---
title: "Phycocyanobilin from Arthrospira platensis as a candidate LYN kinase modulator in systemic lupus e"
id: "plos-one-15-phycocyanobilin-a-potential-bioactive-compound-from-arthrospira-platensis"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-15-phycocyanobilin-a-potential-bioactive-compound-from-arthrospira-platensis"
content_type: "clinical_feed_article"
specialty: "Rheumatology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357093"
published_at: "2026-08-28T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Phycocyanobilin from Arthrospira platensis as a candidate LYN kinase modulator in systemic lupus e
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-15-phycocyanobilin-a-potential-bioactive-compound-from-arthrospira-platensis
- **Specialty:** [Rheumatology](https://medichelpline.com/clinical-feed/rheumatology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357093)
- **Published At:** 2026-08-28T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This study investigated **phycocyanobilin**, a chromophore from Arthrospira platensis (Spirulina), for potential relevance to **systemic lupus erythematosus (SLE)** based on structural similarity to **bilirubin** (Tanimoto score 93%). - Researchers screened 833 algal-derived bioactive compounds against bilirubin using PubChem structural similarity tools and focused on phycocyanobilin due to its bile-pigment–like structure and prior network analyses. - Molecular docking (AutoDock/MGLTools) evaluated binding of phycocyanobilin and bilirubin to multiple protein targets previously implicated in SLE: **EGFR**, **FYN**, **HLA-B**, **LCK**, **LYN**, and **TP53**. - NetPredictor network-based target prediction prioritized **LYN kinase** as a key candidate receptor for phycocyanobilin. - Molecular dynamics (MD) simulations were run on the phycocyanobilin–LYN complex (LYN PDB ID 3A4O) for 60 ns with ff14SB force field in AMBER 16 at physiological conditions (300 K, 1 atm); the last 10 ns of trajectories were used for detailed analyses. - MD analyses included RMSD assessment for protein backbone, complex and ligand, hydrogen bond profiling, residue-level interaction mapping, and binding free energy estimation using MM/PBSA and MM/GBSA approaches. - Results indicated stable binding of phycocyanobilin to **LYN**, with interaction profiles comparable to the native ligand staurosporine; residue contributions and calculated binding free energies supported strong, stable interactions. - Authors conclude that phycocyanobilin may modulate **LYN-associated signaling pathways** relevant to SLE and recommend further investigation; specific numerical binding energies and some methodological parameters are reported in the full manuscript and supporting files. - Funding: none specific. Data: all relevant data are within the manuscript and supporting information. Competing interests: none declared.
## Clinical Analysis & Structured Key Points
Phycocyanobilin: A potential bioactive compound from Arthrospira platensis targeting LYN protein associated with systemic lupus erythematosus | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Peer Review Reader Comments Figures Figures Abstract Phycocyanobilin, a bioactive compound derived from Arthrospira platensis C1, was investigated for its potential role in systemic lupus erythematosus (SLE) based on its structural similarity to bilirubin, with a Tanimoto score of 93%. Molecular docking revealed favorable binding affinities between phycocyanobilin and several protein targets, including EGFR, FYN, HLA-B, LCK, LYN, and TP53. Target prediction further identified LYN kinase as a key candidate. Molecular dynamics simulations demonstrated stable binding of the phycocyanobilin–LYN complex, with interaction profiles comparable to those of the native ligand, staurosporine. Binding free energy and residue-level analyses supported strong and stable interactions, highlighting key contributions to complex stability. Overall, these findings provide mechanistic insight into the interaction between phycocyanobilin and LYN, suggesting that this compound may modulate LYN-associated signaling pathways and warrants further investigation in the context of SLE. Citation: Chaiprasert A, Han P, Laomettachit T, Ruengjitchatchawalya M (2026) Phycocyanobilin: A potential bioactive compound from Arthrospira platensis targeting LYN protein associated with systemic lupus erythematosus. PLoS One 21(8): e0357093. https://doi.org/10.1371/journal.pone.0357093 Editor: Eman Zahran, Mansoura University, EGYPT Received: September 17, 2025; Accepted: August 8, 2026; Published: August 28, 2026 Copyright: © 2026 Chaiprasert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the manuscript and its Supporting Information files. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Abbreviations: SLE, systemic lupus erythematosus; CID, compound identifier Introduction Systemic lupus erythematosus (SLE) is an autoimmune disease with a broad spectrum of clinical manifestations and an unclear etiology. The disease affects multiple organ systems and is associated with increased morbidity and mortality [ 1 , 2 ]. Although immunosuppressive drugs can induce disease remission, relapses may still occur and are often unpredictable. Various factors, including sunlight exposure and stress, can trigger disease flares. Serum bilirubin, a product of heme degradation traditionally considered a marker of liver disease, has been shown to correlate negatively with disease activity in patients with SLE [ 3 ]. Reduced serum bilirubin levels in SLE patients may be associated with inflammatory processes and lupus-related kidney involvement [ 3 – 5 ]. Furthermore, patients with inactive SLE exhibit higher bilirubin levels than those with active disease, and patients without lupus nephritis have higher serum bilirubin levels than those with nephritis [ 6 ]. In addition, bilirubin has emerged as a potent signaling molecule with strong antioxidant properties. It exerts broad inhibitory effects on multiple components of the immune system, contributing to protection against autoimmune and inflammatory diseases [ 7 ]. Spirulina ( Arthrospira or Limnospira platensis ), a photosynthetic cyanobacterium, is commonly used as a food and feed supplement due to its rich nutrient content and diverse bioactive compounds. These include the water-soluble phycobiliprotein phycocyanin, which contains phycocyanobilin as a chromophore. Phycocyanobilin belongs to a group of open-chain tetrapyrrole chromophores that bind to proteins via thioester linkages to cysteine residues [ 8 – 10 ]. This microalga exhibits immunomodulatory activity, and its beneficial effects have been investigated in patients with various diseases [ 11 – 19 ]. Our previous study [ 20 ] identified bioactive compounds from Spirulina ( A. platensis C1) associated with immunological responses in SLE using structural similarity, bioassay similarity, disease-drug-compound network analysis, molecular docking, and molecular dynamics (MD) simulations. A high Tanimoto score indicates structural similarity between Spirulina-derived compounds and immunosuppressive agents; although phycocyanobilin was not among the highest-scoring compounds (90–100%), it was identified within a similarity range of ≥60% (see S5 Table in [ 20 ]). In this study, we aimed to investigate the potential of the algal-derived bioactive compounds, particularly phycocyanobilin. Its chemical structure resembles that of bile pigments, including bilirubin, suggesting that it may be associated with SLE or exhibit similar molecular effects. Materials and methods Structural similarity between bioactive compounds of Spirulina and bilirubin The structural similarity between bioactive compounds of Spirulina ( A. platensis C1) and bilirubin was analyzed using the PubChem structural similarity tool based on compound identifiers (CIDs) ( https://pubchem.ncbi.nlm.nih.gov/score_matrix/score_matrix.cgi ) [ 21 ]. A total of 833 bioactive compounds from A. platensis C1 were identified from the Kyoto Encyclopedia of Genes and Genomes (KEGG; www.genome.jp/kegg/ ), the Spirulina Proteome Repository (SpirPro, see S1 Table in [ 20 ]), and a literature review [ 20 , 22 , 23 ]. Structural similarity matching was performed between bilirubin (CID 5280352) and these 833 algal-derived bioactive compounds, including phycocyanobilin [ 20 ]. Molecular docking and molecular dynamics simulations of bioactive compounds and targets To determine whether phycocyanobilin exhibits molecular signaling effects similar to bilirubin, the binding affinities of these two compounds toward several related receptors were evaluated using molecular docking. This approach aims to identify the most favorable binding modes between ligands and receptors. Molecular docking simulations were performed using MGLTools ( http://mgltools.scripps.edu/ ) with AutoDock. The input files included the 3D