Polymer Labs

polymerlabs

NEW YORK CITY, New York, November 11th, 2024/Chainwire/--Polymer Labs has officially launched Polymer Hub, a real-time interoperability protocol for connecting all Ethereum rollups. By streaming messages, states, and logs over IBC primitives (equivalent to Web2’s TCP/IP), Polymer Hub verifies and stores the headers of all connected rollups, allowing applications to prove any arbitrary state across rollups at vastly reduced overhead. Rollup/L2 ecosystems have historically connected only within their own walled gardens; with Polymer, however, rollups can now communicate across ecosystems and coordinate as fast as they can produce blocks. The protocol offers significant improvements in cross-chain communication latency, bandwidth, and cost for all on-chain primitives compared to existing solutions. Polymer aims to make cross-chain interoperability as fast, efficient, and affordable as blockspace itself, enabling Ethereum applications to scale to the next million users. Increase Bandwidth Reduce Latency Real-time, high-throughput rollups are right around the corner, but existing interoperability protocols (point-to-point and hub-and-spoke models) were not designed to support dense network traffic across hundreds of rollups. According to the team, existing solutions are too slow and expensive for the next generation of applications on Ethereum. As we enter this new era, interoperability solutions must complement their speed and efficiency. Real-time apps require real-time interoperability. Polymer aims to build the fastest and most efficient interoperability protocol for next-generation rollups like MegaETH. The Hub passes messages in real-time via sequencer pre-confirmations, ensuring cross-chain communication latency can keep up with millisecond block times of these rollups. Polymer also leverages EigenDA to scale cross-rollup bandwidth to facilitate data-intensive use cases on-chain. "Real-timeness, the ability to react to inputs with ultra-low latency at massive scale, will enable truly ground-breaking decentralized applications. Readying the infra stack for this revolution will be a joint effort, in which real-time interoperability from Polymer will be crucial." - Lei Yang (Cofounder & CTO MegaETH) Various technologies like shared sequencers and ecosystem-native interoperability intra-nets have fragmented Ethereum into rollup clusters; these clusters, however, can leverage Polymer Hub’s one-to-all architecture to become interoperable with each other within minutes, rather than months. Polymer Hub is also the first interoperability solution to offer re-org protection. This helps enable token bridges and solver networks to safely settle cross-chain transactions in milliseconds and automatically revert them if they deviate from Ethereum’s L1 history. Looking Ahead The next generation of on-chain applications will closely follow the architecture of cloud apps: rollups are the new microservices, AVSs are the new infrastructure services. To enable horizontal scaling on-chain, cross-chain infrastructure must be low-latency, high-bandwidth, and affordably scalable. The Polymer team seeks to improve interoperability performance in order to enable competitive, novel categories of applications, such as high throughput e-commerce and ride-sharing, to be built on-chain. "Building interoperable applications that don’t trade off cost or latency is a requirement to make crypto usable again. At scale, this connective layer needs to be as robust and secure as the Ethereum base layer itself, and Polymer has been uncompromising in achieving this vision." - Vikram Arun (Cofounder & CEO Superform Labs) Starting with the OP stack, Polymer plans to bring real-time interoperability to all rollup ecosystems on Ethereum—enabling swift and cost-effective scaling for applications in the near future. Developers interested in trying Polymer Hub's mainnet can find more information on Polymer Lab's website, and by following Polymer on X (@Polymer_Labs). About Polymer Labs provides real-time, high-throughput interoperability for Ethereum rollups. Polymer lays foundational network infrastructure enabling the next generation of internet-scale apps like Uber to be built on-chain. Contacts Co-Founder Peter Kim Polymer Labs peter@polymerlabs.org Marketing Lead Harry Lam Polymer Labs hlam@polymerlabs.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here NEW YORK CITY, New York, November 11th, 2024/Chainwire/--Polymer Labs has officially launched Polymer Hub, a real-time interoperability protocol for connecting all Ethereum rollups. By streaming messages, states, and logs over IBC primitives (equivalent to Web2’s TCP/IP), Polymer Hub verifies and stores the headers of all connected rollups, allowing applications to prove any arbitrary state across rollups at vastly reduced overhead. NEW YORK CITY, New York, November 11th, 2024/Chainwire/--Polymer Labs Rollup/L2 ecosystems have historically connected only within their own walled gardens; with Polymer, however, rollups can now communicate across ecosystems and coordinate as fast as they can produce blocks. The protocol offers significant improvements in cross-chain communication latency, bandwidth, and cost for all on-chain primitives compared to existing solutions. Polymer aims to make cross-chain interoperability as fast, efficient, and affordable as blockspace itself, enabling Ethereum applications to scale to the next million users. Increase Bandwidth Reduce Latency Real-time, high-throughput rollups are right around the corner, but existing interoperability protocols (point-to-point and hub-and-spoke models) were not designed to support dense network traffic across hundreds of rollups. According to the team, existing solutions are too slow and expensive for the next generation of applications on Ethereum. As we enter this new era, interoperability solutions must complement their speed and efficiency. Real-time apps require real-time interoperability. Polymer aims to build the fastest and most efficient interoperability protocol for next-generation rollups like MegaETH. The Hub passes messages in real-time via sequencer pre-confirmations, ensuring cross-chain communication latency can keep up with millisecond block times of these rollups. Polymer also leverages EigenDA to scale cross-rollup bandwidth to facilitate data-intensive use cases on-chain. "Real-timeness, the ability to react to inputs with ultra-low latency at massive scale, will enable truly ground-breaking decentralized applications. Readying the infra stack for this revolution will be a joint effort, in which real-time interoperability from Polymer will be crucial." - Lei Yang (Cofounder & CTO MegaETH) "Real-timeness, the ability to react to inputs with ultra-low latency at massive scale, will enable truly ground-breaking decentralized applications. Readying the infra stack for this revolution will be a joint effort, in which real-time interoperability from Polymer will be crucial." - Lei Yang (Cofounder & CTO MegaETH) Various technologies like shared sequencers and ecosystem-native interoperability intra-nets have fragmented Ethereum into rollup clusters; these clusters, however, can leverage Polymer Hub’s one-to-all architecture to become interoperable with each other within minutes, rather than months. Polymer Hub is also the first interoperability solution to offer re-org protection. This helps enable token bridges and solver networks to safely settle cross-chain transactions in milliseconds and automatically revert them if they deviate from Ethereum’s L1 history. Looking Ahead The next generation of on-chain applications will closely follow the architecture of cloud apps: rollups are the new microservices, AVSs are the new infrastructure services. To enable horizontal scaling on-chain, cross-chain infrastructure must be low-latency, high-bandwidth, and affordably scalable. The Polymer team seeks to improve interoperability performance in order to enable competitive, novel categories of applications, such as high throughput e-commerce and ride-sharing, to be built on-chain. "Building interoperable applications that don’t trade off cost or latency is a requirement to make crypto usable again. At scale, this connective layer needs to be as robust and secure as the Ethereum base layer itself, and Polymer has been uncompromising in achieving this vision." - Vikram Arun (Cofounder & CEO Superform Labs) Starting with the OP stack, Polymer plans to bring real-time interoperability to all rollup ecosystems on Ethereum—enabling swift and cost-effective scaling for applications in the near future. Developers interested in trying Polymer Hub's mainnet can find more information on Polymer Lab's website , and by following Polymer on X (@Polymer_Labs). Polymer Lab's website Polymer Lab's website X (@Polymer_Labs). X (@Polymer_Labs). About Polymer Labs provides real-time, high-throughput interoperability for Ethereum rollups. Polymer lays foundational network infrastructure enabling the next generation of internet-scale apps like Uber to be built on-chain. Polymer Labs Polymer Labs Contacts Co-Founder Peter Kim Polymer Labs peter@polymerlabs.org Marketing Lead Harry Lam Polymer Labs hlam@polymerlabs.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Polymer Launches Real-Time Interoperability For Ethereum Rollups

