Complementary Roles of Physics-Based Approaches in Predicting VHH Thermal Stability

VHHs are frequently employed in protein therapeutics; however, enhancing their thermal stability (Tm) remains a significant challenge. We previously developed a two‑step in silico strategy to enhance Tm by identifying mutation sites (first round) and selecting favorable substitutions (second round) using dStability, a Gibbs free energy-based score. While dStability performed reasonably in the second round ( r = -0.64), it failed to predict the effect of G97F mutation, ranking it as most stabilizing despite its strong destabilizing impact on experimental Tm. In this study, we re-evaluated this dataset using high-temperature molecular dynamics (MD) simulations, employing Q-values, originally proposed by Bekker et al., as a quantitative metric to assess the extent of structural degradation. This method successfully identified G97F as least stable, demonstrating utility for detecting destabilizing mutations for the first round, though its second‑round performance was weaker ( r  = 0.44). We also developed a temperature‑increase MD protocol, ramping simulation temperature from 300 K to 1000 K over 4 ns to completely degrade protein. Despite being 25-fold faster than fixed-temperature simulations, this approach retained comparable predictive performance. Overall, combining these two physics-based approaches, dStability and Q‑value analysis with optimized MD protocols, enables efficient identification of stabilizing mutations.