Zhou, J. & Rossi, J. Aptamers as focused therapeutics: present potential and challenges. Nat. Rev. Drug Discov. 16, 181–202 (2017).
Google ScholarÂ
Zhang, J., Lang, M., Zhou, Y. & Zhang, Y. Predicting RNA constructions and features by synthetic intelligence. Tendencies Genet. 40, 94–107 (2024).
Google ScholarÂ
Hayes, T. et al. Simulating 500 million years of evolution with a language mannequin. Science 387, 850–858 (2025).
Google ScholarÂ
Zhang, H. et al. Algorithm for optimized mRNA design improves stability and immunogenicity. Nature 621, 396–403 (2023).
Google ScholarÂ
Jiang, Ok. et al. Speedy in silico directed evolution by a protein language mannequin with EVOLVEpro. Science 387, eadr6006 (2025).
Google ScholarÂ
Bunka, D. H. J. & Stockley, P. G. Aptamers come of age – finally. Nat. Rev. Microbiol. 4, 588–596 (2006).
Google ScholarÂ
Zhang, Y., Juhas, M. & Kwok, C. Ok. Aptamers concentrating on SARS-COV-2: a promising device to combat towards COVID-19. Tendencies Biotechnol. 41, 528–544 (2023).
Google ScholarÂ
Zhang, Y., Lai, B. S. & Juhas, M. Latest advances in aptamer discovery and functions. Molecules 24, 941 (2019).
Google ScholarÂ
Vargas-Montes, M. et al. Enzyme-linked aptamer assay (ELAA) for detection of toxoplasma ROP18 protein in human serum. Entrance. Cell. Infect. Microbiol. 9, 386 (2019).
Google ScholarÂ
Tuerk, C. & Gold, L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 249, 505–510 (1990).
Google ScholarÂ
Zhang, J. et al. Repurposing CRISPR/Cas to find SARS-CoV-2 detecting and neutralizing aptamers. Adv. Sci. 10, 2300656 (2023).
Google ScholarÂ
Su-Tobon, Q. et al. CRISPR-Hybrid: a CRISPR-mediated intracellular directed evolution platform for RNA aptamers. Nat. Commun. 16, 595 (2025).
Google ScholarÂ
Iwano, N., Adachi, T., Aoki, Ok., Nakamura, Y. & Hamada, M. Generative aptamer discovery utilizing RaptGen. Nat. Comput. Sci. 2, 378–386 (2022).
Google ScholarÂ
Wang, Z. et al. AptaDiff: de novo design and optimization of aptamers based mostly on diffusion fashions. Temporary. Bioinform. 25, bbae517 (2024).
Google ScholarÂ
Wong, F. et al. Deep generative design of RNA aptamers utilizing structural predictions. Nat. Comput. Sci. 4, 829–839 (2024).
Google ScholarÂ
Zhou, X. et al. ProRefiner: an entropy-based refining technique for inverse protein folding with world graph consideration. Nat. Commun. 14, 7434 (2023).
Google ScholarÂ
Dauparas, J. et al. Strong deep studying–based mostly protein sequence design utilizing ProteinMPNN. Science 378, 49–56 (2022).
Google ScholarÂ
Ren, M., Yu, C., Bu, D. & Zhang, H. Correct and sturdy protein sequence design with CarbonDesign. Nat. Mach. Intell. 6, 536–547 (2024).
Google ScholarÂ
Lee, G., Jang, G. H., Kang, H. Y. & Tune, G. Predicting aptamer sequences that work together with goal proteins utilizing an aptamer-protein interplay classifier and a Monte Carlo tree search method. PLoS ONE 16, e0253760 (2021).
Google ScholarÂ
Shin, I. et al. AptaTrans: a deep neural community for predicting aptamer-protein interplay utilizing pretrained encoders. BMC Bioinformatics 24, 447 (2023).
