1
|
Yang R, Pan M, Guo J, Huang Y, Zhang QC, Deng T, Wang J. Mapping of the influenza A virus genome RNA structure and interactions reveals essential elements of viral replication. Cell Rep 2024; 43:113833. [PMID: 38416642 DOI: 10.1016/j.celrep.2024.113833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 12/04/2023] [Accepted: 02/02/2024] [Indexed: 03/01/2024] Open
Abstract
Influenza A virus (IAV) represents a constant public health threat. The single-stranded, segmented RNA genome of IAV is replicated in host cell nuclei as a series of 8 ribonucleoprotein complexes (vRNPs) with RNA structures known to exert essential function to support viral replication. Here, we investigate RNA secondary structures and RNA interactions networks of the IAV genome and construct an in vivo structure model for each of the 8 IAV genome segments. Our analyses reveal an overall in vivo and in virio resemblance of the IAV genome conformation but also wide disparities among long-range and intersegment interactions. Moreover, we identify a long-range RNA interaction that exerts an essential role in genome packaging. Disrupting this structure displays reduced infectivity, attenuating virus pathogenicity in mice. Our findings characterize the in vivo RNA structural landscape of the IAV genome and reveal viral RNA structures that can be targeted to develop antiviral interventions.
Collapse
Affiliation(s)
- Rui Yang
- The State Key Laboratory of Membrane Biology, Beijing Frontier Research Center for Biological Structure, Tsinghua University-Peking University Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Minglei Pan
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Jiamei Guo
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yong Huang
- The State Key Laboratory of Membrane Biology, Beijing Frontier Research Center for Biological Structure, Tsinghua University-Peking University Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Qiangfeng Cliff Zhang
- The State Key Laboratory of Membrane Biology, Beijing Frontier Research Center for Biological Structure, Tsinghua University-Peking University Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
| | - Tao Deng
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Jianwei Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
| |
Collapse
|
2
|
Xu Q, Bao X, Lin Z, Tang L, He LN, Ren J, Zuo Z, Hu K. AStruct: detection of allele-specific RNA secondary structure in structuromic probing data. BMC Bioinformatics 2024; 25:91. [PMID: 38429654 PMCID: PMC11264973 DOI: 10.1186/s12859-024-05704-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/14/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Uncovering functional genetic variants from an allele-specific perspective is of paramount importance in advancing our understanding of gene regulation and genetic diseases. Recently, various allele-specific events, such as allele-specific gene expression, allele-specific methylation, and allele-specific binding, have been explored on a genome-wide scale due to the development of high-throughput sequencing methods. RNA secondary structure, which plays a crucial role in multiple RNA-associated processes like RNA modification, translation and splicing, has emerged as an essential focus of relevant research. However, tools to identify genetic variants associated with allele-specific RNA secondary structures are still lacking. RESULTS Here, we develop a computational tool called 'AStruct' that enables us to detect allele-specific RNA secondary structure (ASRS) from RT-stop based structuromic probing data. AStruct shows robust performance in both simulated datasets and public icSHAPE datasets. We reveal that single nucleotide polymorphisms (SNPs) with higher AStruct scores are enriched in coding regions and tend to be functional. These SNPs are highly conservative, have the potential to disrupt sites involved in m6A modification or protein binding, and are frequently associated with disease. CONCLUSIONS AStruct is a tool dedicated to invoke allele-specific RNA secondary structure events at heterozygous SNPs in RT-stop based structuromic probing data. It utilizes allelic variants, base pairing and RT-stop information under different cell conditions to detect dynamic and functional ASRS. Compared to sequence-based tools, AStruct considers dynamic cell conditions and outperforms in detecting functional variants. AStruct is implemented in JAVA and is freely accessible at: https://github.com/canceromics/AStruct .
Collapse
Affiliation(s)
- Qingru Xu
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Xiaoqiong Bao
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China
| | - Zhuobin Lin
- Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lin Tang
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China
| | - Li-Na He
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China
| | - Jian Ren
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China
| | - Zhixiang Zuo
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510060, China.
| | - Kunhua Hu
- Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
3
|
Zhu H, Yang Y, Wang Y, Wang F, Huang Y, Chang Y, Wong KC, Li X. Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet. Nat Commun 2023; 14:6824. [PMID: 37884495 PMCID: PMC10603054 DOI: 10.1038/s41467-023-42547-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders.
