Evaluation of Artificial Intelligence-Generated DNA Aptamers Against Treponema pallidum Surface Proteins

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Bruno, Shamsudin Nasaev, Dmitry Ufaev, Jeffrey C. Sivils This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8158423/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract DNA aptamer sequences were selected in silico with assistance from artificial intelligence (AI) using the new Xelari.com platform against three known immunogenic surface proteins of Treponema pallidum designated Tpp17, Tpp47 and Tp0751. The in silico aptamers were synthesized with 5’ Alexa Fluor 647 labels and shown to bind live Treponema pallidum by fluorescence microscopy and spectrofluorometry. While AI-predicted K d values differed somewhat from measured K d values, it is clear that the aptamers bound the T. pallidum surface which is consistent with the molecular docking models for each aptamer with each cognate target protein all of which are thought to be largely accessible for binding (i.e., not predominately embedded in the outer membrane or cell wall). All three aptamers also demonstrated good specificity in cross-reactivity binding studies. agentic aptamer artificial intelligence in silico syphilis Treponema Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Numerous groups have selected nucleic acid aptamer DNA or RNA sequences via in silico computational techniques [ 1 – 7 ]. For our in vitro therapeutic studies, we sought aptamers to bind T. pallidum and kill this pathogen which causes syphilis via opsonization [ 8 ] and complement-mediated lysis [ 9 – 11 ] especially because of shortages in the benzathine penicillin antibiotic supply, the lack of a syphilis vaccine and T. pallidum’s alarming resistance to penicillin alternatives such as azithromycin [ 12 – 16 ]. To date the only aptamers we have found in the literature that were able to bind T. pallidum were a subset of the aptamers developed against T. denticola by Park et al. [ 17 ] and these T. denticola aptamers were partially effective at opsonization of T. pallidum in our hands (DOI: 10.31038/IDT.2025613 ). Still, we sought even higher affinity anti- T. pallidum aptamers for our novel syphilis therapeutics approaches. Thus, we chose to pursue the agentic AI-based approach now available from Xelari.com which is based on a multi-stage process for aptamer modeling using rule-based and stochastic algorithms for geometric transformations and molecular mechanics simulations, as well as artificial neural networks for energy and structural predictions. As input data, the platform uses the 3D structure of the target protein, obtained either from the PDB database (DOI: 10.1093/nar/28.1.235 ) or predicted using tools such as AlphaFold 2 (DOI: 10.1038/s41586-021-03819-2 ). During processing, the platform analyzes the protein surface model to identify the most favorable binding regions. It then performs de novo design of seed structures through stochastic sampling of poses from a predefined list of nucleotides (standard deoxyribonucleotides were used in this study). For each sampled pose, a scoring function is applied to approximate the contribution of each individual nucleotide to the overall interaction energy. The greater the contribution, the more favorable the position of that nucleotide is considered to be. After the seed structures are generated, optimal frameworks are constructed by combining nucleotides with high energetic contributions into continuous chains. Following this assembly step, molecular dynamics simulations are carried out to relax the complex, and the dissociation constant (K d ) of each complex is estimated. This process yields a set of aptamers, from which the sequence with the K d value closest to the desired target is selected. As our initial set of protein targets, we chose three well-characterized immunogenic proteins thought to be loosely associated with the outer membrane or cell wall of T. pallidum . In particular, these proteins as shown in Table 1 are designated Tpp17, Tpp47 and Tp0751 [ 18 – 23 ]. The reader is advised that these proteins go by other similar names or designations in the literature, but the UniProt numbers listed in Table 1 provide clarity for each protein’s identity. The 3D structural PDB files associated with the Table 1 UniProt numbers were fed into Xelari’s platform to generate the three aptamer DNA sequences shown in Table 1 in the Results section. Finally, the AI-generated aptamers were tested for binding to real live T. pallidum cells as well as related and unrelated bacterial species. As shown in this work, the three AI-generated aptamers demonstrated relatively high affinity and good specificity for binding live T. pallidum cells. 2. Materials and methods 2.1. In Silico aptamer (Xelamer) AI selection criteria PDB files for the three UniProt database target protein accession numbers given in Table 1 were loaded into separate 24 hour runs on the Xelari.com platform at https://platform.xelari.com/ . Selection parameters were set as follows: Na + concentration was 137 mM, K + concentration was 2.7 mM, Mg 2+ was 0 mM, pH was 7.2 and temperature was set to 37˚C since the aptamers may eventually be used therapeutically in vivo and need to bind well at body temperature. Target aptamer length was set to 39 nucleotides and desired binding affinity was set to the highest setting K d = 1 nM with associated DNA parameters. The Xelari platform output DNA aptamer sequences between 37 to 42 bases with associated predicted K d values and 3D docked aptamer-target protein PDB structures that were viewed, rotated and analyzed using RasMol version 2.7.5.2 software. 