The structural view of the protein PGD-219aa encoded by the circular RNA CircPGD

The structural view of the protein PGD-219aa encoded by the circular RNA CircPGD | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report The structural view of the protein PGD-219aa encoded by the circular RNA CircPGD Jit Mondal, Sima Biswas, Sreekanya Roy, Anirban Nandy, Dipanjan Guha, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6522942/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Circular RNAs (circRNAs), belonging to the class of non-coding RNA molecules, have emerged as one of the key regulators of gene expression. Some of the circRNAs have proven protein-coding potentials, and their gene products play significant roles in various physiological and pathological processes. One such protein is PGD-219aa, which is derived from the circRNA named CircPGD. The protein has been shown to regulate the SMAD3 and YAP signalling pathways in gastric cancer. Nothing but the amino acid sequence of the protein is available to date. Therefore, we used in silico methods to characterize the protein and decipher its functional roles. Furthermore, we performed pathway analyses to shed light on the biochemical avenues where the protein might have a significant presence. Subsequently, we could propose its association not only with gastric cancer but also with other diseases as well. This is the first such report, and our work may help in future drug development endeavours to combat the spread of gastric cancer tumours. Circular RNA Gastric Cancer PGD-219aa Molecular Dynamics Simulations Pathway Analysis Figures Figure 1 Highlights Unusual protein PGD-219aa derived from circular RNA Structure prediction Functional associations Network analysis Annotations of disease associations Identification of key amino acid residues of PGD-219aa Introduction Circular RNAs (circRNAs), initially rejected as transcriptional junks, are now considered to have multifunctional roles in the regulation of gene expression via microRNA sponging (miRNA) (Meng et al. 2017 ) or by binding to the RNA-binding proteins (RBPs) (Ren et al. 2020 ). However, certain circRNAs contain m6A RNA modification elements, open reading frames (ORFs) (Lei et al. 2020 ), or internal ribosome entry sites (IRES) (Yuan et al. 2023 ; Liu et al. 2021 ), which enable them to form peptides/proteins in a cap-independent way, although by definition they are non-coding RNA molecules. These derived peptides/proteins can regulate the functionalities of different cellular processes (Bagchi 2018 ; Biswas et al. 2023 ; Mookherjee et al. 2022 ); they mostly possess unique properties that inhibit or activate different oncogenes to promote or regulate tumour proliferation, migration, and invasion, apoptosis, differentiation, and angiogenesis to name a few (Khan et al. 2022 ). One such circular RNA is CircPGD, or hsa_circ_0009735, which takes part in SMAD3 regulation of the YAP signalling pathway by sponging the microRNA, mir-16-5p, associated with the onset of Gastric Cancer (GC) (Liu et al. 2022 ; Zhu et al. 2018 ). CircPGD has an open reading frame that codes for a 219 amino acid residue long protein referred to as PGD-219aa, overexpression of which is connected to SMAD3, and it targets the ABL2 axis, thereby promoting cell migration and proliferation, subsequently inhibiting apoptosis in GC patients (Gil-Henn et al. 2013 ). To date, only this much information about the protein is available, with no structural or functional characterizations. Therefore, we used the techniques of computational biology to generate a structural view of the protein and performed molecular modelling and dynamics simulations to predict its most stable three-dimensional form. Side by side, we proposed its functional characteristics by analyzing its ligand binding activities. We were able to propose the role of a specific amino acid Ser177 which might help the protein exert its biochemical activity. After that, we tried to delve deeper into revealing its biochemical roles in different metabolic pathways with the help of pathway enrichment analysis. This is the first such attempt to characterize this unusual protein derived from an apparently non-coding circular RNA. Results from our work may be considered for future bench works to come up with a big picture of the activity of the protein in the onset of Gastric Cancer tumours. Material and Methods Sequence analysis of the protein and model building The following amino acid sequence of PGD-219aa was retrieved from Liu et al. ( 2022 ) MRLNSLFPLLNKSYIRLREAVFARCLSSLKDERIQASKKLKGPQKFQFDGDKKSFLEDIRKALYASKIISYAQGFMLLRQAATEFGWTLNYGGIA LMWRGGCIIRSVFLGKIKDAFDRNPELQNLLLDDFFKSAVENCQDSWRRAVSTGVQAGIPMPCFTTALSFYDGYRHEMLPASLIQAQRDY FGAHTYELLAKPGQFIHTNWTGHGGTVSSSSYNA. The structure of the protein has not yet been identified by any of the wet-lab-based methods. Therefore, we tried to predict its structure from the amino acid sequence itself using the tool HHpred ( https://toolkit.tuebingen.mpg.de/tools/hhpred ). The final stereo-chemically fit structure was obtained after loop modifications by ModLoop ( https://modbase.compbio.ucsf.edu/modloop/ ) and subsequent energy minimization steps in Discovery Studio (DS) 2.5 platform. The process was performed until the RMS gradient of the energy derivative would reach 0.01 kcal/mol at a cycle rate of 5000. The stereochemical fitness parameters were determined using the tool SAVES v6.0 ( https://saves.mbi.ucla.edu ). The model quality was further checked by ProSA ( https://prosa.services.came.sbg.ac.at/prosa.php ) and ProQ ( https://proq.bioinfo.se/cgi-bin/ProQ/ProQ.cgi ) web tools. The ProSA Z-Score and the LG score of ProQ are − 2.96 and 7.816, and both of them would indicate a good model quality. Extraction of physicochemical characteristics from the amino acid sequence We further analyzed the amino acid sequence of the protein to extract its physicochemical features using the tools in the Expasy web server ( https://www.expasy.org/ ). Furthermore, we identified the potential post-translational modification sites (PTMs) on the protein using the tools. NetPhos 3.1 (Luo et al. 2019 ): For prediction of potential phosphorylation sites. NetOGlyc 4.0 (Pakhrin et al. 2024 ): For prediction of potential glycosylation sites with a cut-off score of 0.6. GPS-SUMO 2.0 (Gou et al. 2024 ): For prediction of potential sumoylation sites. Molecular Dynamics Simulation (MDS) of the protein We used GROMACS 5.1.5 (GROningen MAchine for Chemical Simulations), a molecular dynamics simulation package ( https://www.gromacs.org/ ), to comprehend the time-dependent structural evolution of the protein mimicking the human physiological environment. The parameters for the MDS are mentioned below: Forcefield: CHARMm27 ( https://academiccharmm.org/ ) Solvent used: SPC16 water model (Mark and Nilsson. 