1
|
Thiel BC, Bussi G, Poblete S, Hofacker IL. Sampling globally and locally correct RNA 3D structures using Ernwin, SPQR and experimental SAXS data. Nucleic Acids Res 2024:gkae602. [PMID: 39021350 DOI: 10.1093/nar/gkae602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 07/05/2024] [Indexed: 07/20/2024] Open
Abstract
The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray scattering can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent. The low-resolution helix model (Ernwin) deals with the exploration of the huge conformational space making use of the SAXS data, while a nucleotide-level model (SPQR) removes clashes and disentangles the proposed structures, leading the structure to an all-atom representation in explicit water. We apply the procedure on four different known pdb structures up to 159 nucleotides with promising results. Additionally, we predict an all-atom structure for the Plasmodium falceparum signal recognition particle ALU RNA based on SAXS data deposited in the SASBDB, which has an alternate conformation and better fit to the SAXS data than the previously published structure based on the same data but other modelling methods.
Collapse
Affiliation(s)
- Bernhard C Thiel
- Department of Theoretical Chemistry, University of Vienna, Währinger Strasse 17, Vienna 1090, Austria
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, SISSA, via Bonomea 265, Trieste 34136, Italy
| | - Simón Poblete
- Centro BASAL Ciencia & Vida, Avenida del Valle Norte 725, Santiago 8580702, Chile
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Santiago 8420524, Chile
| | - Ivo L Hofacker
- Department of Theoretical Chemistry, University of Vienna, Währinger Strasse 17, Vienna 1090, Austria
- Research group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna 1090, Austria
| |
Collapse
|
2
|
Tosti Guerra F, Poppleton E, Šulc P, Rovigatti L. ANNaMo: Coarse-grained modeling for folding and assembly of RNA and DNA systems. J Chem Phys 2024; 160:205102. [PMID: 38814009 DOI: 10.1063/5.0202829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 05/04/2024] [Indexed: 05/31/2024] Open
Abstract
The folding of RNA and DNA strands plays crucial roles in biological systems and bionanotechnology. However, studying these processes with high-resolution numerical models is beyond current computational capabilities due to the timescales and system sizes involved. In this article, we present a new coarse-grained model for investigating the folding dynamics of nucleic acids. Our model represents three nucleotides with a patchy particle and is parameterized using well-established nearest-neighbor models. Thanks to the reduction of degrees of freedom and to a bond-swapping mechanism, our model allows for simulations at timescales and length scales that are currently inaccessible to more detailed models. To validate the performance of our model, we conducted extensive simulations of various systems: We examined the thermodynamics of DNA hairpins, capturing their stability and structural transitions, the folding of an MMTV pseudoknot, which is a complex RNA structure involved in viral replication, and also explored the folding of an RNA tile containing a k-type pseudoknot. Finally, we evaluated the performance of the new model in reproducing the melting temperatures of oligomers and the dependence on the toehold length of the displacement rate in toehold-mediated displacement processes, a key reaction used in molecular computing. All in all, the successful reproduction of experimental data and favorable comparisons with existing coarse-grained models validate the effectiveness of the new model.
Collapse
Affiliation(s)
- F Tosti Guerra
- Department of Physics, Sapienza University of Rome, Roma, Italy
| | - E Poppleton
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85281, USA
- Biophysical Engineering Group, Max Planck Institute for Medical Research, Heidelberg, Germany
| | - P Šulc
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85281, USA
- Department of Bioscience, School of Natural Sciences, Technical University Munich, Munich, Germany
| | - L Rovigatti
- Department of Physics, Sapienza University of Rome, Roma, Italy
| |
Collapse
|
3
|
Wang F, Xia R, Su Y, Cai P, Xu X. Quantifying RNA structures and interactions with a unified reduced chain representation model. Int J Biol Macromol 2023; 253:127181. [PMID: 37793523 DOI: 10.1016/j.ijbiomac.2023.127181] [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: 06/07/2023] [Revised: 08/30/2023] [Accepted: 09/25/2023] [Indexed: 10/06/2023]
Abstract
RNA is a pivotal molecule that plays critical roles in various cellular processes. Quantifying RNA structures and interactions is essential to understanding RNA function and developing RNA-based therapeutics. Using a unified five-bead model and a non-redundant database, this paper investigates the structural features and interactions of five commonly occurring RNA motifs, i.e., double-stranded helices, hairpin loops, internal/bulge loops, multi-branched junctions, and single-stranded terminal tails. Analyzing detailed distributions of RNA local structural features and base-base interactions reveals a preference for helical structures in both local backbone structures and base orientations. The interactions between adjacent bases exhibit motif-specific and sequence-dependent characteristics, reflecting the distinct topological constraints imposed by different loop-helix connection modes and the varying pairing and stacking interactions among different sequences. These findings shed light on the stability of RNA helices, emphasizing their significance in providing dominant base pairing and stacking interactions for RNA structures and stability. The four non-helix motifs encompass unpaired nucleotide loops and exhibit diverse base-base interactions, contributing to the structural diversity observed in RNA. Overall, the complexity of RNA structure arises from the intricate interplay of base-base interactions.
Collapse
Affiliation(s)
- Fengfei Wang
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China
| | - Renjie Xia
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China
| | - Yangyang Su
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China
| | - Pinggen Cai
- Department of Applied Physics, Zhejiang University of Technology, Hangzhou 310023, China.
| | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China.
| |
Collapse
|
4
|
Dowerah D, V. N. Uppuladinne M, Sarma PJ, Biswakarma N, Sonavane UB, Joshi RR, Ray SK, Namsa ND, Deka RC. Design of LNA Analogues Using a Combined Density Functional Theory and Molecular Dynamics Approach for RNA Therapeutics. ACS OMEGA 2023; 8:22382-22405. [PMID: 37396274 PMCID: PMC10308574 DOI: 10.1021/acsomega.2c07860] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 02/17/2023] [Indexed: 07/04/2023]
Abstract
Antisense therapeutics treat a wide spectrum of diseases, many of which cannot be addressed with the current drug technologies. In the quest to design better antisense oligonucleotide drugs, we propose five novel LNA analogues (A1-A5) for modifying antisense oligonucleotides and establishing each with the five standard nucleic acids: adenine (A), guanine (G), cytosine (C), thymine (T), and uracil (U). Monomer nucleotides of these modifications were considered for a detailed Density Functional Theory (DFT)-based quantum chemical analysis to determine their molecular-level structural and electronic properties. A detailed MD simulation study was done on a 14-mer ASO (5'-CTTAGCACTGGCCT-3') containing these modifications targeting PTEN mRNA. Results from both molecular- and oligomer-level analysis clearly depicted LNA-level stability of the modifications, the ASO/RNA duplexes maintaining stable Watson-Crick base pairing preferring RNA-mimicking A-form duplexes. Notably, monomer MO isosurfaces for both purines and pyrimidines were majorly distributed on the nucleobase region in modifications A1 and A2 and in the bridging unit in modifications A3, A4, and A5, suggesting that A3/RNA, A4/RNA, and A5/RNA duplexes interact more with the RNase H and solvent environment. Accordingly, solvation of A3/RNA, A4/RNA, and A5/RNA duplexes was higher compared to that of LNA/RNA, A1/RNA, and A2/RNA duplexes. This study has resulted in a successful archetype for creating advantageous nucleic acid modifications tailored for particular needs, fulfilling a useful purpose of designing novel antisense modifications, which may overcome the drawbacks and improve the pharmacokinetics of existing LNA antisense modifications.
