023
Summary
Analyzing amino acid composition (AAC) in SARS-CoV-2 variants reveals mutation patterns. Leucine is most abundant, tryptophan least, aiding in predicting viral mutations for drug and vaccine design.
Area of Science:
- Virology
- Computational Biology
- Biochemistry
Background:
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a global health challenge, necessitating rapid therapeutic and vaccine development.
- Understanding viral mutation patterns is crucial for anticipating and countering future viral evolution and designing effective interventions.
- Amino acid composition (AAC) analysis offers a potential method for predicting protein structure, evolutionary rates, and viral characteristics.
Purpose of the Study:
- To elucidate patterns and rates of mutations in SARS-CoV-2 variants through amino acid composition analysis.
- To explore the utility of AAC in predicting viral mutations for early drug and vaccine design.
- To assess the correlations between different SARS-CoV-2 variants based on their amino acid frequencies.
Main Methods:
- Analysis of amino acid residue frequencies in 1637 complete proteomes from 11 SARS-CoV-2 variants/lineages.
- Statistical analysis including correlograms and Shapiro-Wilk normality tests to identify correlations and distribution patterns.
- Calculation of average AAC, mutation intolerance, and correlation coefficients between variants.
Main Results:
- Leucine was the most abundant amino acid (9.658% AAC), while tryptophan was the least abundant (1.11%).
- High correlations (0.999992) were found between B.1.525 (Eta) and B.1.526 (Iota) variants; strong negative correlation between isoleucine and threonine (-0.912); strong positive correlation between cysteine and isoleucine (0.835).
- Tryptophan showed the highest intolerance to mutation; AAC values for most amino acids were normally distributed, allowing prediction via probability and z-scores.
Conclusions:
- AAC analysis provides valuable insights into SARS-CoV-2 variant characteristics and evolutionary patterns.
- AAC can be a significant feature in machine-learning algorithms for predicting viral mutations, aiding in the development of targeted therapeutics and vaccines.
- AAC may also be beneficial for classifying viral strains, predicting disease types, and for diagnostic purposes.
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