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Sabei A, Hognon C, Martin J, Frezza E. Dynamics of Protein-RNA Interfaces Using All-Atom Molecular Dynamics Simulations. J Phys Chem B 2024; 128:4865-4886. [PMID: 38740056 DOI: 10.1021/acs.jpcb.3c07698] [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: 05/16/2024]
Abstract
Facing the current challenges posed by human health diseases requires the understanding of cell machinery at a molecular level. The interplay between proteins and RNA is key for any physiological phenomenon, as well protein-RNA interactions. To understand these interactions, many experimental techniques have been developed, spanning a very wide range of spatial and temporal resolutions. In particular, the knowledge of tridimensional structures of protein-RNA complexes provides structural, mechanical, and dynamical pieces of information essential to understand their functions. To get insights into the dynamics of protein-RNA complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on nine different protein-RNA complexes with different functions and interface size by taking into account the bound and unbound forms. First, we characterized structural changes upon binding and, for the RNA part, the change in the puckering. Second, we extensively analyzed the interfaces, their dynamics and structural properties, and the structural waters involved in the binding, as well as the contacts mediated by them. Based on our analysis, the interfaces rearranged during the simulation time showing alternative and stable residue-residue contacts with respect to the experimental structure.
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Affiliation(s)
- Afra Sabei
- Université Paris Cité, CiTCoM, CNRS, Paris F-75006, France
| | - Cécilia Hognon
- Université Paris Cité, CiTCoM, CNRS, Paris F-75006, France
| | - Juliette Martin
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, UMR 5086 MMSB, Lyon 69367, France
- Laboratory of Biology and Modeling of the Cell, Université de Lyon, ENS de Lyon, Université Claude Bernard, CNRS UMR 5239, Inserm U1293, Lyon 69367, France
| | - Elisa Frezza
- Université Paris Cité, CiTCoM, CNRS, Paris F-75006, France
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2
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Tants JN, Schlundt A. Advances, Applications, and Perspectives in Small-Angle X-ray Scattering of RNA. Chembiochem 2023; 24:e202300110. [PMID: 37466350 DOI: 10.1002/cbic.202300110] [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: 02/10/2023] [Revised: 04/22/2023] [Indexed: 07/20/2023]
Abstract
RNAs exhibit a plethora of functions far beyond transmitting genetic information. Often, RNA functions are entailed in their structure, be it as a regulatory switch, protein binding site, or providing catalytic activity. Structural information is a prerequisite for a full understanding of RNA-regulatory mechanisms. Owing to the inherent dynamics, size, and instability of RNA, its structure determination remains challenging. Methods such as NMR spectroscopy, X-ray crystallography, and cryo-electron microscopy can provide high-resolution structures; however, their limitations make structure determination, even for small RNAs, cumbersome, if at all possible. Although at a low resolution, small-angle X-ray scattering (SAXS) has proven valuable in advancing structure determination of RNAs as a complementary method, which is also applicable to large-sized RNAs. Here, we review the technological and methodological advancements of RNA SAXS. We provide examples of the powerful inclusion of SAXS in structural biology and discuss possible future applications to large RNAs.
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Affiliation(s)
- Jan-Niklas Tants
- Goethe University Frankfurt, Institute for Molecular Biosciences and Biomagnetic Resonance Centre (BMRZ), Max-von-Laue-Str. 9, 60438, Frankfurt, Germany
| | - Andreas Schlundt
- Goethe University Frankfurt, Institute for Molecular Biosciences and Biomagnetic Resonance Centre (BMRZ), Max-von-Laue-Str. 9, 60438, Frankfurt, Germany
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3
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Agarwal A, Kant S, Bahadur RP. Efficient mapping of RNA-binding residues in RNA-binding proteins using local sequence features of binding site residues in protein-RNA complexes. Proteins 2023; 91:1361-1379. [PMID: 37254800 DOI: 10.1002/prot.26528] [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: 09/07/2022] [Revised: 04/13/2023] [Accepted: 05/02/2023] [Indexed: 06/01/2023]
Abstract
Protein-RNA interactions play vital roles in plethora of biological processes such as regulation of gene expression, protein synthesis, mRNA processing and biogenesis. Identification of RNA-binding residues (RBRs) in proteins is essential to understand RNA-mediated protein functioning, to perform site-directed mutagenesis and to develop novel targeted drug therapies. Moreover, the extensive gap between sequence and structural data restricts the identification of binding sites in unsolved structures. However, efficient use of computational methods demanding only sequence to identify binding residues can bridge this huge sequence-structure gap. In this study, we have extensively studied protein-RNA interface in known RNA-binding proteins (RBPs). We find that the interface is highly enriched in basic and polar residues with Gly being the most common interface neighbor. We investigated several amino acid features and developed a method to predict putative RBRs from amino acid sequence. We have implemented balanced random forest (BRF) classifier with local residue features of protein sequences for prediction. With 5-fold cross-validations, the sequence pattern derived dipeptide composition based BRF model (DCP-BRF) resulted in an accuracy of 87.9%, specificity of 88.8%, sensitivity of 82.2%, Mathew's correlation coefficient of 0.60 and AUC of 0.93, performing better than few existing methods. We further validated our prediction model on known human RBPs through RBR prediction and could map ~54% of them. Further, knowledge of binding site preferences obtained from computational predictions combined with experimental validations of potential RNA binding sites can enhance our understanding of protein-RNA interactions. This may serve to accelerate investigations on functional roles of many novel RBPs.
