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Sun X, Xia R, Wang Y, Wang F, Liu Z, Xue G, Zhang G. Neuromedin S regulates goat ovarian granulosa cell proliferation and steroidogenesis via endoplasmic reticulum Ca 2+-YAP1-ATF4-c-Jun pathway. J Cell Physiol 2024:e31368. [PMID: 38982727 DOI: 10.1002/jcp.31368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 06/17/2024] [Accepted: 06/24/2024] [Indexed: 07/11/2024]
Abstract
Neuromedin S (NMS) plays key roles in reproductive regulation, while its function and mechanism in follicular development remain unclear. The current study aims to investigate the specific role and mechanisms of NMS and its receptors in regulating the proliferation and steroidogenesis of ovarian granulosa cells (GCs). Phenotypically, a certain concentration of NMS addition promoted the proliferation and estrogen production of goat GCs, accompanied by an increase in the G1/S cell population and upregulation of the expression levels of cyclin D1, cyclin dependent kinase 6, steroidogenic acute regulatory protein, cytochrome P450, family 11, subfamily A, polypeptide 1, 3beta-hydroxysteroid dehydrogenase, and cytochrome P450, family 11, subfamily A, polypeptide 1, while the effects of NMS treatment were effectively hindered by knockdown of neuromedin U receptor type 2 (NMUR2). Mechanistically, activation of NMUR2 with NMS maintained endoplasmic reticulum (ER) calcium (Ca2+) homeostasis by triggering the PLCG1-IP3R pathway, which helped preserve ER morphology, sustained an appropriate level of endoplasmic reticulum unfolded protein response (UPRer), and suppressed the nuclear translocation of activating transcription factor 4. Moreover, NMS maintained intracellular Ca2+ homeostasis to activate the calmodulin 1-large tumor suppressor kinase 1 pathway, ultimately orchestrating the regulation of goat GC proliferation and estrogen production through the Yes1 associated transcriptional regulator-ATF4-c-Jun pathway. Crucially, the effects of NMS were mitigated by concurrent knockdown of the NMUR2 gene. Collectively, these data suggest that activation of NMUR2 by NMS enhances cell proliferation and estrogen production in goat GCs through modulating the ER and intracellular Ca2+ homeostasis, leading to activation of the YAP1-ATF4-c-Jun pathway. These findings offer valuable insights into the regulatory mechanisms involved in follicular growth and development, providing a novel perspective for future research.
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Affiliation(s)
- Xuan Sun
- Jiangsu Livestock Embryo Engineering Laboratory, Nanjing Agricultural University, Nanjing, China
| | - Rongxin Xia
- Jiangsu Livestock Embryo Engineering Laboratory, Nanjing Agricultural University, Nanjing, China
- College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China
| | - Yifei Wang
- Jiangsu Livestock Embryo Engineering Laboratory, Nanjing Agricultural University, Nanjing, China
- College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China
| | - Feng Wang
- Jiangsu Livestock Embryo Engineering Laboratory, Nanjing Agricultural University, Nanjing, China
| | - Zhipeng Liu
- Jiangsu Livestock Embryo Engineering Laboratory, Nanjing Agricultural University, Nanjing, China
| | - Gang Xue
- Animal Husbandry and Veterinary Station of Haimen District, Nantong City, China
| | - Guomin Zhang
- Jiangsu Livestock Embryo Engineering Laboratory, Nanjing Agricultural University, Nanjing, China
- College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China
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2
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Panei FP, Gkeka P, Bonomi M. Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN. Nat Commun 2024; 15:5725. [PMID: 38977675 PMCID: PMC11231146 DOI: 10.1038/s41467-024-49638-7] [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: 08/12/2023] [Accepted: 06/05/2024] [Indexed: 07/10/2024] Open
Abstract
The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics simulations and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identifies all the experimentally resolved pockets and ranks them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.
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Affiliation(s)
- F P Panei
- Integrated Drug Discovery, Molecular Design Sciences, Sanofi, Vitry-sur-Seine, France
- Institut Pasteur, Université Paris Cité, CNRS UMR 3528, Computational Structural Biology Unit, Paris, France
- Sorbonne Université, Ecole Doctorale Complexité du Vivant, Paris, France
| | - P Gkeka
- Integrated Drug Discovery, Molecular Design Sciences, Sanofi, Vitry-sur-Seine, France.
| | - M Bonomi
- Institut Pasteur, Université Paris Cité, CNRS UMR 3528, Computational Structural Biology Unit, Paris, France.
