1
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Kravchenko A, de Vries SJ, Smaïl-Tabbone M, Chauvot de Beauchene I. HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses. BMC Bioinformatics 2024; 25:129. [PMID: 38532339 DOI: 10.1186/s12859-024-05733-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 03/06/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND The RNA-Recognition motif (RRM) is a protein domain that binds single-stranded RNA (ssRNA) and is present in as much as 2% of the human genome. Despite this important role in biology, RRM-ssRNA interactions are very challenging to study on the structural level because of the remarkable flexibility of ssRNA. In the absence of atomic-level experimental data, the only method able to predict the 3D structure of protein-ssRNA complexes with any degree of accuracy is ssRNA'TTRACT, an ssRNA fragment-based docking approach using ATTRACT. However, since ATTRACT parameters are not ssRNA-specific and were determined in 2010, there is substantial opportunity for enhancement. RESULTS Here we present HIPPO, a composite RRM-ssRNA scoring potential derived analytically from contact frequencies in near-native versus non-native docking models. HIPPO consists of a consensus of four distinct potentials, each extracted from a distinct reference pool of protein-trinucleotide docking decoys. To score a docking pose with one potential, for each pair of RNA-protein coarse-grained bead types, each contact is awarded or penalised according to the relative frequencies of this contact distance range among the correct and incorrect poses of the reference pool. Validated on a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, HIPPO achieved a threefold or higher enrichment for half of the fragments, versus only a quarter with the ATTRACT scoring function. In particular, HIPPO drastically improved the chance of very high enrichment (12-fold or higher), a scenario where the incremental modelling of entire ssRNA chains from fragments becomes viable. However, for the latter result, more research is needed to make it directly practically applicable. Regardless, our approach already improves upon the state of the art in RRM-ssRNA modelling and is in principle extendable to other types of protein-nucleic acid interactions.
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Affiliation(s)
- Anna Kravchenko
- Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France
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2
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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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3
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Baulin EF, Mukherjee S, Moafinejad SN, Wirecki TK, Badepally NG, Jaryani F, Stefaniak F, Amiri Farsani M, Ray A, Rocha de Moura T, Bujnicki JM. RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints. Proteins 2023; 91:1800-1810. [PMID: 37622458 DOI: 10.1002/prot.26575] [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: 04/15/2023] [Revised: 07/06/2023] [Accepted: 07/31/2023] [Indexed: 08/26/2023]
Abstract
Ribonucleic acid (RNA) molecules serve as master regulators of cells by encoding their biological function in the ribonucleotide sequence, particularly their ability to interact with other molecules. To understand how RNA molecules perform their biological tasks and to design new sequences with specific functions, it is of great benefit to be able to computationally predict how RNA folds and interacts in the cellular environment. Our workflow for computational modeling of the 3D structures of RNA and its interactions with other molecules uses a set of methods developed in our laboratory, including MeSSPredRNA for predicting canonical and non-canonical base pairs, PARNASSUS for detecting remote homology based on comparisons of sequences and secondary structures, ModeRNA for comparative modeling, the SimRNA family of programs for modeling RNA 3D structure and its complexes with other molecules, and QRNAS for model refinement. In this study, we present the results of testing this workflow in predicting RNA 3D structures in the CASP15 experiment. The overall high score of the computational models predicted by our group demonstrates the robustness of our workflow and its individual components in terms of predicting RNA 3D structures of acceptable quality that are close to the target structures. However, the variance in prediction quality is still quite high, and the results are still too far from the level of protein 3D structure predictions. This exercise led us to consider several improvements, especially to better predict and enforce stacking interactions and non-canonical base pairs.
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Affiliation(s)
- Eugene F Baulin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Sunandan Mukherjee
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - S Naeim Moafinejad
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Tomasz K Wirecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Nagendar Goud Badepally
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Farhang Jaryani
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Filip Stefaniak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Masoud Amiri Farsani
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Angana Ray
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Tales Rocha de Moura
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
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4
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Hommen P, Hwang J, Huang F, Borgelt L, Hohnen L, Wu P. Chromenopyrazole-Peptide Conjugates as Small-Molecule Based Inhibitors Disrupting the Protein-RNA Interaction of LIN28-let-7. Chembiochem 2023; 24:e202300376. [PMID: 37224100 DOI: 10.1002/cbic.202300376] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 05/24/2023] [Indexed: 05/26/2023]
Abstract
Targeting the protein-RNA interaction of LIN28 and let-7 is a promising strategy for the development of novel anticancer therapeutics. However, a limited number of small-molecule inhibitors disrupting the LIN28-let-7 interaction with potent efficacy are available. Herein, we developed a novel LIN28-inhibiting strategy by targeting selective hotspot amino acids at the LIN28-let-7 binding interface with small-molecule-based bifunctional conjugates. Starting from reported small-molecule LIN28 inhibitors, we identified a feasible linker-attachment position after performing a structure-activity relationship exploration based on the LIN28-targeting chromenopyrazoles. In parallel, a virtual alanine scan identified hotspot residues at the protein-RNA binding interface, based on which we designed a set of peptides to enhance the interaction with the identified hotspot residues. Conjugation of the tailor-designed peptides with linker-attached chromenopyrazoles yielded a series of bifunctional small-molecule-peptide conjugates, represented by compound 83 (PH-223), as a new LIN28-targeting chemical modality. Our result demonstrated an unexplored rational design approach using bifunctional conjugates to target protein-RNA interactions.
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Affiliation(s)
- Pascal Hommen
- Chemical Genomics Centre, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
- Department of Chemical Biology, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn Str. 6, Dortmund, 44227, Germany
| | - Jimin Hwang
- Chemical Genomics Centre, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
- Department of Chemical Biology, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn Str. 6, Dortmund, 44227, Germany
| | - Fubao Huang
- Chemical Genomics Centre, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
- Department of Chemical Biology, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
| | - Lydia Borgelt
- Chemical Genomics Centre, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
- Department of Chemical Biology, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn Str. 6, Dortmund, 44227, Germany
| | - Lisa Hohnen
- Chemical Genomics Centre, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
- Department of Chemical Biology, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
- Faculty of Chemistry and Biochemistry, Ruhr-University Bochum, Universitätsstr. 150, Bochum, 44780, Germany
| | - Peng Wu
- Chemical Genomics Centre, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
- Department of Chemical Biology, Max Planck Institute of Molecular Physiology, Otto-Hahn Str. 11, Dortmund, 44227, Germany
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5
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Hong X, Tong X, Xie J, Liu P, Liu X, Song Q, Liu S, Liu S. An updated dataset and a structure-based prediction model for protein-RNA binding affinity. Proteins 2023; 91:1245-1253. [PMID: 37186412 DOI: 10.1002/prot.26503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 03/08/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023]
Abstract
Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.
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Affiliation(s)
- Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Pinyu Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qi Song
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, Wuhan, China
| | - Sen Liu
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, Wuhan, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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6
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Zeng C, Jian Y, Vosoughi S, Zeng C, Zhao Y. Evaluating native-like structures of RNA-protein complexes through the deep learning method. Nat Commun 2023; 14:1060. [PMID: 36828844 PMCID: PMC9958188 DOI: 10.1038/s41467-023-36720-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/14/2023] [Indexed: 02/26/2023] Open
Abstract
RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computational methods capable of identifying the native-like RNA-protein structures are needed. To address this challenge, we thus develop DRPScore, a deep-learning-based approach for identifying native-like RNA-protein structures. DRPScore is tested on representative sets of RNA-protein complexes with various degrees of binding-induced conformation change ranging from fully rigid docking (bound-bound) to fully flexible docking (unbound-unbound). Out of the top 20 predictions, DRPScore selects native-like structures with a success rate of 91.67% on the testing set of bound RNA-protein complexes and 56.14% on the unbound complexes. DRPScore consistently outperforms existing methods with a roughly 10.53-15.79% improvement, even for the most difficult unbound cases. Furthermore, DRPScore significantly improves the accuracy of the native interface interaction predictions. DRPScore should be broadly useful for modeling and designing RNA-protein complexes.
