Identification of RNA structures and their roles in RNA functions

Nature Reviews Molecular Cell Biology, Journal Year: 2024, Volume and Issue: 25(10), P. 784 - 801

Published: June 26, 2024

Language: Английский

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Codon-optimization in gene therapy: promises, prospects and challenges

Frontiers in Bioengineering and Biotechnology, Journal Year: 2024, Volume and Issue: 12

Published: March 28, 2024

Codon optimization has evolved to enhance protein expression efficiency by exploiting the genetic code's redundancy, allowing for multiple codon options a single amino acid. Initially observed in

Language: Английский

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Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(2)

Published: Jan. 22, 2024

Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated several disease conditions the human body. In this regard, a growing interest observed to probe into potential act as drug targets conditions. To accelerate search for disease-associated novel RNA their small molecular inhibitors, machine learning models binding affinity prediction were developed specific six subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches viral RNAs. We found that differences sequence composition, flexibility polar nature RNA-binding ligands are predicting affinity. Our method showed an average Pearson correlation (r) 0.83 mean absolute error 0.66 upon evaluation using jack-knife test, indicating reliability despite low amount data available subtypes. Further, validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. have web server host models, RNA-Small molecule Affinity Predictor, is freely at: https://web.iitm.ac.in/bioinfo2/RSAPred/.

Language: Английский

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RNA structure prediction using deep learning — A comprehensive review

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 188, P. 109845 - 109845

Published: Feb. 20, 2025

In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of functions and RNA-based drug design. Implementing deep learning techniques for has led tremendous progress in this field, resulting significant improvements accuracy. This comprehensive review aims to provide an overview the diverse strategies employed predicting secondary structures, emphasizing methods. The article categorizes discussion into three main dimensions: feature extraction methods, existing state-of-the-art model architectures, approaches. We present comparative analysis various models highlighting their strengths weaknesses. Finally, we identify gaps literature, discuss current challenges, suggest future approaches enhance performance applicability tasks. provides deeper insight subject paves way further dynamic intersection life sciences artificial intelligence.

Language: Английский

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Discovery and Quantification of Long-Range RNA Base Pairs in Coronavirus Genomes with SEARCH-MaP and SEISMIC-RNA

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 30, 2024

Abstract RNA molecules perform a diversity of essential functions for which their linear sequences must fold into higher-order structures. Techniques including crystallography and cryogenic electron microscopy have revealed 3D structures ribosomal, transfer, other well-structured RNAs; while chemical probing with sequencing facilitates secondary structure modeling any RNAs interest, even within cells. Ongoing efforts continue increasing the accuracy, resolution, ability to distinguish coexisting alternative However, no method can discover quantify base pairs spanning arbitrarily long distances – an obstacle studying viral, messenger, noncoding RNAs, may form long-range pairs. Here, we introduce Structure Ensemble Ablation by Reverse Complement Hybridization Mutational Profiling (SEARCH-MaP) software Inference Sequencing, Mutation Identification, Clustering (SEISMIC-RNA). We use SEARCH-MaP SEISMIC-RNA that frameshift stimulating element SARS coronavirus 2 base-pairs another 1 kilobase downstream in nearly half molecules, this competes pseudoknot stimulates ribosomal frameshifting. Moreover, identify involving coronaviruses transmissible gastroenteritis virus, model full genomic latter. These findings suggest are common regulate frameshifting, is viral synthesis. anticipate will enable solving many ensembles eluded characterization, thereby enhancing our general understanding functions. SEISMIC-RNA, analyzing mutational profiling data at scale, could power future studies on available GitHub Python Package Index.

Language: Английский

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mRNA Vaccine Sequence and Structure Design and Optimization: Advances and Challenges

Journal of Biological Chemistry, Journal Year: 2024, Volume and Issue: unknown, P. 108015 - 108015

Published: Nov. 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.

Language: Английский

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Trends in Aptasensing and the Enhancement of Diagnostic Efficiency and Accuracy

ACS Synthetic Biology, Journal Year: 2025, Volume and Issue: 14(1), P. 21 - 40

Published: Jan. 6, 2025

The field of healthcare diagnostics is navigating complex challenges driven by evolving patient demographics and the rapid advancement new technologies worldwide. In response to these challenges, biosensors offer distinctive advantages over traditional diagnostic methods, such as cost-effectiveness, enhanced specificity, adaptability, making their integration with point-of-care (POC) platforms more feasible. recent years, aptasensors have significantly evolved in capabilities through emerging microfluidics, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) systems, wearable devices, machine learning (ML), driving progress precision medicine global solutions. Moreover, advancements not only improve accuracy but also hold potential revolutionize early detection, reduce costs, outcomes, especially resource-limited settings. This Account examines key advancements, focusing on how scientific breakthroughs, including artificial intelligence (AI), improved sensitivity precision. Additionally, has enabled real-time monitoring data analysis, fostering advances personalized healthcare. Furthermore, commercialization aptasensor could increase availability clinical settings support use widespread solutions for health challenges. Hence, this review discusses technological improvements, practical uses, prospects while surrounding standardization, validation, interdisciplinary collaboration application. Finally, ongoing efforts address are ensure that can be effectively implemented diverse systems.

Language: Английский

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RNA Sequence Analysis Landscape: A Comprehensive Review of Task Types, Databases, Datasets, Word Embedding Methods, and Language Models

Heliyon, Journal Year: 2025, Volume and Issue: 11(2), P. e41488 - e41488

Published: Jan. 1, 2025

Deciphering information of RNA sequences reveals their diverse roles in living organisms, including gene regulation and protein synthesis. Aberrations sequence such as dysregulation mutations can drive a spectrum diseases cancers, genetic disorders, neurodegenerative conditions. Furthermore, researchers are harnessing RNA's therapeutic potential for transforming traditional treatment paradigms into personalized therapies through the development RNA-based drugs therapies. To gain insights biological functions to detect at early stages develop potent therapeutics, performing types analysis tasks. conventional wet-lab methods is expensive, time-consuming error prone. enable large-scale analysis, empowerment experimental with Artificial Intelligence (AI) applications necessitates scientists have comprehensive knowledge both DNA AI fields. While molecular biologists encounter challenges understanding methods, computer often lack basic foundations Considering absence literature that bridges this research gap promotes AI-driven applications, contributions manuscript manifold: It equips 47 distinct sets stage benchmark datasets related tasks by facilitating cruxes 64 different databases. presents word embeddings language models across streamlines new predictors providing survey 58 70 based predictive pipelines performance values well top encoding performances

Language: Английский

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Advances and Mechanisms of RNA–Ligand Interaction Predictions

Life, Journal Year: 2025, Volume and Issue: 15(1), P. 104 - 104

Published: Jan. 15, 2025

The diversity and complexity of RNA include sequence, secondary structure, tertiary structure characteristics. These elements are crucial for RNA's specific recognition other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data uncover the mechanisms complex interactions. However, determining these complexes experimentally can be technically challenging often results low-resolution data. Many machine learning computational approaches have recently emerged learn multiscale-level features predict Predicting interactions remains an unexplored area. Therefore, studying is essential understanding biological processes. In this review, we analyze interaction characteristics by examining structure. Our goal clarify how specifically recognizes ligands. Additionally, systematically discuss methods predicting guide future research directions. We aim inspire creation more reliable prediction tools.

Language: Английский

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Consistent features observed in structural probing data of eukaryotic RNAs

NAR Genomics and Bioinformatics, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 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.

Language: Английский

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