2001) with 30034 water molecules Box details: Cubic box having dimensions of 9.806 nm x 9.806 nm x 9.806 nm at an angle of 90 0 . Number of counter ions added for neutralization: Na + : 55; Cl − : 63 The entire system was subjected to energy minimization by the steepest descent method to make the system free from any kind of constraints. After that the system was equilibrated in two subsequent steps, first by keeping the Number of particles (N), Pressure (P) and Temperature (T) (NPT) and then by Number of particles (N), Volume (V) and Temperature (T) (NVT) of the system to be fixed. Each of the steps was carried out for 100ps at 300K temperature and 1atm pressure to mimic the physiological environment. Electrostatic interactions were specified by the Particle-Mesh Ewald (PME) algorithm during the simulations. The ultimate MD production run was carried out for 200ns. We performed the experiment in triplicate for proper convergence of the results. The progress of the MD simulation was observed by plotting the Root Mean Squared Deviations (RMSDs) of the backbone atoms of the protein with time. The movements of the side chain atoms of the amino acid residues were monitored by checking the Root Mean Squared Fluctuations (RMSFs). The structural compactness of the protein atoms was observed by calculating the Radius of Gyration (Rg) values. Microsoft Excel was used to analyze and visualize all these data. The MD data were used further to compute the conformational distributions of the protein along the principal components. This was used further to determine free energy landscape (FEL) of the protein dynamics. Analysis of the binding site of the protein We used the amino acid sequence data of PGD-219aa as an input to binding site prediction tools, viz., COACH, co-factor, S-site, TM-site, and ConCavity ( https://zhanggroup.org/COACH/ ). The amino acid sequence of PGD-219aa was checked by Consurf (Rubin et al. 2021) to identify the potential conserved and functional residues. Pathway analysis using Cytoscape The protein is known to be associated with GC. However, to assess how the protein exerts its role, we created the Protein-Protein Interaction (PPI) network by protein-related query searches using the STRING database, version 12.0 ( https://string-db.org/ ). Using the output of our primary search results, we were able to identify ten closest interacting protein partners of PGD-219aa using Cytoscape 3.10.2 with the default confidence level having a p-value of > 0.5. A distinct protein is represented by each node in the query, and the interactions between the proteins are displayed by each edge (or line). Confidence (the thickness and colour of the line signifying the level of data support), active interaction sources, databases, co-expression profiles, neighbourhood relationships, gene fusion, and co-occurrence, in addition to text mining and experiments, were used to interpret the network. The list of proteins that we obtained from our first searches, as previously mentioned, was enriched with the application of statistical scores through the use of the Cytoscape plugin. The KEGG ( https://www.genome.jp/kegg/ ) and DISEASE ( https://diseases.jensenlab.org/Search ) databases were utilised in Cytoscape to further enhance the pathways utilising the enriched proteins. The list of proteins that we obtained from our first searches, as previously mentioned, was enriched with the application of statistical scores through the use of the Cytoscape plug-in, and the corresponding fold enrichment was determined. Result Structural details of the protein PGD-219aa The protein PGD-219aa is unique in the sense that the so-called non-coding RNA hsa_circ_0009735, or CircPGD, is its source. The protein belongs to the family of all-alpha class with two small anti-parallel beta strands. The alpha helices and beta strands of the PGD-219aa are arranged independently, being connected by loops and turns (Fig. 1 ). We further computed various physicochemical features of the protein. It was observed that the protein consists of hydrophobic (spanning the regions 20 to 25, 90 to 110, and 150 to 160) and hydrophilic (spanning the regions 40 to 50, 120 to 125, 140 to 145, and 195 to 210) patches. The Grand Average of Hydropathicity (GRAVY), the instability index, and the aliphatic index of the protein are found to be -0.234, 27.78, and 80.73, respectively. These results indicate that PGD-219aa is stable & hydrophilic in nature. The molecular mass & the half-life time of the protein are 24804.50 Da and 30 hours, respectively. The comparatively high molecular mass of the protein could also be an indicator of its stability. The pI is 9.45, signifying that the protein would remain mostly positively charged at the physiological pH of 7.4. The amino acid residues, Ser5, 13, 27, 28, 37, 54, 66, 133, 141, 147, 164, 215, 216, Tyr64 & Thr211, are predicted to be the potential phosphorylation sites. Ser37 has the potential to be glycosylated as well. However, results would indicate that the chances of phosphorylation are much higher than that of glycosylation. The results are presented in Figure S1 , Figure S2, Figure S3, Figure S4, Table S1 and Table S2. Analysis of the structural fluidity of the protein The results from triplicate MDS showed a good level of convergence. The protein was found to attain stability around 180ns and could retain its structural scaffolding till the end of the simulations (Figure S5). The C-terminal part of the protein is more flexible than the N-terminus (Figure S6). The tight and compact structural scaffolding of the protein was determined by Radius of gyration (Rg) (Figure S7) reveals, the protein core is stable, and the protein, though obtained from a non-coding circular RNA, is able to retain its native structure and remain functional. The protein conformational dynamics were further tested with the help of FEL analysis along the PC1 and PC2 directions (Figure S8). It was observed that the PC2 axis was densely populated with protein conformations having lower free energy values than the PC1 axis. PC2 axis represents the data with lesser extent of variations among them (Kumar et al. 2024 ). In this case, it would therefore point towards a lesser variation in the values of the free