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Affiliation(s)
- Ankita Agarwal
- School of Bio Science, Indian Institute of Technology Kharagpur, Kharagpur, India
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Shri Kant
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
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4
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Krepl M, Damberger FF, von Schroetter C, Theler D, Pokorná P, Allain FHT, Šponer J. Recognition of N6-Methyladenosine by the YTHDC1 YTH Domain Studied by Molecular Dynamics and NMR Spectroscopy: The Role of Hydration. J Phys Chem B 2021; 125:7691-7705. [PMID: 34258996 DOI: 10.1021/acs.jpcb.1c03541] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The YTH domain of YTHDC1 belongs to a class of protein "readers", recognizing the N6-methyladenosine (m6A) chemical modification in mRNA. Static ensemble-averaged structures revealed details of N6-methyl recognition via a conserved aromatic cage. Here, we performed molecular dynamics (MD) simulations along with nuclear magnetic resonance (NMR) and isothermal titration calorimetry (ITC) to examine how dynamics and solvent interactions contribute to the m6A recognition and negative selectivity toward an unmethylated substrate. The structured water molecules surrounding the bound RNA and the methylated substrate's ability to exclude bulk water molecules contribute to the YTH domain's preference for m6A. Intrusions of bulk water deep into the binding pocket disrupt binding of unmethylated adenosine. The YTHDC1's preference for the 5'-Gm6A-3' motif is partially facilitated by a network of water-mediated interactions between the 2-amino group of the guanosine and residues in the m6A binding pocket. The 5'-Im6A-3' (where I is inosine) motif can be recognized too, but disruption of the water network lowers affinity. The D479A mutant also disrupts the water network and destabilizes m6A binding. Our interdisciplinary study of the YTHDC1 protein-RNA complex reveals an unusual physical mechanism by which solvent interactions contribute toward m6A recognition.
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Affiliation(s)
- Miroslav Krepl
- Institute of Biophysics of the Czech Academy of Sciences, Kralovopolska 135, 612 65 Brno, Czech Republic
| | - Fred Franz Damberger
- Department of Biology, Institute of Biochemistry, ETH Zürich, 8093 Zürich, Switzerland
| | | | - Dominik Theler
- Department of Biology, Institute of Biochemistry, ETH Zürich, 8093 Zürich, Switzerland
| | - Pavlína Pokorná
- Institute of Biophysics of the Czech Academy of Sciences, Kralovopolska 135, 612 65 Brno, Czech Republic.,National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic
| | - Frédéric H-T Allain
- Department of Biology, Institute of Biochemistry, ETH Zürich, 8093 Zürich, Switzerland
| | - Jiří Šponer
- Institute of Biophysics of the Czech Academy of Sciences, Kralovopolska 135, 612 65 Brno, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Olomouc 783 71, Czech Republic
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5
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Krah A, Huber RG, Bond PJ. How Ligand Binding Affects the Dynamical Transition Temperature in Proteins. Chemphyschem 2020; 21:916-926. [DOI: 10.1002/cphc.201901221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 03/03/2020] [Indexed: 11/11/2022]
Affiliation(s)
- Alexander Krah
- School of Computational SciencesKorea Institute for Advanced Study 85 Hoegiro, Dongdaemun-gu Seoul 02455 Republic of Korea
- Bioinformatics InstituteAgency for Science Technology and Research (A*STAR) 30 Biopolis Str., #07-01 Matrix 138671 Singapore
| | - Roland G. Huber
- Bioinformatics InstituteAgency for Science Technology and Research (A*STAR) 30 Biopolis Str., #07-01 Matrix 138671 Singapore
| | - Peter J. Bond
- Bioinformatics InstituteAgency for Science Technology and Research (A*STAR) 30 Biopolis Str., #07-01 Matrix 138671 Singapore
- National University of SingaporeDepartment of Biological Sciences 14 Science Drive 4 Singapore 117543
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6
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Nithin C, Mukherjee S, Bahadur RP. A structure-based model for the prediction of protein-RNA binding affinity. RNA (NEW YORK, N.Y.) 2019; 25:1628-1645. [PMID: 31395671 PMCID: PMC6859855 DOI: 10.1261/rna.071779.119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Accepted: 08/05/2019] [Indexed: 05/28/2023]
Abstract
Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of -5.7 cal/mol/Å2 in the entire data set. We train regression models to predict the binding affinity from the structural and physicochemical parameters of protein-RNA interfaces. The best fit model with the lowest maximum error is provided with three interface parameters: relative hydrophobicity, conformational change upon binding and relative hydration pattern. This model has been used for predicting the binding affinity on a test data set, generated using mutated structures of yeast aspartyl-tRNA synthetase, for which experimentally determined ΔG values of 40 mutations are available. The predicted ΔGempirical values highly correlate with the experimental observations. The data set provided in this study should be useful for further development of the binding affinity prediction methods. Moreover, the model developed in this study enhances our understanding on the structural basis of protein-RNA binding affinity and provides a platform to engineer protein-RNA interfaces with desired affinity.
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Affiliation(s)
- Chandran Nithin
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Sunandan Mukherjee
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
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7
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Deng L, Yang W, Liu H. PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees. Front Genet 2019; 10:637. [PMID: 31428122 PMCID: PMC6688581 DOI: 10.3389/fgene.2019.00637] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 06/18/2019] [Indexed: 01/24/2023] Open
Abstract
Protein-RNA interactions play essential roles in many biological aspects. Quantifying the binding affinity of protein-RNA complexes is helpful to the understanding of protein-RNA recognition mechanisms and identification of strong binding partners. Due to experimentally measured protein-RNA binding affinity data available is still limited to date, there is a pressing demand for accurate and reliable computational approaches. In this paper, we propose a computational approach, PredPRBA, which can effectively predict protein-RNA binding affinity using gradient boosted regression trees. We build a dataset of protein-RNA binding affinity that includes 103 protein-RNA complex structures manually collected from related literature. Then, we generate 37 kinds of sequence and structural features and explore the relationship between the features and protein-RNA binding affinity. We find that the binding affinity mainly depends on the structure of RNA molecules. According to the type of RNA associated with proteins composed of the protein-RNA complex, we split the 103 protein-RNA complexes into six categories. For each category, we build a gradient boosted regression tree (GBRT) model based on the generated features. We perform a comprehensive evaluation for the proposed method on the binding affinity dataset using leave-one-out cross-validation. We show that PredPRBA achieves correlations ranging from 0.723 to 0.897 among six categories, which is significantly better than other typical regression methods and the pioneer protein-RNA binding affinity predictor SPOT-Seq-RNA. In addition, a user-friendly web server has been developed to predict the binding affinity of protein-RNA complexes. The PredPRBA webserver is freely available at http://PredPRBA.denglab.org/.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China.,School of Software, Xinjiang University, Urumqi, China
| | - Wenyi Yang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hui Liu
- Lab of Information Management, Changzhou University, Changzhou, China
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8
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Foss DV, Schirle NT, MacRae IJ, Pezacki JP. Structural insights into interactions between viral suppressor of RNA silencing protein p19 mutants and small RNAs. FEBS Open Bio 2019; 9:1042-1051. [PMID: 31021526 PMCID: PMC6551489 DOI: 10.1002/2211-5463.12644] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 04/03/2019] [Accepted: 04/24/2019] [Indexed: 12/31/2022] Open
Abstract
Viral suppressors of RNA silencing (VSRSs) are a diverse group of viral proteins that have evolved to disrupt eukaryotic RNA silencing pathways, thereby contributing to viral pathogenicity. The p19 protein is a VSRS that selectively binds to short interfering RNAs (siRNAs) over microRNAs (miRNAs). Mutational analysis has identified single amino acid substitutions that reverse this selectivity through new high-affinity interactions with human miR-122. Herein, we report crystal structures of complexed p19-T111S (2.6 Å), p19-T111H (2.3 Å) and wild-type p19 protein (2.2 Å) from the Carnation Italian ringspot virus with small interfering RNA (siRNA) ligands. Structural comparisons reveal that these mutations do not lead to major changes in p19 architecture, but instead promote subtle rearrangement of residues and solvent molecules along the p19 midline. These observations suggest p19 uses many small interactions to distinguish siRNAs from miRNAs and perturbing these interactions can create p19 variants with novel RNA-recognition properties. DATABASE: Model data are deposited in the PDB database under the accession numbers 6BJG, 6BJH and 6BJV.