2.2. Bacteria and culture conditions Treponema pallidum was purchased from American Type Culture Collection (ATCC; Manassas, VA) as Treponema phagedenis (ex Brumpt) Smibert, Kazan 8 strain (ATCC No. 27087) in lyophilized form. Treponema denticola (ex Flügge; ATCC No. 35404) was shipped frozen from ATCC. The treponeme cultures were rehydrated or thawed in 10 ml of Oral Treponema Enrichment Broth (OTEB) tubes from Anaerobe Systems Inc. (Morgan Hill, CA; Cat. No. AS-603) at 37°C in a slowly rotating closed incubator with a microaerophilic environment induced by Becton Dickinson Gas Pak™ pouches (Cat. No. 260680). T. pallidum cultures were capped and passaged weekly by 1:10 dilution of the previous passage in OTEB. Other bacteria were cultured from Culti-Loops™ purchased from Thermo Fisher and grown on blood agar plates overnight at 37˚C. 2.3. Dye-labeled aptamer synthesis Each of the aptamer DNA sequences shown in Table 1 was synthesized at Integrated DNA Technologies (Coralville, IA) with a 5’ Alexa Fluor (AF) 647 dye label and purified by HPLC. Dry aptamers were rehydrated at a 100 µM concentration in sterile Dulbecco’s phosphate buffer saline (PBS) without Ca 2+ or Mg 2+ at pH 7.2. 2.4. Spectrofluorometry and fluorescence microscopy Aptamer binding was validated by spectrofluorometric analysis of aptamer-AF 647 staining and fluorescence microscopy as follows. The amount of each AF 647-labeled T. pallidum aptamer from Table 1 was varied from 0.1 to 4 µM and added to 100 µl of ~ 10 7 live T. pallidum (5 day cultures in OTEB) after pelleting at 13,000 x g for 5 min and resuspension in 1 ml of sterile PBS without Ca 2+ or Mg 2+ at pH 7.2 (hereby referred to simply as PBS) for 30 min at 37˚C. Samples were then centrifuged again for 5 min at 13,000 x g. The supernates were carefully siphoned out so as not to disturb the stained bacterial pellets which were then resuspended in 1 ml of fresh PBS. The 1 ml samples were then diluted to a total volume of 3 ml in PBS in polystyrene 1 cm path length cuvettes, capped, inverted several times and immediately analyzed using a Cary-Varian Eclipse™ spectrofluorometer with excitation at 650 nm and fluorescence emission scanning from 655 to 700 nm using a photomultiplier tube (PMT) setting of 700 V. Some samples were also placed in separate wells of a sterile 6 well plate and imaged using the red channel of an Invitrogen EVOS M5000 fluorescence microscope with 400X zoom setting. For cross-reactivity studies, all bacterial species were normalized to 1.0 absorbance unit at 620 nm using a plate reader and 100 µl of each species was stained with 2 µM of each aptamer-AF 647 conjugate in 1 ml of PBS for 30 min at 37˚C followed by centrifugal pelleting, removal of the supernates and resuspension in 3 ml of PBS in plastic cuvettes followed by five 650/665 nm ex/em point readings at a PMT setting of 600 V with mean and standard deviation determinations for each group. 2.5 K d value prediction and measurement The Xelari.com platform derived K d values using an evaluation function that estimates relative K d magnitudes based on the geometric characteristics of the complex structure and the usage conditions. At the time of the experiments, a fast evaluation function was employed, which did not fit the prediction results to experimental values. This scoring function was trained on synthetic complex formation energies without conversion to experimental units, which could lead to differences of several orders of magnitude. Nevertheless, the predicted values showed good correlation with the experimentally measured K d values and allowed reliable relative ranking of binding strength within each group of candidates. Some samples were analyzed by simple 650/665nm ex/em fluorescence intensity analyses using the Cary-Varian Eclipse™ spectrofluorometer at a PMT setting of 700 V as a function of aptamer concentration using 100 µl of live day 5 T. pallidum cultures with the same staining protocol as previously described. These simple emission peak reads were fed into GraphPad Prism software to generate data for the measured K d estimations in Table 1 . 3. Results Table 1 summarizes the three aptamer DNA sequences generated by using the Xelari.com platform along with the AI-predicted K d and actual measured K d values obtained by fluorescence peak readings at 665 nm as a function of aptamer concentration via GraphPad Prism software. The top panels of Figs. 1-3 illustrate the 3D docking models generated by the Xelari.com platform for each predicted aptamer (ribbon structure) docked with its cognate T. pallidum protein (molecular surface structure) in the most likely binding region (i.e., “epitope”) as viewed via RasMol software. The bottom panels of Figs. 1-3 use RasMol’s monomer identification and labeling feature to reveal which nucleotides of the aptamer bound which amino acids of the target protein in the putative binding sites. By studying this information in connection with what is known about how the Tpp17, Tp47 and Tp0751 proteins are oriented with respect to the T. pallidum outer membrane and cell wall and the knowledge that these proteins are generally loosely tethered to or associated with the bacterial surface [19-23], we postulated that the majority of all three proteins are accessible for interaction and binding to their respective aptamers in solution. The 3D docked model RasMol files for each of the three aptamer-protein interactions are provided as supplemental materials for interested parties wishing to explore the binding sites and orientation of the proteins with respect to the cell surface in further detail. Physical proof of aptamer binding began with the typical representative fluorescence microscopy appearance applicable to any of the three aptamers bound to T. pallidum as shown in Fig. 4. Fig. 4A shows the appearance of live T. pallidum spirochetes under phase-contrast microscopy at 400X and the Fig. 4B shows the red fluorescing appearance of any of the three AI aptamer-AF 647 conjugates bound to the bacterial surface of live T. pallidum . It is worth noting that treponemes are known to form cyst-like aggregates