structures of the ligands retrieved from PubChem ( https://pubchem.ncbi.nlm.nih.gov ) and the receptor proteins in PDB format, obtained from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) ( https://www.rcsb.org ). Prior to docking, both ligands and receptors were converted from PDB to PDBQT format. Docking simulations were conducted to evaluate the binding of bilirubin and phycocyanobilin to all potential receptors, including EGFR (binding site 1: W2R; binding sites 2 and 3: SO4), FYN, HLA-B, LCK, LYN, and TP53, which were initially identified through our previous report [ 20 ]. To ensure comprehensive coverage of potential binding sites, the grid box was configured to encompass the relevant binding domains of each receptor, enabling thorough sampling of potential binding conformations. For the EGFR kinase domain, the grid box specifically included the W2R binding site and two SO4 sites. Detailed grid coordinates and exhaustiveness parameters were defined for each AutoDock run. The reliability of the docking protocol was validated by comparing the predicted binding energies of the studied compounds with those of native ligands from experimentally resolved crystal structures available in the PDB, using the same grid parameters and search settings. The accuracy of the protocol was confirmed by calculating the root-mean-square deviation (RMSD) between the predicted docking poses and the original crystal structures. In addition to docking, NetPredictor ( https://github.com/abhik1368/netpredictor ), a bioinformatics tool that predicts biological interactions using a network-based approach, was employed to identify potential target receptors of the bioactive compounds. Based on the docking and NetPredictor results, the most promising protein receptor was selected for further investigation using molecular dynamics (MD) simulations. The LYN kinase structure (PDB ID: 3A4O) was prepared by removing all water molecules and heteroatoms to obtain the apo-protein. Protonation states of all ionizable amino acid residues were assigned at pH 7.0 using PROPKA 3.0 to simulate physiological conditions. The structure was then subjected to energy minimization using the sander module in AMBER 16 to resolve steric clashes and optimize geometry. MD simulations were performed for 60 ns using AMBER 16, with the ff14SB force field applied to both the protein and ligand. The system was maintained at a constant temperature of 300 K and a pressure of 1 atm. System setup was carried out using LEaP, and energy minimization was performed using sander . Bond and angle constraints were applied using the SHAKE algorithm. System stability was evaluated by calculating the RMSD of the protein backbone, complex, and ligand using the PTRAJ module. The final 10 ns of MD trajectories were extracted for further analysis, including binding free energy calculations, hydrogen bond analysis, and identification of key residues involved in ligand binding. The MM/PBSA and MM/GBSA methods were used to calculate the binding free energy (ΔG_bind) of the simulated complexes. The total binding free energy consisted of entropy, electrostatic energy, van der Waals (vdW) energy, solvation energy, and polar solvation energy. For comparison , MD simulations were performed on both the algal-derived compounds in complex with their potential protein receptors and the corresponding native protein–ligand complexes. Results Molecular docking of bioactive compounds and targets We first assessed the structural similarity between bioactive compounds of Spirulina ( A. platensis C1) and bilirubin. The results showed that bilirubin ( https://pubchem.ncbi.nlm.nih.gov/compound/5280352#section=2D-Structure ) shares a high Tanimoto score of 93% with phycocyanobilin ( https://pubchem.ncbi.nlm.nih.gov/compound/137699530#section=2D-Structure ), a notable bioactive compound in Spirulina (Figure in S1 Fig ). Both bilirubin and phycocyanobilin are involved in the porphyrin biosynthesis pathway (KEGG pathway map00860). Subsequently, molecular docking studies using MGLTools were conducted to evaluate the binding affinities of bilirubin and phycocyanobilin against their potential target receptors, including EGFR, FYN, HLA-B, LCK, LYN, and TP53. Due to the presence of multiple binding sites within the EGFR kinase domain, including one W2R site and two SO 4 sites, all potential sites were included in the analysis. The grid box was configured to encompass these binding