1
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Gunasinghe KKJ, Ginjom IRH, San HS, Rahman T, Wezen XC. Can Current Molecular Docking Methods Accurately Predict RNA Inhibitors? J Chem Inf Model 2024. [PMID: 39023229 DOI: 10.1021/acs.jcim.4c00235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Ribonucleic acids (RNAs), particularly the noncoding RNAs, play key roles in cancer, making them attractive drug targets. While conventional methods such as high throughput screening are resource-intensive, computational methods such as RNA-ligand docking can be used as an alternative. However, currently available docking methods are fine-tuned to perform protein-ligand and protein-protein docking. In this work, we evaluated three commonly used docking methods─AutoDock Vina, HADDOCK, and HDOCK─alongside RLDOCK, which is specifically designed for RNA-ligand docking. Our evaluation was based on several criteria including cognate docking, blind docking, scoring potential, and ranking potential. In cognate docking, only RLDOCK showed a success rate of 70% for the top-scoring docked pose. Despite this, all four docking methods did not achieve an overall success rate exceeding 50% amidst our attempt to refine the top-scoring docked poses using molecular dynamics simulations. Meanwhile, all four docking methods showed poor performance in scoring potential evaluation. Although AutoDock Vina achieved an area under the receiver operating characteristic curve of 0.70, it showed poor performance in terms of Matthews' correlation coefficient, precision, enrichment factors, and normalized enrichment factors at 1, 2, and 5%. These results highlight the growing need for further optimization of docking methods to assess RNA-ligand interactions.
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Affiliation(s)
| | - Irine Runnie Henry Ginjom
- Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak, Kuching, Sarawak 93350, Malaysia
| | - Hwang Siaw San
- Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak, Kuching, Sarawak 93350, Malaysia
| | - Taufiq Rahman
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, United Kingdom
| | - Xavier Chee Wezen
- Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak, Kuching, Sarawak 93350, Malaysia
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
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2
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Avila Y, Rebolledo LP, Skelly E, de Freitas Saito R, Wei H, Lilley D, Stanley RE, Hou YM, Yang H, Sztuba-Solinska J, Chen SJ, Dokholyan NV, Tan C, Li SK, He X, Zhang X, Miles W, Franco E, Binzel DW, Guo P, Afonin KA. Cracking the Code: Enhancing Molecular Tools for Progress in Nanobiotechnology. ACS APPLIED BIO MATERIALS 2024; 7:3587-3604. [PMID: 38833534 PMCID: PMC11190997 DOI: 10.1021/acsabm.4c00432] [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: 03/28/2024] [Revised: 05/21/2024] [Accepted: 05/27/2024] [Indexed: 06/06/2024]
Abstract
Nature continually refines its processes for optimal efficiency, especially within biological systems. This article explores the collaborative efforts of researchers worldwide, aiming to mimic nature's efficiency by developing smarter and more effective nanoscale technologies and biomaterials. Recent advancements highlight progress and prospects in leveraging engineered nucleic acids and proteins for specific tasks, drawing inspiration from natural functions. The focus is developing improved methods for characterizing, understanding, and reprogramming these materials to perform user-defined functions, including personalized therapeutics, targeted drug delivery approaches, engineered scaffolds, and reconfigurable nanodevices. Contributions from academia, government agencies, biotech, and medical settings offer diverse perspectives, promising a comprehensive approach to broad nanobiotechnology objectives. Encompassing topics from mRNA vaccine design to programmable protein-based nanocomputing agents, this work provides insightful perspectives on the trajectory of nanobiotechnology toward a future of enhanced biomimicry and technological innovation.