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3
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Sun S, Gao L. Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction. Bioinformatics 2024; 40:btae155. [PMID: 38507691 PMCID: PMC11007238 DOI: 10.1093/bioinformatics/btae155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/23/2024] [Accepted: 03/18/2024] [Indexed: 03/22/2024] Open
Abstract
MOTIVATION The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA-small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA-small molecule binding affinity prediction has been proposed. The prediction of RNA-small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA-small molecule binding affinity accurately. RESULTS In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA-small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA-small molecule binding affinity. Our experimental results exhibited RLaffinity's superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening. AVAILABILITY AND IMPLEMENTATION https://github.com/SaisaiSun/RLaffinity.
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Affiliation(s)
- Saisai Sun
- School of Computer Science and Technology, Xidian University, No.266 Xinglong Section of Xi Feng Road, Xi’an, Shaanxi, 710126, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, No.266 Xinglong Section of Xi Feng Road, Xi’an, Shaanxi, 710126, China
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4
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Jiang D, Du H, Zhao H, Deng Y, Wu Z, Wang J, Zeng Y, Zhang H, Wang X, Wang E, Hou T, Hsieh CY. Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes. Phys Chem Chem Phys 2024; 26:10323-10335. [PMID: 38501198 DOI: 10.1039/d3cp04366e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.
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Affiliation(s)
- Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Huifeng Zhao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Yundian Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Haotian Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Xiaorui Wang
- China State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, China
| | - Ercheng Wang
- Zhejiang Laboratory, Hangzhou, Zhejiang 311100, China.
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
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Tan LH, Kwoh CK, Mu Y. RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method. Brief Bioinform 2024; 25:bbae166. [PMID: 38695120 PMCID: PMC11063749 DOI: 10.1093/bib/bbae166] [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: 12/21/2023] [Revised: 03/15/2024] [Accepted: 03/19/2024] [Indexed: 05/04/2024] Open
Abstract
Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.
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Affiliation(s)
- Lai Heng Tan
- Interdisciplinary Graduate School, Nanyang Technological University, 61 Nanyang Drive, 637335 Singapore, Singapore
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Singapore, Singapore
| | - Yuguang Mu
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, 637551 Singapore, Singapore
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6
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Singh S, Choudhary M. Unusual Ni⋯Ni interaction in Ni(ii) complexes as potential inhibitors for the development of new anti-SARS-CoV-2 Omicron drugs. RSC Med Chem 2024; 15:895-915. [PMID: 38516589 PMCID: PMC10953495 DOI: 10.1039/d3md00601h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/05/2024] [Indexed: 03/23/2024] Open
Abstract
Two nickel(ii) coordination complexes [Ni(L)]2(1) and [Ni(L)]n(2) of a tetradentate Schiff base ligand (H2L) derived from 2-hydroxy-1-naphthaldehyde with ethylenediamine were synthesized, designed, and characterized via spectroscopic and single crystal XRD analyses. Both nickel(ii) complexes exhibited unusual Ni⋯Ni interactions and were fully characterized via single-crystal X-ray crystallography. Nickel(ii) complexes [Ni(L)]2(1) and [Ni(L)]n(2) crystallize in monoclinic and triclinic crystal systems with P21/c and P1̄ space groups, respectively, and revealed square planar geometry around each Ni(ii) ion. The structure of both the complexes have established the existence of a new kind of metal system containing nickel(ii)-nickel(ii) interactions with a square planar-like geometry about the nickel(ii) atoms. Both square planar Ni(ii) complexes were often stacked with relatively short Ni⋯Ni distances. The non-bonded Ni-Ni distance (Ni⋯Ni separation) seems to be 3.356 Å and 3.214 Å from the nickel atoms of [Ni(L)]2(1) and [Ni(L)]n(2), respectively. These distances are shorter than the sum of their van der Waals radii (4.80 Å) but longer than the sum of their covalent radii (2.50 Å), indicating that there is a Ni⋯Ni interaction but not a Ni-Ni bond. The discrete molecules are π-stacked and connected via weak intermolecular interactions (C-H⋯O and C-H⋯N). Cyclic voltammetry measurements were obtained for both the complexes, and their pharmacokinetic and chemoinformatics properties were also explored. Detailed structural analysis and non-covalent supramolecular interactions were investigated using single-crystal structure analysis and computational approaches. Both the unique structures show good inhibition performance for the Omicron spike proteins of the SARS CoV-2 virus. To gain insights into potential SARS-CoV-2 Omicron drugs and find inhibitors against the Omicron variants of SARS-CoV-2, we examined the molecular docking of the nickel(ii) complexes [Ni(L)]2(1) and [Ni(L)]n(2) with the SARS-CoV-2 Omicron spike protein (PDB ID: 7WK2 and 7WVO). A strong binding was predicted between Ni(ii) coordination complexes [Ni(L)]2(1) and [Ni(L)]n(2) with the SARS-CoV-2 Omicron variant receptor protein through the negative value of binding affinity. Molecular docking of Nil(ii) complexes [Ni(L)]2(1) and [Ni(L)]n(2) with a DNA duplex (PDB ID: 7D3T) and RNA (PDB ID: 7TDC) binding protein was also studied. Overall, this study suggests that Ni(ii) complexes can be considered as drug candidates against the Omicron variants of SARS-CoV-2.