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Affiliation(s)
- Chengwei Zeng
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China
| | - Yiren Jian
- Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA
| | - Soroush Vosoughi
- Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA
| | - Chen Zeng
- Department of Physics, The George Washington University, Washington, DC, 20052, USA
| | - Yunjie Zhao
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China.
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7
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Mei LC, Hao GF, Yang GF. Thermodynamic database supports deciphering protein-nucleic acid interactions. Trends Biotechnol 2023; 41:140-143. [PMID: 36272818 DOI: 10.1016/j.tibtech.2022.09.018] [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: 07/13/2022] [Revised: 09/16/2022] [Accepted: 09/27/2022] [Indexed: 01/11/2023]
Abstract
The thermodynamics of protein-nucleic acid interactions (PNIs) is crucial for elucidating the mechanisms of molecular recognition and pathological consequences. The Protein-Nucleic Acid Thermodynamics Database (PNATDB) is a database containing experimentally determined thermodynamic parameters along with sequence, structural, and function data, which is available free online.
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Affiliation(s)
- Long-Can Mei
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Ge-Fei Hao
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China; State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang 550000, China.
| | - Guang-Fu Yang
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.
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8
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Roca-Martínez J, Dhondge H, Sattler M, Vranken WF. Deciphering the RRM-RNA recognition code: A computational analysis. PLoS Comput Biol 2023; 19:e1010859. [PMID: 36689472 PMCID: PMC9894542 DOI: 10.1371/journal.pcbi.1010859] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 02/02/2023] [Accepted: 01/07/2023] [Indexed: 01/24/2023] Open
Abstract
RNA recognition motifs (RRM) are the most prevalent class of RNA binding domains in eucaryotes. Their RNA binding preferences have been investigated for almost two decades, and even though some RRM domains are now very well described, their RNA recognition code has remained elusive. An increasing number of experimental structures of RRM-RNA complexes has become available in recent years. Here, we perform an in-depth computational analysis to derive an RNA recognition code for canonical RRMs. We present and validate a computational scoring method to estimate the binding between an RRM and a single stranded RNA, based on structural data from a carefully curated multiple sequence alignment, which can predict RRM binding RNA sequence motifs based on the RRM protein sequence. Given the importance and prevalence of RRMs in humans and other species, this tool could help design RNA binding motifs with uses in medical or synthetic biology applications, leading towards the de novo design of RRMs with specific RNA recognition.
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Affiliation(s)
- Joel Roca-Martínez
- Interuniversity Institute of Bioinformatics in Brussels, VUB/ULB, Brussels, Belgium
- Structural biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
| | | | - Michael Sattler
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich, Neuherberg, Germany
- Bavarian NMR Center, Department of Bioscience, School of Natural Sciences, Technical University of Munich, Garching, Germany
| | - Wim F. Vranken
- Interuniversity Institute of Bioinformatics in Brussels, VUB/ULB, Brussels, Belgium
- Structural biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
- * E-mail:
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9
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Interaction preferences between protein side chains and key epigenetic modifications 5-methylcytosine, 5-hydroxymethycytosine and N 6-methyladenine. Sci Rep 2022; 12:19583. [PMID: 36380112 PMCID: PMC9666514 DOI: 10.1038/s41598-022-23585-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 11/02/2022] [Indexed: 11/16/2022] Open
Abstract
Covalent modifications of standard DNA/RNA nucleobases affect epigenetic regulation of gene expression by modulating interactions between nucleic acids and protein readers. We derive here the absolute binding free energies and analyze the binding modalities between key modified nucleobases 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC) and N6-methyladenine (m6A) and all non-prolyl/non-glycyl protein side chains using molecular dynamics simulations and umbrella sampling in both water and methanol, the latter mimicking the low dielectric environment at the dehydrated nucleic-acid/protein interfaces. We verify the derived affinities by comparing against a comprehensive set of high-resolution structures of nucleic-protein complexes involving 5mC. Our analysis identifies protein side chains that are highly tuned for detecting cytosine methylation as a function of the environment and can thus serve as microscopic readers of epigenetic marks. Conversely, we show that the relative ordering of sidechain affinities for 5hmC and m6A does not differ significantly from those for their precursor bases, cytosine and adenine, respectively, especially in the low dielectric environment. For those two modified bases, the effect is more nuanced and manifests itself primarily at the level of absolute changes in the binding free energy. Our results contribute towards establishing a quantitative foundation for understanding, predicting and modulating the interactions between modified nucleic acids and proteins at the atomistic level.
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10
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Lin J, Wang X, Zhai S, Shi M, Peng C, Deng X, Fu D, Wang J, Shen B. Hypoxia-induced exosomal circPDK1 promotes pancreatic cancer glycolysis via c-myc activation by modulating miR-628-3p/BPTF axis and degrading BIN1. J Hematol Oncol 2022; 15:128. [PMID: 36068586 PMCID: PMC9450374 DOI: 10.1186/s13045-022-01348-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 08/30/2022] [Indexed: 12/21/2022] Open
Abstract
Background circRNA has been established to play a pivotal role in tumorigenesis development in a variety of cancers; nevertheless, the biological functions and molecular mechanisms of hypoxia-induced exosomal circRNAs in pancreatic cancer remain largely unknown. Methods Differentially expressed circRNAs in exosomes between hypoxic exosomes and normoxic exosomes in PC cells were verified by RNA sequencing. The expression of circPDK1 in PC tumors and PC patients was evaluated by qRT-PCR and ISH, and the biological functions of circPDK1 in PC were verified through a series of in vitro and in vivo experiments. Using Western blotting, Co-IP, RNA pull-down, ChIP, RIP, dual-luciferase assays, and rescue experiments, the underlying mechanism of circPDK1 was verified. Results CircPDK1 was highly abundant in PC tumor tissues and serum exosomes and was associated with poor survival. Exosomal circPDK1 significantly promoted PC cell proliferation, migration, and glycolysis both in vitro and in vivo. Mechanistically, circPDK1 could be activated by HIF1A at the transcriptional level and sponges miR-628-3p to activate the BPTF/c-myc axis. In addition, circPDK1 serves as a scaffold that enhances the interaction between UBE2O and BIN1, inducing the UBE2O-mediated degradation of BIN1. Conclusions We found that circPDK1 was activated by HIF1A at the transcriptional level by modulating the miR-628-3p/BPTF axis and degrading BIN1. Exosomal circPDK1 is a promising biomarker for PC diagnosis and prognosis and represents a potential therapeutic target for PC. Supplementary Information The online version contains supplementary material available at 10.1186/s13045-022-01348-7.
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Affiliation(s)
- Jiewei Lin
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,State Key Laboratory of Oncogenes and Related Genes, Shanghai, China.,Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xinjing Wang
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,State Key Laboratory of Oncogenes and Related Genes, Shanghai, China.,Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shuyu Zhai
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,State Key Laboratory of Oncogenes and Related Genes, Shanghai, China.,Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Minmin Shi
- Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,State Key Laboratory of Oncogenes and Related Genes, Shanghai, China.,Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chenghong Peng
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,State Key Laboratory of Oncogenes and Related Genes, Shanghai, China.,Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaxing Deng
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,State Key Laboratory of Oncogenes and Related Genes, Shanghai, China.,Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Da Fu
- Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,State Key Laboratory of Oncogenes and Related Genes, Shanghai, China. .,Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Jiancheng Wang
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,State Key Laboratory of Oncogenes and Related Genes, Shanghai, China. .,Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Baiyong Shen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,State Key Laboratory of Oncogenes and Related Genes, Shanghai, China. .,Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
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11
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Sun S, Yang J, Zhang Z. RNALigands: a database and web server for RNA-ligand interactions. RNA (NEW YORK, N.Y.) 2022; 28:115-122. [PMID: 34732566 PMCID: PMC8906548 DOI: 10.1261/rna.078889.121] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
RNA molecules can fold into complex and stable 3D structures, allowing them to carry out important genetic, structural, and regulatory roles inside the cell. These complex structures often contain 3D pockets made up of secondary structural motifs that can be potentially targeted by small molecule ligands. Indeed, many RNA structures in PDB contain bound small molecules, and high-throughput experimental studies have generated a large number of interacting RNA and ligand pairs. There is considerable interest in developing small molecule lead compounds targeting viral RNAs or those RNAs implicated in neurological diseases or cancer. We hypothesize that RNAs that have similar secondary structural motifs may bind to similar small molecule ligands. Toward this goal, we established a database collecting RNA secondary structural motifs and bound small molecule ligands. We further developed a computational pipeline, which takes as input an RNA sequence, predicts its secondary structure, extracts structural motifs, and searches the database for similar secondary structure motifs and interacting small molecule. We demonstrated the utility of the server by querying α-synuclein mRNA 5' UTR sequence and finding potential matches which were validated as correct. The server is publicly available at http://RNALigands.ccbr.utoronto.ca The source code can also be downloaded at https://github.com/SaisaiSun/RNALigands.