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Affiliation(s)
- Dana V Foss
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Canada
| | - Nicole T Schirle
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ian J MacRae
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - John Paul Pezacki
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Canada
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9
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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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10
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An account of solvent accessibility in protein-RNA recognition. Sci Rep 2018; 8:10546. [PMID: 30002431 PMCID: PMC6043566 DOI: 10.1038/s41598-018-28373-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 06/21/2018] [Indexed: 01/16/2023] Open
Abstract
Protein–RNA recognition often induces conformational changes in binding partners. Consequently, the solvent accessible surface area (SASA) buried in contact estimated from the co-crystal structures may differ from that calculated using their unbound forms. To evaluate the change in accessibility upon binding, we compare SASA of 126 protein-RNA complexes between bound and unbound forms. We observe, in majority of cases the interface of both the binding partners gain accessibility upon binding, which is often associated with either large domain movements or secondary structural transitions in RNA-binding proteins (RBPs), and binding-induced conformational changes in RNAs. At the non-interface region, majority of RNAs lose accessibility upon binding, however, no such preference is observed for RBPs. Side chains of RBPs have major contribution in change in accessibility. In case of flexible binding, we find a moderate correlation between the binding free energy and change in accessibility at the interface. Finally, we introduce a parameter, the ratio of gain to loss of accessibility upon binding, which can be used to identify the native solution among the flexible docking models. Our findings provide fundamental insights into the relationship between flexibility and solvent accessibility, and advance our understanding on binding induced folding in protein-RNA recognition.
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11
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Mukherjee S, Nithin C, Divakaruni Y, Bahadur RP. Dissecting water binding sites at protein–protein interfaces: a lesson from the atomic structures in the Protein Data Bank. J Biomol Struct Dyn 2018; 37:1204-1219. [DOI: 10.1080/07391102.2018.1453379] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Sunandan Mukherjee
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Chandran Nithin
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Yasaswi Divakaruni
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India
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12
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Hu W, Qin L, Li M, Pu X, Guo Y. A structural dissection of protein–RNA interactions based on different RNA base areas of interfaces. RSC Adv 2018; 8:10582-10592. [PMID: 35540439 PMCID: PMC9078961 DOI: 10.1039/c8ra00598b] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Accepted: 03/05/2018] [Indexed: 11/21/2022] Open
Abstract
Protein–RNA interactions are very common cellular processes, but the mechanisms of interactions are not fully understood, mainly due to the complicated RNA structures. By the elaborate investigation on RNA structures of protein–RNA complexes, it was firstly found in this paper that RNAs in these complexes could be clearly classified into three classes (high, medium and low) based on the different levels of Pbase (the percentage of base area buried in the RNA interface). In view of the three RNA classes, more detailed analyses on protein–RNA interactions were comprehensively performed from various aspects, including interface area, structure, composition and interaction force, so as to achieve a deeper understanding of the recognition specificity for the three classes of protein–RNA interactions. According to our classification strategy, the three complex classes have significant differences in terms of almost all properties. Complexes in the high class have short and extended RNA structures and behave like protein–ssDNA interactions. Their hydrogen bonds and hydrophobic interactions are strong. For complexes in low class, their RNA structures are mainly double-stranded, like protein–dsDNA interactions, and electrostatic interactions frequently occur. The complexes in medium class have the longest RNA chains and largest average interface area. Meanwhile, they do not show any preference for the interaction force. On average, in terms of composition, secondary structures and intermolecular physicochemical properties, significant feature preferences can be observed in high and low complexes, but no highly specific features are found for medium complexes. We found that our proposed Pbase is an important parameter which can be used as a new determinant to distinguish protein–RNA complexes. For high and low complexes, we can more easily understand the specificity of the recognition process from the interface features than for medium complexes. In the future, medium complexes should be our research focus to further structurally analyze from more feature aspects. Overall, this study may contribute to further understanding of the mechanism of protein–RNA interactions on a more detailed level. Qualitative and quantitative measurements of the influence of structure and composition of RNA interfaces on protein–RNA interactions.![]()