Collapse
Affiliation(s)
- Dikshita Dowerah
- CMML—Catalysis
and Molecular Modelling Lab, Department of Chemical Sciences, Tezpur University, Napaam, Sonitpur, Assam 784 028, India
| | - Mallikarjunachari V. N. Uppuladinne
- HPC—Medical
& Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC), Panchavati, Pashan, Pune 411008, India
| | - Plaban J. Sarma
- CMML—Catalysis
and Molecular Modelling Lab, Department of Chemical Sciences, Tezpur University, Napaam, Sonitpur, Assam 784 028, India
- Department
of Chemistry, Gargaon College, Sivasagar, Assam 785685, India
| | - Nishant Biswakarma
- CMML—Catalysis
and Molecular Modelling Lab, Department of Chemical Sciences, Tezpur University, Napaam, Sonitpur, Assam 784 028, India
| | - Uddhavesh B. Sonavane
- HPC—Medical
& Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC), Panchavati, Pashan, Pune 411008, India
| | - Rajendra R. Joshi
- HPC—Medical
& Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC), Panchavati, Pashan, Pune 411008, India
| | - Suvendra K. Ray
- Department
of Molecular Biology and Biotechnology, Tezpur University, Napaam, Sonitpur, Assam 784028, India
- Center
for Multidisciplinary Research, Tezpur University, Napaam, Sonitpur, Assam 784028, India
| | - Nima D. Namsa
- Department
of Molecular Biology and Biotechnology, Tezpur University, Napaam, Sonitpur, Assam 784028, India
- Center
for Multidisciplinary Research, Tezpur University, Napaam, Sonitpur, Assam 784028, India
| | - Ramesh Ch. Deka
- CMML—Catalysis
and Molecular Modelling Lab, Department of Chemical Sciences, Tezpur University, Napaam, Sonitpur, Assam 784 028, India
- Center
for Multidisciplinary Research, Tezpur University, Napaam, Sonitpur, Assam 784028, India
| |
Collapse
|
5
|
Li J, Chen SJ. RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling. Nucleic Acids Res 2023; 51:3341-3356. [PMID: 36864729 PMCID: PMC10123122 DOI: 10.1093/nar/gkad122] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 01/14/2023] [Accepted: 02/25/2023] [Indexed: 03/04/2023] Open
Abstract
RNA 3D structures are critical for understanding their functions. However, only a limited number of RNA structures have been experimentally solved, so computational prediction methods are highly desirable. Nevertheless, accurate prediction of RNA 3D structures, especially those containing multiway junctions, remains a significant challenge, mainly due to the complicated non-canonical base pairing and stacking interactions in the junction loops and the possible long-range interactions between loop structures. Here we present RNAJP ('RNA Junction Prediction'), a nucleotide- and helix-level coarse-grained model for the prediction of RNA 3D structures, particularly junction structures, from a given 2D structure. Through global sampling of the 3D arrangements of the helices in junctions using molecular dynamics simulations and in explicit consideration of non-canonical base pairing and base stacking interactions as well as long-range loop-loop interactions, the model can provide significantly improved predictions for multibranched junction structures than existing methods. Moreover, integrated with additional restraints from experiments, such as junction topology and long-range interactions, the model may serve as a useful structure generator for various applications.
Collapse
Affiliation(s)
- Jun Li
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| |
Collapse
|
6
|
Ma H, Pham P, Luo B, Rangan R, Kappel K, Su Z, Das R. Auto-DRRAFTER: Automated RNA Modeling Based on Cryo-EM Density. Methods Mol Biol 2023; 2568:193-211. [PMID: 36227570 DOI: 10.1007/978-1-0716-2687-0_13] [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] [Indexed: 05/14/2023]
Abstract
RNA three-dimensional structures provide rich and vital information for understanding their functions. Recent advances in cryogenic electron microscopy (cryo-EM) allow structure determination of RNAs and ribonucleoprotein (RNP) complexes. However, limited global and local resolutions of RNA cryo-EM maps pose great challenges in tracing RNA coordinates. The Rosetta-based "auto-DRRAFTER" method builds RNA models into moderate-resolution RNA cryo-EM density as part of the Ribosolve pipeline. Here, we describe a step-by-step protocol for auto-DRRAFTER using a glycine riboswitch from Fusobacterium nucleatum as an example. Successful implementation of this protocol allows automated RNA modeling into RNA cryo-EM density, accelerating our understanding of RNA structure-function relationships. Input and output files are being made available at https://github.com/auto-DRRAFTER/springer-chapter .
Collapse
Affiliation(s)
- Haiyun Ma
- The State Key Laboratory of Biotherapy, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Phillip Pham
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Bingnan Luo
- The State Key Laboratory of Biotherapy, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ramya Rangan
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Zhaoming Su
- The State Key Laboratory of Biotherapy, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Rhiju Das
- Biophysics Program, Stanford University, Stanford, CA, USA.
| |
Collapse
|
7
|
Abstract
RNA molecules carry out various cellular functions, and understanding the mechanisms behind their functions requires the knowledge of their 3D structures. Different types of computational methods have been developed to model RNA 3D structures over the past decade. These methods were widely used by researchers although their performance needs to be further improved. Recently, along with these traditional methods, machine-learning techniques have been increasingly applied to RNA 3D structure prediction and show significant improvement in performance. Here we shall give a brief review of the traditional methods and recent related advances in machine-learning approaches for RNA 3D structure prediction.