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Affiliation(s)
- Saisai Sun
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shanxi, China
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Jianyi Yang
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Zhaolei Zhang
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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12
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杨 爽. Analysis of Residue Interface Preference in Protein-DNA Complexes and Its Application in Recognition of Binding Interface. Biophysics (Nagoya-shi) 2022. [DOI: 10.12677/biphy.2022.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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13
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RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery. iScience 2021; 24:103381. [PMID: 34841226 PMCID: PMC8605353 DOI: 10.1016/j.isci.2021.103381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/01/2021] [Accepted: 10/27/2021] [Indexed: 11/29/2022] Open
Abstract
Identifying the factors determining the RBP-RNA interactions remains a big challenge. It involves sparse binding motifs and a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites in RNAs using an ultra-fast inexact k-mers search for statistically significant seeds. The seeds work as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network. The developed models also received support from MD-simulation studies. The implemented software, RBPSpot, scored consistently high for all the performance metrics including average accuracy of ∼90% across a large number of validated datasets. It outperformed the compared tools, including some with much complex deep-learning models, during a comprehensive benchmarking process. RBPSpot can identify RBP binding sites in the human system and can also be used to develop new models, making it a valuable resource in the area of regulatory system studies. Efficient motif anchoring helps to get good quality contextual information on binding Realistic and high granularity datasets ensure better performance of the classifiers DNN models on the contextual features outperform more complex deep learning tools RBPSpot algorithm may be used to develop RBP binding models for other species also
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14
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Statistical potentials for RNA-protein interactions optimized by CMA-ES. J Mol Graph Model 2021; 110:108044. [PMID: 34736056 DOI: 10.1016/j.jmgm.2021.108044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/24/2021] [Accepted: 10/04/2021] [Indexed: 11/23/2022]
Abstract
Characterizing RNA-protein interactions remains an important endeavor, complicated by the difficulty in obtaining the relevant structures. Evaluating model structures via statistical potentials is in principle straight-forward and effective. However, given the relatively small size of the existing learning set of RNA-protein complexes optimization of such potentials continues to be problematic. Notably, interaction-based statistical potentials have problems in addressing large RNA-protein complexes. In this study, we adopted a novel strategy with covariance matrix adaptation (CMA-ES) to calculate statistical potentials, successfully identifying native docking poses.
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15
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Zhou T, Wang H, Zeng C, Zhao Y. RPocket: an intuitive database of RNA pocket topology information with RNA-ligand data resources. BMC Bioinformatics 2021; 22:428. [PMID: 34496744 PMCID: PMC8424408 DOI: 10.1186/s12859-021-04349-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/27/2021] [Indexed: 11/13/2022] Open
Abstract
Background RNA regulates a variety of biological functions by interacting with other molecules. The ligand often binds in the RNA pocket to trigger structural changes or functions. Thus, it is essential to explore and visualize the RNA pocket to elucidate the structural and recognition mechanism for the RNA-ligand complex formation. Results In this work, we developed one user-friendly bioinformatics tool, RPocket. This database provides geometrical size, centroid, shape, secondary structure element for RNA pocket, RNA-ligand interaction information, and functional sites. We extracted 240 RNA pockets from 94 non-redundant RNA-ligand complex structures. We developed RPDescriptor to calculate the pocket geometrical property quantitatively. The geometrical information was then subjected to RNA-ligand binding analysis by incorporating the sequence, secondary structure, and geometrical combinations. This new approach takes advantage of both the atom-level precision of the structure and the nucleotide-level tertiary interactions. The results show that the higher-level topological pattern indeed improves the tertiary structure prediction. We also proposed a potential mechanism for RNA-ligand complex formation. The electrostatic interactions are responsible for long-range recognition, while the Van der Waals and hydrophobic contacts for short-range binding and optimization. These interaction pairs can be considered as distance constraints to guide complex structural modeling and drug design. Conclusion RPocket database would facilitate RNA-ligand engineering to regulate the complex formation for biological or medical applications. RPocket is available at http://zhaoserver.com.cn/RPocket/RPocket.html. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04349-4.
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Affiliation(s)
- Ting Zhou
- Department of Physics, Institute of Biophysics, Central China Normal University, Wuhan, 430079, China
| | - Huiwen Wang
- Department of Physics, Institute of Biophysics, Central China Normal University, Wuhan, 430079, China
| | - Chen Zeng
- Department of Physics, George Washington University, Washington, DC, 20052, USA
| | - Yunjie Zhao
- Department of Physics, Institute of Biophysics, Central China Normal University, Wuhan, 430079, China.
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16
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Mei LC, Wang YL, Wu FX, Wang F, Hao GF, Yang GF. HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid-protein interface with a case study. Brief Bioinform 2021; 22:bbaa373. [PMID: 33406224 PMCID: PMC7929440 DOI: 10.1093/bib/bbaa373] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/19/2020] [Accepted: 11/23/2020] [Indexed: 01/18/2023] Open
Abstract
Protein-nucleic acid interactions play essential roles in many biological processes, such as transcription, replication and translation. In protein-nucleic acid interfaces, hotspot residues contribute the majority of binding affinity toward molecular recognition. Hotspot residues are commonly regarded as potential binding sites for compound molecules in drug design projects. The dynamic property is a considerable factor that affects the binding of ligands. Computational approaches have been developed to expedite the prediction of hotspot residues on protein-nucleic acid interfaces. However, existing approaches overlook hotspot dynamics, despite their essential role in protein function. Here, we report a web server named Hotspots In silico Scanning on Nucleic Acid and Protein Interface (HISNAPI) to analyze hotspot residue dynamics by integrating molecular dynamics simulation and one-step free energy perturbation. HISNAPI is capable of not only predicting the hotspot residues in protein-nucleic acid interfaces but also providing insights into their intensity and correlation of dynamic motion. Protein dynamics have been recognized as a vital factor that has an effect on the interaction specificity and affinity of the binding partners. We applied HISNAPI to the case of SARS-CoV-2 RNA-dependent RNA polymerase, a vital target of the antiviral drug for the treatment of coronavirus disease 2019. We identified the hotspot residues and characterized their dynamic behaviors, which might provide insight into the target site for antiviral drug design. The web server is freely available via a user-friendly web interface at http://chemyang.ccnu.edu.cn/ccb/server/HISNAPI/ and http://agroda.gzu.edu.cn:9999/ccb/server/HISNAPI/.
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Affiliation(s)
- Long-Can Mei
- College of Chemistry, Central China Normal University
| | | | | | | | | | - Guang-Fu Yang
- Pesticide Science from Nankai University, Tianjin, China
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17
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Hognon C, Monari A. Staring at the Naked Goddess: Unraveling the Structure and Reactivity of Artemis Endonuclease Interacting with a DNA Double Strand. Molecules 2021; 26:molecules26133986. [PMID: 34210101 PMCID: PMC8271620 DOI: 10.3390/molecules26133986] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 06/23/2021] [Accepted: 06/26/2021] [Indexed: 11/16/2022] Open
Abstract
Artemis is an endonuclease responsible for breaking hairpin DNA strands during immune system adaptation and maturation as well as the processing of potentially toxic DNA lesions. Thus, Artemis may be an important target in the development of anticancer therapy, both for the sensitization of radiotherapy and for immunotherapy. Despite its importance, its structure has been resolved only recently, and important questions concerning the arrangement of its active center, the interaction with the DNA substrate, and the catalytic mechanism remain unanswered. In this contribution, by performing extensive molecular dynamic simulations, both classically and at the hybrid quantum mechanics/molecular mechanics level, we evidenced the stable interaction modes of Artemis with a model DNA strand. We also analyzed the catalytic cycle providing the free energy profile and key transition states for the DNA cleavage reaction.