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Affiliation(s)
- Wen Hu
- College of Chemistry
- Sichuan University
- Chengdu 610064
- People's Republic of China
| | - Liu Qin
- College of Chemistry
- Sichuan University
- Chengdu 610064
- People's Republic of China
| | - Menglong Li
- College of Chemistry
- Sichuan University
- Chengdu 610064
- People's Republic of China
| | - Xuemei Pu
- College of Chemistry
- Sichuan University
- Chengdu 610064
- People's Republic of China
| | - Yanzhi Guo
- College of Chemistry
- Sichuan University
- Chengdu 610064
- People's Republic of China
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13
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Grechishnikova DA, Poptsova MS. The Physical and Geometric Properties of Human Transposon Stem–Loop Structures under Natural Selection. Biophysics (Nagoya-shi) 2017. [DOI: 10.1134/s0006350917060070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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14
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Krepl M, Blatter M, Cléry A, Damberger FF, Allain FH, Sponer J. Structural study of the Fox-1 RRM protein hydration reveals a role for key water molecules in RRM-RNA recognition. Nucleic Acids Res 2017; 45:8046-8063. [PMID: 28505313 PMCID: PMC5737849 DOI: 10.1093/nar/gkx418] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 04/26/2017] [Accepted: 05/02/2017] [Indexed: 01/07/2023] Open
Abstract
The Fox-1 RNA recognition motif (RRM) domain is an important member of the RRM protein family. We report a 1.8 Å X-ray structure of the free Fox-1 containing six distinct monomers. We use this and the nuclear magnetic resonance (NMR) structure of the Fox-1 protein/RNA complex for molecular dynamics (MD) analyses of the structured hydration. The individual monomers of the X-ray structure show diverse hydration patterns, however, MD excellently reproduces the most occupied hydration sites. Simulations of the protein/RNA complex show hydration consistent with the isolated protein complemented by hydration sites specific to the protein/RNA interface. MD predicts intricate hydration sites with water-binding times extending up to hundreds of nanoseconds. We characterize two of them using NMR spectroscopy, RNA binding with switchSENSE and free-energy calculations of mutant proteins. Both hydration sites are experimentally confirmed and their abolishment reduces the binding free-energy. A quantitative agreement between theory and experiment is achieved for the S155A substitution but not for the S122A mutant. The S155 hydration site is evolutionarily conserved within the RRM domains. In conclusion, MD is an effective tool for predicting and interpreting the hydration patterns of protein/RNA complexes. Hydration is not easily detectable in NMR experiments but can affect stability of protein/RNA complexes.
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Affiliation(s)
- Miroslav Krepl
- Institute of Biophysics, Academy of Sciences of the Czech Republic, Kralovopolska 135, 612 65 Brno, Czech Republic
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 771 46 Olomouc, Czech Republic
| | - Markus Blatter
- Institute of Molecular Biology and Biophysics, Department of Biology, ETH Zurich, CH-8093 Zurich, Switzerland
- Present address: Global Discovery Chemistry, Novartis Institute for BioMedical Research, Basel CH-4002, Switzerland
| | - Antoine Cléry
- Institute of Molecular Biology and Biophysics, Department of Biology, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Fred F. Damberger
- Institute of Molecular Biology and Biophysics, Department of Biology, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Frédéric H.T. Allain
- Institute of Molecular Biology and Biophysics, Department of Biology, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Jiri Sponer
- Institute of Biophysics, Academy of Sciences of the Czech Republic, Kralovopolska 135, 612 65 Brno, Czech Republic
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 771 46 Olomouc, Czech Republic
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15
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Spyrakis F, Ahmed MH, Bayden AS, Cozzini P, Mozzarelli A, Kellogg GE. The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery. J Med Chem 2017; 60:6781-6827. [PMID: 28475332 DOI: 10.1021/acs.jmedchem.7b00057] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.