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Affiliation(s)
- Yelixza
I. Avila
- Nanoscale
Science Program, Department of Chemistry
University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States
| | - Laura P. Rebolledo
- Nanoscale
Science Program, Department of Chemistry
University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States
| | - Elizabeth Skelly
- Nanoscale
Science Program, Department of Chemistry
University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States
| | - Renata de Freitas Saito
- Comprehensive
Center for Precision Oncology, Centro de Investigação
Translacional em Oncologia (LIM24), Departamento
de Radiologia e Oncologia, Faculdade de Medicina da Universidade de
São Paulo and Instituto do Câncer do Estado de São
Paulo, São Paulo, São Paulo 01246-903, Brazil
| | - Hui Wei
- College
of Engineering and Applied Sciences, Nanjing
University, Nanjing, Jiangsu 210023, P. R. China
| | - David Lilley
- School
of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom
| | - Robin E. Stanley
- Signal
Transduction Laboratory, National Institute of Environmental Health
Sciences, National Institutes of Health, Department of Health and Human Services, 111 T. W. Alexander Drive, Research Triangle Park, North Carolina 27709, United States
| | - Ya-Ming Hou
- Thomas
Jefferson
University, Department of Biochemistry
and Molecular Biology, 233 South 10th Street, BLSB 220 Philadelphia, Pennsylvania 19107, United States
| | - Haoyun Yang
- Department
of Chemistry and Biochemistry, The Ohio
State University, Columbus, Ohio 43210, United States
| | - Joanna Sztuba-Solinska
- Vaccine
Research and Development, Early Bioprocess Development, Pfizer Inc., 401 N Middletown Road, Pearl
River, New York 10965, United States
| | - Shi-Jie Chen
- Department
of Physics and Astronomy, Department of Biochemistry, Institute of
Data Sciences and Informatics, University
of Missouri at Columbia, Columbia, Missouri 65211, United States
| | - Nikolay V. Dokholyan
- Departments
of Pharmacology and Biochemistry & Molecular Biology Penn State College of Medicine; Hershey, Pennsylvania 17033, United States
- Departments
of Chemistry and Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Cheemeng Tan
- University of California, Davis, California 95616, United States
| | - S. Kevin Li
- Division
of Pharmaceutical Sciences, James L Winkle
College of Pharmacy, University of Cincinnati, Cincinnati, Ohio 45267, United States
| | - Xiaoming He
- Fischell
Department of Bioengineering, University
of Maryland, College Park, Maryland 20742, United States
| | - Xiaoting Zhang
- Department
of Cancer Biology, Breast Cancer Research Program, and University
of Cincinnati Cancer Center, Vontz Center for Molecular Studies, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267, United States
| | - Wayne Miles
- Department
of Cancer Biology and Genetics, The Ohio
State University, Columbus, Ohio 43210, United States
| | - Elisa Franco
- Department
of Mechanical and Aerospace Engineering, University of California at Los Angeles, Los Angeles, California 90024, United States
| | - Daniel W. Binzel
- Center
for RNA Nanobiotechnology and Nanomedicine; College of Pharmacy, James
Comprehensive Cancer Center, The Ohio State
University, Columbus, Ohio 43210, United States
| | - Peixuan Guo
- Center
for RNA Nanobiotechnology and Nanomedicine; College of Pharmacy, James
Comprehensive Cancer Center, The Ohio State
University, Columbus, Ohio 43210, United States
- Dorothy
M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio 43210, United States
| | - Kirill A. Afonin
- Nanoscale
Science Program, Department of Chemistry
University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States
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3
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Zhou Y, Chen SJ. Advances in machine-learning approaches to RNA-targeted drug design. ARTIFICIAL INTELLIGENCE CHEMISTRY 2024; 2:100053. [PMID: 38434217 PMCID: PMC10904028 DOI: 10.1016/j.aichem.2024.100053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.
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Affiliation(s)
- Yuanzhe Zhou
- Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211-7010, USA
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
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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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5
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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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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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7
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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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8
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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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9
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RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study. Int J Mol Sci 2023; 24:ijms24065497. [PMID: 36982570 PMCID: PMC10058308 DOI: 10.3390/ijms24065497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA’s conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I–III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base–base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications.
