A Complex Prime Numerical Representation of Amino Acids for Protein Function Comparison

Area of Science:

• Bioinformatics
• Computational Biology
• Protein Analysis

Background:

• Assessing protein functional similarity computationally aids knowledge transfer and reduces experimental effort in molecular biology.
• Numerical representation of amino acids is crucial for advanced protein analysis, surpassing symbolic methods.
• Existing methods like electro-ion interaction pseudopotential (EIIP) face degeneracy issues, where different sequences yield identical representations.

Purpose of the Study:

• To introduce a novel numerical representation for amino acids called Complex Prime Numerical Representation (CPNR).
• To address the degeneracy problem inherent in the EIIP method for amino acid representation.
• To evaluate the effectiveness of CPNR in protein functional similarity assessment compared to EIIP.

Main Methods:

• Developed Complex Prime Numerical Representation (CPNR) based on prime number patterns and amino acid codon counts.
• Compared CPNR against the established electro-ion interaction pseudopotential (EIIP) method.
• Created a framework to integrate CPNR and EIIP for enhanced analysis.

Main Results:

• CPNR consistently outperformed EIIP in experimental assessments of protein functional similarity.
• The proposed CPNR method demonstrated reduced degeneracy compared to EIIP.
• The combined framework utilizing both CPNR and EIIP showed improved performance and facilitated unique characteristic studies.

Conclusions:

• CPNR is a more effective numerical representation for amino acids in functional similarity analysis.
• The degeneracy issue in EIIP can be mitigated with alternative representations like CPNR.
• Integrating CPNR and EIIP offers a powerful approach for advancing protein analysis in bioinformatics.