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Affiliation(s)
- Francesca Spyrakis
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino , Via Pietro Giuria 9, 10125 Torino, Italy
| | - Mostafa H Ahmed
- Department of Medicinal Chemistry & Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University , Richmond, Virginia 23298-0540, United States
| | - Alexander S Bayden
- CMD Bioscience , 5 Science Park, New Haven, Connecticut 06511, United States
| | - Pietro Cozzini
- Dipartimento di Scienze degli Alimenti e del Farmaco, Laboratorio di Modellistica Molecolare, Università degli Studi di Parma , Parco Area delle Scienze 59/A, 43121 Parma, Italy
| | - Andrea Mozzarelli
- Dipartimento di Scienze degli Alimenti e del Farmaco, Laboratorio di Biochimica, Università degli Studi di Parma , Parco Area delle Scienze 23/A, 43121 Parma, Italy.,Istituto di Biofisica, Consiglio Nazionale delle Ricerche , Via Moruzzi 1, 56124 Pisa, Italy
| | - Glen E Kellogg
- Department of Medicinal Chemistry & Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University , Richmond, Virginia 23298-0540, United States
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16
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Sharma C, Mohanty D. Molecular Dynamics Simulations for Deciphering the Structural Basis of Recognition of Pre-let-7 miRNAs by LIN28. Biochemistry 2017; 56:723-735. [PMID: 28076679 DOI: 10.1021/acs.biochem.6b00837] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
LIN28 protein inhibits biogenesis of miRNAs belonging to the let-7 family by binding to precursor forms of miRNAs. Overexpression of LIN28 and low levels of let-7 miRNAs are associated with several forms of cancer cells. We have performed multiple explicit solvent molecular dynamics simulations ranging from 200 to 500 ns in length on different isoforms of preE-let-7 in complex with LIN28 and also in isolation to identify structural features and key specificity-determining residues (SDRs) that are important for the inhibitory role of LIN28. Our simulations suggest that a conserved structural feature of the loop regions of preE-let-7 miRNAs is more important for LIN28 recognition than sequence conservation among members of the let-7 family or the presence of the GGAG motif in the 3' region. The loop region consisting of a minimum of five nucleotides helps pre-miRNAs to acquire a conformation ideal for binding to LIN28, but pre-let-7c-2 prefers a conformation with a three-nucleotide loop. Thus, our simulations provide a theoretical rationale for the recent experimental observation of the escape of LIN28-mediated repression by pre-let-7c-2. The essential structural and sequence features highlighted in this study might aid in designing synthetic small molecule inhibitors for modulating LIN28-let-7 interaction in malignant cells. We have also identified crucial SDRs of the LIN28-preE-let-7 complex involving 13 residues of LIN28 and 10 residues of the pre-miRNA. On the basis of the conservation profile of these 13 SDRs, we have identified 10 novel proteins that are not annotated as LIN28 like but are similar in sequence, domain, or fold level to LIN28.
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Affiliation(s)
- Chhaya Sharma
- Bioinformatics Center, National Institute of Immunology , Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Debasisa Mohanty
- Bioinformatics Center, National Institute of Immunology , Aruna Asaf Ali Marg, New Delhi 110067, India
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17
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Liu ZP, Liu S, Chen R, Huang X, Wu LY. Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces. BMC Bioinformatics 2017; 18:27. [PMID: 28077065 PMCID: PMC5225598 DOI: 10.1186/s12859-016-1410-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Accepted: 12/07/2016] [Indexed: 11/23/2022] Open
Abstract
Background Many critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. RNA-binding events often take place at pockets on protein surfaces. The structural classification of local binding pockets determines the major patterns of RNA recognition. Results In this work, we provide a novel framework for systematically identifying the structure motifs of protein-RNA binding sites in the form of pockets on regional protein surfaces via a structure alignment-based method. We first construct a similarity network of RNA-binding pockets based on a non-sequential-order structure alignment method for local structure alignment. By using network community decomposition, the RNA-binding pockets on protein surfaces are clustered into groups with structural similarity. With a multiple structure alignment strategy, the consensus RNA-binding pockets in each group are identified. The crucial recognition patterns, as well as the protein-RNA binding motifs, are then identified and analyzed. Conclusions Large-scale RNA-binding pockets on protein surfaces are grouped by measuring their structural similarities. This similarity network-based framework provides a convenient method for modeling the structural relationships of functional pockets. The local structural patterns identified serve as structure motifs for the recognition with RNA on protein surfaces. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1410-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Shutang Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Ruitang Chen
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
| | - Xiaopeng Huang
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.,National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ling-Yun Wu