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10
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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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11
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Wang KW, Riveros I, DeLoye J, Yildirim I. Dynamic docking of small molecules targeting RNA CUG repeats causing myotonic dystrophy type 1. Biophys J 2023; 122:180-196. [PMID: 36348626 PMCID: PMC9822796 DOI: 10.1016/j.bpj.2022.11.010] [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: 03/15/2022] [Revised: 08/05/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Expansion of RNA CUG repeats causes myotonic dystrophy type 1 (DM1). Once transcribed, the expanded CUG repeats strongly attract muscleblind-like 1 (MBNL1) proteins and disturb their functions in cells. Because of its unique structural form, expanded RNA CUG repeats are prospective drug targets, where small molecules can be utilized to target RNA CUG repeats to inhibit MBNL1 binding and ameliorate DM1-associated defects. In this contribution, we developed two physics-based dynamic docking approaches (DynaD and DynaD/Auto) and applied them to nine small molecules known to specifically target RNA CUG repeats. While DynaD uses a distance-based reaction coordinate to study the binding phenomenon, DynaD/Auto combines results of umbrella sampling calculations performed on 1 × 1 UU internal loops and AutoDock calculations to efficiently sample the energy landscape of binding. Predictions are compared with experimental data, displaying a positive correlation with correlation coefficient (R) values of 0.70 and 0.81 for DynaD and DynaD/Auto, respectively. Furthermore, we found that the best correlation was achieved with MM/3D-RISM calculations, highlighting the importance of solvation in binding calculations. Moreover, we detected that DynaD/Auto performed better than DynaD because of the use of prior knowledge about the binding site arising from umbrella sampling calculations. Finally, we developed dendrograms to present how bound states are connected to each other in a binding process. Results are exciting, as DynaD and DynaD/Auto will allow researchers to utilize two novel physics-based and computer-aided drug-design methodologies to perform in silico calculations on drug-like molecules aiming to target complex RNA loops.
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Affiliation(s)
- Kye Won Wang
- Department of Chemistry and Biochemistry, Florida Atlantic University, Jupiter, Florida; Departments of Biological Sciences and Chemistry, Lehigh University, Bethlehem, Pennsylvania
| | - Ivan Riveros
- Department of Chemistry and Biochemistry, Florida Atlantic University, Jupiter, Florida
| | - James DeLoye
- Department of Chemistry, University of California, Berkeley, Berkeley, California
| | - Ilyas Yildirim
- Department of Chemistry and Biochemistry, Florida Atlantic University, Jupiter, Florida.
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12
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Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions. Comput Struct Biotechnol J 2022; 20:6120-6137. [PMID: 36420155 PMCID: PMC9672130 DOI: 10.1016/j.csbj.2022.11.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022] Open
Abstract
The International Society of RNA Nanotechnology and Nanomedicine (ISRNN) serves to further the development of a wide variety of functional nucleic acids and other related nanotechnology platforms. To aid in the dissemination of the most recent advancements, a biennial discussion focused on biomotors, viral assembly, and RNA nanobiotechnology has been established where international experts in interdisciplinary fields such as structural biology, biophysical chemistry, nanotechnology, cell and cancer biology, and pharmacology share their latest accomplishments and future perspectives. The results summarized here highlight advancements in our understanding of viral biology and the structure-function relationship of frame-shifting elements in genomic viral RNA, improvements in the predictions of SHAPE analysis of 3D RNA structures, and the understanding of dynamic RNA structures through a variety of experimental and computational means. Additionally, recent advances in the drug delivery, vaccine design, nanopore technologies, biomotor and biomachine development, DNA packaging, RNA nanotechnology, and drug delivery are included in this critical review. We emphasize some of the novel accomplishments, major discussion topics, and present current challenges and perspectives of these emerging fields.