Collapse
Affiliation(s)
- Haoran Zhu
- School of Artificial Intelligence, Jilin University, 130012, Changchun, China
| | - Yuning Yang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Yunhe Wang
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Fuzhou Wang
- Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR
| | - Yujian Huang
- College of Computer Science and Cyber Security, Chengdu University of Technology, 610059, Chengdu, China
| | - Yi Chang
- School of Artificial Intelligence, Jilin University, 130012, Changchun, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR.
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, 130012, Changchun, China.
| |
Collapse
|
4
|
Deng P, Tan SQ, Yang QY, Fu L, Wu Y, Zhu HZ, Sun L, Bao Z, Lin Y, Zhang QC, Wang H, Wang J, Liu JJG. Structural RNA components supervise the sequential DNA cleavage in R2 retrotransposon. Cell 2023; 186:2865-2879.e20. [PMID: 37301196 DOI: 10.1016/j.cell.2023.05.032] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 03/14/2023] [Accepted: 05/20/2023] [Indexed: 06/12/2023]
Abstract
Retroelements are the widespread jumping elements considered as major drivers for genome evolution, which can also be repurposed as gene-editing tools. Here, we determine the cryo-EM structures of eukaryotic R2 retrotransposon with ribosomal DNA target and regulatory RNAs. Combined with biochemical and sequencing analysis, we reveal two essential DNA regions, Drr and Dcr, required for recognition and cleavage. The association of 3' regulatory RNA with R2 protein accelerates the first-strand cleavage, blocks the second-strand cleavage, and initiates the reverse transcription starting from the 3'-tail. Removing 3' regulatory RNA by reverse transcription allows the association of 5' regulatory RNA and initiates the second-strand cleavage. Taken together, our work explains the DNA recognition and RNA supervised sequential retrotransposition mechanisms by R2 machinery, providing insights into the retrotransposon and application reprogramming.
Collapse
Affiliation(s)
- Pujuan Deng
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Shun-Qing Tan
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Qi-Yu Yang
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Liangzheng Fu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yachao Wu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Han-Zhou Zhu
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Lei Sun
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University, Qingdao 266237, China
| | - Zhangbin Bao
- Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Yi Lin
- Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Qiangfeng Cliff Zhang
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Haoyi Wang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jia Wang
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China.
| | - Jun-Jie Gogo Liu
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
| |
Collapse
|
5
|
Wei J, Chen S, Zong L, Gao X, Li Y. Protein-RNA interaction prediction with deep learning: structure matters. Brief Bioinform 2022; 23:bbab540. [PMID: 34929730 PMCID: PMC8790951 DOI: 10.1093/bib/bbab540] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/14/2021] [Accepted: 11/22/2021] [Indexed: 12/11/2022] Open
Abstract
Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.
Collapse
Affiliation(s)
- Junkang Wei
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
| | - Siyuan Chen
- Computational Bioscience Research Center (CBRC),
King Abdullah University of Science and Technology (KAUST),
23955-6900, Thuwal, Saudi Arabia
| | - Licheng Zong
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC),
King Abdullah University of Science and Technology (KAUST),
23955-6900, Thuwal, Saudi Arabia
| | - Yu Li
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
- The CUHK Shenzhen Research Institute, Hi-Tech Park, 518057,
Shenzhen, China
| |
Collapse
|
6
|
Piao M, Li P, Zeng X, Wang XW, Kang L, Zhang J, Wei Y, Zhang S, Tang L, Zhu J, Kwok CK, Hu X, Zhang QC. An ultra low-input method for global RNA structure probing uncovers Regnase-1-mediated regulation in macrophages. FUNDAMENTAL RESEARCH 2022; 2:2-13. [PMID: 38933905 PMCID: PMC11197792 DOI: 10.1016/j.fmre.2021.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/10/2021] [Accepted: 12/20/2021] [Indexed: 11/17/2022] Open
Abstract
To enable diverse functions and precise regulation, an RNA sequence often folds into complex yet distinct structures in different cellular states. Probing RNA in its native environment is essential to uncovering RNA structures of biological contexts. However, current methods generally require large amounts of input RNA and are challenging for physiologically relevant use. Here, we report smartSHAPE, a new RNA structure probing method that requires very low amounts of RNA input due to the largely reduced artefact of probing signals and increased efficiency of library construction. Using smartSHAPE, we showcased the profiling of the RNA structure landscape of mouse intestinal macrophages upon inflammation, and provided evidence that RNA conformational changes regulate immune responses. These results demonstrate that smartSHAPE can greatly expand the scope of RNA structure-based investigations in practical biological systems, and also provide a research paradigm for the study of post-transcriptional regulation.