Collapse
Affiliation(s)
- Xiujuan Ou
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Zhang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yiduo Xiong
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Xiao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| |
Collapse
|
8
|
Bheemireddy S, Sandhya S, Srinivasan N, Sowdhamini R. Computational tools to study RNA-protein complexes. Front Mol Biosci 2022; 9:954926. [PMID: 36275618 PMCID: PMC9585174 DOI: 10.3389/fmolb.2022.954926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/20/2022] [Indexed: 11/19/2022] Open
Abstract
RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.
Collapse
Affiliation(s)
- Sneha Bheemireddy
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Sankaran Sandhya
- Department of Biotechnology, Faculty of Life and Allied Health Sciences, M.S. Ramaiah University of Applied Sciences, Bengaluru, India
- *Correspondence: Sankaran Sandhya, ; Ramanathan Sowdhamini,
| | | | - Ramanathan Sowdhamini
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- National Centre for Biological Sciences, TIFR, GKVK Campus, Bangalore, India
- Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
- *Correspondence: Sankaran Sandhya, ; Ramanathan Sowdhamini,
| |
Collapse
|
9
|
3dDNA: A Computational Method of Building DNA 3D Structures. Molecules 2022; 27:molecules27185936. [PMID: 36144680 PMCID: PMC9503956 DOI: 10.3390/molecules27185936] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/04/2022] [Accepted: 09/08/2022] [Indexed: 02/07/2023] Open
Abstract
Considerable progress has been made in the prediction methods of 3D structures of RNAs. In contrast, no such methods are available for DNAs. The determination of 3D structures of the latter is also increasingly needed for understanding their functions and designing new DNA molecules. Since the number of experimental structures of DNA is limited at present, here, we propose a computational and template-based method, 3dDNA, which combines DNA and RNA template libraries to predict DNA 3D structures. It was benchmarked on three test sets with different numbers of chains, and the results show that 3dDNA can predict DNA 3D structures with a mean RMSD of about 2.36 Å for those with one or two chains and fewer than 4 Å with three or more chains.
Collapse
|
10
|
Guo ZH, Yuan L, Tan YL, Zhang BG, Shi YZ. RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures. FRONTIERS IN BIOINFORMATICS 2022; 1:809082. [PMID: 36303785 PMCID: PMC9580920 DOI: 10.3389/fbinf.2021.809082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 12/17/2021] [Indexed: 11/13/2022] Open
Abstract
The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).
Collapse
Affiliation(s)
- Zhi-Hao Guo
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China
- School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China
| | - Li Yuan
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China
- School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China
| | - Ya-Lan Tan
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China
| | - Ben-Gong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China
| | - Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China
- *Correspondence: Ya-Zhou Shi,
| |
Collapse
|
11
|
rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation. Biophys J 2022; 121:142-156. [PMID: 34798137 PMCID: PMC8758408 DOI: 10.1016/j.bpj.2021.11.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/23/2021] [Accepted: 11/10/2021] [Indexed: 01/07/2023] Open
Abstract
Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.
Collapse
|
12
|
Li J, Chen SJ. RNA 3D Structure Prediction Using Coarse-Grained Models. Front Mol Biosci 2021; 8:720937. [PMID: 34277713 PMCID: PMC8283274 DOI: 10.3389/fmolb.2021.720937] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 06/17/2021] [Indexed: 12/12/2022] Open
Abstract
The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequences and the experimentally determined structures enables the thriving development of computational approaches to modeling RNAs. However, computational methods based on all-atom simulations are intractable for large RNA systems, which demand long time simulations. Facing such a challenge, many coarse-grained (CG) models have been developed. Here, we provide a review of CG models for modeling RNA 3D structures, compare the performance of the different models, and offer insights into potential future developments.
Collapse
Affiliation(s)
| | - Shi-Jie Chen
- Departments of Physics and Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, MO, United States
| |
Collapse
|
13
|
Zhang D, Li J, Chen SJ. IsRNA1: De Novo Prediction and Blind Screening of RNA 3D Structures. J Chem Theory Comput 2021; 17:1842-1857. [PMID: 33560836 DOI: 10.1021/acs.jctc.0c01148] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Modeling structures and functions of large ribonucleic acid (RNAs) especially with complicated topologies is highly challenging due to the inefficiency of large conformational sampling and the presence of complicated tertiary interactions. To address this problem, one highly promising approach is coarse-grained modeling. Here, following an iterative simulated reference state approach to decipher the correlations between different structural parameters, we developed a potent coarse-grained RNA model named as IsRNA1 for RNA studies. Molecular dynamics simulations in the IsRNA1 can predict the native structures of small RNAs from a sequence and fold medium-sized RNAs into near-native tertiary structures with the assistance of secondary structure constraints. A large-scale benchmark test on RNA 3D structure prediction shows that IsRNA1 exhibits improved performance for relatively large RNAs of complicated topologies, such as large stem-loop structures and structures containing long-range tertiary interactions. The advantages of IsRNA1 include the consideration of the correlations between the different structural variables, the appropriate characterization of canonical base-pairing and base-stacking interactions, and the better sampling for the backbone conformations. Moreover, a blind screening protocol was developed based on IsRNA1 to identify good structural models from a pool of candidates without prior knowledge of the native structures.
Collapse
Affiliation(s)
- Dong Zhang
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Jun Li
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| |
Collapse
|
14
|
Hurst T, Zhang D, Zhou Y, Chen SJ. A Bayes-inspired theory for optimally building an efficient coarse-grained folding force field. COMMUNICATIONS IN INFORMATION AND SYSTEMS 2021; 21:65-83. [PMID: 34354546 PMCID: PMC8336718 DOI: 10.4310/cis.2021.v21.n1.a4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Because of their potential utility in predicting conformational changes and assessing folding dynamics, coarse-grained (CG) RNA folding models are appealing for rapid characterization of RNA molecules. Previously, we reported the iterative simulated RNA reference state (IsRNA) method for parameterizing a CG force field for RNA folding, which consecutively updates the simulation force field to reflect marginal distributions of folding coordinates in the structure database and extract various energy terms. While the IsRNA model was validated by showing close agreement between the IsRNA-simulated and experimentally observed distributions, here, we expand our theoretical understanding of the model and, in doing so, improve the parameterization process to optimize the subset of included folding coordinates, which leads to accelerated simulations. Using statistical mechanical theory, we analyze the underlying, Bayesian concept that drives parameterization of the energy function, providing a general method for developing predictive, knowledge-based, polymer force fields on the basis of limited data. Furthermore, we propose an optimal parameterization procedure, based on the principal of maximum entropy.
