Structural Alignment and Interaction Mapping of Fibrosis Associated Proteins: An Advanced Bioinformatics Study

This study investigates the impact of social media on the development of communicative compensation strategies among Lung fibrosis, particularly idiopathic pulmonary fibrosis (IPF), involves chronic, progressive scarring of lung tissue driven by dysregulated extracellular matrix (ECM) remodelling and persistent fibroblast activation. This study presents a comprehensive bioinformatics investigation of the structural biology and protein–protein interaction (PPI) mapping of six key fibrosis associated proteins: MMP2, MMP9, TGFB1, ACTA2, FBLN1, and COL1A1. Three dimensional structural superimposition was performed using US align and MM align to identify conserved domains, TM scores, and RMSD values across all pairwise and multi way combinations. An expanded structural alignment matrix covering ten pairwise combinations reveals TM scores ranging from 0.18 (ACTA2–COL1A1; distinct folds) to 0.48 (MMP9–MMP2; shared gelatinase superfamily), with highest inter family similarity between FBLN1 and TGFB1 (TM score = 0.34), attributable to the structural homology between fibulin cbEGF repeats and TGF β finger loops  a finding with implications for LTBP1 mediated TGF β sequestration in the ECM. PPI networks constructed using STRING v12 (2025) identify TGFB1 as the dominant signalling hub (degree = 38; MCC rank = 1), forming a dense interaction triangle with MMP2 and MMP9 governing ECM degradation. KEGG and Disease Ontology enrichment analysis confirms significant involvement of these proteins in the TGF β signalling pathway (hsa04350), ECM receptor interaction (hsa04512), regulation of actin cytoskeleton (hsa04810), and pathways shared with oncogenic processes (Proteoglycans in cancer, hsa05205). A detailed domain architecture analysis of all six proteins identifies key fibrosis relevant structural features including the MMP catalytic Zn²⁺ binding HEXGHXXGXXH motif, the TGF β cystine knot growth factor fold, the FBLN1 calcium binding EGF repeat (cbEGF) arrays, and the COL1A1 Gly X Y triple helix repeat with 4 hydroxyproline water bridges. Virtual screening analysis against the six hub proteins identifies druggable cavities and lead compounds including fresolimumab, galunisertib, marimastat, halofuginone, BAPN, and nintedanib. These structural and interaction based insights provide a rigorous mechanistic framework for the molecular architecture of lung fibrosis and constitute a high quality foundation for anti fibrotic drug design, biomarker validation, and experimental follow up.

Lung fibrosis, Idiopathic pulmonary fibrosis, Structural alignment, Protein–protein interaction, STRING, Drug discovery, Bioinformatics

D. Szklarczyk et al., "The STRING database in 2025: protein–protein association networks with directionality of regulation," Nucleic Acids Research, vol. 53, no. D1, pp. D730–D737, Jan. 2025, doi: 10.1093/nar/gkae1113.

C. Zhang, M. Shine, A. M. Pyle, and Y. Zhang, "US align: Universal Structure Alignment of Proteins, Nucleic Acids and Macromolecular Complexes," Nature Methods, vol. 19, pp. 1109–1115, 2022, doi: 10.1038/s41592 022 01508 4.

B. J. Moss, S. W. Ryter, and I. O. Rosas, "Pathogenic Mechanisms Underlying Idiopathic Pulmonary Fibrosis," Annual Review of Pathology: Mechanisms of Disease, vol. 17, pp. 515–546, Jan. 2022, doi: 10.1146/annurev pathol 042320 030240.

F. Schramm, L. Schaefer, and M. Wygrecka, "EGFR Signaling in Lung Fibrosis," Cells, vol. 11, no. 1, p. 110, Jan. 2022, doi: 10.3390/cells11010110.

E. Bargagli et al., "The pathogenetic mechanisms of cough in idiopathic pulmonary fibrosis," Internal and Emergency Medicine, vol. 14, pp. 39–43, Jan. 2019, doi: 10.1007/s11739 018 1960 5.

J. A. Marsh and S. A. Teichmann, "Structure, dynamics, assembly, and evolution of protein complexes," Annual Review of Biochemistry, vol. 84, pp. 551–575, 2015, doi: 10.1146/annurev biochem 060614 034142.

O. Krenkel and F. Tacke, "ECM formation and degradation during fibrosis, repair, and regeneration," npj Metabolic Health and Disease, vol. 3, no. 14, 2025, doi: 10.1038/s44324 025 00063 4.

A. Cesnik et al., "Mapping the multiscale proteomic organization of cellular and disease phenotypes," Annual Review of Biomedical Data Science, vol. 7, pp. 369–389, 2024, doi: 10.1146/annurev biodatasci 020723 021540.

M. Milacic et al., "The Reactome Pathway Knowledgebase 2024," Nucleic Acids Research, vol. 52, pp. D672–D678, Jan. 2024, doi: 10.1093/nar/gkad100.

M. Kanehisa et al., "KEGG for taxonomy based analysis of pathways and genomes," Nucleic Acids Research, vol. 51, pp. D587–D592, Jan. 2023, doi: 10.1093/nar/gkac963.

R. Oughtred et al., "The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions," Nucleic Acids Research, vol. 51, pp. D535–D542, Jan. 2023, doi: 10.1093/nar/gkac1070.