energies of the protein conformations. In other words, this would point towards the fact that the protein core would remain mostly stable during the course of the MD simulations. Binding site prediction and identification of conserved amino acid residues The N-terminal part of the protein has conserved amino acid residues for binding to nucleotide phosphates, and the protein may act like a kinase. This phenomenon can further be explained by the result that the protein has a higher chance of getting phosphorylated than glycosylated, as presented in section 3.1. We then performed a sequence conservation analysis to predict the functionally important amino acid residues in PGD-219aa, and they are presented as follows: Glu19, Arg24, Ser27, Lys30, Arg33, Lys67, Gln73, Arg99, Arg105, Asn124, Arg143, Gly153, Pro157, Asp167, Arg170, Pro175, Ala176, Ser177, Gln178, Arg183, Glu184, Gly187, Leu193, Ala195, His202, Trp205, Gly209, Tyr217, Ala219 (Figure S9). The majority of the amino acid residues mentioned above, with the exception of Ser177, would map with the functional amino acid residues of 6-Phosphogluconate dehydrogenase (6PGD). It is evident from our analysis that Ser177 has a lesser chance of getting phosphorylated. The amino acid would therefore be considered as a functionally important residue for PGD-219aa. We then performed a site-directed mutagenesis experiment where we substituted the Ser177 by Ala and checked the effect of the change with the help of DDMut (Zhou et al. 2023 ) and DDGEmb (Savojardo et al. 2024 ) tools (Figure S10 and Figure S11). Both the tools predicted the change to be a destabilizing one. The importance of this amino acid residue was further tested by a computational site-directed mutagenesis study with the help of the tool MuPro (Laskar et al. 2023 ). The Ser177 was mutated to Ala, and the overall ΔΔG value was computed. The MuPro result revealed a decrease in the stability of the protein after the mutation. This would further intensify our belief that Ser177 is indeed an important residue for PGD-219aa. The amino acid Ser177 has an average residual fluctuation to the tune of 0.5 Å, which would predict that the residue is open to interactions though it is present in a helix. This result would point towards the importance of the residue. However, the protein PGD-219aa lacks the other known catalytic amino acid residues of 6PGD from humans. For 6PGD the functionally important amino acid residues span the following sequence regions 10–15, 33–35, 75–77, 103, 129–131, 184, 187–188, 191, 192 and 261. These amino acid residues are mainly associated with the formation of the dimerization interface of PGD (Hanau and Helliwell. 2022). The protein PGD-219aa, due to the absence of these amino acid residues, may therefore be considered to exert its function as a monomer. In order to validate our claim that PGD-219aa can act as a monomer, we performed a preliminary molecular docking simulation between PGD-219aa and the typical ligands 6-Phosphogluconate & NADPH. The binding free energy value was found to be -3.4 kcal/mole. The enzyme 6-Phosphogluconate dehydrogenase (bearing the PDB code: 2JKV) acts as a dimer (Goulielmos et al. 2004 ; Morales-Luna et al. 2021 ; Adem et al. 2024 ); we, therefore analysed its binding interactions with the ligands, 6-Phosphogluconate & NADPH. The corresponding binding free energy was found to be -5.1 kcal/mole. From the results, it is apparent that the two binding free energy values are not exceedingly different. The difference in binding free energy values arises due to the molecular arrangements- the wild-type 6-Phosphogluconate dehydrogenase in dimeric form and the PGD-219aa in monomeric form. In other words, the protein PGD-219aa may very well substitute the functionality of the wild-type 6-Phosphogluconate dehydrogenase. Therefore, simply knocking out the wild-type 6-Phosphogluconate dehydrogenase gene or deactivating the enzyme itself will not be sufficient to prevent the spread of GC. The presence of PGD-219aa may carry out the functionalities deemed for the 6-Phosphogluconate dehydrogenase. Therefore, targeting the essential amino acid residues of the protein PGD-219aa opens up a new avenue for the structure based drug designing endeavours. Pathway enrichment analysis of PGD-219aa From our analyses, it was revealed that PGD-219aa might be a part of the biochemical pathways leading to the onsets of three main ailments (Fig. 1 ). On top of that, the protein may also have its roles in six pathways linked to various other disease networks. It is evident that PGD-219aa interacts directly with several proteins, including TKT, TKL1/2, G6PD, H6PD, MPI, PFKL, PGM2 etc. Thus, PGD-219aa and the proteins that are closely linked to it can control a variety of metabolic processes and ailments, including the pentose phosphate pathway, glycolysis/gluconeogenesis, glutathione metabolism, Wernicke-Korsakoff syndrome, favism, and others. This information is depicted in Tables S3 and S4. Discussion This work is on the characterization of the unusual protein PGD-219aa, which is obtained from a so-called non-coding circular RNA. We predicted its structure and possible binding interactions with the probable ligands. From these analyses, we could predict the probable active site residues of the protein. A residue of special importance is Ser177, which may act as the key amino acid in exerting the functions of the protein. The amino acid residues may be targeted to thwart the functionality of the protein necessary for disease onsets. This protein is known to be associated with the onset of GC. However, we tried to extend the knowledge beyond that by analyzing the amino acid sequence of the protein through networking tools and observed that the protein could take part in different cellular processes leading to metabolic disorders, like type 2 diabetes and different forms of carcinomas apart from GC. Therefore, the protein PGD-219aa may safely be considered to be present at the juncture of a variety of pathways leading to the onsets of numerous disorders. Thus, the protein might serve as an important biomarker and target for drug development. This is the first such report of the structure-function study of this protein. Our work may therefore help to streamline future wet-lab-based methodologies to validate the proposed disease associations of this very important protein. Conclusion The main findings of the work are associated with the characterizations of the protein PGD-219aa. This is the first report in this avenue. Furthermore, the