Collapse
Affiliation(s)
- Travis Hurst
- Department of Physics, University of Missouri-Columbia, Columbia, MO 65211, USA
| | - Dong Zhang
- Department of Physics, University of Missouri-Columbia
| | - Yuanzhe Zhou
- Department of Physics, University of Missouri-Columbia, Columbia, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, MU Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO 65211, USA
| |
Collapse
|
15
|
Feng Y, Huang SY. ITScore-NL: An Iterative Knowledge-Based Scoring Function for Nucleic Acid-Ligand Interactions. J Chem Inf Model 2020; 60:6698-6708. [PMID: 33291885 DOI: 10.1021/acs.jcim.0c00974] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Nucleic acid-ligand complexes underlie numerous cellular processes, such as gene function expression and regulation, in which their three-dimensional structures are important to understand their functions and thus to develop therapeutic interventions. Given the high cost and technical difficulties in experimental methods, computational methods such as molecular docking have been actively used to investigate nucleic acid-ligand interactions in which an accurate scoring function is crucial. However, because of the limited number of experimental nucleic acid-ligand binding data and structures, the scoring function development for nucleic acid-ligand interactions falls far behind that for protein-protein and protein-ligand interactions. Here, based on our statistical mechanics-based iterative approach, we have developed an iterative knowledge-based scoring function for nucleic acid-ligand interactions, named as ITScore-NL, by explicitly including stacking and electrostatic potentials. Our ITScore-NL scoring function was extensively evaluated for its ability in the binding mode and binding affinity predictions on three diverse test sets and compared with state-of-the-art scoring functions. Overall, ITScore-NL obtained significantly better performance than the other 12 scoring functions and predicted near-native poses with rmsd ≤ 1.5 Å for 71.43% of the cases when the top three binding modes were considered and a good correlation of R = 0.64 in binding affinity prediction on the large test set of 77 nucleic acid-ligand complexes. These results suggested the accuracy of ITScore-NL and the necessity of explicitly including stacking and electrostatic potentials.
Collapse
Affiliation(s)
- Yuyu Feng
- 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
| |
Collapse
|
16
|
Joshi SY, Deshmukh SA. A review of advancements in coarse-grained molecular dynamics simulations. MOLECULAR SIMULATION 2020. [DOI: 10.1080/08927022.2020.1828583] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Soumil Y. Joshi
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, USA
| | | |
Collapse
|
17
|
Šulc P. The Multiscale Future of RNA Modeling. Biophys J 2020; 119:1270-1272. [PMID: 32941784 PMCID: PMC7462927 DOI: 10.1016/j.bpj.2020.08.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/14/2020] [Accepted: 08/25/2020] [Indexed: 11/28/2022] Open
Affiliation(s)
- Petr Šulc
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, Tempe, Arizona.
| |
Collapse
|
18
|
Zhao C, Zhang D, Jiang Y, Chen SJ. Modeling Loop Composition and Ion Concentration Effects in RNA Hairpin Folding Stability. Biophys J 2020; 119:1439-1455. [PMID: 32949490 PMCID: PMC7568001 DOI: 10.1016/j.bpj.2020.07.042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 06/12/2020] [Accepted: 07/08/2020] [Indexed: 12/21/2022] Open
Abstract
The ability to accurately predict RNA hairpin structure and stability for different loop sequences and salt conditions is important for understanding, modeling, and designing larger RNA folds. However, traditional RNA secondary structure models cannot treat loop-sequence and ionic effects on RNA hairpin folding. Here, we describe a general, three-dimensional (3D) conformation-based computational method for modeling salt concentration-dependent conformational distributions and the detailed 3D structures for a set of three RNA hairpins that contain a variable, 15-nucleotide loop sequence. For a given RNA sequence, the new, to our knowledge, method integrates a Vfold2D two-dimensional structure folding model with IsRNA coarse-grained molecular dynamics 3D folding simulations and Monte Carlo tightly bound ion estimations of ion-mediated electrostatic interactions. The model predicts free-energy landscapes for the different RNA hairpin-forming sequences with variable salt conditions. The theoretically predicted results agree with the experimental fluorescence measurements, validating the strategy. Furthermore, the theoretical model goes beyond the experimental results by enabling in-depth 3D structural analysis, revealing energetic mechanisms for the sequence- and salt-dependent folding stability. Although the computational framework presented here is developed for RNA hairpin systems, the general method may be applied to investigate other RNA systems, such as multiway junctions or pseudoknots in mixed metal ion solutions.
Collapse
Affiliation(s)
- Chenhan Zhao
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Dong Zhang
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Yangwei Jiang
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri.
| |
Collapse
|
19
|
Richardson KE, Adams MS, Kirkpatrick CC, Gohara DW, Znosko BM. Identification and Characterization of New RNA Tetraloop Sequence Families. Biochemistry 2019; 58:4809-4820. [PMID: 31714066 DOI: 10.1021/acs.biochem.9b00535] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
There is an abundance of RNA sequence information available due to the efforts of sequencing projects. However, current techniques implemented to solve the tertiary structures of RNA, such as NMR and X-ray crystallography, are difficult and time-consuming. Therefore, biophysical techniques are not able to keep pace with the abundance of sequence information available. Because of this, there is a need to develop quick and efficient ways to predict RNA tertiary structure from sequence. One promising approach is to identify structural patterns within previously solved 3D structures and apply these patterns to new sequences. RNA tetraloops are one of the most common naturally occurring secondary structure motifs. Here, we use RNA Characterization of Secondary Structure Motifs (CoSSMos), Dissecting the Spatial Structure of RNA (DSSR), and a bioinformatic approach to search for and characterize tertiary structure patterns among tetraloops. Not surprising, we identified the well-known GNRA and UNCG tetraloops, as well as the previously identified RNYA tetraloop. However, some previously identified characteristics of these families were not observed in this data set, and some new characteristics were identified. In addition, we also identified and characterized three new tetraloop sequence families: YGAR, UGGU, and RMSA. This new structural information sheds light on the tertiary structure of tetraloops and contributes to the efforts of RNA tertiary structure prediction from sequence.