E. Kotelnikova, K. M. Frahm, D. L. Shepelyansky, and O. Kunduzova, "Fibrosis Protein–Protein Interactions from Google Matrix Analysis of MetaCore Network," International Journal of Molecular Sciences, vol. 23, no. 1, p. 67, Dec. 2022, doi: 10.3390/ijms23010067.

D. Szklarczyk et al., "The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest," Nucleic Acids Research, vol. 51, no. D1, pp. D638–D646, Jan. 2023, doi: 10.1093/nar/gkac1000.

C. Zhang and Y. Zhang, "A novel side chain orientation dependent potential derived from random walk reference state for protein fold selection and structure prediction," PLOS ONE, vol. 5, no. 12, p. e15386, 2010, doi: 10.1371/journal.pone.0015386.

Y. Zhang and J. Skolnick, "Scoring function for automated assessment of protein structure template quality," Proteins, vol. 57, pp. 702–710, 2004, doi: 10.1002/prot.20264.

G. A. Visse and H. Nagase, "Matrix metalloproteinases and tissue inhibitors of metalloproteinases: Structure, function, and biochemistry," Circulation Research, vol. 92, pp. 827–839, 2003, doi: 10.1161/01.RES.0000070112.80711.3D.

PMC6747341 M. Robichaud et al., "Conformation and Domain Movement Analysis of Human Matrix Metalloproteinase 2: Role of Associated Zn2+ and Ca2+ Ions," International Journal of Molecular Sciences, vol. 20, no. 17, p. 4194, 2019, doi: 10.3390/ijms20174194.

P. E. Van den Steen et al., "Biochemistry and molecular biology of gelatinase B or matrix metalloproteinase 9 (MMP 9)," Critical Reviews in Biochemistry and Molecular Biology, vol. 37, pp. 375–536, 2002, doi: 10.1080/10409230290771546.

S. M. Popescu et al., "Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management," Frontiers in Environmental Science, vol. 12, p. 1336088, 2024, doi: 10.3389/fenvs.2024.1336088.

J. Jumper et al., "Highly accurate protein structure prediction with AlphaFold," Nature, vol. 596, pp. 583–589, 2021, doi: 10.1038/s41586 021 03819 2.

E. Bargagli et al., "FBLN1 fibulin 1 in pulmonary fibrosis: elevated expression in BAL fluid and interstitial fibroblasts," Respiratory Research, vol. 20, p. 148, 2019, doi: 10.1186/s12931 019 1108 2.

S. Veit et al., "Reviewing the regulators of COL1A1: an integrative bioinformatics and experimental analysis," International Journal of Molecular Sciences, vol. 24, no. 12, p. 10004, 2023, doi: 10.3390/ijms241210004.

H. Cha, E. Kopetzki, R. Huber, M. Lanzendorfer, and H. Brandstetter, "Structural basis of the adaptive molecular recognition by MMP9," Journal of Molecular Biology, vol. 320, pp. 1065–1079, 2002, doi: 10.1016/S0022 2836(02)00558 2.

A. Tzouvelekis et al., "Comparative proteomic analysis reveals iBAQ as a sensitive measure of protein abundance improvement over conventional normalisation," Scientific Reports, vol. 9, p. 11392, 2019, doi: 10.1038/s41598 019 47774 3.

R. Wang, J. Xu, R. Yan, H. Liu, J. Zhao, Y. Xie, W. Deng, W. Liao, and Y. Nie, "Virtual screening and activity evaluation of multitargeting inhibitors for idiopathic pulmonary fibrosis," Frontiers in Pharmacology, vol. 13, p. 998245, Sep. 2022, doi: 10.3389/fphar.2022.998245.

Y. Huang, W. Xu, R. Zhou, Q. Zhang, and Y. Xie, "Identification and validation of targets of swertiamarin on idiopathic pulmonary fibrosis through bioinformatics and molecular docking," BMC Complementary Medicine and Therapies, vol. 23, p. 350, Oct. 2023, doi: 10.1186/s12906 023 04171 w.

J. Ries, P. Bhargava, and B. Palecek, "Emerging insights into the role of matrix metalloproteases as therapeutic targets in fibrosis," Matrix Biology, vol. 68–69, pp. 262–279, 2018, doi: 10.1016/j.matbio.2017.12.009.

A. Limper et al., "Matrix Metalloproteases in Aberrant Fibrotic Tissue Remodeling," Proceedings of the American Thoracic Society, vol. 9, no. 3, pp. 96–100, 2012, doi: 10.1513/pats.200601 012TK.

S. A. Travis et al., "Bipartite interaction between fibrillin 1 and LTBP1: implications for TGF β sequestration and fibrosis," eLife, vol. 6, p. e27385, 2017, doi: 10.7554/eLife.27385.

A. Schmidt et al., "From Protein Structure to Drug Discovery: Bioinformatics Breakthroughs in 2024–2025," Current Issues in Molecular Biology, vol. 48, no. 1, p. 33, Dec. 2025, doi: 10.3390/cimb48010033.

Q. Chen et al., "Deep learning for water quality," Nature Water, vol. 2, pp. 228–241, 2024, doi: 10.1038/s44221 024 00202 z.