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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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Jung Y, El-Manzalawy Y, Dobbs D, Honavar VG. Partner-specific prediction of RNA-binding residues in proteins: A critical assessment. Proteins 2018; 87:198-211. [PMID: 30536635 PMCID: PMC6389706 DOI: 10.1002/prot.25639] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 10/10/2018] [Accepted: 11/29/2018] [Indexed: 01/06/2023]
Abstract
RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are "specific", that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are "non-RNA specific." Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.
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Affiliation(s)
- Yong Jung
- Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, Pennsylvania.,Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, Pennsylvania.,The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania
| | - Yasser El-Manzalawy
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, Pennsylvania.,Clinical and Translational Sciences Institute, Pennsylvania State University, University Park, Pennsylvania.,College of Information Sciences and Technology, Pennsylvania State University, Pennsylvania
| | - Drena Dobbs
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa.,Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, Iowa
| | - Vasant G Honavar
- Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, Pennsylvania.,Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, Pennsylvania.,Institute for Cyberscience, Pennsylvania State University, University Park, Pennsylvania.,Clinical and Translational Sciences Institute, Pennsylvania State University, University Park, Pennsylvania.,The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania.,College of Information Sciences and Technology, Pennsylvania State University, Pennsylvania
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3
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Dvir S, Argoetti A, Mandel-Gutfreund Y. Ribonucleoprotein particles: advances and challenges in computational methods. Curr Opin Struct Biol 2018; 53:124-130. [PMID: 30172766 DOI: 10.1016/j.sbi.2018.08.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 08/07/2018] [Indexed: 01/16/2023]
Abstract
RNA-binding proteins (RBPs) interact with RNA to form Ribonucleoprotein Particles (RNPs). The interaction between RBPs and their RNA partners are traditionally thought to be mediated by highly conserved RNA-binding domains (RBDs). Recently, high-throughput studies led to the discovery of hundreds of novel proteins and domains, of which many do not follow the classical definition of RNA-binding. Despite technological innovations, experimental screenings are currently limited to the detection of specific types of RNPs, underscoring the importance of computational methods for predicting novel RBPs and RNA interacting residues and interfaces. Here, we discuss major challenges in computational prediction of RBPs and RBDs and outline new strategies to circumvent current limitations of experimental techniques.
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Affiliation(s)
- Shlomi Dvir
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | - Amir Argoetti
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa 32000, Israel
| | - Yael Mandel-Gutfreund
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa 32000, Israel; Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel.
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Nithin C, Ghosh P, Bujnicki JM. Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes. Genes (Basel) 2018; 9:genes9090432. [PMID: 30149645 PMCID: PMC6162694 DOI: 10.3390/genes9090432] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 07/26/2018] [Accepted: 08/21/2018] [Indexed: 12/29/2022] Open
Abstract
RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.
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Affiliation(s)
- Chandran Nithin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland.
| | - Pritha Ghosh
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland.
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland.
- Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, PL-61-614 Poznan, Poland.
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5
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Hamada M. In silico approaches to RNA aptamer design. Biochimie 2017; 145:8-14. [PMID: 29032056 DOI: 10.1016/j.biochi.2017.10.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 10/09/2017] [Indexed: 10/18/2022]
Abstract
RNA aptamers are ribonucleic acids that bind to specific target molecules. An RNA aptamer for a disease-related protein has great potential for development into a new drug. However, huge time and cost investments are required to develop an RNA aptamer into a pharmaceutical. Recently, SELEX combined with high-throughput sequencers (i.e., HT-SELEX) has been widely used to select candidate RNA aptamers that bind to a target protein with high affinity and specificity. After candidate selection, further optimizations such as shortening and modifying candidate sequences are performed. In these steps, in silico approaches are expected to reduce the time and cost associated with aptamer drug development. In this article, we review existing in silico approaches to RNA aptamer development, including a method for ranking the candidates of RNA aptamers from HT-SELEX data, clustering a huge number of aptamer sequences, and finding motifs amidst a set of significant RNA aptamers. It is expected that further studies in addition to these methods will be utilized for in silico RNA aptamer design, permitting a minimal number of experiments to be performed through the utilization of sophisticated computational methods.
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Affiliation(s)
- Michiaki Hamada
- Bioinformatics Laboratory, Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan; Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), 63-520, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan; Institute for Medical-oriented Structural Biology, Waseda University, 2-2, Wakamatsu-cho Shinjuku-ku, Tokyo 162-8480, Japan; Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-ku, Tokyo 135-0064, Japan; Graduate School of Medicine, Nippon Medical School, 1-1-5, Sendagi, Bunkyo-ku, Tokyo 113-8602, Japan.
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Alfinito E, Reggiani L, Cataldo R, De Nunzio G, Giotta L, Guascito MR. Modeling the microscopic electrical properties of thrombin binding aptamer (TBA) for label-free biosensors. NANOTECHNOLOGY 2017; 28:065502. [PMID: 28050975 DOI: 10.1088/1361-6528/aa510f] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Aptamers are chemically produced oligonucleotides, able to bind a variety of targets such as drugs, proteins and pathogens with high sensitivity and selectivity. Therefore, aptamers are largely employed for producing label-free biosensors (aptasensors), with significant applications in diagnostics and drug delivery. In particular, the anti-thrombin aptamers are biomolecules of high interest for clinical use, because of their ability to recognize and bind the thrombin enzyme. Among them, the DNA 15-mer aptamer (TBA), has been widely explored around the possibility of using it in aptasensors. This paper proposes a microscopic model of the electrical properties of TBA and of the aptamer-thrombin complex, combining information from both structure and function, following the issues addressed in an emerging branch of electronics known as proteotronics. The theoretical results are compared and validated with measurements reported in the literature. Finally, the model suggests resistance measurements as a novel tool for testing aptamer-target affinity.
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Affiliation(s)
- Eleonora Alfinito
- Dipartimento di Ingegneria dell'Innovazione. Università del Salento, via Monteroni, Lecce, Italy
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Structural studies of RNA-protein complexes: A hybrid approach involving hydrodynamics, scattering, and computational methods. Methods 2016; 118-119:146-162. [PMID: 27939506 DOI: 10.1016/j.ymeth.2016.12.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Revised: 12/01/2016] [Accepted: 12/05/2016] [Indexed: 01/01/2023] Open
Abstract
The diverse functional cellular roles played by ribonucleic acids (RNA) have emphasized the need to develop rapid and accurate methodologies to elucidate the relationship between the structure and function of RNA. Structural biology tools such as X-ray crystallography and Nuclear Magnetic Resonance are highly useful methods to obtain atomic-level resolution models of macromolecules. However, both methods have sample, time, and technical limitations that prevent their application to a number of macromolecules of interest. An emerging alternative to high-resolution structural techniques is to employ a hybrid approach that combines low-resolution shape information about macromolecules and their complexes from experimental hydrodynamic (e.g. analytical ultracentrifugation) and solution scattering measurements (e.g., solution X-ray or neutron scattering), with computational modeling to obtain atomic-level models. While promising, scattering methods rely on aggregation-free, monodispersed preparations and therefore the careful development of a quality control pipeline is fundamental to an unbiased and reliable structural determination. This review article describes hydrodynamic techniques that are highly valuable for homogeneity studies, scattering techniques useful to study the low-resolution shape, and strategies for computational modeling to obtain high-resolution 3D structural models of RNAs, proteins, and RNA-protein complexes.
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