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Affiliation(s)
- Simranjeet Singh
- Department of Chemistry, National Institute of Technology Patna Patna-800005 Bihar India
| | - Mukesh Choudhary
- Department of Chemistry, National Institute of Technology Patna Patna-800005 Bihar India
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7
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Rinaldi S, Moroni E, Rozza R, Magistrato A. Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation. J Chem Theory Comput 2024; 20:993-1018. [PMID: 38287883 DOI: 10.1021/acs.jctc.3c01239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.
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Affiliation(s)
- Silvia Rinaldi
- National Research Council of Italy (CNR) - Institute of Chemistry of OrganoMetallic Compounds (ICCOM), c/o Area di Ricerca CNR di Firenze Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy
| | - Elisabetta Moroni
- National Research Council of Italy (CNR) - Institute of Chemical Sciences and Technologies (SCITEC), via Mario Bianco 9, 20131 Milano, Italy
| | - Riccardo Rozza
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
| | - Alessandra Magistrato
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
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8
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Krishnan SR, Roy A, Gromiha MM. Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning. Brief Bioinform 2024; 25:bbae002. [PMID: 38261341 PMCID: PMC10805179 DOI: 10.1093/bib/bbae002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 12/21/2023] [Accepted: 12/24/2023] [Indexed: 01/24/2024] Open
Abstract
Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.
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Affiliation(s)
- Sowmya R Krishnan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- TCS Research (Life Sciences division), Tata Consultancy Services, Hyderabad 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences division), Tata Consultancy Services, Hyderabad 500081, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan
- Department of Computer Science, National University of Singapore, Singapore 117543
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9
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Agarwal R, T RR, Smith JC. Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking. J Chem Inf Model 2023; 63:7444-7452. [PMID: 37972310 DOI: 10.1021/acs.jcim.3c01533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein-ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA-small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand-target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ∼27%. Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand-protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein-target docking illustrates a need for further methods refinement.
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Affiliation(s)
- Rupesh Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6309, United States
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996-1939, United States
| | - Rajitha Rajeshwar T
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6309, United States
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996-1939, United States
| | - Jeremy C Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6309, United States
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996-1939, United States
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10
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Chan KH, Wang Y, Zheng BX, Long W, Feng X, Wong WL. RNA-Selective Small-Molecule Ligands: Recent Advances in Live-Cell Imaging and Drug Discovery. ChemMedChem 2023; 18:e202300271. [PMID: 37649155 DOI: 10.1002/cmdc.202300271] [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: 05/20/2023] [Revised: 08/13/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023]
Abstract
RNA structures, including those formed from coding and noncoding RNAs, alternative to protein-based drug targets, could be a promising target of small molecules for drug discovery against various human diseases, particularly in anticancer, antibacterial and antivirus development. The normal cellular activity of cells is critically dependent on the function of various RNA molecules generated from DNA transcription. Moreover, many studies support that mRNA-targeting small molecules may regulate the synthesis of disease-related proteins via the non-covalent mRNA-ligand interactions that do not involve gene modification. RNA-ligand interaction is thus an attractive approach to address the challenge of "undruggable" proteins in drug discovery because the intracellular activity of these proteins is hard to be suppressed with small molecule ligands. We selectively surveyed a specific area of RNA structure-selective small molecule ligands in fluorescence live cell imaging and drug discovery because the area was currently underexplored. This state-of-the-art review thus mainly focuses on the research published within the past three years and aims to provide the most recent information on this research area; hopefully, it could be complementary to the previously reported reviews and give new insights into the future development on RNA-specific small molecule ligands for live cell imaging and drug discovery.