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13
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Mollica L, Cupaioli FA, Rossetti G, Chiappori F. An overview of structural approaches to study therapeutic RNAs. Front Mol Biosci 2022; 9:1044126. [PMID: 36387283 PMCID: PMC9649582 DOI: 10.3389/fmolb.2022.1044126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 10/18/2022] [Indexed: 11/07/2023] Open
Abstract
RNAs provide considerable opportunities as therapeutic agent to expand the plethora of classical therapeutic targets, from extracellular and surface proteins to intracellular nucleic acids and its regulators, in a wide range of diseases. RNA versatility can be exploited to recognize cell types, perform cell therapy, and develop new vaccine classes. Therapeutic RNAs (aptamers, antisense nucleotides, siRNA, miRNA, mRNA and CRISPR-Cas9) can modulate or induce protein expression, inhibit molecular interactions, achieve genome editing as well as exon-skipping. A common RNA thread, which makes it very promising for therapeutic applications, is its structure, flexibility, and binding specificity. Moreover, RNA displays peculiar structural plasticity compared to proteins as well as to DNA. Here we summarize the recent advances and applications of therapeutic RNAs, and the experimental and computational methods to analyze their structure, by biophysical techniques (liquid-state NMR, scattering, reactivity, and computational simulations), with a focus on dynamic and flexibility aspects and to binding analysis. This will provide insights on the currently available RNA therapeutic applications and on the best techniques to evaluate its dynamics and reactivity.
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Affiliation(s)
- Luca Mollica
- Department of Medical Biotechnologies and Translational Medicine, L.I.T.A/University of Milan, Milan, Italy
| | | | | | - Federica Chiappori
- National Research Council—Institute for Biomedical Technologies, Milan, Italy
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14
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Kognole AA, Hazel A, MacKerell AD. SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules. J Chem Theory Comput 2022; 18:5672-5691. [PMID: 35913731 PMCID: PMC9474704 DOI: 10.1021/acs.jctc.2c00381] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.
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Affiliation(s)
- Abhishek A Kognole
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
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15
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Albadawy R, Hasanin AH, Agwa SHA, Hamady S, Aboul-Ela YM, Raafat MH, Kamar SS, Othman M, Yahia YA, Matboli M. Rosavin Ameliorates Hepatic Inflammation and Fibrosis in the NASH Rat Model via Targeting Hepatic Cell Death. Int J Mol Sci 2022; 23:ijms231710148. [PMID: 36077546 PMCID: PMC9456245 DOI: 10.3390/ijms231710148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) represents the most common form of chronic liver disease that urgently needs effective therapy. Rosavin, a major constituent of the Rhodiola Rosea plant of the family Crassulaceae, is believed to exhibit multiple pharmacological effects on diverse diseases. However, its effect on non-alcoholic steatohepatitis (NASH), the progressive form of NAFLD, and the underlying mechanisms are not fully illustrated. Aim: Investigate the pharmacological activity and potential mechanism of rosavin treatment on NASH management via targeting hepatic cell death-related (HSPD1/TNF/MMP14/ITGB1) mRNAs and their upstream noncoding RNA regulators (miRNA-6881-5P and lnc-SPARCL1-1:2) in NASH rats. Results: High sucrose high fat (HSHF) diet-induced NASH rats were treated with different concentrations of rosavin (10, 20, and 30 mg/kg/day) for the last four weeks of dietary manipulation. The data revealed that rosavin had the ability to modulate the expression of the hepatic cell death-related RNA panel through the upregulation of both (HSPD1/TNF/MMP14/ITGB1) mRNAs and their epigenetic regulators (miRNA-6881-5P and lnc-SPARCL1-1:2). Moreover, rosavin ameliorated the deterioration in both liver functions and lipid profile, and thereby improved the hepatic inflammation, fibrosis, and apoptosis, as evidenced by the decreased protein levels of IL6, TNF-α, and caspase-3 in liver sections of treated animals compared to the untreated NASH rats. Conclusion: Rosavin has demonstrated a potential ability to attenuate disease progression and inhibit hepatic cell death in the NASH animal model. The produced effect was correlated with upregulation of the hepatic cell death-related (HSPD1, TNF, MMP14, and ITGB1) mRNAs—(miRNA-6881-5P—(lnc-SPARCL1-1:2) RNA panel.