Polymer lanza interoperabilidad en tiempo real para los rollups de Ethereum

Polymer lanseeraa reaaliaikaisen yhteentoimivuuden Ethereum Rolluppeille

Polymer lance l'interopérabilité en temps réel pour les rollups Ethereum

Polymer lanza a interoperabilidade en tempo real para Ethereum Rollups

פולימר משיק יכולת פעולה הדדית בזמן אמת עבור אוסף Ethereum

Polymer pokreće interoperabilnost u stvarnom vremenu za Ethereum Rollups

ポリマー、イーサリアム ロールアップのリアルタイム相互運用性を開始

Polymère Ebandi Interopérabilité Na Tango Réel Pona Ba Rollups Ya Ethereum

پولیمر د ایتیریم رول اپونو لپاره د ریښتیني وخت مداخله پیل کوي

Polymer Inotangisa Chaiyo-Nguva Kudyidzana KweEthereum Rollups

Polymer เปิดตัวการทำงานร่วมกันแบบเรียลไทม์สำหรับ Ethereum Rollups

Polimer “Ethereum Rollups” üçin hakyky wagtda işleşip başlaýar

Analyzing the Impact of Model Scaling on Long-Form Factuality

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 7 Conclusion We present a method that efficiently scales thermodynamic stability prediction from single mutations to higher-order mutations. Our key insight is that the effects of mutations on the same protein are correlated. Thus, for a target protein, it suffices to run a deep backbone once and decode the effect of all mutations simultaneously using a shallow decoder. With the AlphaFold model as our backbone, our method outperforms existing methods on a variety of single and multiple mutation benchmarks. Our method scales to millions of mutations with minimal computational overhead and runs in a fraction of the time it would take prior works. 8 Acknowledgements This work is supported by the NSF AI Institute for Foundations of Machine Learning (IFML). References [1] Randomization of genes by pcr mutagenesis. Genome research 2(1), 28–33 (1992) 2 [2] Adams, J.P., Brown, M.J., Diaz-Rodriguez, A., Lloyd, R.C., Roiban, G.D.: Biocatalysis: A Pharma Perspective. Advanced Synthesis and Catalysis 361(11), 2421–2432 (2019). https://doi.org/10.1002/adsc.201900424 1 [3] Ahdritz, G., Bouatta, N., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T.J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., Nowaczynski, A., Wang, B., Stepniewska-Dziubinska, M.M., Zhang, S., Ojewole, A., Guney, M.E., Biderman, S., Watkins, A.M., Ra, S., Lorenzo, P.R., Nivon, L., Weitzner, B., Ban, Y.E.A., Sorger, P.K., Mostaque, E., Zhang, Z., Bonneau, R., AlQuraishi, M.: Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization. bioRxiv (2022). https://doi.org/10.1101/2022.11.20.517210 6 [4] Arnold, F.H.: Design by directed evolution. Accounts of chemical research 31(3), 125–131 (1998) 1, 2, 3 [5] Bell, E.L., Finnigan, W., France, S.P., Green, A.P., Hayes, M.A., Hepworth, L.J., Lovelock, S.L., Niikura, H., Osuna, S., Romero, E., Ryan, K.S., Turner, N.J., Flitsch, S.L.: Biocatalysis. Nature Reviews Methods Primers 1(1), 1–21 (2021). https://doi.org/10.1038/s43586-021-00044-z 1 [6] Benevenuta, S., Pancotti, C., Fariselli, P., Birolo, G., Sanavia, T.: An antisymmetric neural network to predict free energy changes in protein variants. Journal of Physics D: Applied Physics 54(24), 245403 [7] Benevenuta, S., Pancotti, C., Fariselli, P., Birolo, G., Sanavia, T.: An antisymmetric neural network to predict free energy changes in protein variants. Journal of Physics D: Applied Physics 54(24), 245403 (2021) 8 [8] Benevenuta, S., Birolo, G., Sanavia, T., Capriotti, E., Fariselli, P.: Challenges in predicting stabilizing variations: An exploration. Frontiers in Molecular Biosciences 9, 1075570 (2023) 5, 6, 17 [9] Brückner, A., Polge, C., Lentze, N., Auerbach, D., Schlattner, U.: Yeast two-hybrid, a powerful tool for systems biology. International journal of molecular sciences 10(6), 2763–2788 (2009) 1 [10] Cadet, F., Saavedra, E., Syren, P.O., Gontero, B.: Machine learning, epistasis, and protein engineering: From sequence-structure-function relationships to regulation of metabolic pathways. Frontiers in Molecular Biosciences 9, 1098289 (2022) 1 [11] Capriotti, E., Fariselli, P., Casadio, R.: I-mutant2. 