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Affiliation(s)
- Ka Hin Chan
- State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR 999077, P. R. China
| | - Yakun Wang
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518057, P. R. China
| | - Bo-Xin Zheng
- State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR 999077, P. R. China
| | - Wei Long
- State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR 999077, P. R. China
| | - Xinxin Feng
- State Key Laboratory of Chem-/Bio-Sensing and Chemometrics, Hunan Provincial Key Laboratory of Biomacromolecular Chemical Biology and School of Chemistry and Chemical Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Wing-Leung Wong
- State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR 999077, P. R. China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518057, P. R. China
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11
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Yu H, Mao G, Pei Z, Cen J, Meng W, Wang Y, Zhang S, Li S, Xu Q, Sun M, Xiao K. In Vitro Affinity Maturation of Nanobodies against Mpox Virus A29 Protein Based on Computer-Aided Design. Molecules 2023; 28:6838. [PMID: 37836685 PMCID: PMC10574621 DOI: 10.3390/molecules28196838] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Mpox virus (MPXV), the most pathogenic zoonotic orthopoxvirus, caused worldwide concern during the SARS-CoV-2 epidemic. Growing evidence suggests that the MPXV surface protein A29 could be a specific diagnostic marker for immunological detection. In this study, a fully synthetic phage display library was screened, revealing two nanobodies (A1 and H8) that specifically recognize A29. Subsequently, an in vitro affinity maturation strategy based on computer-aided design was proposed by building and docking the A29 and A1 three-dimensional structures. Ligand-receptor binding and molecular dynamics simulations were performed to predict binding modes and key residues. Three mutant antibodies were predicted using the platform, increasing the affinity by approximately 10-fold compared with the parental form. These results will facilitate the application of computers in antibody optimization and reduce the cost of antibody development; moreover, the predicted antibodies provide a reference for establishing an immunological response against MPXV.
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Affiliation(s)
- Haiyang Yu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
| | - Guanchao Mao
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
| | - Zhipeng Pei
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
| | - Jinfeng Cen
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
| | - Wenqi Meng
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
| | - Yunqin Wang
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
| | - Shanshan Zhang
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
| | - Songling Li
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
| | - Qingqiang Xu
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
| | - Mingxue Sun
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
| | - Kai Xiao
- Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China; (G.M.); (Z.P.); (J.C.); (W.M.); (Y.W.); (S.Z.); (S.L.)
