Biophysical characterization of high-confidence, small human proteins

Significant efforts have been made to characterize the biophysical properties of proteins. Small proteins have received less attention because their annotation has historically been less reliable. However, recent improvements in sequencing, proteomics, and bioinformatics techniques have led to the high-confidence annotation of small open reading frames (smORFs) that encode for functional proteins, producing smORF-encoded proteins (SEPs). SEPs have been found to perform critical functions in several species, including humans. While significant efforts have been made to annotate SEPs, less attention has been given to the biophysical properties of these proteins. We characterized the distributions of predicted and curated biophysical properties, including sequence composition, structure, localization, function, and disease association of a conservative list of previously identified human SEPs. We found significant differences between SEPs and both larger proteins and control sets. Additionally, we provide an example of how our characterization of biophysical properties can contribute to distinguishing protein-coding smORFs from non-coding ones in otherwise ambiguous cases. Why it MattersThe identification of proteins smaller than 100 amino acids has lagged being that of larger proteins in part because their small size makes proteomic and genomic approaches more challenging. However, small proteins can perform significant functions. There are now enough proteins with high confidence of function to enable meaningful comparison with larger proteins, and to determine whether putative small proteins might be identified, based on their biophysical properties. As an example, we found that small proteins often contain transmembrane helices but are relatively devoid of beta sheets, which helped to support the annotation of a putative protein as functional. We also identified a wide range of biological functions and localization for small proteins, including many with disease associations.