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18
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Mei LC, Hao GF, Yang GF. Computational methods for predicting hotspots at protein-RNA interfaces. WILEY INTERDISCIPLINARY REVIEWS-RNA 2021; 13:e1675. [PMID: 34080311 DOI: 10.1002/wrna.1675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/13/2021] [Accepted: 05/14/2021] [Indexed: 11/10/2022]
Abstract
Protein-RNA interactions play essential roles in many critical biological events. A comprehensive understanding of the mechanisms underlying these interactions is helpful when studying cellular activities and therapeutic applications. Hotspots are a small portion of residues contributing much toward protein-RNA binding affinity. In pharmaceutical research, the hotspot residues are seen as the best option for designing small molecules to target proteins of therapeutic interest. With the accumulation of experimental data about protein-RNA interactions, computational methods have been produced for hotspot prediction on a large scale. In this review, we first present an overview of the existing databases for protein-RNA binding data. Furthermore, we outline the most adopted computational methods for hotspots prediction in protein-RNA interactions. Finally, we discuss the applications of hotspot prediction. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Methods > RNA Analyses In Vitro and In Silico.
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Affiliation(s)
- Long-Can Mei
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China.,International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, China
| | - Ge-Fei Hao
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China.,International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, China.,State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang, China
| | - Guang-Fu Yang
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, China.,International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, China.,Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, China
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19
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Xian Y, Xie Y, Silva SM, Karki CB, Qiu W, Li L. StructureMan: A Structure Manipulation Tool to Study Large Scale Biomolecular Interactions. Front Mol Biosci 2021; 7:627087. [PMID: 33505991 PMCID: PMC7831659 DOI: 10.3389/fmolb.2020.627087] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 12/10/2020] [Indexed: 11/22/2022] Open
Abstract
Studying biomolecular interactions is a crucial but challenging task. Due to their large scales, many biomolecular interactions are difficult to be simulated via all atom models. An effective approach to investigate the biomolecular interactions is highly demanded in many areas. Here we introduce a Structure Manipulation (StructureMan) program to operate the structures when studying the large-scale biomolecular interactions. This novel StructureMan tool provides comprehensive operations which can be utilized to study the interactions in various large biological systems. Combining with electrostatic calculation programs such as DelPhi and DelPhiForce, StructureMan was implemented to reveal the detailed electrostatic features in two large biological examples, the viral capsid and molecular motor-microtubule complexes. Applications on these two examples revealed interesting binding mechanisms in the viral capsid and molecular motor. Such applications demonstrated that the StructureMan can be widely used when studying the biomolecular interactions in large scale biological problems. This novel tool provides an alternative approach to efficiently study the biomolecular interactions, especially for large scale biology systems. The StructureMan tool is available at our website: http://compbio.utep.edu/static/downloads/script-for-munipulation2.zip.
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Affiliation(s)
- Yuejiao Xian
- Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX, United States
| | - Yixin Xie
- Computational Science Program, University of Texas at El Paso, El Paso, TX, United States
| | - Sebastian Miki Silva
- Department of Physics, University of Texas at El Paso, El Paso, TX, United States
| | - Chitra B Karki
- Computational Science Program, University of Texas at El Paso, El Paso, TX, United States
| | - Weihong Qiu
- Department of Physics, Oregon State University, Corvallis, OR, United States.,Department of Biochemistry & Biophysics, Oregon State University, Corvallis, OR, United States
| | - Lin Li
- Computational Science Program, University of Texas at El Paso, El Paso, TX, United States.,Department of Physics, University of Texas at El Paso, El Paso, TX, United States
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20
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Abstract
Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes.
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21
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Aderinwale T, Christoffer CW, Sarkar D, Alnabati E, Kihara D. Computational structure modeling for diverse categories of macromolecular interactions. Curr Opin Struct Biol 2020; 64:1-8. [PMID: 32599506 PMCID: PMC7665979 DOI: 10.1016/j.sbi.2020.05.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 05/06/2020] [Accepted: 05/21/2020] [Indexed: 01/23/2023]
Abstract
Computational protein-protein docking is one of the most intensively studied topics in structural bioinformatics. The field has made substantial progress through over three decades of development. The development began with methods for rigid-body docking of two proteins, which have now been extended in different directions to cover the various macromolecular interactions observed in a cell. Here, we overview the recent developments of the variations of docking methods, including multiple protein docking, peptide-protein docking, and disordered protein docking methods.
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Affiliation(s)
- Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | | | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Eman Alnabati
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
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22
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Zheng J, Hong X, Xie J, Tong X, Liu S. P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking. Bioinformatics 2020; 36:96-103. [PMID: 31173056 DOI: 10.1093/bioinformatics/btz478] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 05/24/2019] [Accepted: 06/04/2019] [Indexed: 01/02/2023] Open
Abstract
MOTIVATION The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy. RESULTS In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html. AVAILABILITY AND IMPLEMENTATION www.rnabinding.com/P3DOCK/P3DOCK.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jinfang Zheng
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
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23
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He J, Tao H, Huang SY. Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models. Bioinformatics 2020; 35:4994-5002. [PMID: 31086984 DOI: 10.1093/bioinformatics/btz388] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 04/28/2019] [Accepted: 05/03/2019] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB). RESULTS Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions. AVAILABILITY AND IMPLEMENTATION http://huanglab.phys.hust.edu.cn/mprdock/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiahua He
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huanyu Tao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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24
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Pradhan P, Srivastava A, Singh J, Biswas B, Saini A, Siddique I, Kumari P, Khan MA, Mishra A, Yadav PK, Kumar S, Bhavesh NS, Venkatraman P, Vivekanandan P, Kundu B. Prion protein transcription is auto-regulated through dynamic interactions with G-quadruplex motifs in its own promoter. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2020; 1863:194479. [PMID: 31931179 DOI: 10.1016/j.bbagrm.2019.194479] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 12/26/2019] [Accepted: 12/28/2019] [Indexed: 11/19/2022]
Abstract
Cellular prion protein (PrP) misfolds into an aberrant and infectious scrapie form (PrPSc) that lead to fatal transmissible spongiform encephalopathies (TSEs). Association of prions with G-quadruplex (GQ) forming nucleic acid motifs has been reported, but implications of these interactions remain elusive. Herein, we show that the promoter region of the human prion gene (PRNP) contains two putative GQ motifs (Q1 and Q2) that assume stable, hybrid, intra-molecular quadruplex structures and bind with high affinity to PrP. Here, we investigate the ability of PrP to bind to the quadruplexes in its own promoter. We used a battery of techniques including SPR, NMR, CD, MD simulations and cell culture-based reporter assays. Our results show that PrP auto-regulates its expression by binding and resolving the GQs present in its own promoter. Furthermore, we map this resolvase-like activity to the N-terminal region (residues 23-89) of PrP. Our findings highlight a positive transcriptional-translational feedback regulation of the PRNP gene by PrP through dynamic unwinding of GQs in its promoter. Taken together, our results shed light on a yet unknown mechanism of regulation of the PRNP gene. This work provides the necessary framework for a plethora of studies on understanding the regulation of PrP levels and its implications in prion pathogenesis.