Collapse
Affiliation(s)
- Meiling Piao
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Pan Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Xiaomin Zeng
- Institute for Immunology and School of Medicine, Tsinghua University, Beijing, China
- Beijing Key Laboratory for Immunological Research on Chronic Diseases, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China
| | - Xi-Wen Wang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Lan Kang
- Institute for Immunology and School of Medicine, Tsinghua University, Beijing, China
- Beijing Key Laboratory for Immunological Research on Chronic Diseases, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China
| | - Jinsong Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Yifan Wei
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Shaojun Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Lei Tang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Jianghui Zhu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Chun Kit Kwok
- Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong, China
- Shenzhen Research Institute of City University of Hong Kong, Shenzhen, China
| | - Xiaoyu Hu
- Institute for Immunology and School of Medicine, Tsinghua University, Beijing, China
- Beijing Key Laboratory for Immunological Research on Chronic Diseases, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| |
Collapse
|
7
|
Gong J, Xu K, Ma Z, Lu ZJ, Zhang QC. A deep learning method for recovering missing signals in transcriptome-wide RNA structure profiles from probing experiments. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00412-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
8
|
Manigrasso J, Marcia M, De Vivo M. Computer-aided design of RNA-targeted small molecules: A growing need in drug discovery. Chem 2021. [DOI: 10.1016/j.chempr.2021.05.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
9
|
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 264] [Impact Index Per Article: 88.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
Collapse
Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
| |
Collapse
|
10
|
Li J, Li G, Qi Y, Lu Y, Wang H, Shi K, Li D, Shi J, Stovall DB, Sui G. SRSF5 regulates alternative splicing of DMTF1 pre-mRNA through modulating SF1 binding. RNA Biol 2021; 18:318-336. [PMID: 34291726 DOI: 10.1080/15476286.2021.1947644] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
ABBREVIATIONS ARF: alternative reading frame, that is, p14ARF, or CDKN2A (cyclin-dependent kinase inhibitor 2A); β-gal: β-galactosidase; CLIP-seq: crosslinking and immunoprecipitation-sequencing; DMTF1: the cyclin D binding myb-like transcription factor 1; ESS/ESE: exonic splicing silencer/enhancer; Ex: exon; FBS: fetal bovine serum; Gluc: Gaussia luciferase; hnRNPs: heterogeneous nuclear ribonucleoproteins; In: intron; ISS/ISE: intronic splicing silencer/enhancer; PBS: phosphate-buffered saline; PCR: polymerase chain reaction; PSI: percent-splice-in; qPCR: quantitative real-time PCR; RIP: RNA immunoprecipitation; RNAseq: RNA sequencing; RT: reverse transcription; SF1: splicing factor 1; SR: serine/arginine-rich proteins; SRSF5: serine and arginine-rich splicing factor 5; TCGA: the cancer genome atlas; UCSC: University of California, Santa Cruz. WT: Wild type.
