Probing the potentials of γ-globin genes for Hemoglobinopathies (Sickle Cell Anemia)

Abstract

Hemoglobinopathies are a cluster of hereditary disorders that impact the structure and functionality of hemoglobin. These illnesses include a broad spectrum of irregularities, with sickle cell anemia being one of the more well-known forms. Sickle cell anemia arises from a genetic mutation GLU6VAL in the HBB gene that results in the synthesis of abnormal hemoglobin (HbS) and the characteristic deformation of red blood cells into a sickle shape. Sickle-shaped cells then ultimately block the flow of normal blood cells causing anemia, organ damage, pain crises, and other infections that lead to reduced life expectancy. The treatment of sickle cell anemia is complex because of its genetic origin, needing precise treatments that address the underlying mutation in the HBB gene. The research used Molecular Dynamics simulations to investigate the molecular complexities of normal and fetal hemoglobin, to discover crucial interaction patterns that contribute to their antisickling abilities. Thus, the objective is to probe the binding pattern and the stability of the alphagamma chain complex of the fetal hemoglobin for the modulation of the beta chain in adults to rescue the mutated function of sickled hemoglobin. The study used a Structural Bioinformatics approach to conduct Molecular Dynamics (MD) simulations for 200ns, aiming to examine the dynamic interactions occurring inside the alpha and beta chains of both wild-type sickled and normal hemoglobin Furthermore, the scope of the investigation was broadened to examine the patterns of alpha-gamma interaction within fetal hemoglobin. The findings of the MD simulations not only enhanced understanding of the structural dynamics but also provided crucial insights into the modified interactions that underlie the manifestation of sickle cell anemia. Studying the alpha-gamma patterns in fetal hemoglobin has enhanced knowledge of the molecular characteristics associated with hemoglobinopathies. Thus, the identification of interaction patterns not only demonstrates the potential of computational methods in addressing complex genetic diseases at the molecular level but also offers a practical pathway for the identification of therapeutic target