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Affiliation(s)
- Prashant Pradhan
- Kusuma School of Biological Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Ankit Srivastava
- Kusuma School of Biological Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Jasdeep Singh
- Kusuma School of Biological Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Banhi Biswas
- Kusuma School of Biological Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Akanksha Saini
- Kusuma School of Biological Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Ibrar Siddique
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Pooja Kumari
- Transcription Regulation group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Mohd Asim Khan
- Department of Biochemistry, University of Delhi, South Campus, New Delhi 110021, India
| | - Akhilesh Mishra
- Kusuma School of Biological Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Pramod Kumar Yadav
- Kusuma School of Biological Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Shivani Kumar
- Kusuma School of Biological Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India
| | - Neel Sarovar Bhavesh
- Transcription Regulation group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Prasanna Venkatraman
- Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer, Kharghar, Navi Mumbai 410210, India; Homi Bhabha National Institute, 2nd floor, BARC Training School Complex, Anushaktinagar, Mumbai, Maharashtra 400094, India
| | - Perumal Vivekanandan
- Kusuma School of Biological Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India.
| | - Bishwajit Kundu
- Kusuma School of Biological Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India.
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25
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Qiu L, Zou X. Scoring Functions for Protein-RNA Complex Structure Prediction: Advances, Applications, and Future Directions. COMMUNICATIONS IN INFORMATION AND SYSTEMS 2020; 20:1-22. [PMID: 33867869 DOI: 10.4310/cis.2020.v20.n1.a1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Protein-RNA interaction is among the most essential of biological events in living cells, being involved in protein synthesizing, RNA processing and transport, DNA transcription, and regulation of gene expression, and many other critical bio-molecular activities. A thorough understanding of this interaction is of paramount importance in fundamental study of a variety of vital cellular processes and therapeutic application for remedy of a broad range of diseases. Experimental high-resolution 3D structure determination is the primary source of knowledge for protein-RNA complexes. However, due to technical limitations, the existing techniques for experimental structure determination couldn't match the demand from fast growing interest in academia and industry. This problem necessitates the alternative high-throughput computational method for protein-RNA complex structure prediction. Similar to the in silico methods used for protein-protein and protein-DNA interactions, a reliable prediction of protein-RNA complex structure requires a scoring function with commensurate discriminatory power. Derived from determined structures and purposed to predict the to-be-determined structures, the scoring function is not only a predictive tool but also a gauge of our knowledge of protein-RNA interaction. In this review, we present an overview of the status of existing scoring functions and the scientific principle behind their constructions as well as their strengths and limitations. Finally, we will discuss about future directions of the scoring function development for protein-RNA structure prediction.
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Affiliation(s)
- Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri 65211
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri 65211.,Department of Physics & Astronomy, University of Missouri, Columbia, Missouri 65211.,Department of Biochemistry, University of Missouri, Columbia, Missouri 65211.,Informatics Institute, University of Missouri, Columbia, Missouri 65211
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26
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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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27
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Magnus M, Kappel K, Das R, Bujnicki JM. RNA 3D structure prediction guided by independent folding of homologous sequences. BMC Bioinformatics 2019; 20:512. [PMID: 31640563 PMCID: PMC6806525 DOI: 10.1186/s12859-019-3120-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 10/01/2019] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule's sequence. The prediction of tertiary structures of complex RNAs is still a challenging task. RESULTS Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence. CONCLUSION This work, for the first time to our knowledge, demonstrates the importance of the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure "foldability" or "predictability" of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identifying limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.
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Affiliation(s)
- Marcin Magnus
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, Poland
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA USA
| | - Rhiju Das
- Biophysics Program, Stanford University, Stanford, CA USA
- Department of Biochemistry, Stanford University, Stanford, CA USA
- Department of Physics, Stanford University, Stanford, CA USA
| | - Janusz M. Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, Poland
- Laboratory of Bioinformatics, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Poznan, Poland
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28
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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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29
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Yan Y, Wen Z, Zhang D, Huang SY. Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method. Nucleic Acids Res 2019; 46:e56. [PMID: 29506237 PMCID: PMC5961370 DOI: 10.1093/nar/gky113] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Accepted: 02/08/2018] [Indexed: 11/15/2022] Open
Abstract
RNA–RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA–RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Here, we have developed a physics-based double-iterative strategy to determine the effective potentials for RNA–RNA interactions based on a training set of 97 diverse RNA–RNA complexes. The double-iterative strategy circumvented the reference state problem in knowledge-based scoring functions by updating the potentials through iteration and also overcame the decoy-dependent limitation in previous iterative methods by constructing the decoys iteratively. The derived scoring function, which is referred to as DITScoreRR, was evaluated on an RNA–RNA docking benchmark of 60 test cases and compared with three other scoring functions. It was shown that for bound docking, our scoring function DITScoreRR obtained the excellent success rates of 90% and 98.3% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 63.3% and 71.7% for van der Waals interactions, 45.0% and 65.0% for ITScorePP, and 11.7% and 26.7% for ZDOCK 2.1, respectively. For unbound docking, DITScoreRR achieved the good success rates of 53.3% and 71.7% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 13.3% and 28.3% for van der Waals interactions, 11.7% and 26.7% for our ITScorePP, and 3.3% and 6.7% for ZDOCK 2.1, respectively. DITScoreRR also performed significantly better in ranking decoys and obtained significantly higher score-RMSD correlations than the other three scoring functions. DITScoreRR will be of great value for the prediction and design of RNA structures and RNA–RNA complexes.
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Affiliation(s)
- Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China
| | - Zeyu Wen
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China
| | - Di Zhang
- 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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30
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Lengert N, Spies J, Drossel B. Rad54 Phosphorylation Promotes Homologous Recombination by Balancing Rad54 Mobility and DNA Binding. Biophys J 2019; 116:1406-1419. [PMID: 30961891 DOI: 10.1016/j.bpj.2019.03.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 02/28/2019] [Accepted: 03/02/2019] [Indexed: 11/30/2022] Open
Abstract
The repair of DNA double-strand breaks by homologous recombination is of crucial importance for maintaining genomic stability. Two major players during this repair pathway are Rad51 and Rad54. Previously, it was shown that Rad54 exists as a monomer or oligomer when bound to DNA and drives the displacement of Rad51 by translocating along the DNA. Moreover, phosphorylation of Rad54 was reported to stimulate this clearance of Rad51 from DNA. However, it is currently unclear how phosphorylation of Rad54 modulates its molecular-structural function and how it affects the activity of monomeric or oligomeric Rad54 during the removal of Rad51. To examine the impact of Rad54 phosphorylation on a molecular-structural level, we applied molecular dynamics simulations of Rad54 monomers and hexamers in the absence or presence of DNA. Our results suggest that 1) phosphorylation of Rad54 stabilizes the monomeric form by reducing the interlobe movement of Rad54 monomers and might therefore facilitate multimer formation around DNA and 2) phosphorylation of Rad54 in a higher-order hexamer reduces its binding strength to DNA, which is a requirement for efficient mobility on DNA. To further address the relationship between the mobility of Rad54 and its phosphorylation state, we performed fluorescence recovery after photobleaching experiments in living cells, which expressed different versions of the Rad54 protein. Here, we could measure that the phosphomimetic version of Rad54 was highly mobile on DNA, whereas a nonphosphorylatable mutant displayed a mobility defect. Taken together, these data show that the phosphorylation of Rad54 is a critical event in balancing the DNA binding strength and mobility of Rad54 and might therefore provide optimal conditions for DNA translocation and subsequent removal of Rad51 during homologous recombination repair.