potential amino acid residue for exerting its functionality is predicted. All these findings will help to streamline future wet-lab- based validations of the structure-function relationship of PGD-219aa. Declarations Acknowledgment The infrastructural support from the DBT-funded Bioinformatics Infrastructure Facility Centre (Project Sanction no: BT/PR40162/BTIS/137/48/2022, dated 31.10.2022) and National Network Project (Project Sanction no: BT/PR40192/BTIS/137/69/2023, dated 19.12.2023), sanctioned to Prof. Angshuman Bagchi, were utilized in this work. Author contributions JM: Experiment performed, Formal analysis, Drafting and Editing; SB: Analysis of results; Editing; DG: Editing; SR: Analysis, Editing; AN: Analysis, Editing; AB: Conceptualization, Supervision, Editing. Funding The financial support from the Department of Biotechnology (DBT), Government of India for BIF centre. Jit Mondal (Student ID: 211610197059) receives funding from UGC. Conflict of interest The authors declare that they have no conflict of interest. Ethics approval and consent to participate Not applicable. Consent for publication All the authors approved the final version of the manuscript for publication. 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Oncol Rep 50:160. https://doi.org/10.3892/or.2023.8597 Zhou Y, Pan Q, Pires DEV, Rodrigues CHM, Ascher DB (2023) DDMut: predicting effects of mutations on protein stability using deep learning. Nucleic Acids Res 51:W122–W128. https://doi.org/10.1093/nar/gkad472 Zhu C, Huang Q, Zhu H (2018) Melatonin inhibits the proliferation of gastric cancer cells through regulating the miR-16-5p-Smad3 pathway. DNA Cell Biol 37:244–252. https://doi.org/10.1089/dna.2017.4040 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.doc Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 May, 2025 Editor assigned by journal 26 Apr, 2025 Submission checks completed at journal 26 Apr, 2025 First submitted to journal 24 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6522942","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":453469057,"identity":"5af3931c-bdaf-4116-85fc-47eea12c0b3a","order_by":0,"name":"Jit Mondal","email":"","orcid":"","institution":"University of Kalyani","correspondingAuthor":false,"prefix":"","firstName":"Jit","middleName":"","lastName":"Mondal","suffix":""},{"id":453469058,"identity":"ed1eb946-7d08-4c19-90eb-3718f17c8139","order_by":1,"name":"Sima Biswas","email":"","orcid":"","institution":"University of Kalyani","correspondingAuthor":false,"prefix":"","firstName":"Sima","middleName":"","lastName":"Biswas","suffix":""},{"id":453469060,"identity":"b34bc0ff-44db-4cf1-9b75-3ecbc40a7466","order_by":2,"name":"Sreekanya Roy","email":"","orcid":"","institution":"University of Kalyani","correspondingAuthor":false,"prefix":"","firstName":"Sreekanya","middleName":"","lastName":"Roy","suffix":""},{"id":453469061,"identity":"ea2c473f-46f7-4c0c-b34f-40f14acc985e","order_by":3,"name":"Anirban Nandy","email":"","orcid":"","institution":"University of Kalyani","correspondingAuthor":false,"prefix":"","firstName":"Anirban","middleName":"","lastName":"Nandy","suffix":""},{"id":453469062,"identity":"9ffea556-098d-4cc4-8c99-8ccc5353927f","order_by":4,"name":"Dipanjan Guha","email":"","orcid":"","institution":"University of Kalyani","correspondingAuthor":false,"prefix":"","firstName":"Dipanjan","middleName":"","lastName":"Guha","suffix":""},{"id":453469063,"identity":"ecf811a8-ec9d-4267-8207-59d0b2449742","order_by":5,"name":"Angshuman Bagchi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYJACZgYGGwM+ECsBzGdsIEZLmgEbqVoOQ7QQBfhnt1/+XNh23phNIsfswcM2Bnn+Bua2B/i0SNw5UyY9s+22GVCLuUFiG4PhjAOM7QZ4rbmRk8bM23bbBmSLRMIZBsYNDIxtEvh0yN/ISf7M23YOrsWeoBaDG+kHpHnbDphBtFQwJBLUYngjh02a51yyMRvPszKgFonkGYcJaJG7kf74M0+ZnWE/e/I2yR8GNrb97e3P8GphYOCBBo9AAoiUAEcTAcD+AELzHyCodBSMglEwCkYoAADLNEDH41+zuwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Kalyani","correspondingAuthor":true,"prefix":"","firstName":"Angshuman","middleName":"","lastName":"Bagchi","suffix":""}],"badges":[],"createdAt":"2025-04-24 17:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6522942/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6522942/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84774552,"identity":"b5dba208-77a4-4166-a33f-54b433b850de","added_by":"auto","created_at":"2025-06-17 08:42:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":346484,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eCartoon view of PGD-219aa. Colour code: Red: Helices, Cyan: Strand. \u003cstrong\u003eB. \u003c/strong\u003eThe first ten clusters linked with the protein sequence in the STRING server.\u003cstrong\u003e C.\u003c/strong\u003e The Cytoscape PPI analysis shows PGD-219aa and its associated proteins connected to different kinds of diseases and pathways. The diseases and pathways are represented as hexagons and squares, respectively. The main diseases are Wernicke-Korsakoff syndrome [Red], Favism [Magenta], Disease metabolism [Pink]; whereas the pathways where PDG-219aa participates are pentose phosphate signalling pathway [Sky Blue], pentose and glucuronate interconversion pathway [Neon Green], amino sugar and nucleotide sugar metabolism [Pale Yellow], fructose and mannose metabolism [Blue], glycolysis/gluconeogenesis [Purple], and glutathione metabolism [Teal Blue].\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6522942/v1/312673195c961165e4d89f9a.png"},{"id":84774569,"identity":"d858f1bd-c65d-4944-a68c-c36d2830df5e","added_by":"auto","created_at":"2025-06-17 08:42:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":898273,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6522942/v1/98bba9cb-3f9a-4146-9688-d13dda31bf44.pdf"},{"id":84774568,"identity":"17dee270-2593-4965-ab11-a24e2462ccd9","added_by":"auto","created_at":"2025-06-17 08:42:34","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18582016,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.doc","url":"https://assets-eu.researchsquare.com/files/rs-6522942/v1/ad588acaac5c809f931836d8.