0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic acids research 33(suppl_2), W306–W310 (2005) 3, 8 [12] Chen, K., Arnold, F.H.: Tuning the activity of an enzyme for unusual environments: sequential random mutagenesis of subtilisin e for catalysis in dimethylformamide. Proceedings of the National Academy of Sciences 90(12), 5618–5622 (1993) 1, 3 [13] Chen, T., Gong, C., Diaz, D.J., Chen, X., Wells, J.T., Wang, Z., Ellington, A., Dimakis, A., Klivans, A., et al.: Hotprotein: A novel framework for protein thermostability prediction and editing. In: The Eleventh International Conference on Learning Representations (2022) 3 [14] Chen, Y., Lu, H., Zhang, N., Zhu, Z., Wang, S., Li, M.: Premps: Predicting the impact of missense mutations on protein stability. PLoS computational biology 16(12), e1008543 (2020) 3, 8 [15] Cheng, J., Randall, A., Baldi, P.: Prediction of protein stability changes for single-site mutations using support vector machines. Proteins: Structure, Function, and Bioinformatics 62(4), 1125–1132 (2006) 3, 8 [16] Dehghanpoor, R., Ricks, E., Hursh, K., Gunderson, S., Farhoodi, R., Haspel, N., Hutchinson, B., Jagodzinski, F.: Predicting the effect of single and multiple mutations on protein structural stability. Molecules 23(2), 251 (2018) 4, 7 [17] Dehouck, Y., Kwasigroch, J.M., Gilis, D., Rooman, M.: Popmusic 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC bioinformatics 12(1), 1–12 (2011) 3, 8 [18] Diaz, D.J., Gong, C., Ouyang-Zhang, J., Loy, J.M., Wells, J.T., Yang, D., Ellington, A.J., Dimakis, A., Klivans, A.R.: Stability oracle: A structure-based graph-transformer for identifying stabilizing mutations (2023). https://doi.org/10.1101/2023.05.15.540857 3, 4, 7, 8, 16, 17, 18, 19 [19] Diaz, D.J., Kulikova, A.V., Ellington, A.D., Wilke, C.O.: Using machine learning to predict the effects and consequences of mutations in proteins. Current Opinion in Structural Biology 78, 102518 (2023) 3 [20] d’Oelsnitz, S., Diaz, D.J., Acosta, D.J., Schechter, M.W., Minus, M.B., Howard, J.R., Do, H., Loy, J., Alper, H., Ellington, A.D.: Synthetic microbial sensing and biosynthesis of amaryllidaceae alkaloids. bioRxiv pp. 2023–04 (2023) 3 [21] d’Oelsnitz, S., Kim, W., Burkholder, N.T., Javanmardi, K., Thyer, R., Zhang, Y., Alper, H.S., Ellington, A.D.: Using fungible biosensors to evolve improved alkaloid biosyntheses. Nature Chemical Biology 18(9), 981–989 (2022) 1 [22] Ellefson, J.W., Gollihar, J., Shroff, R., Shivram, H., Iyer, V.R., Ellington, A.D.: Synthetic evolutionary origin of a proofreading reverse transcriptase. Science 352(6293), 1590–1593 (2016) 1 [23] Feng, X., Sanchis, J., Reetz, M.T., Rabitz, H.: Enhancing the efficiency of directed evolution in focused enzyme libraries by the adaptive substituent reordering algorithm. Chemistry–A European Journal 18(18), 5646–5654 (2012) 3 [24] Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J.K., Brock, K., Gal, Y., Marks, D.S.: Disease variant prediction with deep generative models of evolutionary data. Nature 599(7883), 91–95 (2021) 9 [25] Gebauer, M., Skerra, A.: Engineered protein scaffolds as next-generation therapeutics. Annual Review of Pharmacology and Toxicology 60, 391–415 (2020). https://doi.org/10.1146/annurev-pharmtox-010818- 021118 1 [26] Gerasimavicius, L., Livesey, B.J., Marsh, J.A.: Correspondence between functional scores from deep mutational scans and predicted effects on protein stability. bioRxiv pp. 2023–02 (2023) 9 [27] Giver, L., Gershenson, A., Freskgard, P.O., Arnold, F.H.: Directed evolution of a thermostable esterase. Proceedings of the National Academy of Sciences 95(22), 12809–12813 (1998) 2, 3 [28] Hie, B.L., Shanker, V.R., Xu, D., Bruun, T.U., Weidenbacher, P.A., Tang, S., Wu, W., Pak, J.E., Kim, P.S.: Efficient evolution of human antibodies from general protein language models. Nature Biotechnology (2023) 3 [29] Jaroszewicz, W., Morcinek-Orłowska, J., Pierzynowska, K., Gaffke, L., W˛egrzyn, G.: Phage display and other peptide display technologies. FEMS Microbiology Reviews 46(2), fuab052 (2022) 1 [30] Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (TOIS) 20(4), 422–446 (2002) 6, 17 [31] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al.: Highly accurate protein structure prediction with alphafold. Nature 596(7873), 583–589 (2021) 2, 3, 4, 6, 9, 10 [32] Kellogg, E.H., Leaver-Fay, A., Baker, D.: Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins: Structure, Function, and Bioinformatics 79(3), 830–838 (2011) 3 [33] Klabunde, T., Petrassi, H.M., Oza, V.B., Raman, P., Kelly, J.W., Sacchettini, J.C.: Rational design of potent human transthyretin amyloid disease inhibitors. Nature structural biology 7(4), 312–321 (2000) 1, 2 [34] Kouba, P., Kohout, P., Haddadi, F., Bushuiev, A., Samusevich, R., Sedlar, J., Damborsky, J., Pluskal, T., Sivic, J., Mazurenko, S.: Machine learning-guided protein engineering. ACS Catalysis 13, 13863–13895 (2023) 3 [35] Laimer, J., Hofer, H., Fritz, M., Wegenkittl, S., Lackner, P.: Maestro-multi agent stability prediction upon point mutations. BMC bioinformatics 16(1), 1–13 (2015) 3, 4, 7, 8 [36] Li, B., Yang, Y.T., Capra, J.A., Gerstein, M.B.: Predicting changes in protein thermodynamic stability upon