Integrating Sequence, Structure, and Graph-based Features for Elucidating the Stability of Thermophilic Proteins

Proteins from thermophilic organisms exhibit remarkable stability under extreme thermal conditions. Understanding the molecular mechanisms underlying thermostability is essential for studying protein evolution and engineering robust enzymes. In this study, we systematically analyzed four sets of mesophilic-thermophilic protein pairs to investigate the molecular basis of thermal adaptation. We have constructed independent datasets of mesophilic-thermophilic protein pairs defined by sequence identity and optimal growth temperature (OGT): (a) >90% identity and 60-80 °C OGT, (b) 50-90% identity and >80 °C OGT, (c) 50-90% identity and 60-80 °C OGT, and (d) 50-90% identity and 40-60 °C OGT. Mutational analysis revealed that thermophilic proteins consistently reduced polar, uncharged residues while enriching charged, hydrophobic, and aromatic residues, particularly in extreme thermophiles (>80 °C). Further, by integrating multiple known protein features into a hierarchical rule-based classifier, we identified the thermostable protein from a pair of sequences and also assessed the relative importance of features across datasets to provide interpretable insights into protein thermostability. The hierarchical rule-based method identified stabilizing residues as the primary distinguishing factor, followed by electrostatic energy, volume, and localized electrical effects, which correctly classified 99% of thermophilic proteins. A bagging model trained on the same features achieved a balanced accuracy of 92% in 5-fold cross-validation and 91% on the 20% hold-out test set. Furthermore, independent validation using multiple mutations in proteins accurately identified 94% of stabilizing and destabilizing mutations. The results obtained in this work provide valuable insights to understand the thermal adaptation of proteins and reliably identify thermostable proteins.