- Marine Biomedical Science and Technology Innovation Platform of Lingang Special Area, Shanghai 201306, China
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12
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Jiang D, Zhao H, Du H, Deng Y, Wu Z, Wang J, Zeng Y, Zhang H, Wang X, Wu J, Hsieh CY, Hou T. How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation. J Chem Theory Comput 2023; 19:5633-5647. [PMID: 37480347 DOI: 10.1021/acs.jctc.3c00507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Nucleic acid (NA)-ligand interactions are of paramount importance in a variety of biological processes, including cellular reproduction and protein biosynthesis, and therefore, NAs have been broadly recognized as potential drug targets. Understanding NA-ligand interactions at the atomic scale is essential for investigating the molecular mechanism and further assisting in NA-targeted drug discovery. Molecular docking is one of the predominant computational approaches for predicting the interactions between NAs and small molecules. Despite the availability of versatile docking programs, their performance profiles for NA-ligand complexes have not been thoroughly characterized. In this study, we first compiled the largest structure-based NA-ligand binding data set to date, containing 800 noncovalent NA-ligand complexes with clearly identified ligands. Based on this extensive data set, eight frequently used docking programs, including six protein-ligand docking programs (LeDock, Surflex-Dock, UCSF Dock6, AutoDock, AutoDock Vina, and PLANTS) and two specific NA-ligand docking programs (rDock and RLDOCK), were systematically evaluated in terms of binding pose and binding affinity predictions. The results demonstrated that some protein-ligand docking programs, specifically PLANTS and LeDock, produced more promising or comparable results compared with the specialized NA-ligand docking programs. Among the programs evaluated, PLANTS, rDock, and LeDock showed the highest performance in binding pose prediction, and their top-1 and best root-mean-square deviation (rmsd) success rates were as follows: PLANTS (35.93 and 76.05%), rDock (27.25 and 72.16%), and LeDock (27.40 and 64.37%). Compared with the moderate level of binding pose prediction, few programs were successful in binding affinity prediction, and the best correlation (Rp = -0.461) was observed with PLANTS. Finally, further comparison with the latest NA-ligand docking program (NLDock) on four well-established data sets revealed that PLANTS and LeDock outperformed NLDock in terms of binding pose prediction on all data sets, demonstrating their significant potential for NA-ligand docking. To the best of our knowledge, this study is the most comprehensive evaluation of popular molecular docking programs for NA-ligand systems.
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Affiliation(s)
- Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Huifeng Zhao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yundian Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Haotian Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xiaorui Wang
- China State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, China
| | - Jian Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310006, Zhejiang, China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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13
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Wang X, Yu S, Lou E, Tan YL, Tan ZJ. RNA 3D Structure Prediction: Progress and Perspective. Molecules 2023; 28:5532. [PMID: 37513407 PMCID: PMC10386116 DOI: 10.3390/molecules28145532] [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: 06/14/2023] [Revised: 07/05/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.
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Affiliation(s)
- Xunxun Wang
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Shixiong Yu
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - En Lou
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Lan Tan
- School of Bioengineering and Health, Wuhan Textile University, Wuhan 430200, China
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, China
| | - Zhi-Jie Tan
- Department of Physics, Key Laboratory of Artificial Micro & Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
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14
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Comparative Assessment of Docking Programs for Docking and Virtual Screening of Ribosomal Oxazolidinone Antibacterial Agents. Antibiotics (Basel) 2023; 12:antibiotics12030463. [PMID: 36978331 PMCID: PMC10044086 DOI: 10.3390/antibiotics12030463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
Oxazolidinones are a broad-spectrum class of synthetic antibiotics that bind to the 50S ribosomal subunit of Gram-positive and Gram-negative bacteria. Many crystal structures of the ribosomes with oxazolidinone ligands have been reported in the literature, facilitating structure-based design using methods such as molecular docking. It would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. We examined the performance of five molecular docking programs (AutoDock 4, AutoDock Vina, DOCK 6, rDock, and RLDock) for their ability to model ribosomal–ligand interactions with oxazolidinones. Eleven ribosomal crystal structures with oxazolidinones as the ligands were docked. The accuracy was evaluated by calculating the docked complexes’ root-mean-square deviation (RMSD) and the program’s internal scoring function. The rankings for each program based on the median RMSD between the native and predicted were DOCK 6 > AD4 > Vina > RDOCK >> RLDOCK. Results demonstrate that the top-performing program, DOCK 6, could accurately replicate the ligand binding in only four of the eleven ribosomes due to the poor electron density of said ribosomal structures. In this study, we have further benchmarked the performance of the DOCK 6 docking algorithm and scoring in improving virtual screening (VS) enrichment using the dataset of 285 oxazolidinone derivatives against oxazolidinone binding sites in the S. aureus ribosome. However, there was no clear trend between the structure and activity of the oxazolidinones in VS. Overall, the docking performance indicates that the RNA pocket’s high flexibility does not allow for accurate docking prediction, highlighting the need to validate VS. protocols for ligand-RNA before future use. Later, we developed a re-scoring method incorporating absolute docking scores and molecular descriptors, and the results indicate that the descriptors greatly improve the correlation of docking scores and pMIC values. Morgan fingerprint analysis was also used, suggesting that DOCK 6 underpredicted molecules with tail modifications with acetamide, n-methylacetamide, or n-ethylacetamide and over-predicted molecule derivatives with methylamino bits. Alternatively, a ligand-based approach similar to a field template was taken, indicating that each derivative’s tail groups have strong positive and negative electrostatic potential contributing to microbial activity. These results indicate that one should perform VS. campaigns of ribosomal antibiotics with care and that more comprehensive strategies, including molecular dynamics simulations and relative free energy calculations, might be necessary in conjunction with VS. and docking.