point mutation with deep 3d convolutional neural networks. PLoS computational biology 16(11), e1008291 (2020) 3, 4, 8 [37] Li, G., Panday, S.K., Alexov, E.: Saafec-seq: a sequence-based method for predicting the effect of single point mutations on protein thermodynamic stability. International journal of molecular sciences 22(2), 606 (2021) 3 [38] Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., et al.: Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv (2022) 3, 4, 6, 9, 10, 18 [39] Loell, K., Nanda, V.: Marginal protein stability drives subcellular proteome isoelectric point. Proceedings of the National Academy of Sciences 115(46), 11778–11783 (2018) 1 [40] Lu, H., Diaz, D.J., Czarnecki, N.J., Zhu, C., Kim, W., Shroff, R., Acosta, D.J., Alexander, B.R., Cole, H.O., Zhang, Y., et al.: Machine learning-aided engineering of hydrolases for pet depolymerization. Nature 604(7907), 662–667 (2022) 3 [41] Meghwanshi, G.K., Kaur, N., Verma, S., Dabi, N.K., Vashishtha, A., Charan, P.D., Purohit, P., Bhandari, H.S., Bhojak, N., Kumar, R.: Enzymes for pharmaceutical and therapeutic applications. Biotechnology and Applied Biochemistry 67(4), 586–601 (2020). https://doi.org/10.1002/bab.1919 1 [42] Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., Rives, A.: Language models enable zero-shot prediction of the effects of mutations on protein function. Advances in Neural Information Processing Systems 34, 29287–29303 (2021) 9, 18 [43] Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., Steinegger, M.: Colabfold: making protein folding accessible to all. Nature methods 19(6), 679–682 (2022) 6 [44] Montanucci, L., Capriotti, E., Frank, Y., Ben-Tal, N., Fariselli, P.: Ddgun: an untrained method for the prediction of protein stability changes upon single and multiple point variations. BMC bioinformatics 20, 1–10 (2019) 3, 4, 7, 8, 16, 18 [45] Montanucci, L., Savojardo, C., Martelli, P.L., Casadio, R., Fariselli, P.: On the biases in predictions of protein stability changes upon variations: the inps test case. Bioinformatics 35(14), 2525–2527 (2019) 16 [46] Musdal, Y., Govindarajan, S., Mannervik, B.: Exploring sequence-function space of a poplar glutathione transferase using designed information-rich gene variants. Protein Engineering, Design and Selection 30(8), 543–549 (2017) 3 [47] Nijkamp, E., Ruffolo, J., Weinstein, E.N., Naik, N., Madani, A.: Progen2: exploring the boundaries of protein language models. arXiv preprint arXiv:2206.13517 (2022) 9 [48] Nikam, R., Kulandaisamy, A., Harini, K., Sharma, D., Gromiha, M.M.: Prothermdb: thermodynamic database for proteins and mutants revisited after 15 years. Nucleic acids research 49(D1), D420–D424 (2021) 2, 16 [49] Notin, P., Dias, M., Frazer, J., Hurtado, J.M., Gomez, A.N., Marks, D., Gal, Y.: Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. In: International Conference on Machine Learning. pp. 16990–17017. PMLR (2022) 2, 3, 9, 16, 18 [50] Notin, P.M., Van Niekerk, L., Kollasch, A.W., Ritter, D., Gal, Y., Marks, D.: Trancepteve: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction. bioRxiv pp. 2022–12 (2022) 9 [51] Nutschel, C., Fulton, A., Zimmermann, O., Schwaneberg, U., Jaeger, K.E., Gohlke, H.: Systematically scrutinizing the impact of substitution sites on thermostability and detergent tolerance for bacillus subtilis lipase a. Journal of chemical information and modeling 60(3), 1568–1584 (2020) 9 [52] Paik, I., Ngo, P.H., Shroff, R., Diaz, D.J., Maranhao, A.C., Walker, D.J., Bhadra, S., Ellington, A.D.: Improved bst dna polymerase variants derived via a machine learning approach. Biochemistry (2021) 3 [53] Pak, M.A., Markhieva, K.A., Novikova, M.S., Petrov, D.S., Vorobyev, I.S., Maksimova, E.S., Kondrashov, F.A., Ivankov, D.N.: Using alphafold to predict the impact of single mutations on protein stability and function. Plos one 18(3), e0282689 (2023) 3 [54] Pancotti, C., Benevenuta, S., Birolo, G., Alberini, V., Repetto, V., Sanavia, T., Capriotti, E., Fariselli, P.: Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset. Briefings in Bioinformatics 23(2), bbab555 (2022) 2, 8, 16, 17 [55] Pancotti, C., Benevenuta, S., Repetto, V., Birolo, G., Capriotti, E., Sanavia, T., Fariselli, P.: A deep-learning sequence-based method to predict protein stability changes upon genetic variations. Genes 12(6), 911 (2021) 8 [56] Pires, D.E., Ascher, D.B., Blundell, T.L.: Duet: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic acids research 42(W1), W314–W319 (2014) 3, 8 [57] Pires, D.E., Ascher, D.B., Blundell, T.L.: mcsm: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30(3), 335–342 (2014) 