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Affiliation(s)
- Reda Albadawy
- Department of Gastroenterology, Hepatology & Infectious Disease, Faculty of Medicine, Benha University, Benha 13518, Egypt
- Correspondence: (R.A.); or (M.M.)
| | - Amany Helmy Hasanin
- Clinical Pharmacology Department, Faculty of Medicine, Ain Shams University, Cairo 11566, Egypt
| | - Sara H. A. Agwa
- Clinical Pathology and Molecular Genomics Unit, Medical Ain Shams Research Institute (MASRI), Faculty of Medicine, Ain Shams University, Cairo 11382, Egypt
| | - Shaimaa Hamady
- Department of Biochemistry, Faculty of Science, Ain Shams University, Cairo 11566, Egypt
| | - Yasmin M. Aboul-Ela
- Clinical Pharmacology Department, Faculty of Medicine, Ain Shams University, Cairo 11566, Egypt
| | - Mona Hussien Raafat
- Histology and Cell Biology Department, Faculty of Medicine, Ain Shams University, Cairo 11566, Egypt
| | - Samaa Samir Kamar
- Histology and Cell Biology Department, Kasralainy Faculty of Medicine, Cairo University, Giza 12613, Egypt
| | - Mohamed Othman
- Gastroenterology and Hepatology Section, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yahia A. Yahia
- Biochemistry Department, Faculty of Pharmacy, Misr University for Science and Technology, Giza 12566, Egypt or
- Chemistry Department, School of Science and Engineering, American University in Cairo, New Cairo 11835, Egypt
| | - Marwa Matboli
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo 11566, Egypt
- Correspondence: (R.A.); or (M.M.)
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16
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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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17
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Zhou Y, Jiang Y, Chen SJ. RNA-ligand molecular docking: advances and challenges. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2022; 12:e1571. [PMID: 37293430 PMCID: PMC10250017 DOI: 10.1002/wcms.1571] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 07/20/2021] [Indexed: 12/16/2022]
Abstract
With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.
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Affiliation(s)
- Yuanzhe Zhou
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
| | - Yangwei Jiang
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
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18
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Jiang Y, Chen SJ. RLDOCK method for predicting RNA-small molecule binding modes. Methods 2022; 197:97-105. [PMID: 33549725 PMCID: PMC8333169 DOI: 10.1016/j.ymeth.2021.01.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/24/2021] [Accepted: 01/27/2021] [Indexed: 01/03/2023] Open
Abstract
RNA molecules play critical roles in cellular functions at the level of gene expression and regulation. The intricate 3D structures and the functional roles of RNAs make RNA molecules ideal targets for therapeutic drugs. The rational design of RNA-targeted drug requires accurate modeling of RNA-ligand interactions. Recently a new computational tool, RLDOCK, was developed to predict ligand binding sites and binding poses. Using an iterative multiscale sampling and search algorithm and a energy-based evaluation of ligand poses, the method enables efficient and accurate predictions for RNA-ligand interactions. Here we present a detailed illustration of the computational procedure for the practical implementation of the RLDOCK method. Using Flavin mononucleotide (FMN) docking to F. nucleatum FMN riboswitch as an example, we illustrate the computational protocol for RLDOCK-based prediction of RNA- ligand interactions. The RLDOCK software is freely accessible at http://https://github.com/Vfold-RNA/RLDOCK.
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Affiliation(s)
- Yangwei Jiang
- Department of Physics, MU Institute for Data Science and Informatics, Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics, MU Institute for Data Science and Informatics, Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA.