Collapse
Affiliation(s)
- Katherine E Richardson
- Department of Chemistry , Saint Louis University , Saint Louis , Missouri 63103 , United States
| | - Miranda S Adams
- Department of Chemistry , Saint Louis University , Saint Louis , Missouri 63103 , United States
| | - Charles C Kirkpatrick
- Department of Chemistry , Saint Louis University , Saint Louis , Missouri 63103 , United States
| | - David W Gohara
- Department of Biochemistry and Molecular Biology , Saint Louis University , Saint Louis , Missouri 63103 , United States
| | - Brent M Znosko
- Department of Chemistry , Saint Louis University , Saint Louis , Missouri 63103 , United States
| |
Collapse
|
20
|
Jin L, Tan YL, Wu Y, Wang X, Shi YZ, Tan ZJ. Structure folding of RNA kissing complexes in salt solutions: predicting 3D structure, stability, and folding pathway. RNA (NEW YORK, N.Y.) 2019; 25:1532-1548. [PMID: 31391217 PMCID: PMC6795135 DOI: 10.1261/rna.071662.119] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 08/02/2019] [Indexed: 05/08/2023]
Abstract
RNA kissing complexes are essential for genomic RNA dimerization and regulation of gene expression, and their structures and stability are critical to their biological functions. In this work, we used our previously developed coarse-grained model with an implicit structure-based electrostatic potential to predict three-dimensional (3D) structures and stability of RNA kissing complexes in salt solutions. For extensive RNA kissing complexes, our model shows great reliability in predicting 3D structures from their sequences, and our additional predictions indicate that the model can capture the dependence of 3D structures of RNA kissing complexes on monovalent/divalent ion concentrations. Moreover, the comparisons with extensive experimental data show that the model can make reliable predictions on the stability for various RNA kissing complexes over wide ranges of monovalent/divalent ion concentrations. Notably, for RNA kissing complexes, our further analyses show the important contribution of coaxial stacking to the 3D structures and stronger stability than the corresponding kissing-interface duplexes at high salts. Furthermore, our comprehensive analyses for RNA kissing complexes reveal that the thermally folding pathway for a complex sequence is mainly determined by the relative stability of two possible folded states of kissing complex and extended duplex, which can be significantly modulated by its sequence.
Collapse
Affiliation(s)
- Lei Jin
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Lan Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Yao Wu
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Xunxun Wang
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan 430073, China
| | - Zhi-Jie Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| |
Collapse
|
21
|
Zhang BG, Qiu HH, Jiang J, Liu J, Shi YZ. 3D structure stability of the HIV-1 TAR RNA in ion solutions: A coarse-grained model study. J Chem Phys 2019; 151:165101. [PMID: 31675878 DOI: 10.1063/1.5126128] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
As an extremely common structural motif, RNA hairpins with bulge loops [e.g., the human immunodeficiency virus type 1 (HIV-1) transactivation response (TAR) RNA] can play essential roles in normal cellular processes by binding to proteins and small ligands, which could be very dependent on their three-dimensional (3D) structures and stability. Although the structures and conformational dynamics of the HIV-1 TAR RNA have been extensively studied, there are few investigations on the thermodynamic stability of the TAR RNA, especially in ion solutions, and the existing studies also have some divergence on the unfolding process of the RNA. Here, we employed our previously developed coarse-grained model with implicit salt to predict the 3D structure, stability, and unfolding pathway for the HIV-1 TAR RNA over a wide range of ion concentrations. As compared with the extensive experimental/theoretical results, the present model can give reliable predictions on the 3D structure stability of the TAR RNA from the sequence. Based on the predictions, our further comprehensive analyses on the stability of the TAR RNA as well as its variants revealed that the unfolding pathway of an RNA hairpin with a bulge loop is mainly determined by the relative stability between different states (folded state, intermediate state, and unfolded state) and the strength of the coaxial stacking between two stems in folded structures, both of which can be apparently modulated by the ion concentrations as well as the sequences.
Collapse
Affiliation(s)
- Ben-Gong Zhang
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| | - Hua-Hai Qiu
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| | - Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| |
Collapse
|
22
|
Tan YL, Feng CJ, Jin L, Shi YZ, Zhang W, Tan ZJ. What is the best reference state for building statistical potentials in RNA 3D structure evaluation? RNA (NEW YORK, N.Y.) 2019; 25:793-812. [PMID: 30996105 PMCID: PMC6573789 DOI: 10.1261/rna.069872.118] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Accepted: 04/06/2019] [Indexed: 05/14/2023]
Abstract
Knowledge-based statistical potentials have been shown to be efficient in protein structure evaluation/prediction, and the core difference between various statistical potentials is attributed to the choice of reference states. However, for RNA 3D structure evaluation, a comprehensive examination on reference states is still lacking. In this work, we built six statistical potentials based on six reference states widely used in protein structure evaluation, including averaging, quasi-chemical approximation, atom-shuffled, finite-ideal-gas, spherical-noninteracting, and random-walk-chain reference states, and we examined the six reference states against three RNA test sets including six subsets. Our extensive examinations show that, overall, for identifying native structures and ranking decoy structures, the finite-ideal-gas and random-walk-chain reference states are slightly superior to others, while for identifying near-native structures, there is only a slight difference between these reference states. Our further analyses show that the performance of a statistical potential is apparently dependent on the quality of the training set. Furthermore, we found that the performance of a statistical potential is closely related to the origin of test sets, and for the three realistic test subsets, the six statistical potentials have overall unsatisfactory performance. This work presents a comprehensive examination on the existing reference states and statistical potentials for RNA 3D structure evaluation.
Collapse
Affiliation(s)
- Ya-Lan Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Chen-Jie Feng
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Lei Jin
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan 430073, China
| | - Wenbing Zhang
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Zhi-Jie Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| |
Collapse
|
23
|
Pucci F, Schug A. Shedding light on the dark matter of the biomolecular structural universe: Progress in RNA 3D structure prediction. Methods 2019; 162-163:68-73. [DOI: 10.1016/j.ymeth.2019.04.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 04/12/2019] [Accepted: 04/22/2019] [Indexed: 11/25/2022] Open
|
24
|
Ponce-Salvatierra A, Astha, Merdas K, Nithin C, Ghosh P, Mukherjee S, Bujnicki JM. Computational modeling of RNA 3D structure based on experimental data. Biosci Rep 2019; 39:BSR20180430. [PMID: 30670629 PMCID: PMC6367127 DOI: 10.1042/bsr20180430] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 01/19/2019] [Accepted: 01/21/2019] [Indexed: 01/02/2023] Open
Abstract
RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.