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Affiliation(s)
- Nicor Lengert
- Institute for Condensed Matter Physics, Darmstadt University of Technology, Darmstadt, Germany.
| | - Julian Spies
- Radiation Biology and DNA Repair, Darmstadt University of Technology, Darmstadt, Germany
| | - Barbara Drossel
- Institute for Condensed Matter Physics, Darmstadt University of Technology, Darmstadt, Germany
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31
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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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32
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Huang Y, Li H, Xiao Y. 3dRPC: a web server for 3D RNA-protein structure prediction. Bioinformatics 2018; 34:1238-1240. [PMID: 29186336 DOI: 10.1093/bioinformatics/btx742] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 11/24/2017] [Indexed: 12/26/2022] Open
Abstract
RNA-protein interactions occur in many biological processes. To understand the mechanism of these interactions one needs to know three-dimensional (3D) structures of RNA-protein complexes. 3dRPC is an algorithm for prediction of 3D RNA-protein complex structures and consists of a docking algorithm RPDOCK and a scoring function 3dRPC-Score. RPDOCK is used to sample possible complex conformations of an RNA and a protein by calculating the geometric and electrostatic complementarities and stacking interactions at the RNA-protein interface according to the features of atom packing of the interface. 3dRPC-Score is a knowledge-based potential that uses the conformations of nucleotide-amino-acid pairs as statistical variables and that is used to choose the near-native complex-conformations obtained from the docking method above. Recently, we built a web server for 3dRPC. The users can easily use 3dRPC without installing it locally. RNA and protein structures in PDB (Protein Data Bank) format are the only needed input files. It can also incorporate the information of interface residues or residue-pairs obtained from experiments or theoretical predictions to improve the prediction. Availability and implementation The address of 3dRPC web server is http://biophy.hust.edu.cn/3dRPC. Contact yxiao@hust.edu.cn.
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Affiliation(s)
- Yangyu Huang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Haotian Li
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yi Xiao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
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33
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Kappel K, Das R. Sampling Native-like Structures of RNA-Protein Complexes through Rosetta Folding and Docking. Structure 2018; 27:140-151.e5. [PMID: 30416038 DOI: 10.1016/j.str.2018.10.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 08/27/2018] [Accepted: 10/05/2018] [Indexed: 10/27/2022]
Abstract
RNA-protein complexes underlie numerous cellular processes including translation, splicing, and posttranscriptional regulation of gene expression. The structures of these complexes are crucial to their functions but often elude high-resolution structure determination. Computational methods are needed that can integrate low-resolution data for RNA-protein complexes while modeling de novo the large conformational changes of RNA components upon complex formation. To address this challenge, we describe RNP-denovo, a Rosetta method to simultaneously fold-and-dock RNA to a protein surface. On a benchmark set of diverse RNA-protein complexes not solvable with prior strategies, RNP-denovo consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges involving the spliceosome, telomerase, and a methyltransferase-ribosomal RNA complex in which previous methods gave poor results. When coupled with the same sparse FRET, crosslinking, and functional data used previously, RNP-denovo gave models with significantly improved accuracy. These results open a route to modeling global folds of RNA-protein complexes from low-resolution data.
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Affiliation(s)
- Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA 94305, USA
| | - Rhiju Das
- Biophysics Program, Stanford University, Stanford, CA 94305, USA; Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Physics, Stanford University, Stanford, CA 94305, USA.
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34
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Krüger DM, Neubacher S, Grossmann TN. Protein-RNA interactions: structural characteristics and hotspot amino acids. RNA (NEW YORK, N.Y.) 2018; 24:1457-1465. [PMID: 30093489 PMCID: PMC6191724 DOI: 10.1261/rna.066464.118] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 08/06/2018] [Indexed: 06/01/2023]
Abstract
Structural information about protein-RNA complexes supports the understanding of crucial recognition processes in the cell, and it can allow the development of high affinity ligands to interfere with these processes. In this respect, the identification of amino acid hotspots is particularly important. In contrast to protein-protein interactions, in silico approaches for protein-RNA interactions lag behind in their development. Herein, we report an analysis of available protein-RNA structures. We assembled a data set of 322 crystal and NMR structures and analyzed them regarding interface properties. In addition, we describe a computational alanine-scanning approach which provides interaction scores for interface amino acids, allowing the identification of potential hotspots in protein-RNA interfaces. We have made the computational approach available as an online tool, which allows interaction scores to be calculated for any structure of a protein-RNA complex by uploading atomic coordinates to the PRI HotScore web server (https://pri-hotscore.labs.vu.nl).
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Affiliation(s)
- Dennis M Krüger
- Chemical Genomics Centre of the Max Planck Society, 44227 Dortmund, Germany
| | - Saskia Neubacher
- Department of Chemistry and Pharmaceutical Sciences, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Tom N Grossmann
- Chemical Genomics Centre of the Max Planck Society, 44227 Dortmund, Germany
- Department of Chemistry and Pharmaceutical Sciences, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands
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35
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Zagrovic B, Bartonek L, Polyansky AA. RNA-protein interactions in an unstructured context. FEBS Lett 2018; 592:2901-2916. [PMID: 29851074 PMCID: PMC6175095 DOI: 10.1002/1873-3468.13116] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 05/12/2018] [Accepted: 05/13/2018] [Indexed: 02/02/2023]
Abstract
Despite their importance, our understanding of noncovalent RNA-protein interactions is incomplete. This especially concerns the binding between RNA and unstructured protein regions, a widespread class of such interactions. Here, we review the recent experimental and computational work on RNA-protein interactions in an unstructured context with a particular focus on how such interactions may be shaped by the intrinsic interaction affinities between individual nucleobases and protein side chains. Specifically, we articulate the claim that the universal genetic code reflects the binding specificity between nucleobases and protein side chains and that, in turn, the code may be seen as the Rosetta stone for understanding RNA-protein interactions in general.
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Affiliation(s)
- Bojan Zagrovic
- Department of Structural and Computational BiologyMax F. Perutz LaboratoriesUniversity of ViennaAustria
| | - Lukas Bartonek
- Department of Structural and Computational BiologyMax F. Perutz LaboratoriesUniversity of ViennaAustria
| | - Anton A. Polyansky
- Department of Structural and Computational BiologyMax F. Perutz LaboratoriesUniversity of ViennaAustria,MM Shemyakin and Yu A Ovchinnikov Institute of Bioorganic ChemistryRussian Academy of SciencesMoscowRussia
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36
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Chen F, Sun H, Wang J, Zhu F, Liu H, Wang Z, Lei T, Li Y, Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes. RNA (NEW YORK, N.Y.) 2018; 24:1183-1194. [PMID: 29930024 PMCID: PMC6097651 DOI: 10.1261/rna.065896.118] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 06/13/2018] [Indexed: 05/10/2023]
Abstract
Molecular docking provides a computationally efficient way to predict the atomic structural details of protein-RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein-RNA docking, but their prediction performance for protein-RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein-RNA systems with different solvent models and interior dielectric constants (εin). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GBGBn1 model with εin = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GBGBn1 model distinguished the near-native binding structures within the top 10 decoys for 117 out of the 148 protein-RNA systems (79.1%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein-RNA systems.
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Affiliation(s)
- Fu Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China
- College of Life and Environmental Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Huiyong Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hui Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Zhe Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Tailong Lei
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China
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37
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Krüger A, Zimbres FM, Kronenberger T, Wrenger C. Molecular Modeling Applied to Nucleic Acid-Based Molecule Development. Biomolecules 2018; 8:E83. [PMID: 30150587 PMCID: PMC6163985 DOI: 10.3390/biom8030083] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 08/12/2018] [Accepted: 08/16/2018] [Indexed: 12/15/2022] Open
Abstract
Molecular modeling by means of docking and molecular dynamics (MD) has become an integral part of early drug discovery projects, enabling the screening and enrichment of large libraries of small molecules. In the past decades, special emphasis was drawn to nucleic acid (NA)-based molecules in the fields of therapy, diagnosis, and drug delivery. Research has increased dramatically with the advent of the SELEX (systematic evolution of ligands by exponential enrichment) technique, which results in single-stranded DNA or RNA sequences that bind with high affinity and specificity to their targets. Herein, we discuss the role and contribution of docking and MD to the development and optimization of new nucleic acid-based molecules. This review focuses on the different approaches currently available for molecular modeling applied to NA interaction with proteins. We discuss topics ranging from structure prediction to docking and MD, highlighting their main advantages and limitations and the influence of flexibility on their calculations.
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Affiliation(s)
- Arne Krüger
- Unit for Drug Discovery, Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP 05508-000, Brazil.
| | - Flávia M Zimbres
- Department of Biochemistry and Molecular Biology and Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, GA 30602, USA.
| | - Thales Kronenberger
- Department of Internal Medicine VIII, University Hospital of Tübingen, 72076 Tübingen, Germany.
| | - Carsten Wrenger
- Unit for Drug Discovery, Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP 05508-000, Brazil.