Google ScholarÂ
Patel, S. et al. AptaBLE: an enhanced deep studying platform for aptamer protein interplay prediction and design. In Machine Studying for Structural Biology Workshop, NeurIPS 2024 (2024).
Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep studying. Nat. Biotechnol. 33, 831–838 (2015).
Google ScholarÂ
Nithin, C., Kmiecik, S., Błaszczyk, R., Nowicka, J. & Tuszyńska, I. Comparative evaluation of RNA 3D construction prediction strategies: in direction of enhanced modeling of RNA–ligand interactions. Nucleic Acids Res. 52, 7465–7486 (2024).
Google ScholarÂ
Chen, L. T. et al. Goal sequence-conditioned design of peptide binders utilizing masked language modeling. Nat. Biotechnol. (2025).
Torres, M. D. T., Chen, L. T., Wan, F., Chatterjee, P. & de la Fuente-Nunez, C. Generative latent diffusion language modeling yields anti-infective artificial peptides. Cell Biomat. (2025).
Shen, T. et al. Correct RNA 3D construction prediction utilizing a language model-based deep studying method. Nat. Strategies 21, 2287–2298 (2024).
Google ScholarÂ
Akiyama, M. & Sakakibara, Y. Informative RNA base embedding for RNA structural alignment and clustering by deep illustration studying. NAR Genom. Bioinform. 4, lqac012 (2022).
Google ScholarÂ
Wang, N. et al. Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning. Nat. Mach. Intell. 6, 548–557 (2024).
Google ScholarÂ
Patel, S., Peng, F. Z., Fraser, Ok., Chatterjee, P. & Yao, S. EvoFlow-RNA: producing and representing non-coding RNA with a language mannequin. Preprint at bioRxiv (2025).
Penić, R. J., Vlašić, T., Huber, R. G., Wan, Y. & Šikić, M. RiNALMo: general-purpose RNA language fashions can generalize effectively on construction prediction duties. Nat. Commun. 16, 5671 (2025).
Google ScholarÂ
Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336 (2024).
Google ScholarÂ
Ishida, R. et al. RaptRanker: in silico RNA aptamer choice from HT-SELEX experiment based mostly on native sequence and construction data. Nucleic Acids Res. 48, e82 (2020).
Google ScholarÂ
Li, W. & Godzik, A. Cd-hit: a quick program for clustering and evaluating massive units of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
Google ScholarÂ
Freage, L., Jamal, D., Williams, N. B. & Mallikaratchy, P. R. A homodimeric aptamer variant generated from ligand-guided choice prompts the T cell receptor cluster of differentiation 3 advanced. Mol. Ther. Nucleic Acids 22, 167–178 (2020).
Google ScholarÂ
Zumrut, H. et al. Ligand-guided choice with artificially expanded genetic data methods towards TCR-CD3ε. Biochemistry 59, 552–562 (2020).
Google ScholarÂ
Zumrut, H. E. et al. Integrating ligand-receptor interactions and in vitro evolution for streamlined discovery of synthetic nucleic acid ligands. Mol. Ther. Nucleic Acids 17, 150–163 (2019).
Google ScholarÂ
Raddatz, M.-S. L. et al. Enrichment of cell-targeting and population-specific aptamers by fluorescence-activated cell sorting. Angew. Chem. Int. Ed. Engl. 47, 5190–5193 (2008).
Google ScholarÂ
Nakhjavani, M. et al. A circulate cytometry-based cell floor protein binding assay for assessing selectivity and specificity of an anticancer aptamer. J. Vis. Exp. 187, e64304 (2022).
Abramson, J. et al. Correct construction prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Google ScholarÂ
Li, J., Zhang, S., Zhang, D. & Chen, S.-J. Vfold-Pipeline: an internet server for RNA 3D construction prediction from sequences. Bioinformatics 38, 4042–4043 (2022).
Google ScholarÂ
Kretsch, R. C. et al. Purposeful relevance of CASP16 nucleic acid predictions as evaluated by construction suppliers. Proteins 94, 51–78 (2026).