in culture as previously described in the literature [24] which accounts for the clumping seen in Figs. 4A and 4B. We next sought to characterize the affinity of the AI-generated aptamers and to study their binding by fluorometry as a function of aptamer concentration in an attempt to determine K d values for each aptamer. Fig. 5 shows concentration-dependent binding and fluorescence responses of each aptamer from 0.1, 0.2, 0.5, 1, 2 and 4 µM aptamer binding for 30 min at 37˚C in PBS along with blanks (no aptamers; red spectral traces in panels 5A – 5C). Based on these data, we took 5 point readings of each cuvette with ex/em at 650 and 665 nm to obtain mean fluorescence emission values at 665 nm and derive the K d values reported in Table 1 for each aptamer using GraphPad Prism software and to plot the aptamer concentration-dependent binding curves shown in Fig. 6. The measured K d values do not correlate perfectly with the predicted K d values derived by Xelari, but it is important to point out that bacterial particles or clumps of bacteria in suspension are not comparable to truly soluble targets in solution for the purposes of determining accurate K d values. The particulate and clumped aggregate nature of T. pallidum [24, 25] probably also accounts for some of the rough spiked emission spectra seen in Fig. 5 despite using a smoothing function provided in the Cary-Varian software. Finally, the three AI-generated aptamers exhibited good specificity for the Treponema genus by binding well to T. pallidum and and its close relative T. denticola , but not binding well to the unrelated bacterial species seen in Fig. 7. By comparing peak heights in Fig. 7, it is apparent that the aptamers bound approximately 3-10 times better to Treponema species versus the unrelated bacterial species. 4. Discussion The advent of AI has provided unprecedented opportunities for rapid in silico generation of novel ligands and binding molecules such as antibodies and aptamers. The new Xelari.com platform in particular has now demonstrated rapid 24 hour DNA aptamer (Xelamer) generation ability and been validated by binding live target bacteria of medical significance ( T. pallidum ) with relatively high affinity (Table 1 ) and good specificity versus unrelated and related ( T. denticola ) bacterial species. An interesting question in the present work was if aptamers generated against the entire T. pallidum proteins using their 3D structural PDB files would be able to bind these proteins if part of the protein was embedded in the spirochete surface (i.e., if the binding “epitope” was not extracellular and accessible from the outside environment). The available knowledge base suggested that all three T. pallidum surface proteins should be largely accessible to aptamer binding from solution since none of the proteins appeared to be significantly inserted into the outer lipid membrane of the cell wall [ 18 , 25 ]. In particular, for Tpp17 (also called TP0435), Brautigam et al. [ 20 ] developed a structural model in which Tpp17 inserts into T. pallidum ’s outer membrane in such a way that it is essentially just tethered to the cell and exposes almost all of the protein to the extracellular liquid environment, although the protein can dimerize under oxidizing conditions to limit aptamer binding. For Tpp47, the 3D docking model shown in Fig. 2 shows Domain D of the protein being distal to the aptamer binding site and Domain D with the N-terminus is known to face the outer membrane of T. pallidum [ 22 ] making the suspected aptamer binding site readily available for docking with the cognate aptamer in solution. Similarly, according to Parker et al. [ 23 ] the N-terminus of Tp0751 (Pallilysin) is anchored in the T. pallidum outer membrane by a lipid moiety, making most of that surface protein accessible from extracellular solution. In summary, the empirical binding data (Figs. 4 – 6 ) correlate well with the hypothesis that the surface proteins are readily accessible for aptamer binding in solution as predicted theoretically from Figs. 1 – 3 . Our next step in future AI-generated aptamer development will be to use the new aptamers for therapeutics experiments in animals to determine if when coupled to the Fc tail of IgG they enhance phagocytosis (opsonization) and complement-mediated lysis of T. pallidum as previously proven with other aptamer systems [ 8 , 10 , 11 ] in vitro. And in the future, we may be able to predict in silico using AI how nucleotide substitutions with unnatural or exotic bases or chemical modifications of aptamers may impact their binding to known surface target proteins to accelerate and enhance empirical diagnostic and therapeutic experimentation in vitro and in vivo. Declarations Declaration of competing interest The authors declare that with the exception of J.C. Sivils, they are all shareholders in Xelari. Acknowledgements This work was funded by a Phase I SBIR contract (No. 75N93025C00010) from NIAID. The authors thank John Spells for weekly passage and maintenance of all bacterial species used in this project. References M. Ashrafuzzaman, C. Y. Tseng, J. Kapty, J. R. Mercer, and J. A. Tuszynski, "A computationally designed DNA aptamer template with specific binding to phosphatidylserine," Nucleic Acid Ther, vol. 23, pp. 418-26, Dec 2013. A. A. Buglak and A. V. Samokhvalov, "Methods and Applications of In Silico Aptamer Design and Modeling," vol. 21, Nov 10 2020. M. Hamada, "In silico approaches to RNA aptamer design," Biochimie, vol. 145, pp. 8-14, Feb 2018. F. Morena, C. Argentati, I. Tortorella, C. Emiliani, and S. Martino, "De novo ssRNA Aptamers against the SARS-CoV-2 Main Protease: In Silico Design and Molecular Dynamics Simulation," Int J Mol Sci, vol. 22, Jun 26 2021. M. Nosrati and J. 