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. .,National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
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18
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Nithin C, Mukherjee S, Bahadur RP. A non-redundant protein-RNA docking benchmark version 2.0. Proteins 2016; 85:256-267. [PMID: 27862282 DOI: 10.1002/prot.25211] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 10/27/2016] [Accepted: 11/08/2016] [Indexed: 12/23/2022]
Abstract
We present an updated version of the protein-RNA docking benchmark, which we first published four years back. The non-redundant protein-RNA docking benchmark version 2.0 consists of 126 test cases, a threefold increase in number compared to its previous version. The present version consists of 21 unbound-unbound cases, of which, in 12 cases, the unbound RNAs are taken from another complex. It also consists of 95 unbound-bound cases where only the protein is available in the unbound state. Besides, we introduce 10 new bound-unbound cases where only the RNA is found in the unbound state. Based on the degree of conformational change of the interface residues upon complex formation the benchmark is classified into 72 rigid-body cases, 25 semiflexible cases and 19 full flexible cases. It also covers a wide range of conformational flexibility including small side chain movement to large domain swapping in protein structures as well as flipping and restacking in RNA bases. This benchmark should provide the docking community with more test cases for evaluating rigid-body as well as flexible docking algorithms. Besides, it will also facilitate the development of new algorithms that require large number of training set. The protein-RNA docking benchmark version 2.0 can be freely downloaded from http://www.csb.iitkgp.ernet.in/applications/PRDBv2. Proteins 2017; 85:256-267. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Chandran Nithin
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, 721302, India
| | - Sunandan Mukherjee
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, 721302, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, 721302, India
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19
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Protein-RNA interactions: structural biology and computational modeling techniques. Biophys Rev 2016; 8:359-367. [PMID: 28510023 DOI: 10.1007/s12551-016-0223-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 09/20/2016] [Indexed: 12/30/2022] Open
Abstract
RNA-binding proteins are functionally diverse within cells, being involved in RNA-metabolism, translation, DNA damage repair, and gene regulation at both the transcriptional and post-transcriptional levels. Much has been learnt about their interactions with RNAs through structure determination techniques and computational modeling. This review gives an overview of the structural data currently available for protein-RNA complexes, and discusses the technical issues facing structural biologists working to solve their structures. The review focuses on three techniques used to solve the 3-dimensional structure of protein-RNA complexes at atomic resolution, namely X-ray crystallography, solution nuclear magnetic resonance (NMR) and cryo-electron microscopy (cryo-EM). The review then focuses on the main computational modeling techniques that use these atomic resolution data: discussing the prediction of RNA-binding sites on unbound proteins, docking proteins, and RNAs, and modeling the molecular dynamics of the systems. In conclusion, the review looks at the future directions this field of research might take.
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20
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Choi SJ, Ban C. Crystal structure of a DNA aptamer bound to PvLDH elucidates novel single-stranded DNA structural elements for folding and recognition. Sci Rep 2016; 6:34998. [PMID: 27725738 PMCID: PMC5057103 DOI: 10.1038/srep34998] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 09/22/2016] [Indexed: 02/07/2023] Open
Abstract
Structural elements are key elements for understanding single-stranded nucleic acid folding. Although various RNA structural elements have been documented, structural elements of single-stranded DNA (ssDNA) have rarely been reported. Herein, we determined a crystal structure of PvLDH in complex with a DNA aptamer called pL1. This aptamer folds into a hairpin-bulge contact by adopting three novel structural elements, viz, DNA T-loop-like motif, base-phosphate zipper, and DNA G·G metal ion zipper. Moreover, the pL1:PvLDH complex shows unique properties compared with other protein:nucleic acid complexes. Generally, extensive intermolecular hydrogen bonds occur between unpaired nucleotides and proteins for specific recognitions. Although most protein-interacting nucleotides of pL1 are unpaired nucleotides, pL1 recognizes PvLDH by predominant shape complementarity with many bridging water molecules owing to the combination of three novel structural elements making protein-binding unpaired nucleotides stable. Moreover, the additional set of Plasmodium LDH residues which were shown to form extensive hydrogen bonds with unpaired nucleotides of 2008s does not participate in the recognition of pL1. Superimposition of the pL1:PvLDH complex with hLDH reveals steric clashes between pL1 and hLDH in contrast with no steric clashes between 2008s and hLDH. Therefore, specific protein recognition mode of pL1 is totally different from that of 2008s.