Google ScholarÂ
Shannon, C. E. A mathematical concept of communication. Bell Syst. Tech. J. 27, 379–423 (1948).
Google ScholarÂ
Yan, Y., Tao, H., He, J. & Huang, S.-Y. The HDOCK server for built-in protein–protein docking. Nat. Protocols 15, 1829–1852 (2020).
Google ScholarÂ
Valero, J. et al. A serum-stable RNA aptamer particular for SARS-CoV-2 neutralizes viral entry. Proc. Natl Acad. Sci. USA 118, e2112942118 (2021).
Google ScholarÂ
Solar, M. et al. Aptamer blocking technique inhibits SARS-CoV-2 virus an infection. Angew. Chem. Int. Ed. Engl. 60, 10266–10272 (2021).
Google ScholarÂ
Bartoschik, T. et al. Close to-native, site-specific and purification-free protein labeling for quantitative protein interplay evaluation by MicroScale Thermophoresis. Sci. Rep. 8, 4977 (2018).
Google ScholarÂ
Tune, Y. et al. Discovery of aptamers concentrating on the receptor-binding area of the SARS-CoV-2 spike glycoprotein. Anal. Chem. 92, 9895–9900 (2020).
Google ScholarÂ
Li, J. et al. Various high-affinity DNA aptamers for wild-type and B.1.1.7 SARS-CoV-2 spike proteins from a pre-structured DNA library. Nucleic Acids Res. 49, 7267–7279 (2021).
Google ScholarÂ
Liu, X. et al. Neutralizing aptamers block S/RBD−ACE2 interactions and stop host cell an infection. Angew. Chem. Int. Ed. Engl. 60, 10273–10278 (2021).
Google ScholarÂ
Yang, G. et al. Identification of SARS-CoV-2-against aptamer with excessive neutralization exercise by blocking the RBD area of spike protein 1. Sign Transduct. Goal Ther. 6, 227 (2021).
Google ScholarÂ
Alves Ferreira-Bravo, I. & DeStefano, J. J. Xeno-nucleic acid (XNA) 2′-fluoro-arabino nucleic acid (FANA) aptamers to the receptor-binding area of SARS-CoV-2 S protein block ACE2 binding. Viruses 13, 1983 (2021).
Saify Nabiabad, H., Amini, M. & Demirdas, S. Particular delivering of RNAi utilizing spike’s aptamer-functionalized lipid nanoparticles for concentrating on SARS-CoV-2: a robust anti-Covid drug in a scientific case research. Chem. Biol. Drug Des. 99, 233–246 (2022).
Google ScholarÂ
Lorenz, R. et al. ViennaRNA Package deal 2.0. Algorithms Mol. Biol. 6, 26 (2011).
Google ScholarÂ
Guo, C. et al. Transversions have bigger regulatory results than transitions. BMC Genomics 18, 394 (2017).
Google ScholarÂ
Solar, M. et al. Spherical neutralizing aptamer inhibits SARS-CoV-2 an infection and suppresses mutational escape. J. Am. Chem. Soc. 143, 21541–21548 (2021).
Google ScholarÂ
Dang, C. V., Reddy, E. P., Shokat, Ok. M. & Soucek, L. Drugging the ‘undruggable’ most cancers targets. Nat. Rev. Most cancers 17, 502–508 (2017).
Google ScholarÂ
Wang, Y. et al. Antitumor impact of anti-c-Myc aptamer-based PROTAC for degradation of the c-Myc protein. Adv. Sci. 11, 2309639 (2024).
Google ScholarÂ
He, J., Spokoyny, D., Neubig, G. & Berg-Kirkpatrick, T. Lagging inference networks and posterior collapse in variational autoencoders. In The Seventh Worldwide Convention on Studying Representations (ICLR, 2019).
