(A-11) Structural Dynamics and Function of Hepcidin In the Context of Haemochromatosis Using AlphaFold2 and VMD.

Introduction:: Hepcidin is a peptide consisting of 84 amino acids produced by the liver that acts as a key regulator of iron metabolism. Hepcidin metabolizes iron in the human body. The increase in hepcidin levels leads to diseases like hereditary hemochromatosis, inflammation, and chronic kidney diseases. On the other hand, a decrease in hepcidin levels leads to iron deficiency anemia. Hemochromatosis is an autosomal recessive disorder of iron metabolism that is characterized by increased duodenal iron absorption, which leads to excessive iron deposition in the parenchymal cells of the liver and major organs. If untreated, hemochromatosis can lead to cirrhosis, which may increase susceptibility to hepatocarcinoma, diabetes, cardiac failure, endocrine complications, and arthritis. So, the study of hepcidin and its regulation is of great importance for understanding and treating iron-related diseases. We aim at predicting the 3-dimensional structure of hepcidin and its interactions with other proteins using Alphafold2 which uses a deep learning algorithm and by using the visual molecular dynamics (VMD) tool to provide insights into its function and mechanism of action. With VMD, the predicted structure of hepcidin can be visualized and its interactions with the environment will be quantified. We aim to understand, the structure and function of hepcidin protein and its role in regulating iron metabolism by combining the capabilities of Alphafold2 and VMD.


Materials and Methods:: The amino acid sequence of Hepcidin and its variants were extracted from the Alphafold Protein Structure Database.AlphaFold2 and Visual Molecular Dynamics were employed to understand the structural conformations of the Hepcidin protein and its variants. AlphaFold2 predicted the structure of all the variants with the changed amino acid. Additionally, Visual Molecular Dynamics was used to understand the functional aspect of Hepcidin and its variants with Ferroportin.


Results, Conclusions, and Discussions::

Table 1: Hepcidin and its Variants pae, PTM and pLDDT values.

S.No.	Sample	Maximum Predicted Aligned Error	Standard Deviation of pLDDT Value	PTM(Post Translational Modifications)	Average pLDDT Value
1	Hepcidin Normal	31.75	19.706	0.258	59.56
2	Hepcidin(VAR_042512)	31.75	15.718	0.258	46.738
3	Hepcidin(VAR_042513)	31.75	18.292	0.266	58.372
4	Hepcidin(VAR_026648)	31.24	18.556	0.258	58.542
5	Hepcidin(VAR_042514)	31.13	17.918	0.272	56.33

For Hepcidin and its variants, the highest reported maximum PAE was 31.75 residue units, while the least was 31.13 for Hepcidin (VAR_042514). This means that the predicted alignment for that protein had a maximum difference of 31.75 residue units from the true alignment. A high PAE value suggests that the predicted alignment may have some significant deviations from the true alignment. The Standard Deviation of pLDDT Value varies between samples, ranging from 15.718 to 19.706. This indicates differences in the confidence of the predicted protein structures. Post-translational modifications are chemical modifications that can occur to proteins after they are synthesized and can have a significant impact on protein function. The value of PTM was 0.272 to 0.258, which explains that it is estimated to have an average of 0.272 to 0.258 post-translational modifications per residue. An average pLDDT score of 58.372% to 46.738% suggests that the predicted protein structure may have some inaccuracies, but it is still relatively confident compared to other protein structure prediction methods. Predicting protein structure is vulnerable because it varies depending on factors such as protein size, complexity, and the availability of structural templates.

AlphaFold2 and VMD together can provide a powerful set of tools for studying protein structures and their behavior. Alphafold2 helped to predict the 3d structure of Hepcidin and its variants, while VMD help to draw the structure and devise relationships with other chemical components.




Acknowledgements (Optional): : Indebted to my parents, Mr. Motiram Tiwari and Late Januka Tiwari for the life they have given me. Secondly to my wife, Pramila, and daughter, Aashra for love, trust, and encouragement. Appreciate the contribution of Roshan, Ramya, Odhran Casey, Asst. Prof Bhusan and am always thankful to Prof. Prabir Patra for his timely remarks and suggestions.

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