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38
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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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39
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Abstract
Protein-RNA interactions play an important role in many biological processes. Computational methods such as docking have been developed to complement existing biophysical and structural biology techniques. Computational prediction of protein-RNA complex structures includes two steps: generating candidate structures from the individual protein and RNA parts and scoring the generated poses to pick out the correct one. In this work, we considered three recently developed data sets of protein-RNA complexes to evaluate and improve the performance of the FFT-based rigid-body docking algorithm implemented in the ICM package. An electrostatic term describing interactions between negatively charged phosphate groups and positively charged protein residues was added to the energy function used during the docking step to take into account the greater role that electrostatic interactions play in protein-RNA complexes. Next, the docking results were used to optimize a scoring function including van der Waals, electrostatic, and solvation terms. This optimization yielded a much smaller weight for the solvation term indicating that solvation energy may be less important for the scoring of protein-RNA structures. Rescoring of the generated poses with the new scoring function led to much higher success rates, while pose clustering by contact fingerprints produced further improvements, achieving a success rate of 0.66 for the top 100 structures.
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Affiliation(s)
- Yelena A Arnautova
- Molsoft L.L.C., 11199 Sorrento Valley Road, S209 , San Diego , California 92121 , United States
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Maxim Totrov
- Molsoft L.L.C., 11199 Sorrento Valley Road, S209 , San Diego , California 92121 , United States
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40
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Inhibition of protein interactions: co-crystalized protein-protein interfaces are nearly as good as holo proteins in rigid-body ligand docking. J Comput Aided Mol Des 2018; 32:769-779. [PMID: 30003468 DOI: 10.1007/s10822-018-0124-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Accepted: 05/22/2018] [Indexed: 12/15/2022]
Abstract
Modulating protein interaction pathways may lead to the cure of many diseases. Known protein-protein inhibitors bind to large pockets on the protein-protein interface. Such large pockets are detected also in the protein-protein complexes without known inhibitors, making such complexes potentially druggable. The inhibitor-binding site is primary defined by the side chains that form the largest pocket in the protein-bound conformation. Low-resolution ligand docking shows that the success rate for the protein-bound conformation is close to the one for the ligand-bound conformation, and significantly higher than for the apo conformation. The conformational change on the protein interface upon binding to the other protein results in a pocket employed by the ligand when it binds to that interface. This proof-of-concept study suggests that rather than using computational pocket-opening procedures, one can opt for an experimentally determined structure of the target co-crystallized protein-protein complex as a starting point for drug design.
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41
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Deciphering RNA-Recognition Patterns of Intrinsically Disordered Proteins. Int J Mol Sci 2018; 19:ijms19061595. [PMID: 29843482 PMCID: PMC6032373 DOI: 10.3390/ijms19061595] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 05/10/2018] [Accepted: 05/16/2018] [Indexed: 02/06/2023] Open
Abstract
Intrinsically disordered regions (IDRs) and protein (IDPs) are highly flexible owing to their lack of well-defined structures. A subset of such proteins interacts with various substrates; including RNA; frequently adopting regular structures in the final complex. In this work; we have analysed a dataset of protein–RNA complexes undergoing disorder-to-order transition (DOT) upon binding. We found that DOT regions are generally small in size (less than 3 residues) for RNA binding proteins. Like structured proteins; positively charged residues are found to interact with RNA molecules; indicating the dominance of electrostatic and cation-π interactions. However, a comparison of binding frequency shows that interface hydrophobic and aromatic residues have more interactions in only DOT regions than in a protein. Further; DOT regions have significantly higher exposure to water than their structured counterparts. Interactions of DOT regions with RNA increase the sheet formation with minor changes in helix forming residues. We have computed the interaction energy for amino acids–nucleotide pairs; which showed the preference of His–G; Asn–U and Ser–U at for the interface of DOT regions. This study provides insights to understand protein–RNA interactions and the results could also be used for developing a tool for identifying DOT regions in RNA binding proteins.
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42
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A pair-conformation-dependent scoring function for evaluating 3D RNA-protein complex structures. PLoS One 2017; 12:e0174662. [PMID: 28358834 PMCID: PMC5373608 DOI: 10.1371/journal.pone.0174662] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 03/13/2017] [Indexed: 01/04/2023] Open
Abstract
Computational prediction of RNA-protein complex 3D structures includes two basic steps: one is sampling possible structures and another is scoring the sampled structures to pick out the correct one. At present, constructing accurate scoring functions is still not well solved and the performances of the scoring functions usually depend on used benchmarks. Here we propose a pair-conformation-dependent scoring function, 3dRPC-Score, for 3D RNA-protein complex structure prediction by considering the nucleotide-residue pairs having the same energy if their conformations are similar, instead of the distance-only dependence of the most existing scoring functions. Benchmarking shows that 3dRPC-Score has a consistent performance in three test sets.
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43
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Using 3dRPC for RNA-protein complex structure prediction. BIOPHYSICS REPORTS 2017; 2:95-99. [PMID: 28317012 PMCID: PMC5334405 DOI: 10.1007/s41048-017-0034-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 01/05/2017] [Indexed: 02/07/2023] Open
Abstract
3dRPC is a computational method designed for three-dimensional RNA–protein complex structure prediction. Starting from a protein structure and a RNA structure, 3dRPC first generates presumptive complex structures by RPDOCK and then evaluates the structures by RPRANK. RPDOCK is an FFT-based docking algorithm that takes features of RNA–protein interactions into consideration, and RPRANK is a knowledge-based potential using root mean square deviation as a measure. Here we give a detailed description of the usage of 3dRPC. The source code is available at http://biophy.hust.edu.cn/3dRPC.html.
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44
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Zhang Z, Lu L, Zhang Y, Hua Li C, Wang CX, Zhang XY, Tan JJ. A combinatorial scoring function for protein-RNA docking. Proteins 2017; 85:741-752. [PMID: 28120375 DOI: 10.1002/prot.25253] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 01/16/2017] [Accepted: 01/17/2017] [Indexed: 12/13/2022]
Abstract
Protein-RNA docking is still an open question. One of the main challenges is to develop an effective scoring function that can discriminate near-native structures from the incorrect ones. To solve the problem, we have constructed a knowledge-based residue-nucleotide pairwise potential with secondary structure information considered for nonribosomal protein-RNA docking. Here we developed a weighted combined scoring function RpveScore that consists of the pairwise potential and six physics-based energy terms. The weights were optimized using the multiple linear regression method by fitting the scoring function to L_rmsd for the bound docking decoys from Benchmark II. The scoring functions were tested on 35 unbound docking cases. The results show that the scoring function RpveScore including all terms performs best. Also RpveScore was compared with the statistical mechanics-based method derived potential ITScore-PR, and the united atom-based statistical potentials QUASI-RNP and DARS-RNP. The success rate of RpveScore is 71.6% for the top 1000 structures and the number of cases where a near-native structure is ranked in top 30 is 25 out of 35 cases. For 32 systems (91.4%), RpveScore can find the binding mode in top 5 that has no lower than 50% native interface residues on protein and nucleotides on RNA. Additionally, it was found that the long-range electrostatic attractive energy plays an important role in distinguishing near-native structures from the incorrect ones. This work can be helpful for the development of protein-RNA docking methods and for the understanding of protein-RNA interactions. RpveScore program is available to the public at http://life.bjut.edu.cn/kxyj/kycg/2017116/14845362285362368_1.html Proteins 2017; 85:741-752. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Zhao Zhang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Lin Lu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Yue Zhang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Chun Hua Li
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Cun Xin Wang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Xiao Yi Zhang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Jian Jun Tan
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
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45
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Basu A, Basak P, Sarkar A. Molecular-Docking-Based Anti-Allergic Drug Design. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Allergens are foreign proteins that when come in contact of part(s) of human body stimulate the production of immunoglobulin types of proteins (antibodies). These allergens react with antibodies (immunoglobulin type E or IgE) produces allergic reactions, also known as immediate-type hypersensitivity reactions. As much as 20% of the general population may be affected by grass pollen as a major cause of allergic disease. EXPB class of proteins are known in the immunological literature as group-1 grass pollen allergens Molecular docking method can be used to identify the predicated the interaction of pollen allergen EXPB1 (Zea m 1), a beta-expansin and group-1 pollen allergen from maize with IgE molecules of human. The World Health Organization recognised allergen immunotherapy, as therapeutics for allergic diseases. RNA Interference (RNAi) is a biological process in which RNA molecules e.g. Small Interfering RNAs (siRNAs) inhibit gene expression, by cleavage and destruction of specific mRNA molecules. Use of Small Interfering RNA (siRNA) is a novel method in the induction of RNA Interference (RNAi), which is a potent method for therapeutics of allergic reactions. Due to various effects of STAT 6 proteins during hypersensitivity reactions caused by pollen allergens, mRNA of STAT6 gene is selected as target gene for allergy therapeutics via Post-Transcriptional Gene Silencing (PTGS). Using molecular docking study a specific sense siRNA is identified as anti allergic drug to treat allergic asthma during immediate type of hypersensitivity reaction, caused by Zea m 1 pollen allergen.