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15
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Kallert E, Fischer TR, Schneider S, Grimm M, Helm M, Kersten C. Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ 1-Riboswitch. J Chem Inf Model 2022; 62:4134-4148. [PMID: 35994617 DOI: 10.1021/acs.jcim.2c00751] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Targeting RNA with small molecules is an emerging field. While several ligands for different RNA targets are reported, structure-based virtual screenings (VSs) against RNAs are still rare. Here, we elucidated the general capabilities of protein-based docking programs to reproduce native binding modes of small-molecule RNA ligands and to discriminate known binders from decoys by the scoring function. The programs were found to perform similar compared to the RNA-based docking tool rDOCK, and the challenges faced during docking, namely, protomer and tautomer selection, target dynamics, and explicit solvent, do not largely differ from challenges in conventional protein-ligand docking. A prospective VS with the Bacillus subtilis preQ1-riboswitch aptamer domain performed with FRED, HYBRID, and FlexX followed by microscale thermophoresis assays identified six active compounds out of 23 tested VS hits with potencies between 29.5 nM and 11.0 μM. The hits were selected not solely based on their docking score but for resembling key interactions of the native ligand. Therefore, this study demonstrates the general feasibility to perform structure-based VSs against RNA targets, while at the same time it highlights pitfalls and their potential solutions when executing RNA-ligand docking.
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Affiliation(s)
- Elisabeth Kallert
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Tim R Fischer
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Simon Schneider
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Maike Grimm
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Mark Helm
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Christian Kersten
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
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16
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Guo P, Han D. Targeting Pathogenic DNA and RNA Repeats: A Conceptual Therapeutic Way for Repeat Expansion Diseases. Chemistry 2022; 28:e202201749. [PMID: 35727679 DOI: 10.1002/chem.202201749] [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: 06/07/2022] [Indexed: 11/06/2022]
Abstract
Expansions of short tandem repeats (STRs) in the human genome cause nearly 50 neurodegenerative diseases, which are mostly inheritable, nonpreventable and incurable, posing as a huge threat to human health. Non-B DNAs formed by STRs are thought to be structural intermediates that can cause repeat expansions. The subsequent transcripts harboring expanded RNA repeats can further induce cellular toxicity through forming specific structures. Direct targeting of these pathogenic DNA and RNA repeats has emerged as a new potential therapeutic strategy to cure repeat expansion diseases. In this conceptual review, we first introduce the roles of DNA and RNA structures in the genetic instabilities and pathomechanisms of repeat expansion diseases, then describe structural features of DNA and RNA repeats with a focus on the tertiary structures determined by X-ray crystallography and solution nuclear magnetic resonance spectroscopy, and finally discuss recent progress and perspectives of developing chemical tools that target pathogenic DNA and RNA repeats for curing repeat expansion diseases.
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Affiliation(s)
- Pei Guo
- The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital), Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, P. R. China
| | - Da Han
- The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital), Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, P. R. China.,Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, School of Medicine Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
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17
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Feng Y, Yan Y, He J, Tao H, Wu Q, Huang SY. Docking and scoring for nucleic acid-ligand interactions: Principles and current status. Drug Discov Today 2021; 27:838-847. [PMID: 34718205 DOI: 10.1016/j.drudis.2021.10.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 09/06/2021] [Accepted: 10/20/2021] [Indexed: 12/24/2022]
Abstract
Nucleic acid (NA)-ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein-ligand interactions might not be applicable to NA-ligand docking. Therefore, various sampling strategies and scoring functions for NA-ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA-ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.
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Affiliation(s)
- Yuyu Feng
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Jiahua He
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Huanyu Tao
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Qilong Wu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China.
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