Characterization of the Operational Trna Code for Amino Acids Through Supervised Machine Learning

The operational tRNA code for amino acids is defined as a set of sequence- or structure-dependent rules governing the aminoacylation of tRNAs outside and independent of the anticodon. Previous efforts to uncover these rules have included experimental studies using synthetic and chemically modified transfer RNAs (tRNAs), as well as computational analyses aimed at predicting the cognate amino acid from tRNA primary sequences. In this study, we examined (1) whether tRNA primary sequences contain sufficient information to specify amino acid identity in the absence of the anticodon, (2) which regions are most important for aminoacylation, and (3) whether the operational code is universal or taxon-specific. Using supervised machine-learning classifiers trained on tRNA sequences, we found that the anticodon is not the sole determinant of tRNA identity. Even when the anticodon region was removed, classification accuracy remained high, indicating that other portions of the primary sequence encode meaningful recognition signals. Logistic-regression and odds-ratio analyses identified the acceptor stem and D-loop/stem as the regions most strongly associated with tRNA identity, whereas the anticodon and T-loop/stem were less consistently implicated. In contrast to the near universality of the classical genetic code, the operational RNA code for amino acids does not appear to be universal. Reciprocity tests revealed asymmetric predictability among species, suggesting species-specific features or potential model overfitting. Overall, our findings demonstrate that while tRNA recognition extends beyond the anticodon, the operational RNA code is not universal and is evolutionarily divergent. These results imply that tRNA identity is encoded through multiple, distributed sequence features that vary among species and amino acids, underscoring the importance of considering both structural and evolutionary contexts in future studies of tRNA–synthetase recognition.

Name thesis:degree_name

Doctor of Philosophy

Discipline thesis:degree_discipline

Biology

Grantor

University of Houston

Year dc:date.issued

2025

Author dc:creator

• Gospodinov, Denis Ivov 1998-

Advisor dc:contributor.advisor

• Graur, Dan

Committee members dc:contributor.committeemember

• Konstantinidis, Ioannis
• Dauwalder , Brigitte
• Daane, Jacob

• Transfer RNA
• Aminoacyl tRNA synthetase
• Supervised machine learning

Language dc:language.iso

English

Handle dc:identifier.uri

https://hdl.handle.net/10657/20842

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/20842