Collapse
Affiliation(s)
- Almudena Ponce-Salvatierra
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Astha
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Katarzyna Merdas
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Chandran Nithin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Pritha Ghosh
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Sunandan Mukherjee
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, Warsaw PL-02-109, Poland
- Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, Poznan PL-61-614, Poland
| |
Collapse
|
25
|
Jin L, Shi YZ, Feng CJ, Tan YL, Tan ZJ. Modeling Structure, Stability, and Flexibility of Double-Stranded RNAs in Salt Solutions. Biophys J 2018; 115:1403-1416. [PMID: 30236782 DOI: 10.1016/j.bpj.2018.08.030] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 08/10/2018] [Accepted: 08/24/2018] [Indexed: 11/16/2022] Open
Abstract
Double-stranded (ds) RNAs play essential roles in many processes of cell metabolism. The knowledge of three-dimensional (3D) structure, stability, and flexibility of dsRNAs in salt solutions is important for understanding their biological functions. In this work, we further developed our previously proposed coarse-grained model to predict 3D structure, stability, and flexibility for dsRNAs in monovalent and divalent ion solutions through involving an implicit structure-based electrostatic potential. The model can make reliable predictions for 3D structures of extensive dsRNAs with/without bulge/internal loops from their sequences, and the involvement of the structure-based electrostatic potential and corresponding ion condition can improve the predictions for 3D structures of dsRNAs in ion solutions. Furthermore, the model can make good predictions for thermal stability for extensive dsRNAs over the wide range of monovalent/divalent ion concentrations, and our analyses show that the thermally unfolding pathway of dsRNA is generally dependent on its length as well as its sequence. In addition, the model was employed to examine the salt-dependent flexibility of a dsRNA helix, and the calculated salt-dependent persistence lengths are in good accordance with experiments.
Collapse
Affiliation(s)
- Lei Jin
- Center for Theoretical Physics and Key Laboratory of Artificial Micro- & Nanostructures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| | - Chen-Jie Feng
- Center for Theoretical Physics and Key Laboratory of Artificial Micro- & Nanostructures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Ya-Lan Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro- & Nanostructures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Zhi-Jie Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro- & Nanostructures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China.
| |
Collapse
|
26
|
Jain S, Bayrak CS, Petingi L, Schlick T. Dual Graph Partitioning Highlights a Small Group of Pseudoknot-Containing RNA Submotifs. Genes (Basel) 2018; 9:E371. [PMID: 30044451 PMCID: PMC6115904 DOI: 10.3390/genes9080371] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/26/2018] [Accepted: 06/26/2018] [Indexed: 12/31/2022] Open
Abstract
RNA molecules are composed of modular architectural units that define their unique structural and functional properties. Characterization of these building blocks can help interpret RNA structure/function relationships. We present an RNA secondary structure motif and submotif library using dual graph representation and partitioning. Dual graphs represent RNA helices as vertices and loops as edges. Unlike tree graphs, dual graphs can represent RNA pseudoknots (intertwined base pairs). For a representative set of RNA structures, we construct dual graphs from their secondary structures, and apply our partitioning algorithm to identify non-separable subgraphs (or blocks) without breaking pseudoknots. We report 56 subgraph blocks up to nine vertices; among them, 22 are frequently occurring, 15 of which contain pseudoknots. We then catalog atomic fragments corresponding to the subgraph blocks to define a library of building blocks that can be used for RNA design, which we call RAG-3Dual, as we have done for tree graphs. As an application, we analyze the distribution of these subgraph blocks within ribosomal RNAs of various prokaryotic and eukaryotic species to identify common subgraphs and possible ancestry relationships. Other applications of dual graph partitioning and motif library can be envisioned for RNA structure analysis and design.
Collapse
Affiliation(s)
- Swati Jain
- Department of Chemistry, New York University, New York, NY 10003, USA.
| | - Cigdem S Bayrak
- Department of Chemistry, New York University, New York, NY 10003, USA.
| | - Louis Petingi
- Computer Science Department, College of Staten Island, City University of New York, Staten Island, New York, NY 10314, USA.
| | - Tamar Schlick
- Department of Chemistry, New York University, New York, NY 10003, USA.
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA.
- NYU-East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai 3663, China.
| |
Collapse
|
27
|
Shi YZ, Jin L, Feng CJ, Tan YL, Tan ZJ. Predicting 3D structure and stability of RNA pseudoknots in monovalent and divalent ion solutions. PLoS Comput Biol 2018; 14:e1006222. [PMID: 29879103 PMCID: PMC6007934 DOI: 10.1371/journal.pcbi.1006222] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 06/19/2018] [Accepted: 05/22/2018] [Indexed: 01/30/2023] Open
Abstract
RNA pseudoknots are a kind of minimal RNA tertiary structural motifs, and their three-dimensional (3D) structures and stability play essential roles in a variety of biological functions. Therefore, to predict 3D structures and stability of RNA pseudoknots is essential for understanding their functions. In the work, we employed our previously developed coarse-grained model with implicit salt to make extensive predictions and comprehensive analyses on the 3D structures and stability for RNA pseudoknots in monovalent/divalent ion solutions. The comparisons with available experimental data show that our model can successfully predict the 3D structures of RNA pseudoknots from their sequences, and can also make reliable predictions for the stability of RNA pseudoknots with different lengths and sequences over a wide range of monovalent/divalent ion concentrations. Furthermore, we made comprehensive analyses on the unfolding pathway for various RNA pseudoknots in ion solutions. Our analyses for extensive pseudokonts and the wide range of monovalent/divalent ion concentrations verify that the unfolding pathway of RNA pseudoknots is mainly dependent on the relative stability of unfolded intermediate states, and show that the unfolding pathway of RNA pseudoknots can be significantly modulated by their sequences and solution ion conditions.
Collapse
Affiliation(s)
- Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Lei Jin
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Chen-Jie Feng
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Ya-Lan Tan
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Zhi-Jie Tan
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| |
Collapse
|
28
|
Hurst T, Xu X, Zhao P, Chen SJ. Quantitative Understanding of SHAPE Mechanism from RNA Structure and Dynamics Analysis. J Phys Chem B 2018; 122:4771-4783. [PMID: 29659274 DOI: 10.1021/acs.jpcb.8b00575] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) method probes RNA local structural and dynamic information at single nucleotide resolution. To gain quantitative insights into the relationship between nucleotide flexibility, RNA 3D structure, and SHAPE reactivity, we develop a 3D Structure-SHAPE Relationship model (3DSSR) to rebuild SHAPE profiles from 3D structures. The model starts from RNA structures and combines nucleotide interaction strength and conformational propensity, ligand (SHAPE reagent) accessibility, and base-pairing pattern through a composite function to quantify the correlation between SHAPE reactivity and nucleotide conformational stability. The 3DSSR model shows the relationship between SHAPE reactivity and RNA structure and energetics. Comparisons between the 3DSSR-predicted SHAPE profile and the experimental SHAPE data show correlation, suggesting that the extracted analytical function may have captured the key factors that determine the SHAPE reactivity profile. Furthermore, the theory offers an effective method to sieve RNA 3D models and exclude models that are incompatible with experimental SHAPE data.