Dieng, A. B., Kim, Y., Rush, A. M. & Blei, D. M. Avoiding latent variable collapse with generative skip fashions. In Proc. Twenty-Second Worldwide Convention on Synthetic Intelligence and Statistics (eds Kamalika, C. & Masashi, S.) 2397–2405 (PMLR, 2019).
Hawkins-Hooker, A. et al. Producing useful protein variants with variational autoencoders. PLoS Comput. Biol. 17, e1008736 (2021).
Google ScholarÂ
Zhang, C., Shine, M., Pyle, A. M. & Zhang, Y. US-align: common construction alignments of proteins, nucleic acids, and macromolecular complexes. Nat. Strategies 19, 1109–1115 (2022).
Google ScholarÂ
Madeira, F. et al. The EMBL-EBI Job Dispatcher sequence evaluation instruments framework in 2024. Nucleic Acids Res. 52, W521–W525 (2024).
Google ScholarÂ
Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence emblem generator. Genome Res. 14, 1188–1190 (2004).
Google ScholarÂ
Ferruz, N., Schmidt, S. & Höcker, B. ProtGPT2 is a deep unsupervised language mannequin for protein design. Nat. Commun. 13, 4348 (2022).
Google ScholarÂ
Ni, B., Kaplan, D. L. & Buehler, M. J. ForceGen: end-to-end de novo protein era based mostly on nonlinear mechanical unfolding responses utilizing a language diffusion mannequin. Sci. Adv. 10, eadl4000 (2024).
Google ScholarÂ
Madani, A. et al. Massive language fashions generate useful protein sequences throughout numerous households. Nat. Biotechnol. 41, 1099–1106 (2023).
Google ScholarÂ
Zhang, Q. et al. Integrating protein language fashions and computerized biofoundry for enhanced protein evolution. Nat. Commun. 16, 1553 (2025).
Google ScholarÂ
Singh, A. Protein language fashions information directed antibody evolution. Nat. Strategies 20, 785 (2023).
Google ScholarÂ
Dalla-Torre, H. et al. Nucleotide Transformer: constructing and evaluating sturdy basis fashions for human genomics. Nat. Strategies 22, 287–297 (2025).
Google ScholarÂ
Vaswani, A. et al. Consideration is all you want. In Proc. thirty first Worldwide Convention on Neural Info Processing Programs 6000–6010 (Curran Associates, 2017).
Castro, E. et al. Transformer-based protein era with regularized latent house optimization. Nat. Mach. Intell. 4, 840–851 (2022).
Google ScholarÂ
Ferruz, N. & Höcker, B. Controllable protein design with language fashions. Nat. Mach. Intell. 4, 521–532 (2022).
Google ScholarÂ
Nguyen Quang, N., Bouvier, C., Henriques, A., Lelandais, B. & Ducongé, F. Time-lapse imaging of molecular evolution by high-throughput sequencing. Nucleic Acids Res. 46, 7480–7494 (2018).
Google ScholarÂ
Chan, C. Y. et al. A structural interpretation of the impact of GC-content on effectivity of RNA interference. BMC Bioinformatics 10, S33 (2009).
Google ScholarÂ
Mathews, D. H., Sabina, J., Zuker, M. & Turner, D. H. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary construction. J. Mol. Biol. 288, 911–940 (1999).
Google ScholarÂ
Molejon, N. A. et al. Choice of G-rich ssDNA aptamers for the detection of enterotoxins of the cholera toxin household. Anal. Biochem. 669, 115118 (2023).
Google ScholarÂ
Melikishvili, M. et al. SELEX identifies high-affinity RNA targets for chromatin-binding proteins PARP1 and MeCP2. iScience 28, 113299 (2025).
Thiel, W. H. et al. Nucleotide bias noticed with a brief SELEX RNA aptamer library. Nucleic Acid Ther. 21, 253–263 (2011).
Google ScholarÂ
Zhang, J. et al. CRISmers NGS information used and generated by GRAPE-LM (1.0). Zenodo (2025).
Leave a Reply