Engineering a hemoglobin variant with enhanced stability and oxygen affinity using a protein language model.
- 1Laboratory of Chemoinformatics, Infochemistry Scientific Center, ITMO University, Saint Petersburg, Russia.
- 2Department of Bioinformatics, Biocenter, Am Hubland, Julius-Maximilians University of Würzburg, Würzburg, Germany.
- 3Department of Biology and General Genetics, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
Abstract
Introduction
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.Methods
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).Results
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.Discussion
This work establishes a generalizable framework for pLM-driven protein engineering applicable to therapeutic Hb variants and beyond.
Related
- 895 Engineering GPRC5D multispecific antibodies: parallel engineering to optimize affinity, developability, and IP
- Benchmarking Large Language Models for Assay-Aware Drug-Target Binding Affinity Prediction
- Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction
- Antibody-Antigen Affinity Prediction with Chain-Aware Protein Language Modeling
- Clinical-Anchored Incremental Survival Learning: A Stability-Constrained Framework for Molecularly Enhanced Survival Prediction in Lung Adenocarcinoma
BibTeX
@article{shityakov2026engineering,
title = {{Engineering a hemoglobin variant with enhanced stability and oxygen affinity using a protein language model.}},
author = {Sergey Shityakov and Alexander S Novikov and Ekaterina V Skorb and Thomas Dandekar},
journal = {Frontiers in chemistry},
year = {2026},
month = oct,
doi = {10.3389/fchem.2026.1898272},
url = {https://europepmc.org/article/MED/42898797},
note = {CC BY},
}
RIS
TY - JOUR
TI - Engineering a hemoglobin variant with enhanced stability and oxygen affinity using a protein language model.
AU - Sergey Shityakov
AU - Alexander S Novikov
AU - Ekaterina V Skorb
AU - Thomas Dandekar
PY - 2026
DA - 2026/10/05
JO - Frontiers in chemistry
DO - 10.3389/fchem.2026.1898272
UR - https://europepmc.org/article/MED/42898797
AB - Introduction 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.Methods 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).Results 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.Discussion This work establishes a generalizable framework for pLM-driven protein engineering applicable to therapeutic Hb variants and beyond.
SN - 2296-2646
ER -