Collapse
Affiliation(s)
- Travis Hurst
- Department of Physics, Department of Biochemistry , and University of Missouri Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
| | - Xiaojun Xu
- Department of Physics, Department of Biochemistry , and University of Missouri Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
| | - Peinan Zhao
- Department of Physics, Department of Biochemistry , and University of Missouri Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry , and University of Missouri Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
| |
Collapse
|
29
|
Šponer J, Bussi G, Krepl M, Banáš P, Bottaro S, Cunha RA, Gil-Ley A, Pinamonti G, Poblete S, Jurečka P, Walter NG, Otyepka M. RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview. Chem Rev 2018; 118:4177-4338. [PMID: 29297679 PMCID: PMC5920944 DOI: 10.1021/acs.chemrev.7b00427] [Citation(s) in RCA: 336] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Indexed: 12/14/2022]
Abstract
With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.
Collapse
Affiliation(s)
- Jiří Šponer
- Institute of Biophysics of the Czech Academy of Sciences , Kralovopolska 135 , Brno 612 65 , Czech Republic
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Miroslav Krepl
- Institute of Biophysics of the Czech Academy of Sciences , Kralovopolska 135 , Brno 612 65 , Czech Republic
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
| | - Pavel Banáš
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
| | - Sandro Bottaro
- Structural Biology and NMR Laboratory, Department of Biology , University of Copenhagen , Copenhagen 2200 , Denmark
| | - Richard A Cunha
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Alejandro Gil-Ley
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Giovanni Pinamonti
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Simón Poblete
- Scuola Internazionale Superiore di Studi Avanzati , Via Bonomea 265 , Trieste 34136 , Italy
| | - Petr Jurečka
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
| | - Nils G Walter
- Single Molecule Analysis Group and Center for RNA Biomedicine, Department of Chemistry , University of Michigan , Ann Arbor , Michigan 48109 , United States
| | - Michal Otyepka
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science , Palacky University Olomouc , 17. listopadu 12 , Olomouc 771 46 , Czech Republic
| |
Collapse
|
30
|
Zhang D, Chen SJ. IsRNA: An Iterative Simulated Reference State Approach to Modeling Correlated Interactions in RNA Folding. J Chem Theory Comput 2018; 14:2230-2239. [PMID: 29499114 DOI: 10.1021/acs.jctc.7b01228] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Coarse-grained RNA folding models promise great potential for RNA structure prediction. A key component in a coarse-grained folding model is the force field. One of the challenges in the coarse-grained force field calculation is how to treat the correlation between the different degrees of freedoms. Here, we describe a new approach (IsRNA) to extract the correlated energy functions from the known structures. Through iterative molecular dynamics simulations, we build the correlation effects into the reference states, from which we extract the energy functions. The validity of IsRNA is supported by the close agreement between the simulated Boltzmann-like probability distributions for all the structure parameters and those observed from the experimentally determined structures. The correlated energy functions derived here may provide a new tool for RNA 3D structure prediction.
Collapse
Affiliation(s)
- Dong Zhang
- Department of Physics, Department of Biochemistry, and MU Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and MU Informatics Institute , University of Missouri , Columbia , Missouri 65211 , United States
| |
Collapse
|
31
|
Effects and limitations of a nucleobase-driven backmapping procedure for nucleic acids using steered molecular dynamics. Biochem Biophys Res Commun 2018; 498:352-358. [DOI: 10.1016/j.bbrc.2017.12.057] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 11/13/2017] [Accepted: 12/11/2017] [Indexed: 11/19/2022]
|
32
|
Jain S, Schlick T. F-RAG: Generating Atomic Coordinates from RNA Graphs by Fragment Assembly. J Mol Biol 2017; 429:3587-3605. [PMID: 28988954 PMCID: PMC5693719 DOI: 10.1016/j.jmb.2017.09.017] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 09/12/2017] [Accepted: 09/22/2017] [Indexed: 10/18/2022]
Abstract
Coarse-grained models represent attractive approaches to analyze and simulate ribonucleic acid (RNA) molecules, for example, for structure prediction and design, as they simplify the RNA structure to reduce the conformational search space. Our structure prediction protocol RAGTOP (RNA-As-Graphs Topology Prediction) represents RNA structures as tree graphs and samples graph topologies to produce candidate graphs. However, for a more detailed study and analysis, construction of atomic from coarse-grained models is required. Here we present our graph-based fragment assembly algorithm (F-RAG) to convert candidate three-dimensional (3D) tree graph models, produced by RAGTOP into atomic structures. We use our related RAG-3D utilities to partition graphs into subgraphs and search for structurally similar atomic fragments in a data set of RNA 3D structures. The fragments are edited and superimposed using common residues, full atomic models are scored using RAGTOP's knowledge-based potential, and geometries of top scoring models is optimized. To evaluate our models, we assess all-atom RMSDs and Interaction Network Fidelity (a measure of residue interactions) with respect to experimentally solved structures and compare our results to other fragment assembly programs. For a set of 50 RNA structures, we obtain atomic models with reasonable geometries and interactions, particularly good for RNAs containing junctions. Additional improvements to our protocol and databases are outlined. These results provide a good foundation for further work on RNA structure prediction and design applications.
