Large Numbers of New Human Paralogs Discovered.

Area of Science:

• Genomics and Bioinformatics
• Structural Biology
• Computational Biology

Background:

• Identifying protein paralogs is vital for protein evolution, function studies, and drug design.
• Existing sequence-based methods struggle to detect distant paralogs, leaving many human proteins unannotated.
• A comprehensive approach is needed to overcome limitations in current homology detection.

Purpose of the Study:

• To develop and validate an integrated framework for discovering novel human protein paralogs.
• To systematically identify and characterize paralogs, including their catalytic residues.
• To enhance the understanding of protein functional landscapes and evolutionary relationships.

Main Methods:

• An integrated homolog detection framework combining BLASTp, MMseqs2, Foldseek, and PROST (protein language model-based tool).
• All-versus-all comparisons across 20,647 human proteins.
• Validation using structural comparisons and catalytic residue analysis, particularly for enzymes.

Main Results:

• Discovery of 14 previously uncharacterized human serine carboxypeptidases, with 11 showing conserved catalytic triads.
• Identification of 203 new human kinase paralogs, including 163 in major clusters representing novel subtypes.
• Identification of 30 putative novel human transcription factors.
• Prediction of catalytic residues for unannotated serine proteases using structural alignments.

Conclusions:

• The integrated framework successfully identifies a significant number of novel human protein paralogs.
• Combining sequence, structure, and AI-based methods expands the discovery of protein functional landscapes.
• These findings lay the groundwork for future research in protein function, evolution, and therapeutic applications.