Collapse
Affiliation(s)
- Jialiang Li
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, College of Life Science, Northeast Forestry University, Harbin, China
| | - Guangyue Li
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, College of Life Science, Northeast Forestry University, Harbin, China
| | - Yige Qi
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, College of Life Science, Northeast Forestry University, Harbin, China
| | - Yao Lu
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, College of Life Science, Northeast Forestry University, Harbin, China
| | - Hao Wang
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, College of Life Science, Northeast Forestry University, Harbin, China
| | - Ke Shi
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, College of Life Science, Northeast Forestry University, Harbin, China
| | - Dangdang Li
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, College of Life Science, Northeast Forestry University, Harbin, China
| | - Jinming Shi
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, College of Life Science, Northeast Forestry University, Harbin, China
| | - Daniel B Stovall
- College of Arts and Sciences, Winthrop University, Rock Hill, SC, USA
| | - Guangchao Sui
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, College of Life Science, Northeast Forestry University, Harbin, China
| |
Collapse
|
11
|
Sun L, Xu K, Huang W, Yang YT, Li P, Tang L, Xiong T, Zhang QC. Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures. Cell Res 2021; 31:495-516. [PMID: 33623109 PMCID: PMC7900654 DOI: 10.1038/s41422-021-00476-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/19/2021] [Indexed: 01/31/2023] Open
Abstract
Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP-RNA interactions employ RNA sequence and/or predicted RNA structures, and therefore do not capture their condition-dependent nature. Here, after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types, we developed PrismNet, a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular conditions. PrismNet results for 168 RBPs support its utility for both understanding CLIP-seq results and largely extending such interaction data to accurately analyze additional cell types. Further, PrismNet employs an "attention" strategy to computationally identify exact RBP-binding nucleotides, and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants (riboSNitches), which can link genetic diseases with dysregulated RBP bindings. Our rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP-RNA interactions, with clear utility for understanding and treating human diseases.
Collapse
Affiliation(s)
- Lei Sun
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Kui Xu
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Wenze Huang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Yucheng T Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Pan Li
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Lei Tang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Tuanlin Xiong
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China.
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
| |
Collapse
|
12
|
Conserved long-range base pairings are associated with pre-mRNA processing of human genes. Nat Commun 2021; 12:2300. [PMID: 33863890 PMCID: PMC8052449 DOI: 10.1038/s41467-021-22549-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 03/20/2021] [Indexed: 02/07/2023] Open
Abstract
The ability of nucleic acids to form double-stranded structures is essential for all living systems on Earth. Current knowledge on functional RNA structures is focused on locally-occurring base pairs. However, crosslinking and proximity ligation experiments demonstrated that long-range RNA structures are highly abundant. Here, we present the most complete to-date catalog of conserved complementary regions (PCCRs) in human protein-coding genes. PCCRs tend to occur within introns, suppress intervening exons, and obstruct cryptic and inactive splice sites. Double-stranded structure of PCCRs is supported by decreased icSHAPE nucleotide accessibility, high abundance of RNA editing sites, and frequent occurrence of forked eCLIP peaks. Introns with PCCRs show a distinct splicing pattern in response to RNAPII slowdown suggesting that splicing is widely affected by co-transcriptional RNA folding. The enrichment of 3'-ends within PCCRs raises the intriguing hypothesis that coupling between RNA folding and splicing could mediate co-transcriptional suppression of premature pre-mRNA cleavage and polyadenylation.
Collapse
|
13
|
Sun L, Li P, Ju X, Rao J, Huang W, Ren L, Zhang S, Xiong T, Xu K, Zhou X, Gong M, Miska E, Ding Q, Wang J, Zhang QC. In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs. Cell 2021; 184:1865-1883.e20. [PMID: 33636127 PMCID: PMC7871767 DOI: 10.1016/j.cell.2021.02.008] [Citation(s) in RCA: 123] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/01/2020] [Accepted: 02/02/2021] [Indexed: 01/10/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. By using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep-learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.
Collapse
Affiliation(s)
- Lei Sun
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Pan Li
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Xiaohui Ju
- Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Jian Rao
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Wenze Huang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Lili Ren
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Shaojun Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Tuanlin Xiong
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Kui Xu
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Xiaolin Zhou
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Mingli Gong
- Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Eric Miska
- Wellcome Trust/Cancer Research UK Gurdon Institute, Department of Genetics, University of Cambridge, Cambridge CB2 1QN, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
| | - Qiang Ding
- Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing 100084, China.
| | - Jianwei Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
| |
Collapse
|
14
|
|