8 [58] Pucci, F., Schwersensky, M., Rooman, M.: Artificial intelligence challenges for predicting the impact of mutations on protein stability. Current opinion in structural biology 72, 161–168 (2022) 17 [59] Qiu, Y., Wei, G.W.: Persistent spectral theory-guided protein engineering. Nature Computational Science pp. 1–15 (2023) 3, 6, 17 [60] Rao, R.M., Liu, J., Verkuil, R., Meier, J., Canny, J., Abbeel, P., Sercu, T., Rives, A.: Msa transformer. In: International Conference on Machine Learning. pp. 8844–8856. PMLR (2021) 3, 9, 10 [61] Riesselman, A.J., Ingraham, J.B., Marks, D.S.: Deep generative models of genetic variation capture the effects of mutations. Nature methods 15(10), 816–822 (2018) 9 [62] Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., Fergus, R.: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. PNAS (2019). https://doi.org/10.1101/622803 3, 7 [63] Rodrigues, C.H., Pires, D.E., Ascher, D.B.: Dynamut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic acids research 46(W1), W350–W355 (2018) 3, 8 [64] Rodrigues, C.H., Pires, D.E., Ascher, D.B.: Dynamut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations. Protein Science 30(1), 60–69 (2021) 3, 4, 7 [65] Sanavia, T., Birolo, G., Montanucci, L., Turina, P., Capriotti, E., Fariselli, P.: Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine. Computational and Structural Biotechnology Journal 18, 1968–1979 (2020). https://doi.org/10.1016/j.csbj.2020.07.011, https://doi.org/10.1016/j.csbj.2020.07.011 6, 17 [66] Savojardo, C., Fariselli, P., Martelli, P.L., Casadio, R.: Inps-md: a web server to predict stability of protein variants from sequence and structure. Bioinformatics 32(16), 2542–2544 (2016) 3, 8 [67] Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., Serrano, L.: The foldx web server: an online force field. Nucleic acids research 33(suppl_2), W382–W388 (2005) 3, 7, 8, 18 [68] Shroff, R., Cole, A.W., Diaz, D.J., Morrow, B.R., Donnell, I., Annapareddy, A., Gollihar, J., Ellington, A.D., Thyer, R.: Discovery of novel gain-of-function mutations guided by structure-based deep learning. ACS synthetic biology 9(11), 2927–2935 (2020) 3 [69] Starr, T.N., Thornton, J.W.: Epistasis in protein evolution. Protein science 25(7), 1204–1218 (2016) 1 [70] Steinegger, M., Soding, J.: Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature biotechnology 35(11), 1026–1028 (2017) 6 [71] Stemmer, W.P.: Rapid evolution of a protein in vitro by dna shuffling. Nature 370(6488), 389–391 (1994) 2, 3 [72] Tsuboyama, K., Dauparas, J., Chen, J., Laine, E., Mohseni Behbahani, Y., Weinstein, J.J., Mangan, N.M., Ovchinnikov, S., Rocklin, G.J.: Mega-scale experimental analysis of protein folding stability in biology and protein design. bioRxiv pp. 2022–12 (2022) 1, 2, 6, 16 [73] Tung, J.W., Heydari, K., Tirouvanziam, R., Sahaf, B., Parks, D.R., Herzenberg, L.A., Herzenberg, L.A.: Modern flow cytometry: a practical approach. Clinics in laboratory medicine 27(3), 453–468 (2007) 1 [74] Umerenkov, D., Shashkova, T.I., Strashnov, P.V., Nikolaev, F., Sindeeva, M., Ivanisenko, N.V., Kardymon, O.L.: Prostata: Protein stability assessment using transformers. bioRxiv pp. 2022–12 (2022) 3, 4, 6, 7, 8, 18, 19 [75] Wittmann, B.J., Johnston, K.E., Wu, Z., Arnold, F.H.: Advances in machine learning for directed evolution. Current opinion in structural biology 69, 11–18 (2021) 3 [76] Wittmann, B.J., Yue, Y., Arnold, F.H.: Informed training set design enables efficient machine learningassisted directed protein evolution. Cell Systems 12(11), 1026–1045 (2021) 3 [77] Worth, C.L., Preissner, R., Blundell, T.L.: Sdm—a server for predicting effects of mutations on protein stability and malfunction. Nucleic acids research 39(suppl_2), W215–W222 (2011) 3, 8 [78] Wu, S., Snajdrova, R., Moore, J.C., Baldenius, K., Bornscheuer, U.T.: Biocatalysis: Enzymatic Synthesis for Industrial Applications. Angewandte Chemie - International Edition 60(1), 88–119 (2021). https://doi.org/10.1002/anie.202006648 1 [79] Yeung, N., Lin, Y.W., Gao, Y.G., Zhao, X., Russell, B.S., Lei, L., Miner, K.D., Robinson, H., Lu, Y.: Rational design of a structural and functional nitric oxide reductase. Nature 462(7276), 1079–1082 (2009) 1, 2 [80] Zhou, B., Lv, O., Yi, K., Xiong, X., Tan, P., Hong, L., Wang, Y.G.: Accurate and definite mutational effect prediction with lightweight equivariant graph neural networks. arXiv preprint arXiv:2304.08299 (2023) 3 [81] Zhou, Y., Pan, Q., Pires, D.E., Rodrigues, C.H., Ascher, D.B.: Ddmut: predicting effects of mutations on protein stability using deep learning. Nucleic Acids Research p. gkad472 (2023) 3

