A generative framework for enhanced cell-type specificity in rationally designed mRNAs DOI Creative Commons
Matvei Khoroshkin, Arsenii Zinkevich,

Elizaveta Aristova

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 31, 2024

Abstract mRNA delivery offers new opportunities for disease treatment by directing cells to produce therapeutic proteins. However, designing highly stable mRNAs with programmable cell type-specificity remains a challenge. To address this, we measured the regulatory activity of 60,000 5’ and 3’ untranslated regions (UTRs) across six types developed PARADE (Prediction And RAtional DEsign UTRs), generative AI framework engineer RNA tailored type-specific activity. We validated testing 15,800 de novo-designed sequences these lines identified many that demonstrated superior specificity compared existing therapeutics. PARADE-engineered UTRs also exhibited robust tissue-specific in animal models, achieving selective expression liver spleen. leveraged enhance stability, significantly increasing protein output durability vivo. These advancements translated notable increases efficacy, as PARADE-designed oncosuppressor mRNAs, namely PTEN P16, effectively reduced tumor growth patient-derived neuroglioma xenograft models orthotopic mouse models. Collectively, findings establish versatile platform safer, more precise, therapies.

Язык: Английский

Artificial intelligence for medicine 2025: Navigating the endless frontier DOI
Jiyan Dai, Huiyu Xu, Tao Chen

и другие.

The Innovation Medicine, Год журнала: 2025, Номер unknown, С. 100120 - 100120

Опубликована: Янв. 1, 2025

<p>Artificial intelligence (AI) is driving transformative changes in the field of medicine, with its successful application relying on accurate data and rigorous quality standards. By integrating clinical information, pathology, medical imaging, physiological signals, omics data, AI significantly enhances precision research into disease mechanisms patient prognoses. technologies also demonstrate exceptional potential drug development, surgical automation, brain-computer interface (BCI) research. Through simulation biological systems prediction intervention outcomes, enables researchers to rapidly translate innovations practical applications. While challenges such as computational demands, software ethical considerations persist, future remains highly promising. plays a pivotal role addressing societal issues like low birth rates aging populations. can contribute mitigating rate through enhanced ovarian reserve evaluation, menopause forecasting, optimization Assisted Reproductive Technologies (ART), sperm analysis selection, endometrial receptivity fertility remote consultations. In posed by an population, facilitate development dementia models, cognitive health monitoring strategies, early screening systems, AI-driven telemedicine platforms, intelligent smart companion robots, environments for aging-in-place. profoundly shapes medicine.</p>

Язык: Английский

Процитировано

1
RNA language models predict mutations that improve RNA function DOI Creative Commons
Yekaterina Shulgina, Marena Trinidad, Conner J. Langeberg

и другие.

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Дек. 5, 2024

Abstract Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. structure prediction is not yet possible due a lack high-quality reference data associated with organismal phenotypes that could inform function. We present GARNET (Gtdb Acquired RNa Environmental Temperatures), new database for structural and functional analysis anchored Genome Taxonomy Database (GTDB). links sequences experimental predicted optimal growth temperatures GTDB organisms. Using GARNET, we develop sequence- structure-aware generative models, overlapping triplet tokenization providing encoding GPT-like model. Leveraging hyperthermophilic RNAs in these identify mutations ribosomal confer increased thermostability Escherichia coli ribosome. The GTDB-derived deep learning models presented here provide foundation understanding connections between sequence, structure,

Язык: Английский

Процитировано

5
Consistent features observed in structural probing data of eukaryotic RNAs DOI Creative Commons
Kazuhiko Yamamura, Kiyoshi Asai, Junichi Iwakiri

и другие.

NAR Genomics and Bioinformatics, Год журнала: 2025, Номер 7(1)

Опубликована: Янв. 7, 2025

Abstract Understanding RNA structure is crucial for elucidating its regulatory mechanisms. With the recent commercialization of messenger vaccines, profound impact on stability and translation efficiency has become increasingly evident, underscoring importance understanding structure. Chemical probing emerged as a powerful technique investigating in living cells. This approach utilizes chemical probes that selectively react with accessible regions RNA, by measuring reactivity, openness potential protein binding or base pairing can be inferred. Extensive experimental data generated using have significantly contributed to our However, it acknowledge biases ensure an accurate interpretation. In this study, we comprehensively analyzed transcriptome-scale eukaryotes report common features. Notably, all experiments, number bases modified was small, showing top 10% reactivity well reflected known secondary structure, high were more likely exposed solvent low did not reflect exposure, which important information analysis data.

Язык: Английский

Процитировано

0
mRNA Vaccine Sequence and Structure Design and Optimization: Advances and Challenges DOI Creative Commons
Lei Jin, Yuanzhe Zhou, Sicheng Zhang

и другие.