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"The structural view of the protein PGD-219aa encoded by the circular RNA CircPGD","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eUnusual protein PGD-219aa derived from circular RNA\u003c/li\u003e\n \u003cli\u003eStructure prediction\u003c/li\u003e\n \u003cli\u003eFunctional associations\u003c/li\u003e\n \u003cli\u003eNetwork analysis\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAnnotations of disease associations\u003c/li\u003e\n \u003cli\u003eIdentification of key amino acid residues of PGD-219aa\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eCircular RNAs (circRNAs), initially rejected as transcriptional junks, are now considered to have multifunctional roles in the regulation of gene expression via microRNA sponging (miRNA) (Meng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) or by binding to the RNA-binding proteins (RBPs) (Ren et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, certain circRNAs contain m6A RNA modification elements, open reading frames (ORFs) (Lei et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), or internal ribosome entry sites (IRES) (Yuan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which enable them to form peptides/proteins in a cap-independent way, although by definition they are non-coding RNA molecules. These derived peptides/proteins can regulate the functionalities of different cellular processes (Bagchi \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Biswas et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mookherjee et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); they mostly possess unique properties that inhibit or activate different oncogenes to promote or regulate tumour proliferation, migration, and invasion, apoptosis, differentiation, and angiogenesis to name a few (Khan et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). One such circular RNA is CircPGD, or hsa_circ_0009735, which takes part in SMAD3 regulation of the YAP signalling pathway by sponging the microRNA, mir-16-5p, associated with the onset of Gastric Cancer (GC) (Liu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). CircPGD has an open reading frame that codes for a 219 amino acid residue long protein referred to as PGD-219aa, overexpression of which is connected to SMAD3, and it targets the ABL2 axis, thereby promoting cell migration and proliferation, subsequently inhibiting apoptosis in GC patients (Gil-Henn et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). To date, only this much information about the protein is available, with no structural or functional characterizations. Therefore, we used the techniques of computational biology to generate a structural view of the protein and performed molecular modelling and dynamics simulations to predict its most stable three-dimensional form. Side by side, we proposed its functional characteristics by analyzing its ligand binding activities. We were able to propose the role of a specific amino acid Ser177 which might help the protein exert its biochemical activity. After that, we tried to delve deeper into revealing its biochemical roles in different metabolic pathways with the help of pathway enrichment analysis. This is the first such attempt to characterize this unusual protein derived from an apparently non-coding circular RNA. Results from our work may be considered for future bench works to come up with a big picture of the activity of the protein in the onset of Gastric Cancer tumours.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSequence analysis of the protein and model building\u003c/h2\u003e \u003cp\u003eThe following amino acid sequence of PGD-219aa was retrieved from Liu et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMRLNSLFPLLNKSYIRLREAVFARCLSSLKDERIQASKKLKGPQKFQFDGDKKSFLEDIRKALYASKIISYAQGFMLLRQAATEFGWTLNYGGIA LMWRGGCIIRSVFLGKIKDAFDRNPELQNLLLDDFFKSAVENCQDSWRRAVSTGVQAGIPMPCFTTALSFYDGYRHEMLPASLIQAQRDY FGAHTYELLAKPGQFIHTNWTGHGGTVSSSSYNA.\u003c/p\u003e \u003cp\u003eThe structure of the protein has not yet been identified by any of the wet-lab-based methods. Therefore, we tried to predict its structure from the amino acid sequence itself using the tool HHpred (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://toolkit.tuebingen.mpg.de/tools/hhpred\u003c/span\u003e\u003cspan address=\"https://toolkit.tuebingen.mpg.de/tools/hhpred\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The final stereo-chemically fit structure was obtained after loop modifications by ModLoop (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://modbase.compbio.ucsf.edu/modloop/\u003c/span\u003e\u003cspan address=\"https://modbase.compbio.ucsf.edu/modloop/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and subsequent energy minimization steps in Discovery Studio (DS) 2.5 platform. The process was performed until the RMS gradient of the energy derivative would reach 0.01 kcal/mol at a cycle rate of 5000. The stereochemical fitness parameters were determined using the tool SAVES v6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saves.mbi.ucla.edu\u003c/span\u003e\u003cspan address=\"https://saves.mbi.ucla.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The model quality was further checked by ProSA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prosa.services.came.sbg.ac.at/prosa.php\u003c/span\u003e\u003cspan address=\"https://prosa.services.came.sbg.ac.at/prosa.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ProQ (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://proq.bioinfo.se/cgi-bin/ProQ/ProQ.cgi\u003c/span\u003e\u003cspan address=\"https://proq.bioinfo.se/cgi-bin/ProQ/ProQ.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) web tools. The ProSA Z-Score and the LG score of ProQ are − 2.96 and 7.816, and both of them would indicate a good model quality.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExtraction of physicochemical characteristics from the amino acid sequence\u003c/h3\u003e\n\u003cp\u003eWe further analyzed the amino acid sequence of the protein to extract its physicochemical features using the tools in the Expasy web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.expasy.org/\u003c/span\u003e\u003cspan address=\"https://www.expasy.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Furthermore, we identified the potential post-translational modification sites (PTMs) on the protein using the tools.\u003c/p\u003e \u003cp\u003eNetPhos 3.1 (Luo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e): For prediction of potential phosphorylation sites.\u003c/p\u003e \u003cp\u003eNetOGlyc 4.0 (Pakhrin et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e): For prediction of potential glycosylation sites with a cut-off score of 0.6.\u003c/p\u003e \u003cp\u003eGPS-SUMO 2.0 (Gou et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e): For prediction of potential sumoylation sites.