sites, after which bilirubin and phycocyanobilin were docked to each receptor. The AutoDock results for both ligands, bilirubin and phycocyanobilin, against the selected receptors, as obtained using MGLTools, are summarized in Table 1 . In addition to AutoDock, we performed molecular docking using FlexX and iGEMDOCK (Figure in S2 Fig ). The results from these independent docking methods were consistent with those obtained using AutoDock, with LYN consistently ranking among the highest-scoring targets for phycocyanobilin across all evaluated receptors. Download: PNG larger image TIFF original image Table 1. AutoDock-predicted binding energies of bilirubin and phycocyanobilin with selected protein receptors. https://doi.org/10.1371/journal.pone.0357093.t001 The docking results of Spirulina compounds with potential targets were compared with those of native ligands from experimentally resolved crystal structures retrieved from the PDB. The results showed that FYN, HLA-B, LYN, and TP53 exhibited more favorable binding energies with phycocyanobilin than with their respective native ligands ( Fig 1 ). To further strengthen the target selection process, we performed KEGG pathway enrichment analysis. Pathways were mapped using the KEGG database, and enrichment was assessed using a hypergeometric test. The selected proteins were significantly enriched in immune-related pathways, including T-cell receptor signaling, NF-kappa B signaling, Fc epsilon RI signaling, and natural killer cell-mediated cytotoxicity (Figure in S3 Fig ). These findings further support the biological relevance of the selected targets in immune regulation and SLE pathogenesis. Download: PNG larger image TIFF original image Fig 1. Comparison of docking interaction energies between phycocyanobilin and native ligands across protein receptors. Binding energies (kcal/mol) predicted by AutoDock are shown for phycocyanobilin and the corresponding native ligands obtained from experimentally resolved crystal structures in the Protein Data Bank (PDB). More negative values indicate stronger predicted binding affinity. Phycocyanobilin exhibits more favorable binding energies than the native ligands for FYN, HLA-B, LYN, and TP53. https://doi.org/10.1371/journal.pone.0357093.g001 Possible targets of phycocyanobilin identified by NetPredictor Some Spirulina compounds have few or no known targets available for analysis. Therefore, we used NetPredictor, based on network-based inference, to predict potential protein targets for phycocyanobilin. The predicted protein targets of phycocyanobilin, ranked according to their scores, were as follows: PHF1, LYN, WDFY4, TNFSF4, AIF1, TNFSF11, ERCC2, TRIM31, MERTK, and NCR3 ( Table 2 ). Interestingly, LYN kinase was consistently identified as a predicted target, which is in agreement with the molecular docking results (Figure in S4 Fig and Table in S1 Table ). The in silico prediction analysis further supports the potential of LYN as a key protein target for phycocyanobilin. The identification of LYN as a primary target was consistently supported by both analyses, leading to its selection as the most promising protein receptor for further investigation using MD simulations. Download: PNG larger image TIFF original image Table 2. Protein targets of phycocyanobilin identified using NetPredictor. https://doi.org/10.1371/journal.pone.0357093.t002 MD simulations of phycocyanobilin and the potential protein target LYN Comparative MD simulations were conducted for phycocyanobilin in complex with LYN kinase, alongside the native ligand, staurosporine (PDB ID: 3A4O), a potent inhibitor of protein kinase C. To evaluate the stability of the simulated model, the RMSD of the protein backbone, complex, and ligand was calculated. The results shown in Fig 2 illustrate the RMSD values of the complex (blue), backbone (black), and ligand (red) for the phycocyanobilin–LYN system, in comparison with the staurosporine–LYN complex. The RMSD values of both complexes remained within ~2.5–3 Å, indicating overall structure stability. The RMSD of the phycocyanobilin complex exhibited minor fluctuations during 10–20 ns and reached equilibrium at approximately 27 ns. In addition, the RMSD of the backbone followed a similar fluctuation pattern to that of the complex, suggesting coordinated structural behavior. The RMSD of the phycocyanobilin ligand was relatively more stable than that of staurosporine, and the trajectories of both systems showed