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46
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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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47
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Pérez-Cano L, Romero-Durana M, Fernández-Recio J. Structural and energy determinants in protein-RNA docking. Methods 2016; 118-119:163-170. [PMID: 27816523 DOI: 10.1016/j.ymeth.2016.11.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 10/14/2016] [Accepted: 11/01/2016] [Indexed: 01/02/2023] Open
Abstract
Deciphering the structural and energetic determinants of protein-RNA interactions harbors the potential to understand key cell processes at molecular level, such as gene expression and regulation. With this purpose, computational methods like docking aim to complement current biophysical and structural biology efforts. However, the few reported docking algorithms for protein-RNA interactions show limited predictive success rates, mainly due to incomplete sampling of the conformational space of both the protein and the RNA molecules, as well as to the difficulties of the scoring function in identifying the correct docking models. Here, we have tested the predictive value of a variety of knowledge-based and energetic scoring functions on a recently published protein-RNA docking benchmark and developed a scoring function able to efficiently discriminate docking decoys. We first performed docking calculations with the bound conformation, which allowed us to analyze the problem in optimal conditions. We found that geometry-based terms and electrostatics were the most important scoring terms, while binding propensities and desolvation were much less relevant for the scoring of protein-RNA models. This is in contrast with what we observed for protein-protein docking. The results also showed an interesting dependence of the predictive rates on the flexibility of the protein molecule, which arises from the observed higher positive charge of flexible interfaces and provides hints for future development of more efficient protein-RNA docking methods.
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Affiliation(s)
- Laura Pérez-Cano
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center (BSC), Jordi Girona 29, Barcelona 08034, Spain; Center for Neurobehavioral Genetics and Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Miguel Romero-Durana
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center (BSC), Jordi Girona 29, Barcelona 08034, Spain
| | - Juan Fernández-Recio
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center (BSC), Jordi Girona 29, Barcelona 08034, Spain.
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48
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Zheng J, Kundrotas PJ, Vakser IA, Liu S. Template-Based Modeling of Protein-RNA Interactions. PLoS Comput Biol 2016; 12:e1005120. [PMID: 27662342 PMCID: PMC5035060 DOI: 10.1371/journal.pcbi.1005120] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 08/25/2016] [Indexed: 12/29/2022] Open
Abstract
Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes. Structures of protein-RNA complexes are important for characterization of biological processes. The number of experimentally determined protein-RNA complexes is limited. Thus modeling of these complexes is important. Reliable structural predictions of proteins and their complexes are provided by comparative modeling, which takes advantage of similar complexes with experimentally determined structures. Thus, in the case of protein-RNA complexes, it is important to determine if similar proteins and RNAs bind in a similar way. We show that, similarly to the earlier published results on protein-protein complexes, such correlation of the protein-RNA binding mode and the monomers similarity indeed exists, and is stronger when the similarity is determined by structure rather than sequence alignment. The data shows clear transition from random to similar binding mode with the increase of the structural similarity of the monomers. On the basis of the results we designed and implemented a predictive tool, which should be useful for the biological community interested in modeling of protein-RNA interactions.
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Affiliation(s)
- Jinfang Zheng
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Petras J. Kundrotas
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
| | - Ilya A. Vakser
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (IAV); (SL)
| | - Shiyong Liu
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail: (IAV); (SL)
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49
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Iwakiri J, Hamada M, Asai K, Kameda T. Improved Accuracy in RNA-Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function. J Chem Theory Comput 2016; 12:4688-97. [PMID: 27494732 DOI: 10.1021/acs.jctc.6b00254] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
RNA-protein interactions play fundamental roles in many biological processes. To understand these interactions, it is necessary to know the three-dimensional structures of RNA-protein complexes. However, determining the tertiary structure of these complexes is often difficult, suggesting that an accurate rigid body docking for RNA-protein complexes is needed. In general, the rigid body docking process is divided into two steps: generating candidate structures from the individual RNA and protein structures and then narrowing down the candidates. In this study, we focus on the former problem to improve the prediction accuracy in RNA-protein docking. Our method is based on the integration of physicochemical information about RNA into ZDOCK, which is known as one of the most successful computer programs for protein-protein docking. Because recent studies showed the current force field for molecular dynamics simulation of protein and nucleic acids is quite accurate, we modeled the physicochemical information about RNA by force fields such as AMBER and CHARMM. A comprehensive benchmark of RNA-protein docking, using three recently developed data sets, reveals the remarkable prediction accuracy of the proposed method compared with existing programs for docking: the highest success rate is 34.7% for the predicted structure of the RNA-protein complex with the best score and 79.2% for 3,600 predicted ones. Three full atomistic force fields for RNA (AMBER94, AMBER99, and CHARMM22) produced almost the same accurate result, which showed current force fields for nucleic acids are quite accurate. In addition, we found that the electrostatic interaction and the representation of shape complementary between protein and RNA plays the important roles for accurate prediction of the native structures of RNA-protein complexes.
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Affiliation(s)
- Junichi Iwakiri
- Graduate School of Frontier Sciences, The University of Tokyo , 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Michiaki Hamada
- 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.,Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST) , 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Kiyoshi Asai
- Graduate School of Frontier Sciences, The University of Tokyo , 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan.,Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST) , 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Tomoshi Kameda
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST) , 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
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50
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Madan B, Kasprzak JM, Tuszynska I, Magnus M, Szczepaniak K, Dawson WK, Bujnicki JM. Modeling of Protein-RNA Complex Structures Using Computational Docking Methods. Methods Mol Biol 2016; 1414:353-372. [PMID: 27094302 DOI: 10.1007/978-1-4939-3569-7_21] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
A significant part of biology involves the formation of RNA-protein complexes. X-ray crystallography has added a few solved RNA-protein complexes to the repertoire; however, it remains challenging to capture these complexes and often only the unbound structures are available. This has inspired a growing interest in finding ways to predict these RNA-protein complexes. In this study, we show ways to approach this problem by computational docking methods, either with a fully automated NPDock server or with a workflow of methods for generation of many alternative structures followed by selection of the most likely solution. We show that by introducing experimental information, the structure of the bound complex is rendered far more likely to be within reach. This study is meant to help the user of docking software understand how to grapple with a typical realistic problem in RNA-protein docking, understand what to expect in the way of difficulties, and recognize the current limitations.
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Affiliation(s)
- Bharat Madan
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109, Warsaw, Poland
| | - Joanna M Kasprzak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109, Warsaw, Poland
- Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, 61-614, Poznan, Poland
| | - Irina Tuszynska
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109, Warsaw, Poland
- Institute of Informatics, University of Warsaw, ul. Banacha 2, 02-097, Warsaw, Poland
| | - Marcin Magnus
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109, Warsaw, Poland
| | - Krzysztof Szczepaniak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109, Warsaw, Poland
| | - Wayne K Dawson
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 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, 02-109, Warsaw, Poland.
- Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, 61-614, Poznan, Poland.
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