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Affiliation(s)
- Sung-Jin Choi
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
| | - Changill Ban
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
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21
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Jeszenői N, Bálint M, Horváth I, van der Spoel D, Hetényi C. Exploration of Interfacial Hydration Networks of Target–Ligand Complexes. J Chem Inf Model 2016; 56:148-58. [DOI: 10.1021/acs.jcim.5b00638] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Norbert Jeszenői
- Department
of Genetics, Eötvös Loránd University, Pázmány
Péter sétány 1/C, 1117 Budapest, Hungary
- MTA
NAP-B Molecular Neuroendocrinology Group, Institute of Physiology,
Szentágothai Research Center, Center for Neuroscience, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
| | - Mónika Bálint
- Department
of Biochemistry, Eötvös Loránd University, Pázmány
Péter sétány 1/C, 1117 Budapest, Hungary
| | - István Horváth
- Chemistry
Doctoral School, University of Szeged, Dugonics tér 13, 6720 Szeged, Hungary
| | - David van der Spoel
- Uppsala
Center for Computational Chemistry, Science for Life Laboratory, Department
of Cell and Molecular Biology, University of Uppsala, Box 596, SE-75124 Uppsala, Sweden
| | - Csaba Hetényi
- MTA-ELTE
Molecular Biophysics Research Group, Hungarian Academy of Sciences, Pázmány sétány 1/C, 1117 Budapest, Hungary
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22
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Barik A, Nithin C, Karampudi NBR, Mukherjee S, Bahadur RP. Probing binding hot spots at protein-RNA recognition sites. Nucleic Acids Res 2015; 44:e9. [PMID: 26365245 PMCID: PMC4737170 DOI: 10.1093/nar/gkv876] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 08/23/2015] [Indexed: 01/30/2023] Open
Abstract
We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein–RNA interfaces to probe the binding hot spots at protein–RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein–protein and protein–RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental ΔΔG values in a particular class, and provides a stepping-stone towards the engineering of protein–RNA recognition sites with desired affinity.
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Affiliation(s)
- Amita Barik
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302, India
| | - Chandran Nithin
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302, India
| | | | - Sunandan Mukherjee
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur-721302, India Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur-721302, India
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23
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Barik A, C N, Pilla SP, Bahadur RP. Molecular architecture of protein-RNA recognition sites. J Biomol Struct Dyn 2015; 33:2738-51. [PMID: 25562181 DOI: 10.1080/07391102.2015.1004652] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
The molecular architecture of protein-RNA interfaces are analyzed using a non-redundant dataset of 152 protein-RNA complexes. We find that an average protein-RNA interface is smaller than an average protein-DNA interface but larger than an average protein-protein interface. Among the different classes of protein-RNA complexes, interfaces with tRNA are the largest, while the interfaces with the single-stranded RNA are the smallest. Significantly, RNA contributes more to the interface area than its partner protein. Moreover, unlike protein-protein interfaces where the side chain contributes less to the interface area compared to the main chain, the main chain and side chain contributions flipped in protein-RNA interfaces. We find that the protein surface in contact with the RNA in protein-RNA complexes is better packed than that in contact with the DNA in protein-DNA complexes, but loosely packed than that in contact with the protein in protein-protein complexes. Shape complementarity and electrostatic potential are the two major factors that determine the specificity of the protein-RNA interaction. We find that the H-bond density at the protein-RNA interfaces is similar with that of protein-DNA interfaces but higher than the protein-protein interfaces. Unlike protein-DNA interfaces where the deoxyribose has little role in intermolecular H-bonds, due to the presence of an oxygen atom at the 2' position, the ribose in RNA plays significant role in protein-RNA H-bonds. We find that besides H-bonds, salt bridges and stacking interactions also play significant role in stabilizing protein-nucleic acids interfaces; however, their contribution at the protein-protein interfaces is insignificant.
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Affiliation(s)
- Amita Barik
- a Computational Structural Biology Laboratory, Department of Biotechnology , Indian Institute of Technology Kharagpur , Kharagpur , India
| | - Nithin C
- a Computational Structural Biology Laboratory, Department of Biotechnology , Indian Institute of Technology Kharagpur , Kharagpur , India
| | - Smita P Pilla
- a Computational Structural Biology Laboratory, Department of Biotechnology , Indian Institute of Technology Kharagpur , Kharagpur , India
| | - Ranjit Prasad Bahadur
- a Computational Structural Biology Laboratory, Department of Biotechnology , Indian Institute of Technology Kharagpur , Kharagpur , India
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