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Supplementary Files TP0751DockedwAptamer.pdb Tpp47DockedwAptamer.pdb Tpp17DockedwAptamer.pdb Cite Share Download PDF Status: Posted Version 1 posted 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. 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14:09:17","extension":"xml","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64021,"visible":true,"origin":"","legend":"","description":"","filename":"f558a4d41c7d41ccadf43fa3bec3c1da1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/34760547eddf2c9705bd0e1e.xml"},{"id":96825337,"identity":"20570a81-614f-44c5-a01b-b8ca7dbc4fc1","added_by":"auto","created_at":"2025-11-26 12:45:30","extension":"html","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72376,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/1af11ebd45448d73d6372159.html"},{"id":96825302,"identity":"d9fdbba5-6d42-497a-9bab-84fdbdb494f3","added_by":"auto","created_at":"2025-11-26 12:45:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":481816,"visible":true,"origin":"","legend":"\u003cp\u003eTop panel – AI-generated aptamer (Xelamer) ribbon structure docked with its cognate Tpp17 protein (molecular surface structure) using the Xelari.com platform. Bottom panel – Xelamer and Tpp17 backbone structures showing the nucleotide and amino acid labels used to study binding accessibility based on known orientation of the protein from the literature. This analysis also demonstrated that the AF-647 label on the 5’ end was also likely to be free to fluoresce in solution (i.e., not quenched by proximity to amino acids).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/093760022a667b4ba18fba82.png"},{"id":96825308,"identity":"6247c29e-f1ab-473f-9599-7dd91447dafb","added_by":"auto","created_at":"2025-11-26 12:45:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":497340,"visible":true,"origin":"","legend":"\u003cp\u003eSame as for Fig. 1, but for the Tpp47 system.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/cd615418bfe600f7730df570.png"},{"id":96917818,"identity":"8713db12-17ad-4bc6-b3a3-6f57638e3f97","added_by":"auto","created_at":"2025-11-27 14:10:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":480978,"visible":true,"origin":"","legend":"\u003cp\u003eSame as for Figs. 1 and 2, but for the Tp0751 system.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/dc9a92c225f54d15d8d92792.png"},{"id":96825306,"identity":"92ae3cef-cc06-4010-aeac-99792d119bf8","added_by":"auto","created_at":"2025-11-26 12:45:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":426987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e 400X Phase-contrast microscopy of a live day 5 \u003cem\u003eT. pallidum\u003c/em\u003e culture. \u003cstrong\u003eB.\u003c/strong\u003eRepresentative red fluorescent appearance of all three aptamer-AF 647 conjugates after staining live \u003cem\u003eT. pallidum\u003c/em\u003e and washing in PBS with excitation at 650 nm (400X magnification). Bacterial aggregation is common in \u003cem\u003eT. pallidum\u003c/em\u003e cultures [24] which accounts for the visible clumps.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/8f0ba648478205e6bbdfa354.png"},{"id":96825311,"identity":"2cfe71c5-1db2-452f-a080-98030736ddef","added_by":"auto","created_at":"2025-11-26 12:45:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":184707,"visible":true,"origin":"","legend":"\u003cp\u003eSpectroflurometric results (measured in RFUs; relative fluorescence units) from staining of live \u003cem\u003eT. pallidum\u003c/em\u003e with 0.1 to 4 µM concentrations of each of the three aptamer-AF 647 conjugates. Red traces correspond to blanks with no aptamer-AF 647 addition. Excitation was at 650 nm with 5 nm slits and PMT setting of 700 V.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/e1f6e63c07ff9245778c3c2a.png"},{"id":96918262,"identity":"8a3be87d-a360-41d2-9bed-1930dd4a757d","added_by":"auto","created_at":"2025-11-27 14:11:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":128899,"visible":true,"origin":"","legend":"\u003cp\u003eBinding curves for each of the aptamer-AF 647 conjugates generated from fluorometric measurements measuring peak heights at 665 nm after excitation at 650 nm to generate the K\u003csub\u003ed\u003c/sub\u003e values reported in Table 1 following analysis by GraphPad Prism software.\u0026nbsp;\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/ae706117805d398e7d5714f1.png"},{"id":96825315,"identity":"4d6e50cb-945b-467b-b328-cab656926213","added_by":"auto","created_at":"2025-11-26 12:45:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":97037,"visible":true,"origin":"","legend":"\u003cp\u003eResults of a fluorometric cross-reactivity study utilizing equal numbers of \u003cem\u003eT. pallidum\u003c/em\u003e and related (\u003cem\u003eT. denticola\u003c/em\u003e) and unrelated bacterial species stained by 2 µM aptamer-AF 647 conjugates for 30 min at 37˚C after washing in PBS. Mean fluorescence (bar heights) and standard deviations (error bars) of 5 independent readings are plotted from ex/em peak readings at 650/665 nm.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/14c4de4baf26bcccf0d3722d.png"},{"id":97368955,"identity":"0589a378-9699-4003-ab50-1d8311a0e463","added_by":"auto","created_at":"2025-12-03 16:23:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2914311,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/caf5afaa-7ddd-4e77-b83c-4cf809da655a.pdf"},{"id":96919259,"identity":"fc18f902-e1d8-4474-810a-c6ba7ee8e03b","added_by":"auto","created_at":"2025-11-27 14:13:30","extension":"pdb","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":273139,"visible":true,"origin":"","legend":"","description":"","filename":"TP0751DockedwAptamer.pdb","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/ff88c648ef788b1a55bbfbc8.pdb"},{"id":96825304,"identity":"d3e95648-0a70-422b-b680-9abf7226d464","added_by":"auto","created_at":"2025-11-26 