Engineering a hemoglobin variant with enhanced stability and oxygen affinity using a protein language model

Engineered hemoglobin variants offer therapeutic promise for artificial blood substitutes and oxygen therapeutics, but rational design is constrained by protein stability and heme-binding. Protein language models (pLMs) predict mutation fitness, yet their integration with biophysical validation remains limited.

We developed a hybrid pipeline combining pLM (ESM3) with molecular docking, Monte Carlo simulation, molecular dynamics, MM-GBSA/PBSA binding free energy calculations, and density functional theory (DFT) to engineer a stabilized hemoglobin variant (esmHb) with enhanced oxygen affinity, benchmarking against the clinically characterized Hb Fort de France mutation (α45 His→Arg).

ESM3 accurately assessed the fitness effect of the Hb Fort de France mutation and generated structurally stable esmHb models. Compared with the wild-type variant (wtHb), esmHb exhibited improved thermodynamic stability (ΔG: -18.48 vs. -12.22 kcal/mol), increased heme-binding affinity (ΔGbind: -11.79 vs. -8.42 kcal/mol), and greater thermodynamic favorability of oxidation according to DFT calculations (ΔE: -173.1 vs. -149.9 kcal/mol). MM-GBSA and MM-PBSA binding free energy calculations further supported improved protein-heme and protein-protein interactions, with esmHb showing more favorable values in both implicit solvation models. Statistical significance (p < 0.05) was reached for protein-protein interactions via both methods and for protein-heme interactions via MM-PBSA. We interpret the MM-GBSA results as a suggestive trend rather than definitive evidence.

This work establishes a generalizable framework for pLM-driven protein engineering applicable to therapeutic Hb variants and beyond.