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19
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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]
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20
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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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21
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Feng Y, Zhang K, Wu Q, Huang SY. NLDock: a Fast Nucleic Acid-Ligand Docking Algorithm for Modeling RNA/DNA-Ligand Complexes. J Chem Inf Model 2021; 61:4771-4782. [PMID: 34468128 DOI: 10.1021/acs.jcim.1c00341] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Nucleic acid-ligand interactions play an important role in numerous cellular processes such as gene function expression and regulation. Therefore, nucleic acids such as RNAs have become more and more important drug targets, where the structural determination of nucleic acid-ligand complexes is pivotal for understanding their functions and thus developing therapeutic interventions. Molecular docking has been a useful computational tool in predicting the complex structure between molecules. However, although a number of docking algorithms have been developed for protein-ligand interactions, only a few docking programs were presented for nucleic acid-ligand interactions. Here, we have developed a fast nucleic acid-ligand docking algorithm, named NLDock, by implementing our intrinsic scoring function ITScoreNL for nucleic acid-ligand interactions into a modified version of the MDock program. NLDock was extensively evaluated on four test sets and compared with five other state-of-the-art docking algorithms including AutoDock, DOCK 6, rDock, GOLD, and Glide. It was shown that our NLDock algorithm obtained a significantly better performance than the other docking programs in binding mode predictions and achieved the success rates of 73%, 36%, and 32% on the largest test set of 77 complexes for local rigid-, local flexible-, and global flexible-ligand docking, respectively. In addition, our NLDock approach is also computationally efficient and consumed an average of as short as 0.97 and 2.08 min for a local flexible-ligand docking job and a global flexible-ligand docking job, respectively. These results suggest the good performance of our NLDock in both docking accuracy and computational efficiency.
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Affiliation(s)
- Yuyu Feng
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Keqiong Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Qilong Wu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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22
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Maucort C, Vo DD, Aouad S, Charrat C, Azoulay S, Di Giorgio A, Duca M. Design and Implementation of Synthetic RNA Binders for the Inhibition of miR-21 Biogenesis. ACS Med Chem Lett 2021; 12:899-906. [PMID: 34141067 DOI: 10.1021/acsmedchemlett.0c00682] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 05/03/2021] [Indexed: 12/17/2022] Open
Abstract
Targeting RNAs using small molecules is an emerging field of medicinal chemistry and holds promise for the discovery of efficient tools for chemical biology. MicroRNAs are particularly interesting targets since they are involved in a number of pathologies such as cancers. Indeed, overexpressed microRNAs in cancer are oncogenic and various series of inhibitors of microRNAs biogenesis have been developed in recent years. Here, we describe the structure-based design of new efficient inhibitors of microRNA-21. Starting from a previously identified hit, we performed biochemical studies and molecular docking to design a new series of optimized conjugates of neomycin aminoglycoside with artificial nucleobases and amino acids. Investigation about the mode of action and the site of the interaction of the newly synthesized compounds allowed for the description of structure-activity relationships and the identification of the most important parameters for miR-21 inhibition.