Predicting a Protein’s Stability
under a Million Mutations: Conclusion, Acknowledgement & References

Intelligent DevOps: 8 Lessons Learned from Nature

ইন্টেলিজেন্ট ডিওঅপস: প্রকৃতি থেকে শেখা ৮টি পাঠ

Intelligentes DevOps: 8 Lektionen aus der Natur

DevOps inteligente: 8 lecciones aprendidas de la naturaleza

DevOps intelligent : 8 leçons tirées de la nature

इंटेलिजेंट DevOps: प्रकृति से सीखे गए 8 सबक

インテリジェントな DevOps: 自然から学んだ 8 つの教訓

지능형 DevOps: 자연에서 배운 8가지 교훈

DevOps Inteligente: 8 Lições Aprendidas com a Natureza

Интеллектуальный DevOps: 8 уроков, извлеченных из природы

Akıllı DevOps: Doğadan Öğrenilen 8 Ders

DevOps thông minh: 8 bài học rút ra từ thiên nhiên

智能 DevOps：从大自然中汲取的 8 个教训

The major recent breakthroughs in diagnostics and treatment. Even 50 or 70 years ago, personalized medicine existed solely as the talking point of science fiction writers and scientific visionaries. However, as genetic technologies emerged, the individual approach to diagnostics and therapy of many hereditary and incurable diseases became a routine or at least a common solution. These diseases include various cancer types, autoimmune disorders, and numerous orphan (i.e., rare) pathologies. Although such developments are infamous for their high (sometimes exorbitant) costs, they can no longer be considered unavailable. Genomics and molecular genetics have laid the groundwork for a complete overhaul of the healthcare system, following the P4 principle[1] where both personalization and predictability play pivotal roles. It is becoming possible to detect genetic predisposition to a disease before conception, prevent an illness during pregnancy, learn about individual genetic specifics, and define a personal healthy lifestyle. This market is on the upturn: Mordor Intelligence marketing agency forecasts that these services will develop most intensively in Asia-Pacific countries. Europe and North America also demonstrate a slightly lower yet constantly growing demand, while Russia, Africa, and South America are falling behind .[2] These changes are expected to revolutionize medicine and shift the focus from disease treatment to health preservation. For patients with orphan and cancer diseases, gene therapy already gives a chance to get a cure. Given the overwhelming amount of global research, this therapy will soon become the ultimate method of treating other non-infectious diseases that cause early death or disability and have a detrimental effect on the quality of life. In this article, I will provide an overview of major genetic technologies in medicine that proved their effectiveness and became part of clinical practice, as well as of new developments at the final stages of their trials. Genetic testing: finding the “weakest link” of a malignant tumor The late 1990s were the milestone of cancer diagnostics, as genetic information on mutations became the starting point for specifying a targeted therapy. 3] [ Every tumor has its specific mutations, its gene "breakdowns" that the targeted effect of medication seeks to cope with. For example, the information on BRCA1/2 mutation helped establish a targeted breast cancer treatment and prevent this disease among the patient's relatives. Eventually, this experience has converted into the list of most common disorders, both hereditary and acquired, with molecular gene testing becoming a must of all medical guidelines and changing the course of pharmaceutical development. There is a gradual shift from developing medications against specific nosologies to adopting the target concept. For instance, the translocation of the NTRK gene can be found in different tumors but can be effectively treated by the same drug. Although this mutation is rare (found only in 1% of all tumors), its therapy can provide a positive response in 80% of all cases. 4] [ At first, "damages" in tumor tissues and the blood were diagnosed using low-effective methods that enabled the study of small DNA sequences, such as polymerase chain reaction (PCR) and Sanger sequencing, known since the 1970-the 1980s, and fluorescence in situ hybridization (FISH) of the 1990s. These tools were of great assistance, helping patients with solid tumors to avoid a needless or mismatching therapy and get a significant clinical effect; besides, oncohematology patients got an alternative to expensive procedures like marrow transplants. Nowadays, attempts are made to apply gene testing for specifying the diagnosis and even predicting the treatment efficiency. Another breakthrough happened in the 2010s: the Next Generation Sequencing (NGS) technology made it available to check the whole genome for damages. Here is the classic example of this method's capacities: it took over a decade and 3 billion dollars from the US budget to complete the Human Genome Project that described 3 billion DNA nucleotides using the standard tools. Meanwhile, the NGS sequenator can reveal the whole genome sequence in less than a year, at the cost of several thousand dollars. 5] [ Higher speed (several days compared to weeks and months) and fewer expenses are the self-evident advantages offered by the NGS. What is also important, this is almost the only way to collect more data from a smaller amount of material, e.g., from the blood plasma. Thanks to the NGS, liquid biopsy could provide information on potential mutations of up to 400 genes and has been approved for use against non-small-cell lung arcinoma and metastatic castration-resistant prostate cancer. Besides, the NGS makes it possible to perform population screenings and reveal various hereditary mutations, including breast cancer. Scientists believe that in high— or medium-income countries, it is easier and less expensive to test all women above 30, even those without family anamnesis, and then perform additional radiology diagnostics for the mutation carriers .[6] There is only one problem with the NGS application in oncology — it provides too much information, and we still do not fully understand how to use it, especially if a person is healthy. However, implementation of genome data in the clinical process is not as far away as it may seem — many countries, including Russia, are already considering collecting and storing large volumes of genetic data, creating specialized scientific centers, and adopting national strategies for developing genetic technologies .[7] Non-invasive prenatal testing (NIPT) Attempts to detect the risk of various genetic disorders (Down syndrome being the most commonly known) at the gestation stage were made even before the NGS, but this method made this procedure non-invasive — and thus much safer. Mutations can be found since the 10th week of pregnancy by analyzing the fetus' DNA circulating in the mother's blood — to do this, only 0.01 ml of the mother's plasma is needed. 8] [ The person who revolutionized the embryonic-stage diagnostics of Down syndrome was Dennis Lo, a professor at the Chinese University of Hong Kong. At the turn of the millennium, he discovered the way to distinguish the fetal DNA in the pregnant women’s blood, giving an impetus to the rapid development of NITP methods that are harmless both to the mother and the future child. The main prerequisites for NIPT involve chromosome anomalies in the mother's or father's family history. Although this possibility is very low, the testing remains widespread, even though this procedure is not covered by general medical insurance in Russia and many other countries (except for some regional programs). There is another genetic test that enables risk assessment long before pregnancy. It focuses on gene mutations that cause disorders like mucoviscidosis, spinal muscular atrophy (SMA), and others — around 24 hereditary diseases in total. An Australian study shows that couples undergoing infertility treatment often turn out to be carriers of CFTR mutations (mucoviscidosis). 9] In some countries, this screening is offered to all couples applying for IVF, despite the ethical ambiguity of this initiative. 10] [ [ While the medical community keeps discussing the potential social and cultural damage of mass preconception screenings, the IT industry is looking for ways to optimize child diagnostics. It may take days to complete an NGS analysis, so in cases when any delay poses a risk to life and a decision needs to be made within 24 hours, the scientists suggest using artificial intelligence. AI can "notice" a genome decrypted by the NGS faster, even if no description is available. A recent comparative study demonstrates that AI can find a mutated gene in 92% of cases, while other methods fail to pass the 60%-threshold. 