12:45:29","extension":"pdb","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":614149,"visible":true,"origin":"","legend":"","description":"","filename":"Tpp47DockedwAptamer.pdb","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/6a76dbf6f925199412bca6f1.pdb"},{"id":96917623,"identity":"1363061f-ccf9-4456-8530-c5c7904c8528","added_by":"auto","created_at":"2025-11-27 14:10:15","extension":"pdb","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":251917,"visible":true,"origin":"","legend":"","description":"","filename":"Tpp17DockedwAptamer.pdb","url":"https://assets-eu.researchsquare.com/files/rs-8158423/v1/8c27ae95a57535dbb6c9e83e.pdb"}],"financialInterests":"Competing interest reported. J.G.B., S.N. and D.U. are shareholders in Xelari.com.","formattedTitle":"Evaluation of Artificial Intelligence-Generated DNA Aptamers Against Treponema pallidum Surface Proteins","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNumerous groups have selected nucleic acid aptamer DNA or RNA sequences via in silico computational techniques [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For our in vitro therapeutic studies, we sought aptamers to bind \u003cem\u003eT. pallidum\u003c/em\u003e and kill this pathogen which causes syphilis via opsonization [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and complement-mediated lysis [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] especially because of shortages in the benzathine penicillin antibiotic supply, the lack of a syphilis vaccine and \u003cem\u003eT. pallidum\u0026rsquo;s\u003c/em\u003e alarming resistance to penicillin alternatives such as azithromycin [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo date the only aptamers we have found in the literature that were able to bind \u003cem\u003eT. pallidum\u003c/em\u003e were a subset of the aptamers developed against \u003cem\u003eT. denticola\u003c/em\u003e by Park et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and these \u003cem\u003eT. denticola\u003c/em\u003e aptamers were partially effective at opsonization of \u003cem\u003eT. pallidum\u003c/em\u003e in our hands (DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31038/IDT.2025613\u003c/span\u003e\u003cspan address=\"10.31038/IDT.2025613\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Still, we sought even higher affinity anti-\u003cem\u003eT. pallidum\u003c/em\u003e aptamers for our novel syphilis therapeutics approaches. Thus, we chose to pursue the agentic AI-based approach now available from Xelari.com which is based on a multi-stage process for aptamer modeling using rule-based and stochastic algorithms for geometric transformations and molecular mechanics simulations, as well as artificial neural networks for energy and structural predictions.\u003c/p\u003e\u003cp\u003eAs input data, the platform uses the 3D structure of the target protein, obtained either from the PDB database (DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/28.1.235\u003c/span\u003e\u003cspan address=\"10.1093/nar/28.1.235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) or predicted using tools such as AlphaFold 2 (DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-021-03819-2\u003c/span\u003e\u003cspan address=\"10.1038/s41586-021-03819-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). During processing, the platform analyzes the protein surface model to identify the most favorable binding regions. It then performs de novo design of seed structures through stochastic sampling of poses from a predefined list of nucleotides (standard deoxyribonucleotides were used in this study). For each sampled pose, a scoring function is applied to approximate the contribution of each individual nucleotide to the overall interaction energy. The greater the contribution, the more favorable the position of that nucleotide is considered to be. After the seed structures are generated, optimal frameworks are constructed by combining nucleotides with high energetic contributions into continuous chains. Following this assembly step, molecular dynamics simulations are carried out to relax the complex, and the dissociation constant (K\u003csub\u003ed\u003c/sub\u003e) of each complex is estimated. This process yields a set of aptamers, from which the sequence with the K\u003csub\u003ed\u003c/sub\u003e value closest to the desired target is selected.\u003c/p\u003e\u003cp\u003eAs our initial set of protein targets, we chose three well-characterized immunogenic proteins thought to be loosely associated with the outer membrane or cell wall of \u003cem\u003eT. pallidum\u003c/em\u003e. In particular, these proteins as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e are designated Tpp17, Tpp47 and Tp0751 [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The reader is advised that these proteins go by other similar names or designations in the literature, but the UniProt numbers listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provide clarity for each protein\u0026rsquo;s identity. The 3D structural PDB files associated with the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e UniProt numbers were fed into Xelari\u0026rsquo;s platform to generate the three aptamer DNA sequences shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the Results section.\u003c/p\u003e\u003cp\u003eFinally, the AI-generated aptamers were tested for binding to real live \u003cem\u003eT. pallidum\u003c/em\u003e cells as well as related and unrelated bacterial species. As shown in this work, the three AI-generated aptamers demonstrated relatively high affinity and good specificity for binding live \u003cem\u003eT. pallidum\u003c/em\u003e cells.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. In Silico aptamer (Xelamer) AI selection criteria\u003c/h2\u003e\u003cp\u003ePDB files for the three UniProt database target protein accession numbers given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were loaded into separate 24 hour runs on the Xelari.com platform at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://platform.xelari.com/\u003c/span\u003e\u003cspan address=\"https://platform.xelari.