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Affiliation(s)
- Chloé Maucort
- Université Côte d’Azur, CNRS, Institute of Chemistry of Nice (ICN), 28 avenue Valrose, 06100 Nice, France
| | - Duc Duy Vo
- Université Côte d’Azur, CNRS, Institute of Chemistry of Nice (ICN), 28 avenue Valrose, 06100 Nice, France
| | - Samy Aouad
- Université Côte d’Azur, CNRS, Institute of Chemistry of Nice (ICN), 28 avenue Valrose, 06100 Nice, France
| | - Coralie Charrat
- Université Côte d’Azur, CNRS, Institute of Chemistry of Nice (ICN), 28 avenue Valrose, 06100 Nice, France
| | - Stéphane Azoulay
- Université Côte d’Azur, CNRS, Institute of Chemistry of Nice (ICN), 28 avenue Valrose, 06100 Nice, France
| | - Audrey Di Giorgio
- Université Côte d’Azur, CNRS, Institute of Chemistry of Nice (ICN), 28 avenue Valrose, 06100 Nice, France
| | - Maria Duca
- Université Côte d’Azur, CNRS, Institute of Chemistry of Nice (ICN), 28 avenue Valrose, 06100 Nice, France
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23
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Binzel DW, Li X, Burns N, Khan E, Lee WJ, Chen LC, Ellipilli S, Miles W, Ho YS, Guo P. Thermostability, Tunability, and Tenacity of RNA as Rubbery Anionic Polymeric Materials in Nanotechnology and Nanomedicine-Specific Cancer Targeting with Undetectable Toxicity. Chem Rev 2021; 121:7398-7467. [PMID: 34038115 DOI: 10.1021/acs.chemrev.1c00009] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
RNA nanotechnology is the bottom-up self-assembly of nanometer-scale architectures, resembling LEGOs, composed mainly of RNA. The ideal building material should be (1) versatile and controllable in shape and stoichiometry, (2) spontaneously self-assemble, and (3) thermodynamically, chemically, and enzymatically stable with a long shelf life. RNA building blocks exhibit each of the above. RNA is a polynucleic acid, making it a polymer, and its negative-charge prevents nonspecific binding to negatively charged cell membranes. The thermostability makes it suitable for logic gates, resistive memory, sensor set-ups, and NEM devices. RNA can be designed and manipulated with a level of simplicity of DNA while displaying versatile structure and enzyme activity of proteins. RNA can fold into single-stranded loops or bulges to serve as mounting dovetails for intermolecular or domain interactions without external linking dowels. RNA nanoparticles display rubber- and amoeba-like properties and are stretchable and shrinkable through multiple repeats, leading to enhanced tumor targeting and fast renal excretion to reduce toxicities. It was predicted in 2014 that RNA would be the third milestone in pharmaceutical drug development. The recent approval of several RNA drugs and COVID-19 mRNA vaccines by FDA suggests that this milestone is being realized. Here, we review the unique properties of RNA nanotechnology, summarize its recent advancements, describe its distinct attributes inside or outside the body and discuss potential applications in nanotechnology, medicine, and material science.
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Affiliation(s)
- Daniel W Binzel
- Center for RNA Nanobiotechnology and Nanomedicine, College of Pharmacy, Dorothy M. Davis Heart and Lung Research Institute, James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States
| | - Xin Li
- Center for RNA Nanobiotechnology and Nanomedicine, College of Pharmacy, Dorothy M. Davis Heart and Lung Research Institute, James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States
| | - Nicolas Burns
- Center for RNA Nanobiotechnology and Nanomedicine, College of Pharmacy, Dorothy M. Davis Heart and Lung Research Institute, James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States
| | - Eshan Khan
- Department of Cancer Biology and Genetics, The Ohio State University Comprehensive Cancer Center, College of Medicine, Center for RNA Biology, The Ohio State University, Columbus, Ohio 43210, United States
| | - Wen-Jui Lee
- TMU Research Center of Cancer Translational Medicine, School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Department of Laboratory Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Li-Ching Chen
- TMU Research Center of Cancer Translational Medicine, School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Department of Laboratory Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Satheesh Ellipilli
- Center for RNA Nanobiotechnology and Nanomedicine, College of Pharmacy, Dorothy M. Davis Heart and Lung Research Institute, James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States
| | - Wayne Miles
- Department of Cancer Biology and Genetics, The Ohio State University Comprehensive Cancer Center, College of Medicine, Center for RNA Biology, The Ohio State University, Columbus, Ohio 43210, United States
| | - Yuan Soon Ho
- TMU Research Center of Cancer Translational Medicine, School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Graduate Institute of Medical Sciences, College of Medicine, Taipei Medical University, Department of Laboratory Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Peixuan Guo
- Center for RNA Nanobiotechnology and Nanomedicine, College of Pharmacy, Dorothy M. Davis Heart and Lung Research Institute, James Comprehensive Cancer Center, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States
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