\u003c/p\u003e\n\u003ch3\u003eMolecular Dynamics Simulation (MDS) of the protein\u003c/h3\u003e\n\u003cp\u003eWe used GROMACS 5.1.5 (GROningen MAchine for Chemical Simulations), a molecular dynamics simulation package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gromacs.org/\u003c/span\u003e\u003cspan address=\"https://www.gromacs.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), to comprehend the time-dependent structural evolution of the protein mimicking the human physiological environment. The parameters for the MDS are mentioned below:\u003c/p\u003e \u003cp\u003eForcefield: CHARMm27 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://academiccharmm.org/\u003c/span\u003e\u003cspan address=\"https://academiccharmm.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSolvent used: SPC16 water model (Mark and Nilsson. 2001) with 30034 water molecules\u003c/p\u003e \u003cp\u003eBox details: Cubic box having dimensions of 9.806 nm x 9.806 nm x 9.806 nm at an angle of 90\u003csup\u003e0\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNumber of counter ions added for neutralization: Na\u003csup\u003e+\u003c/sup\u003e: 55; Cl\u003csup\u003e−\u003c/sup\u003e: 63\u003c/p\u003e \u003cp\u003eThe entire system was subjected to energy minimization by the steepest descent method to make the system free from any kind of constraints. After that the system was equilibrated in two subsequent steps, first by keeping the Number of particles (N), Pressure (P) and Temperature (T) (NPT) and then by Number of particles (N), Volume (V) and Temperature (T) (NVT) of the system to be fixed. Each of the steps was carried out for 100ps at 300K temperature and 1atm pressure to mimic the physiological environment. Electrostatic interactions were specified by the Particle-Mesh Ewald (PME) algorithm during the simulations. The ultimate MD production run was carried out for 200ns. We performed the experiment in triplicate for proper convergence of the results. The progress of the MD simulation was observed by plotting the Root Mean Squared Deviations (RMSDs) of the backbone atoms of the protein with time. The movements of the side chain atoms of the amino acid residues were monitored by checking the Root Mean Squared Fluctuations (RMSFs). The structural compactness of the protein atoms was observed by calculating the Radius of Gyration (Rg) values. Microsoft Excel was used to analyze and visualize all these data. The MD data were used further to compute the conformational distributions of the protein along the principal components. This was used further to determine free energy landscape (FEL) of the protein dynamics.\u003c/p\u003e\n\u003ch3\u003eAnalysis of the binding site of the protein\u003c/h3\u003e\n\u003cp\u003eWe used the amino acid sequence data of PGD-219aa as an input to binding site prediction tools, viz., COACH, co-factor, S-site, TM-site, and ConCavity (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zhanggroup.org/COACH/\u003c/span\u003e\u003cspan address=\"https://zhanggroup.org/COACH/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The amino acid sequence of PGD-219aa was checked by Consurf (Rubin et al. 2021) to identify the potential conserved and functional residues.\u003c/p\u003e\n\u003ch3\u003ePathway analysis using Cytoscape\u003c/h3\u003e\n\u003cp\u003eThe protein is known to be associated with GC. However, to assess how the protein exerts its role, we created the Protein-Protein Interaction (PPI) network by protein-related query searches using the STRING database, version 12.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Using the output of our primary search results, we were able to identify ten closest interacting protein partners of PGD-219aa using Cytoscape 3.10.2 with the default confidence level having a p-value of \u0026gt; 0.5. A distinct protein is represented by each node in the query, and the interactions between the proteins are displayed by each edge (or line). Confidence (the thickness and colour of the line signifying the level of data support), active interaction sources, databases, co-expression profiles, neighbourhood relationships, gene fusion, and co-occurrence, in addition to text mining and experiments, were used to interpret the network.\u003c/p\u003e \u003cp\u003eThe list of proteins that we obtained from our first searches, as previously mentioned, was enriched with the application of statistical scores through the use of the Cytoscape plugin. The KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and DISEASE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://diseases.jensenlab.org/Search\u003c/span\u003e\u003cspan address=\"https://diseases.jensenlab.org/Search\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e databases were utilised in Cytoscape to further enhance the pathways utilising the enriched proteins. The list of proteins that we obtained from our first searches, as previously mentioned, was enriched with the application of statistical scores through the use of the Cytoscape plug-in, and the corresponding fold enrichment was determined.\u003c/p\u003e"},{"header":"Result","content":"\u003ch2\u003eStructural details of the protein PGD-219aa\u003c/h2\u003e\u003cp\u003eThe protein PGD-219aa is unique in the sense that the so-called non-coding RNA hsa_circ_0009735, or CircPGD, is its source. The protein belongs to the family of all-alpha class with two small anti-parallel beta strands. The alpha helices and beta strands of the PGD-219aa are arranged independently, being connected by loops and turns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe further computed various physicochemical features of the protein. It was observed that the protein consists of hydrophobic (spanning the regions 20 to 25, 90 to 110, and 150 to 160) and hydrophilic (spanning the regions 40 to 50, 120 to 125, 140 to 145, and 195 to 210) patches. The Grand Average of Hydropathicity (GRAVY), the instability index, and the aliphatic index of the protein are found to be -0.234, 27.78, and 80.73, respectively. These results indicate that PGD-219aa is stable \u0026amp; hydrophilic in nature. The molecular mass \u0026amp; the half-life time of the protein are 24804.50 Da and 30 hours, respectively. The comparatively high molecular mass of the protein could also be an indicator of its stability. The pI is 9.45, signifying that the protein would remain mostly positively charged at the physiological pH of 7.4. The amino acid residues, Ser5, 13, 27, 28, 37, 54, 66, 133, 141, 147, 164, 215, 216, Tyr64 \u0026amp; Thr211, are predicted to be the potential phosphorylation sites. Ser37 has the potential to be glycosylated as well. However, results would indicate that the chances of phosphorylation are much higher than that of glycosylation. The results are presented in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Figure S2, Figure S3, Figure S4, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S2.