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Selection parameters were set as follows: Na\u003csup\u003e+\u003c/sup\u003e concentration was 137 mM, K\u003csup\u003e+\u003c/sup\u003e concentration was 2.7 mM, Mg\u003csup\u003e2+\u003c/sup\u003e was 0 mM, pH was 7.2 and temperature was set to 37˚C since the aptamers may eventually be used therapeutically in vivo and need to bind well at body temperature. Target aptamer length was set to 39 nucleotides and desired binding affinity was set to the highest setting K\u003csub\u003ed\u003c/sub\u003e = 1 nM with associated DNA parameters. The Xelari platform output DNA aptamer sequences between 37 to 42 bases with associated predicted K\u003csub\u003ed\u003c/sub\u003e values and 3D docked aptamer-target protein PDB structures that were viewed, rotated and analyzed using RasMol version 2.7.5.2 software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Bacteria and culture conditions\u003c/h2\u003e\u003cp\u003e\u003cem\u003eTreponema pallidum\u003c/em\u003e was purchased from American Type Culture Collection (ATCC; Manassas, VA) as \u003cem\u003eTreponema phagedenis\u003c/em\u003e (ex Brumpt) Smibert, Kazan 8 strain (ATCC No. 27087) in lyophilized form. \u003cem\u003eTreponema denticola\u003c/em\u003e (ex Fl\u0026uuml;gge; ATCC No. 35404) was shipped frozen from ATCC. The treponeme cultures were rehydrated or thawed in 10 ml of Oral Treponema Enrichment Broth (OTEB) tubes from Anaerobe Systems Inc. (Morgan Hill, CA; Cat. No. AS-603) at 37\u0026deg;C in a slowly rotating closed incubator with a microaerophilic environment induced by Becton Dickinson Gas Pak\u0026trade; pouches (Cat. No. 260680). \u003cem\u003eT. pallidum\u003c/em\u003e cultures were capped and passaged weekly by 1:10 dilution of the previous passage in OTEB. Other bacteria were cultured from Culti-Loops\u0026trade; purchased from Thermo Fisher and grown on blood agar plates overnight at 37˚C.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Dye-labeled aptamer synthesis\u003c/h2\u003e\u003cp\u003eEach of the aptamer DNA sequences shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was synthesized at Integrated DNA Technologies (Coralville, IA) with a 5\u0026rsquo; Alexa Fluor (AF) 647 dye label and purified by HPLC. Dry aptamers were rehydrated at a 100 \u0026micro;M concentration in sterile Dulbecco\u0026rsquo;s phosphate buffer saline (PBS) without Ca\u003csup\u003e2+\u003c/sup\u003e or Mg\u003csup\u003e2+\u003c/sup\u003e at pH 7.2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Spectrofluorometry and fluorescence microscopy\u003c/h2\u003e\u003cp\u003eAptamer binding was validated by spectrofluorometric analysis of aptamer-AF 647 staining and fluorescence microscopy as follows. The amount of each AF 647-labeled \u003cem\u003eT. pallidum\u003c/em\u003e aptamer from Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was varied from 0.1 to 4 \u0026micro;M and added to 100 \u0026micro;l of ~\u0026thinsp;10\u003csup\u003e7\u003c/sup\u003e live \u003cem\u003eT. pallidum\u003c/em\u003e (5 day cultures in OTEB) after pelleting at 13,000 x g for 5 min and resuspension in 1 ml of sterile PBS without Ca\u003csup\u003e2+\u003c/sup\u003e or Mg\u003csup\u003e2+\u003c/sup\u003e at pH 7.2 (hereby referred to simply as PBS) for 30 min at 37˚C. Samples were then centrifuged again for 5 min at 13,000 x g. The supernates were carefully siphoned out so as not to disturb the stained bacterial pellets which were then resuspended in 1 ml of fresh PBS. The 1 ml samples were then diluted to a total volume of 3 ml in PBS in polystyrene 1 cm path length cuvettes, capped, inverted several times and immediately analyzed using a Cary-Varian Eclipse\u0026trade; spectrofluorometer with excitation at 650 nm and fluorescence emission scanning from 655 to 700 nm using a photomultiplier tube (PMT) setting of 700 V. Some samples were also placed in separate wells of a sterile 6 well plate and imaged using the red channel of an Invitrogen EVOS M5000 fluorescence microscope with 400X zoom setting.\u003c/p\u003e\u003cp\u003eFor cross-reactivity studies, all bacterial species were normalized to 1.0 absorbance unit at 620 nm using a plate reader and 100 \u0026micro;l of each species was stained with 2 \u0026micro;M of each aptamer-AF 647 conjugate in 1 ml of PBS for 30 min at 37˚C followed by centrifugal pelleting, removal of the supernates and resuspension in 3 ml of PBS in plastic cuvettes followed by five 650/665 nm ex/em point readings at a PMT setting of 600 V with mean and standard deviation determinations for each group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 K\u003csub\u003ed\u003c/sub\u003e value prediction and measurement\u003c/h2\u003e\u003cp\u003eThe Xelari.com platform derived K\u003csub\u003ed\u003c/sub\u003e values using an evaluation function that estimates relative K\u003csub\u003ed\u003c/sub\u003e magnitudes based on the geometric characteristics of the complex structure and the usage conditions. At the time of the experiments, a fast evaluation function was employed, which did not fit the prediction results to experimental values. This scoring function was trained on synthetic complex formation energies without conversion to experimental units, which could lead to differences of several orders of magnitude. Nevertheless, the predicted values showed good correlation with the experimentally measured K\u003csub\u003ed\u003c/sub\u003e values and allowed reliable relative ranking of binding strength within each group of candidates.