Journal of Biological Chemistry, Год журнала: 2024, Номер unknown, С. 108015 - 108015

Опубликована: Ноя. 1, 2024

Messenger RNA (mRNA) vaccines have emerged as a powerful tool against communicable diseases and cancers, demonstrated by their huge success during the coronavirus disease 2019 (COVID-19) pandemic. Despite outstanding achievements, mRNA still face challenges such stringent storage requirements, insufficient antigen expression, unexpected immune responses. Since intrinsic properties of molecules significantly impact vaccine performance, optimizing design is crucial in preclinical development. In this review, we outline four key principles for optimal sequence design: enhancing ribosome loading translation efficiency through untranslated region (UTR) optimization, improving via codon increasing structural stability refining global sequence, extending in-cell lifetime expression fidelity adjusting local structures. We also explore recent advancements computational models designing sequences following these principles. By integrating current knowledge, addressing challenges, examining advanced methods, review aims to promote application approaches development inspire novel solutions existing obstacles.

Язык: Английский

Процитировано

4
RNA function follows form – why is it so hard to predict? DOI Creative Commons

Diana Kwon

Nature, Год журнала: 2025, Номер 639(8056), С. 1106 - 1108

Опубликована: Март 24, 2025

Язык: Английский

Процитировано

0
RNA language models predict mutations that improve RNA function DOI Creative Commons
Yekaterina Shulgina, Marena Trinidad, Conner J. Langeberg

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Апрель 6, 2024

Abstract Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. While advances in deep learning enable prediction accurate protein structural models, structure is not possible present due a lack abundant high-quality reference data 1 . Furthermore, available sequence are generally associated with organismal phenotypes that could inform function 2–4 We created GARNET (Gtdb Acquired RNa Environmental Temperatures), new database for and functional analysis anchored Genome Taxonomy Database (GTDB) 5 links sequences derived GTDB genomes experimental predicted optimal growth temperatures organisms. This enables construction diverse alignments be used machine learning. Using GARNET, we define minimal requirements sequence- structure-aware generative model. also develop GPT-like language model which overlapping triplet tokenization provides encoding. Leveraging hyperthermophilic RNAs these identified mutations ribosomal confer increased thermostability Escherichia coli ribosome. The GTDB- models presented here provide foundation understanding connections between sequence, structure, function.

Язык: Английский

Процитировано

2
From computational models of the splicing code to regulatory mechanisms and therapeutic implications DOI
Charlotte Capitanchik, Oscar G. Wilkins, Nils Wagner

и другие.

Nature Reviews Genetics, Год журнала: 2024, Номер unknown

Опубликована: Окт. 2, 2024

Язык: Английский

Процитировано

2
OpenASO: RNA Rescue—designing splice-modulating antisense oligonucleotides through community science DOI Creative Commons

Victor Tse,

Martin Guiterrez,

Jill Townley

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 17, 2024

ABSTRACT Splice-modulating antisense oligonucleotides (ASOs) are precision RNA-based drugs that becoming an established modality to treat human disease. Previously, we reported the discovery of ASOs target a novel, putative intronic RNA structure rescue splicing multiple pathogenic variants F8 exon 16 cause hemophilia A. However, conventional approach discovering splice-modulating is both laborious and expensive. Here, describe alternative paradigm integrates data-driven prediction community science discover ASOs. Using splicing-deficient variant as model, show 25% top-scoring molecules designed in Eterna OpenASO challenge have statistically significant impact on enhancing splicing. Additionally, distinct combination by players can additively enhance inclusion variant. Together, our data suggests crowdsourcing designs from citizen scientists may accelerate with potential

Язык: Английский

Процитировано

0
A generative framework for enhanced cell-type specificity in rationally designed mRNAs DOI Creative Commons
Matvei Khoroshkin, Arsenii Zinkevich,

Elizaveta Aristova

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 31, 2024

Abstract mRNA delivery offers new opportunities for disease treatment by directing cells to produce therapeutic proteins. However, designing highly stable mRNAs with programmable cell type-specificity remains a challenge. To address this, we measured the regulatory activity of 60,000 5’ and 3’ untranslated regions (UTRs) across six types developed PARADE (Prediction And RAtional DEsign UTRs), generative AI framework engineer RNA tailored type-specific activity. We validated testing 15,800 de novo-designed sequences these lines identified many that demonstrated superior specificity compared existing therapeutics. PARADE-engineered UTRs also exhibited robust tissue-specific in animal models, achieving selective expression liver spleen. leveraged enhance stability, significantly increasing protein output durability vivo. These advancements translated notable increases efficacy, as PARADE-designed oncosuppressor mRNAs, namely PTEN P16, effectively reduced tumor growth patient-derived neuroglioma xenograft models orthotopic mouse models. Collectively, findings establish versatile platform safer, more precise, therapies.

Язык: Английский

Процитировано

0