\u003c/p\u003e\u003ch3\u003eAnalysis of the structural fluidity of the protein\u003c/h3\u003e\u003cp\u003eThe results from triplicate MDS showed a good level of convergence. The protein was found to attain stability around 180ns and could retain its structural scaffolding till the end of the simulations (Figure S5). The C-terminal part of the protein is more flexible than the N-terminus (Figure S6). The tight and compact structural scaffolding of the protein was determined by Radius of gyration (Rg) (Figure S7) reveals, the protein core is stable, and the protein, though obtained from a non-coding circular RNA, is able to retain its native structure and remain functional. The protein conformational dynamics were further tested with the help of FEL analysis along the PC1 and PC2 directions (Figure S8). It was observed that the PC2 axis was densely populated with protein conformations having lower free energy values than the PC1 axis. PC2 axis represents the data with lesser extent of variations among them (Kumar et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this case, it would therefore point towards a lesser variation in the values of the free energies of the protein conformations. In other words, this would point towards the fact that the protein core would remain mostly stable during the course of the MD simulations.\u003c/p\u003e\u003ch2\u003eBinding site prediction and identification of conserved amino acid residues\u003c/h2\u003e\u003cp\u003eThe N-terminal part of the protein has conserved amino acid residues for binding to nucleotide phosphates, and the protein may act like a kinase. This phenomenon can further be explained by the result that the protein has a higher chance of getting phosphorylated than glycosylated, as presented in section 3.1. We then performed a sequence conservation analysis to predict the functionally important amino acid residues in PGD-219aa, and they are presented as follows: Glu19, Arg24, Ser27, Lys30, Arg33, Lys67, Gln73, Arg99, Arg105, Asn124, Arg143, Gly153, Pro157, Asp167, Arg170, Pro175, Ala176, Ser177, Gln178, Arg183, Glu184, Gly187, Leu193, Ala195, His202, Trp205, Gly209, Tyr217, Ala219 (Figure S9). The majority of the amino acid residues mentioned above, with the exception of Ser177, would map with the functional amino acid residues of 6-Phosphogluconate dehydrogenase (6PGD). It is evident from our analysis that Ser177 has a lesser chance of getting phosphorylated. The amino acid would therefore be considered as a functionally important residue for PGD-219aa. We then performed a site-directed mutagenesis experiment where we substituted the Ser177 by Ala and checked the effect of the change with the help of DDMut (Zhou et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and DDGEmb (Savojardo et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) tools (Figure S10 and Figure S11). Both the tools predicted the change to be a destabilizing one. The importance of this amino acid residue was further tested by a computational site-directed mutagenesis study with the help of the tool MuPro (Laskar et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Ser177 was mutated to Ala, and the overall ΔΔG value was computed. The MuPro result revealed a decrease in the stability of the protein after the mutation. This would further intensify our belief that Ser177 is indeed an important residue for PGD-219aa.\u003c/p\u003e\u003cp\u003eThe amino acid Ser177 has an average residual fluctuation to the tune of 0.5 Å, which would predict that the residue is open to interactions though it is present in a helix. This result would point towards the importance of the residue.\u003c/p\u003e\u003cp\u003eHowever, the protein PGD-219aa lacks the other known catalytic amino acid residues of 6PGD from humans. For 6PGD the functionally important amino acid residues span the following sequence regions 10–15, 33–35, 75–77, 103, 129–131, 184, 187–188, 191, 192 and 261. These amino acid residues are mainly associated with the formation of the dimerization interface of PGD (Hanau and Helliwell. 2022). The protein PGD-219aa, due to the absence of these amino acid residues, may therefore be considered to exert its function as a monomer. In order to validate our claim that PGD-219aa can act as a monomer, we performed a preliminary molecular docking simulation between PGD-219aa and the typical ligands 6-Phosphogluconate \u0026amp; NADPH. The binding free energy value was found to be -3.4 kcal/mole. The enzyme 6-Phosphogluconate dehydrogenase (bearing the PDB code: 2JKV) acts as a dimer (Goulielmos et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Morales-Luna et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Adem et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); we, therefore analysed its binding interactions with the ligands, 6-Phosphogluconate \u0026amp; NADPH. The corresponding binding free energy was found to be -5.1 kcal/mole. From the results, it is apparent that the two binding free energy values are not exceedingly different. The difference in binding free energy values arises due to the molecular arrangements- the wild-type 6-Phosphogluconate dehydrogenase in dimeric form and the PGD-219aa in monomeric form. In other words, the protein PGD-219aa may very well substitute the functionality of the wild-type 6-Phosphogluconate dehydrogenase. Therefore, simply knocking out the wild-type 6-Phosphogluconate dehydrogenase gene or deactivating the enzyme itself will not be sufficient to prevent the spread of GC. The presence of PGD-219aa may carry out the functionalities deemed for the 6-Phosphogluconate dehydrogenase. Therefore, targeting the essential amino acid residues of the protein PGD-219aa opens up a new avenue for the structure based drug designing endeavours.\u003c/p\u003e\u003ch2\u003ePathway enrichment analysis of PGD-219aa\u003c/h2\u003e\u003cp\u003eFrom our analyses, it was revealed that PGD-219aa might be a part of the biochemical pathways leading to the onsets of three main ailments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On top of that, the protein may also have its roles in six pathways linked to various other disease networks. It is evident that PGD-219aa interacts directly with several proteins, including TKT, TKL1/2, G6PD, H6PD, MPI, PFKL, PGM2 etc. Thus, PGD-219aa and the proteins that are closely linked to it can control a variety of metabolic processes and ailments, including the pentose phosphate pathway, glycolysis/gluconeogenesis, glutathione metabolism, Wernicke-Korsakoff syndrome, favism, and others. This information is depicted in Tables S3 and S4.