\u003c/p\u003e\u003cp\u003eSome samples were analyzed by simple 650/665nm ex/em fluorescence intensity analyses using the Cary-Varian Eclipse\u0026trade; spectrofluorometer at a PMT setting of 700 V as a function of aptamer concentration using 100 \u0026micro;l of live day 5 \u003cem\u003eT. pallidum\u003c/em\u003e cultures with the same staining protocol as previously described. These simple emission peak reads were fed into GraphPad Prism software to generate data for the measured K\u003csub\u003ed\u003c/sub\u003e estimations in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Table 1 summarizes the three aptamer DNA sequences generated by using the Xelari.com platform along with the AI-predicted K\u003csub\u003ed\u0026nbsp;\u003c/sub\u003eand actual measured K\u003csub\u003ed\u003c/sub\u003e values obtained by fluorescence peak readings at 665 nm as a function of aptamer concentration via GraphPad Prism software. \u0026nbsp;The top panels of Figs. 1-3 illustrate the 3D docking models generated by the Xelari.com platform for each predicted aptamer (ribbon structure) docked with its cognate \u003cem\u003eT. pallidum\u003c/em\u003e protein (molecular surface structure) in the most likely binding region (i.e., \u0026ldquo;epitope\u0026rdquo;) as viewed via RasMol software. \u0026nbsp;The bottom panels of Figs. 1-3 use RasMol\u0026rsquo;s monomer identification and labeling feature to reveal which nucleotides of the aptamer bound which amino acids of the target protein in the putative binding sites. \u0026nbsp;By studying this information in connection with what is known about how the Tpp17, Tp47 and Tp0751 proteins are oriented with respect to the \u003cem\u003eT. pallidum\u003c/em\u003e outer membrane and cell wall and the knowledge that these proteins are generally loosely tethered to or associated with the bacterial surface [19-23], we postulated that the majority of all three proteins are accessible for interaction and binding to their respective aptamers in solution. The 3D docked model RasMol files for each of the three aptamer-protein interactions are provided as supplemental materials for interested parties wishing to explore the binding sites and orientation of the proteins with respect to the cell surface in further detail. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Physical proof of aptamer binding began with the typical representative fluorescence microscopy appearance applicable to any of the three aptamers bound to \u003cem\u003eT. pallidum\u003c/em\u003e as shown in Fig. 4. \u0026nbsp;Fig. 4A shows the appearance of live \u003cem\u003eT. pallidum\u003c/em\u003e spirochetes under phase-contrast microscopy at 400X and the Fig. 4B shows the red fluorescing appearance of any of the three AI aptamer-AF 647 conjugates bound to the bacterial surface of live \u003cem\u003eT. pallidum\u003c/em\u003e. \u0026nbsp;It is worth noting that treponemes are known to form cyst-like aggregates in culture as previously described in the literature [24] which accounts for the clumping seen in Figs. 4A and 4B. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; We next sought to characterize the affinity of the AI-generated aptamers and to study their binding by fluorometry as a function of aptamer concentration in an attempt to determine K\u003csub\u003ed\u003c/sub\u003e values for each aptamer. \u0026nbsp;Fig. 5 shows concentration-dependent binding and fluorescence responses of each aptamer from 0.1, 0.2, 0.5, 1, 2 and 4 \u0026micro;M aptamer binding for 30 min at 37˚C in PBS along with blanks (no aptamers; red spectral traces in panels 5A \u0026ndash; 5C).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Based on these data, we took 5 point readings of each cuvette with ex/em at 650 and 665 nm to obtain mean fluorescence emission values at 665 nm and derive the K\u003csub\u003ed\u003c/sub\u003e values reported in Table 1 for each aptamer using GraphPad Prism software and to plot the aptamer concentration-dependent binding curves shown in Fig. 6. The measured K\u003csub\u003ed\u003c/sub\u003e values do not correlate perfectly with the predicted K\u003csub\u003ed\u003c/sub\u003e values derived by Xelari, but it is important to point out that bacterial particles or clumps of bacteria in suspension are not comparable to truly soluble targets in solution for the purposes of determining accurate K\u003csub\u003ed\u003c/sub\u003e values. \u0026nbsp;The particulate and clumped aggregate nature of \u003cem\u003eT. pallidum\u003c/em\u003e [24, 25] probably also accounts for some of the rough spiked emission spectra seen in Fig. 5 despite using a smoothing function provided in the Cary-Varian software.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Finally, the three AI-generated aptamers exhibited good specificity for the \u003cem\u003eTreponema\u003c/em\u003e genus by binding well to \u003cem\u003eT. pallidum\u003c/em\u003e and and its close relative \u003cem\u003eT. denticola\u003c/em\u003e, but not binding well to the unrelated bacterial species seen in Fig. 7. \u0026nbsp;By comparing peak heights in Fig. 7, it is apparent that the aptamers bound approximately 3-10 times better to \u003cem\u003eTreponema\u003c/em\u003e species versus the unrelated bacterial species.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe advent of AI has provided unprecedented opportunities for rapid in silico generation of novel ligands and binding molecules such as antibodies and aptamers. The new Xelari.com platform in particular has now demonstrated rapid 24 hour DNA aptamer (Xelamer) generation ability and been validated by binding live target bacteria of medical significance (\u003cem\u003eT. pallidum\u003c/em\u003e) with relatively high affinity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and good specificity versus unrelated and related (\u003cem\u003eT. denticola\u003c/em\u003e) bacterial species.