11] [ Gene therapy for orphan diseases The international scientific community began to take gene therapy seriously in the 1970s when Theodore Friedmann and Richard Roblin published their paper in Science. Back then, there were no tools for specifying the mutations, so Friedmann and Roblin emphasized that gene therapy required a careful approach. 12] However, the outcome was the opposite: the research continued, and the first clinical trials of gene therapy methods were launched for severe and rare diseases. [ When one patient died during the trials, those initiatives were put to stop (the US even suspended their specialized federal program) until 2003, when China relaunched the process and licensed gene therapy for head and neck squamous cell carcinoma (HNSCC). 13] Although this genetic product aimed to affect “broken” genes saw no further development, pharmaceutical companies from all over the world realized their ideas could finally be greenlit. [ As a result, gene therapy drugs began to be applied in various branches of medicine. In 2018, for example, the first virus vector-based drug was approved for use in ophthalmology. Luxturna targets RPE65 mutations that cause blindness. Oncohematology has a pool of developments built around CAR-T technology, the first of them being Kymriah, approved in the US in 2017. This method seeks to modify the patient's T cells genetically and codify the antigen receptor to target tumor cells. Four medications of this type are now used; Russia also has the practice of introducing its developments. 14] Zolgensma, approved in the US in 2019, deserves a special mention, as only one injection of this medicine can cure SMA. It is one of the most expensive drugs on the market, with one vial costing approximately 2.1 million USD. [ The FDA predicts that by 2025, they will be approving 10 to 20 cell and gene therapy products a year. 15] These expectations seem realistic — first of all, many clinical trials are undergoing their later stages right now. One of the examples is von Gierke disease (glycogen storage disease type I) that for years has been treated only by regular consumption of corn starch. [ Via a single injection of a virus, a new copy of a gene is delivered to the patient’s liver and “reboots” the body's glucose control. The first positive clinical trial results were published in 2019. 16] The drug has already received orphan medication status in the US and the UK, with its Stage III trial planned to be completed this December. 17] [ [ Meanwhile, we are witnessing the CRISPR gene editing technology in its prime. As I wrote earlier, its founders were rightfully awarded the Nobel Prize in 2020. The method involves "cutting" the DNA using Cas9 nuclease and putting together the ends of this "cut" in a way that makes a defect impossible. The most successful case of integrating CRISPR into clinical practice was demonstrated by Sarah Cannon Research Institute specialists — in summer 2019, they applied this therapy for the first time to a patient with sickle cell anemia. This disease (a mutation in the ) causes red blood cells to deform and lose their elasticity and flexibility, as well as leads to dangerous drops in the hemoglobin level. 18] After a year of therapy, the medics reported qualitative improvements in the patient's life: her hemoglobin level went up, and she needed inpatient assistance less often. In March 2021, similar trials began in several US research centers. 19] β-globin gene [ [ COVID-19 etiology Countermeasures against the COVID-19 pandemic — testing systems, vaccines, and medication — require the assistance of genetics. In early 2021, the WHO emphasized the importance of collecting genome data on SARS-CoV-2 and sequencing the virus. 20] To facilitate this process, scientists propose using AI, the way it is applied for disease diagnostics. 21] [ [ At the same time, research has been conducted to find mutations that make their carriers vulnerable to the coronavirus. The COVID-19 Host Genetics Initiative consortium found 40 "candidate genes" that affect susceptibility to this virus. The scientists analyzed the genomes of about 50,000 people affected by the disease with various degrees of severity. They concluded that the TYK2 gene and its variations increase susceptibility to viruses, bacteria, and fungi. Another potentially dangerous "breakdown" was found in the DPP9 gene that also increases the risk of lung fibrosis. 22] [ The French scientists have found a set of genes among patients hospitalized in critical condition, with ADAM9 being particularly expressed. All these patients did not belong to the general risk group: they had no chronic diseases and were younger than 50. The study shows that this gene not only determines a more severe progression of COVID-19 but can also be potentially affected by targeted therapy. 23] [ As of now, this "COVID" research is more probing by nature than applicable here and now. Advanced technologies enable us to study the coronavirus more assiduously than any other virus in human history; in the next decade, they can give us many opportunities to defeat dozens of infectious diseases. How can we do it? We can study how a virus evolves online — so we will know what to be prepared for. Genetic technologies today and tomorrow How soon will all these achievements become part of our everyday life? The answer to this question depends on their availability and cost. The coronavirus pandemic, with new strains being detected much faster in countries applying advanced genome sequencing technologies, has emphasized the importance of the economic factor. Furthering the IT progress is the way to make genetic technologies less expensive and more widespread: availability of big data to the global medical and scientific communities helps save time and money by avoiding repetitive studies or therapeutic developments. While the importance of big data has become clear to almost every scientist, data transparency will become the key issue for the next few decades. About the Author Rustam Gilfanov is an IT entrepreneur and a venture partner of the LongeVC fund. In 2006, Rustam Gilfanov, together with his partners, opened an international IT outsourcing company in Kyiv. Today, this company is the largest developer of software for the gaming, marketing and finance industries. [1] P4 — predictive, preventive, personalized, and participatory medicine [2] https://www.mordorintelligence.com/industry-reports/global-genetic-testing-market-industry [3] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6243123/ [4] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7815373/ [5] von Bubnoff A. Next-generation sequencing: the race is on. Cell. 2008;132(5):721–723^ doi: 10.1016/j.cell.2008.02.028. [6] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7409094/ [7] https://www.mja.com.au/journal/2014/201/1/impact-genomics-future-medicine-and-health#5 [8] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3893900/ [9] https://pubmed.ncbi.nlm.nih.gov/21875427/ [10] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880037/ [11] https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-021-00965-0 [12] https://www.science.org/doi/10.1126/science.175.4025.949 [13] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7097065/ [14] https://vademec.ru/article/car-t-_dengi_i_stvoly-_kto_pervym_legalizuet_v_rossii_perspektivnyy_metod_lecheniya_raka_krovi/ [15] https://www.fda.gov/news-events/press-announcements/statement-fda-commissioner-scott-gottlieb-md-and-peter-marks-md-phd-director-center-biologics [16] https://www.sciencedaily.com/releases/2019/09/190920124645.htm [17] https://clinicaltrials.gov/ct2/show/NCT04708015?term=DTX401&draw=2&rank=3 [18] http://stm.sciencemag.org/content/8/360/360ra134 [19] https://innovativegenomics.org/news/crispr-clinical-trials-2021/ [20] https://apps.who.int/iris/bitstream/handle/10665/338480/9789240018440-eng.pdf?sequence=1&isAllowed=y [21] https://link.springer.com/article/10.1007/s12539-021-00465-0 [22] https://www.nature.com/articles/d41586-021-01773-7 [23] https://www.science.org/doi/10.1126/scitranslmed.abj7521