comparable trends over the simulation period. Download: PNG larger image TIFF original image Fig 2. Root-mean-square deviation (RMSD) analysis of LYN-ligand complexes during molecular dynamics simulations. RMSD plots of the protein–ligand complex (blue), protein backbone (black), and ligand (red) are shown for the staurosporine–LYN (A) and phycocyanobilin–LYN (B) systems over a 60 ns molecular dynamics simulation. https://doi.org/10.1371/journal.pone.0357093.g002 Calculations using the MM/PBSA and MM/GBSA methods yielded consistent estimates of binding free energy. Electrostatic and vdW interactions were the major contributors to the non-covalent binding energy. As shown in Table 3 , the non-covalent interaction energy of phycocyanobilin (−98.81 ± 5.84 kcal/mol) was lower than that of the native ligand, staurosporine (−77.33 ± 5.33 kcal/mol), suggesting a trend toward more favorable binding of phycocyanobilin to LYN. The calculated binding free energy (ΔG_bind) of phycocyanobilin (−39.42 ± 8.41 kcal/mol) was likewise more favorable than that of staurosporine (−33.24 ± 5.57 kcal/mol). Download: PNG larger image TIFF original image Table 3. Non-covalent interaction energies of two simulated complexes. https://doi.org/10.1371/journal.pone.0357093.t003 To determine the contribution to the total free energy, per-residue binding free energy decomposition was calculated using the MM/PBSA method. The contribution of each amino acid residue to protein–ligand binding is shown in Fig 3 . Phycocyanobilin and staurosporine share several overlapping residues that contribute to binding free energy. More than 10 residues (16–17, 20–21, 24, 36, 66, 84–85, 88–89, 137, 147) were identified as contributing to ligand binding stability in both complexes. Among these, P21, V24, G88, and L137 exhibited the highest contributions to the total free energy. In addition, P21, T84, M85, and L137 were identified as key residues contributing to binding stabilization. Most of the shared residues between phycocyanobilin and staurosporine contributed favorably to ligand binding stability. Notably, P21 showed the strongest stabilizing effect in the phycocyanobilin complex, with a contribution of −5.27 kcal/mol, indicating a significant role in binding affinity. Download: PNG larger image TIFF original image Fig 3. Per-residue decomposition of binding free energy for the phycocyanobilin–LYN and staurosporine–LYN complexes. Negative values indicate stabilizing contributions, whereas positive values indicate destabilizing contributions to ligand binding. https://doi.org/10.1371/journal.pone.0357093.g003 The binding orientation of phycocyanobilin and staurosporine is displayed in Fig 4 . Hydrogen bond formation is a critical factor influencing the binding strength of protein–ligand complexes. The hydrogen bond interactions were evaluated by measuring the distance between hydrogen donor and acceptor atoms. For the phycocyanobilin–LYN complex, one strong hydrogen bond (>90% occupancy) with residue T82 was identified, contributing to binding stabilization. In contrast, the staurosporine–LYN complex exhibited two strong hydrogen bonds (>90% occupancy), involving residues M85 and M260, which also contributed to binding stability ( Fig 5 ). Additional lower-occupancy hydrogen bond interactions were observed in both complexes. Download: PNG larger image TIFF original image Fig 4. Binding orientation of staurosporine and phycocyanobilin in the LYN kinase active site. (A) Binding orientation of staurosporine and (B) phycocyanobilin from the final snapshot of the molecular dynamics simulation. The protein surface is shown with energy-based coloring (red, lowest energy; yellow, intermediate; gray, highest energy). Key interacting residues are labeled. Molecular graphics and analyses were performed using UCSF Chimera [ 24 ], developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311. https://doi.org/10.1371/journal.pone.0357093.g004 Download: PNG larger image TIFF original image Fig 5. Percentage of hydrogen bond occupancy for amino acid residues contributing to ligand binding in the phycocyanobilin–LYN and staurosporine–LYN complexes. Hydrogen bond occupancy (%) was calculated over the last 10 ns of a 60 ns molecular dynamics simulation. https://doi.org/10.1371/journal.pone.0357093.g005 To investigate the effect of solvent accessibility on Spirulina compound binding with potential SL
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