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis work is on the characterization of the unusual protein PGD-219aa, which is obtained from a so-called non-coding circular RNA. We predicted its structure and possible binding interactions with the probable ligands. From these analyses, we could predict the probable active site residues of the protein. A residue of special importance is Ser177, which may act as the key amino acid in exerting the functions of the protein. The amino acid residues may be targeted to thwart the functionality of the protein necessary for disease onsets. This protein is known to be associated with the onset of GC. However, we tried to extend the knowledge beyond that by analyzing the amino acid sequence of the protein through networking tools and observed that the protein could take part in different cellular processes leading to metabolic disorders, like type 2 diabetes and different forms of carcinomas apart from GC. Therefore, the protein PGD-219aa may safely be considered to be present at the juncture of a variety of pathways leading to the onsets of numerous disorders. Thus, the protein might serve as an important biomarker and target for drug development. This is the first such report of the structure-function study of this protein. Our work may therefore help to streamline future wet-lab-based methodologies to validate the proposed disease associations of this very important protein.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe main findings of the work are associated with the characterizations of the protein PGD-219aa. This is the first report in this avenue. Furthermore, the potential amino acid residue for exerting its functionality is predicted. All these findings will help to streamline future wet-lab- based validations of the structure-function relationship of PGD-219aa.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003eThe infrastructural support from the DBT-funded Bioinformatics Infrastructure Facility Centre (Project Sanction no: BT/PR40162/BTIS/137/48/2022, dated 31.10.2022) and National Network Project (Project Sanction no: BT/PR40192/BTIS/137/69/2023, dated 19.12.2023), sanctioned to Prof. Angshuman Bagchi, were utilized in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e JM: Experiment performed, Formal analysis, Drafting and Editing; SB: Analysis of results; Editing; DG: Editing; SR: Analysis, Editing; AN: Analysis, Editing; AB: Conceptualization, Supervision, Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe financial support from the Department of Biotechnology (DBT), Government of India for BIF centre. Jit Mondal (Student ID: 211610197059) receives funding from UGC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e All the authors approved the final version of the manuscript for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdem Ş, Yırtıcı \u0026Uuml;, Aydın M, Rawat R, Ey\u0026uuml;poğlu V (2024) Natural flavonoids as promising 6-phosphogluconate dehydrogenase inhibitor candidates: In silico and in vitro assessments. \u003cem\u003eArch Pharm\u003c/em\u003e 357:e2300326. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ardp.202300326\u003c/span\u003e\u003cspan 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class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-modeling","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmmo","sideBox":"Learn more about [Journal of Molecular Modeling](https://www.springer.com/journal/894)","snPcode":"894","submissionUrl":"https://submission.nature.com/new-submission/894/3","title":"Journal of Molecular Modeling","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Circular RNA, Gastric Cancer, PGD-219aa, Molecular Dynamics Simulations, Pathway Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6522942/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6522942/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCircular RNAs (circRNAs), belonging to the class of non-coding RNA molecules, have emerged as one of the key regulators of gene expression. Some of the circRNAs have proven protein-coding potentials, and their gene products play significant roles in various physiological and pathological processes. One such protein is PGD-219aa, which is derived from the circRNA named CircPGD. The protein has been shown to regulate the SMAD3 and YAP signalling pathways in gastric cancer. Nothing but the amino acid sequence of the protein is available to date. Therefore, we used \u003cem\u003ein silico\u003c/em\u003e methods to characterize the protein and decipher its functional roles. Furthermore, we performed pathway analyses to shed light on the biochemical avenues where the protein might have a significant presence. Subsequently, we could propose its association not only with gastric cancer but also with other diseases as well. This is the first such report, and our work may help in future drug development endeavours to combat the spread of gastric cancer tumours.\u003c/p\u003e","manuscriptTitle":"The structural view of the protein PGD-219aa encoded by the circular RNA CircPGD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 08:42:28","doi":"10.21203/rs.3.rs-6522942/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-07T22:42:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-26T10:15:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-26T10:14:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Modeling","date":"2025-04-24T17:34:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-modeling","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmmo","sideBox":"Learn more about [Journal of Molecular Modeling](https://www.springer.com/journal/894)","snPcode":"894","submissionUrl":"https://submission.nature.com/new-submission/894/3","title":"Journal of Molecular Modeling","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3125a623-8d46-4237-bdae-907867526dce","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-22T07:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 08:42:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6522942","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6522942","identity":"rs-6522942","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}