How Genetic Technologies Transform Medicine

DNA Technology: An Overview DNA Technology, otherwise known as Recombinant DNA technology, involves joining DNA molecules from different species of organisms that are inserted into a host organism to produce new genetic combinations. These genetic combinations are important in the fields of healthcare, agriculture, and science. In simple terms, recombinant that have applications in healthcare, agriculture, and science. DNA technology uses genes to create new genetic combinations Drugs like insulin, somatropin, etc., owe their origin to the existence of DNA technology. These drugs have been used to manage conditions like diabetes mellitus and Turner's syndrome, respectively. DNA Technology: Origin Humans have not always been able to combine genes from plant and animal species to form products. In the late 1960s, Werner, Arber, and Hamilton Smith discovered what are known as restriction . Restriction enzymes are proteins that bind to specific regions of double-stranded DNA, making cuts in the DNA molecule. The process of making cuts is known as cleaving. enzymes In 1970, Howard Temin and David Baltimore independently discovered the enzyme reverse transcriptase from retroviruses. Reverse transcriptase was then used to develop complementary DNA from messenger . RNA In 1975, Southern blotting was discovered by Edwin Southern. Southern blotting is a laboratory technique that helps identify a specific DNA sequence in the midst of many samples. In the 1980s, there was a lot of development in DNA technology. The foot and mouth viral antigen was cloned, there was the cloning and development of the human cancer gene, insulin was commercially produced, and the gene gun was . developed In 1997, the world's first mammalian clone named Dolly was developed from a non-reproductive cell of an animal through cloning by nuclear transplantation. Today, scientists can switch selected genes on and off with the aid of the CRISPR-Cas9 reversible gene-editing . system Types of DNA Technology : DNA sequencing involves determining the order of nucleotides in deoxyribonucleic acid(DNA). The correct sequence is Adenine, Guanine, Cytosine, and Thymine. DNA sequences can reveal changes that lead to the development of disease. This is why DNA sequencing is used in diagnosis, treatment, and epidemiology . DNA sequencing studies : This involves isolating and producing multiple copies of a particular gene. DNA cloning is used to create recombinant versions of a gene for different purposes: identifying a normal gene and separating it from an abnormal one, identifying genetic mutations, and for gene studies. DNA cloning : Polymerase Chain Reaction is a fast method of creating multiple copies of small gene segments. It is used for forensic purposes, the determination of genetic disorders, and the diagnosis of viral conditions like Human Virus(HIV). Polymerase Chain Reaction Immunodeficiency : This is a laboratory method used to separate DNA, RNA, and protein molecules based on molecular size and electric charge. It is used in the separation of DNA fragments for DNA fingerprinting, the analysis of polymerase chain reaction results, and in gene studies. Gel Electrophoresis DNA Technology and Medicine: How Technology Improves Healthcare Recombinant DNA technology has changed the practice of medicine; the prevention, diagnosis, and treatment of disease conditions is a much easier process. Insulin is one of the miracles recombinant DNA technology has given to medicine: the human insulin injection formulation is used in the management of diabetes mellitus as a stabilizer for blood glucose levels. Another gift that DNA technology has provided is : recombinant DNA vaccines have been designed to prevent diseases in human beings and animals. Examples include vaccine formulations for the following viruses: influenza virus, rabies virus, and Ebola virus. vaccines Diagnosis of some clinical conditions like measles and tuberculosis is made simpler with the aid of recombinant DNA technology. can now be easily identified early for prompt management and counseling. DNA technology is the basis of diagnostic tests that are used to detect the presence of HIV, COVID19, and other viruses. Genetic disorders DNA technology has made a great impact in clinical pharmacology. Synthetic monoclonal antibodies are produced with the aid of recombinant DNA technology, eliminating the need for animals in the drug development process. These synthetic monoclonal antibodies are used in research, cancer therapy, and even in the treatment of . COVID19 Somatropin, the human growth hormone, has been produced with the assistance of DNA technology. It is used in the treatment of conditions like dwarfism, chronic kidney disease, Turner's syndrome, Prader Willi syndrome, short bowel syndrome, HIV-associated muscle , etc. wasting DNA technology has multiple applications in medicine that make disease prevention, diagnosis, and treatment better, thereby improving healthcare all over the world. DNA Technology in 2021 and Beyond: The Future In April 2021, researchers at UT San Francisco and the Whitehead Institute described a new CRISPR tool that allows scientists to switch almost all human genes on and off without altering the human genetic . This has great clinical significance: genes that are linked with certain diseases can simply be turned off, thus improving health and prolonging life in a large percentage of the human population. code There are many speculations about the of DNA technology. No one knows if human genes will be modifiable for a longer than normal lifespan or there will be the development of a higher, more intelligent species. However, one fact is sure: DNA technology will continue to improve disease prevention, detection, and treatment greatly. In addition, there will be the provision of more personalized health care plans for patients, especially those based on gene studies. future

DNA Technology: How Genetics Research Improves Healthcare

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