\u003c/p\u003e\u003cp\u003eAn interesting question in the present work was if aptamers generated against the entire \u003cem\u003eT. pallidum\u003c/em\u003e proteins using their 3D structural PDB files would be able to bind these proteins if part of the protein was embedded in the spirochete surface (i.e., if the binding \u0026ldquo;epitope\u0026rdquo; was not extracellular and accessible from the outside environment). The available knowledge base suggested that all three \u003cem\u003eT. pallidum\u003c/em\u003e surface proteins should be largely accessible to aptamer binding from solution since none of the proteins appeared to be significantly inserted into the outer lipid membrane of the cell wall [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In particular, for Tpp17 (also called TP0435), Brautigam et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] developed a structural model in which Tpp17 inserts into \u003cem\u003eT. pallidum\u003c/em\u003e\u0026rsquo;s outer membrane in such a way that it is essentially just tethered to the cell and exposes almost all of the protein to the extracellular liquid environment, although the protein can dimerize under oxidizing conditions to limit aptamer binding. For Tpp47, the 3D docking model shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows Domain D of the protein being distal to the aptamer binding site and Domain D with the N-terminus is known to face the outer membrane of \u003cem\u003eT. pallidum\u003c/em\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] making the suspected aptamer binding site readily available for docking with the cognate aptamer in solution. Similarly, according to Parker et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] the N-terminus of Tp0751 (Pallilysin) is anchored in the \u003cem\u003eT. pallidum\u003c/em\u003e outer membrane by a lipid moiety, making most of that surface protein accessible from extracellular solution. In summary, the empirical binding data (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) correlate well with the hypothesis that the surface proteins are readily accessible for aptamer binding in solution as predicted theoretically from Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOur next step in future AI-generated aptamer development will be to use the new aptamers for therapeutics experiments in animals to determine if when coupled to the Fc tail of IgG they enhance phagocytosis (opsonization) and complement-mediated lysis of \u003cem\u003eT. pallidum\u003c/em\u003e as previously proven with other aptamer systems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] in vitro. And in the future, we may be able to predict in silico using AI how nucleotide substitutions with unnatural or exotic bases or chemical modifications of aptamers may impact their binding to known surface target proteins to accelerate and enhance empirical diagnostic and therapeutic experimentation in vitro and in vivo.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; The authors declare that with the exception of J.C. Sivils, they are all shareholders in Xelari.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; This work was funded by a Phase I SBIR contract (No. 75N93025C00010) from NIAID. \u0026nbsp;The authors thank John Spells for weekly passage and maintenance of all bacterial species used in this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM. Ashrafuzzaman, C. Y. Tseng, J. Kapty, J. R. Mercer, and J. A. Tuszynski, \u0026quot;A computationally designed DNA aptamer template with specific binding to phosphatidylserine,\u0026quot; \u003cem\u003eNucleic Acid Ther, \u003c/em\u003evol. 23, pp. 418-26, Dec 2013.\u003c/li\u003e\n\u003cli\u003eA. A. Buglak and A. V. Samokhvalov, \u0026quot;Methods and Applications of In Silico Aptamer Design and Modeling,\u0026quot; vol. 21, Nov 10 2020.\u003c/li\u003e\n\u003cli\u003eM. 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Norris, \u0026quot;Cellular architecture of Treponema pallidum: novel flagellum, periplasmic cone, and cell envelope as revealed by cryo electron tomography,\u0026quot; \u003cem\u003eJ Mol Biol, \u003c/em\u003evol. 403, pp. 546-61, Nov 5 2010.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"agentic, aptamer, artificial intelligence, in silico, syphilis, Treponema","lastPublishedDoi":"10.21203/rs.3.rs-8158423/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8158423/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDNA aptamer sequences were selected in silico with assistance from artificial intelligence (AI) using the new Xelari.com platform against three known immunogenic surface proteins of Treponema pallidum designated Tpp17, Tpp47 and Tp0751. The in silico aptamers were synthesized with 5\u0026rsquo; Alexa Fluor 647 labels and shown to bind live \u003cem\u003eTreponema pallidum\u003c/em\u003e by fluorescence microscopy and spectrofluorometry. While AI-predicted K\u003csub\u003ed\u003c/sub\u003e values differed somewhat from measured K\u003csub\u003ed\u003c/sub\u003e values, it is clear that the aptamers bound the \u003cem\u003eT. pallidum\u003c/em\u003e surface which is consistent with the molecular docking models for each aptamer with each cognate target protein all of which are thought to be largely accessible for binding (i.e., not predominately embedded in the outer membrane or cell wall). All three aptamers also demonstrated good specificity in cross-reactivity binding studies.\u003c/p\u003e","manuscriptTitle":"Evaluation of Artificial Intelligence-Generated DNA Aptamers Against Treponema pallidum Surface Proteins","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 12:45:24","doi":"10.21203/rs.3.rs-8158423/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3612b826-558f-4a14-b719-a6fb8143e091","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-02T18:38:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 12:45:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8158423","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8158423","identity":"rs-8158423","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}