Computational Modeling of Low-Abundance Proteins in Venom Gland Transcriptomes: and .

Snake venoms contain numerous toxic proteins, but low-abundance proteins often remain uncharacterized due to identification challenges. This study employs a bioinformatics approach to identify and structurally model low-abundance proteins from the venom gland transcriptomes of and . Using tools such as tblastn, Jalview, and CHIMERA, we analyzed sequences and structural features of proteins including arylsulfatase, CRISP (Cysteine-Rich Secretory Protein), von Willebrand factor type D (vWFD), and dihydroorotate dehydrogenase (DHODH), and identified potential new isoforms of SVMP-PIIIb (Ba_1) and botrocetin in . Protein models were generated with AlphaFold2, compared with crystallized structures from the Protein Data Bank (PDB), and validated using Procheck, ERRAT, and Verify3D. Conserved motifs and domains were annotated through Pfam and InterPro, revealing structural elements that suggest possible roles in venom physiology and toxicity. These findings emphasize the potential of computational biology to characterize structurally relevant but experimentally inaccessible venom proteins, and to lay the groundwork for future functional validation.