Collapse
Affiliation(s)
- Swati Jain
- Department of Chemistry, New York University, 1001 Silver, 100 Washington Square East, New York, NY 10003, USA
| | - Tamar Schlick
- Department of Chemistry, New York University, 1001 Silver, 100 Washington Square East, New York, NY 10003, USA; Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA; New York University-East China Normal University Center for Computational Chemistry at New York University Shanghai, Room 340, Geography Building, North Zhongshan Road, 3663 Shanghai, China.
| |
Collapse
|
33
|
Sun LZ, Kranawetter C, Heng X, Chen SJ. Predicting Ion Effects in an RNA Conformational Equilibrium. J Phys Chem B 2017; 121:8026-8036. [PMID: 28780864 DOI: 10.1021/acs.jpcb.7b03873] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
We develop a partial charge-based tightly bound ion (PCTBI) model for the ion effects in RNA folding. On the basis of the Monte Carlo tightly bound ion (MCTBI) approach, the model can account for ion fluctuation and correlation effects, and can predict the ion distribution around the RNA. Furthermore, unlike the previous coarse-grained RNA charge models, where negative charges are placed on the phosphates only, the current new model considers the detailed all-atom partial charge distribution on the RNA. Thus, the model not only keeps the advantage of the MCTBI model, but also has the potential to provide important detailed information unattainable by the previous MCTBI models. For example, the model predicts the reduction in ion binding upon protein binding and ion-induced conformational switches. For hepatitis C virus genomic RNA, the model predicts a Mg2+-induced stabilization of a kissing motif for a cis-acting regulatory element in the genomic RNA. Extensive theory-experiment comparisons support the reliability of the theoretical predictions. Therefore, the model may serve as a robust starting point for further development of an accurate method for ion effects in an RNA conformational equilibrium and RNA-cofactor interactions.
Collapse
Affiliation(s)
- Li-Zhen Sun
- Department of Physics, Department of Biochemistry, and Informatics Institute and ‡Department of Biochemistry, University of Missouri , Columbia, Missouri 65211, United States
| | - Clayton Kranawetter
- Department of Physics, Department of Biochemistry, and Informatics Institute and ‡Department of Biochemistry, University of Missouri , Columbia, Missouri 65211, United States
| | - Xiao Heng
- Department of Physics, Department of Biochemistry, and Informatics Institute and ‡Department of Biochemistry, University of Missouri , Columbia, Missouri 65211, United States
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Informatics Institute and ‡Department of Biochemistry, University of Missouri , Columbia, Missouri 65211, United States
| |
Collapse
|
34
|
Abstract
Inspired by the recent success of scientific-discovery games for predicting protein tertiary and RNA secondary structures, we have developed an open software for coarse-grained RNA folding simulations, guided by human intuition. To determine the extent to which interactive simulations can accurately predict 3D RNA structures of increasing complexity and lengths (four RNAs with 22-47 nucleotides), an interactive experiment was conducted with 141 participants who had very little knowledge of nucleic acids systems and computer simulations, and had received only a brief description of the important forces stabilizing RNA structures. Their structures and full trajectories have been analyzed statistically and compared to standard replica exchange molecular dynamics simulations. Our analyses show that participants gain easily chemical intelligence to fold simple and nontrivial topologies, with little computer time, and this result opens the door for the use of human-guided simulations to RNA folding. Our experiment shows that interactive simulations have better chances of success when the user widely explores the conformational space. Interestingly, providing on-the-fly feedback of the root mean square deviation with respect to the experimental structure did not improve the quality of the proposed models.
Collapse
|
35
|
Uusitalo JJ, Ingólfsson HI, Marrink SJ, Faustino I. Martini Coarse-Grained Force Field: Extension to RNA. Biophys J 2017. [PMID: 28633759 DOI: 10.1016/j.bpj.2017.05.043] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
RNA has an important role not only as the messenger of genetic information but also as a regulator of gene expression. Given its central role in cell biology, there is significant interest in studying the structural and dynamic behavior of RNA in relation to other biomolecules. Coarse-grain molecular dynamics simulations are a key tool to that end. Here, we have extended the coarse-grain Martini force field to include RNA after our recent extension to DNA. In the same way DNA was modeled, the tertiary structure of RNA is constrained using an elastic network. This model, therefore, is not designed for applications involving RNA folding but rather offers a stable RNA structure for studying RNA interactions with other (bio)molecules. The RNA model is compatible with all other Martini models and opens the way to large-scale explicit-solvent molecular dynamics simulations of complex systems involving RNA.
Collapse
Affiliation(s)
- Jaakko J Uusitalo
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Groningen, the Netherlands
| | - Helgi I Ingólfsson
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Groningen, the Netherlands
| | - Siewert J Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Groningen, the Netherlands.
| | - Ignacio Faustino
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Groningen, the Netherlands
| |
Collapse
|
36
|
Abstract
In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion-RNA and ligand-RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data-directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.
Collapse
Affiliation(s)
- Li-Zhen Sun
- Department of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, Missouri 65211;
| | - Dong Zhang
- Department of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, Missouri 65211;
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, Missouri 65211;
| |
Collapse
|
37
|
Bell DR, Cheng SY, Salazar H, Ren P. Capturing RNA Folding Free Energy with Coarse-Grained Molecular Dynamics Simulations. Sci Rep 2017; 7:45812. [PMID: 28393861 PMCID: PMC5385882 DOI: 10.1038/srep45812] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 03/06/2017] [Indexed: 01/25/2023] Open
Abstract
We introduce a coarse-grained RNA model for molecular dynamics simulations, RACER (RnA CoarsE-gRained). RACER achieves accurate native structure prediction for a number of RNAs (average RMSD of 2.93 Å) and the sequence-specific variation of free energy is in excellent agreement with experimentally measured stabilities (R2 = 0.93). Using RACER, we identified hydrogen-bonding (or base pairing), base stacking, and electrostatic interactions as essential driving forces for RNA folding. Also, we found that separating pairing vs. stacking interactions allowed RACER to distinguish folded vs. unfolded states. In RACER, base pairing and stacking interactions each provide an approximate stability of 3-4 kcal/mol for an A-form helix. RACER was developed based on PDB structural statistics and experimental thermodynamic data. In contrast with previous work, RACER implements a novel effective vdW potential energy function, which led us to re-parameterize hydrogen bond and electrostatic potential energy functions. Further, RACER is validated and optimized using a simulated annealing protocol to generate potential energy vs. RMSD landscapes. Finally, RACER is tested using extensive equilibrium pulling simulations (0.86 ms total) on eleven RNA sequences (hairpins and duplexes).
Collapse
Affiliation(s)
- David R. Bell
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Sara Y. Cheng
- Department of Physics, University of Texas at Austin, Austin, Texas 78712, United States
| | - Heber Salazar
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Pengyu Ren
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| |
Collapse
|
38
|
Barth SA, Menge C, Eichhorn I, Semmler T, Pickard D, Geue L. Evaluation of applicability of DNA microarray–based characterization of bovine Shiga toxin–producing Escherichia coli isolates using whole genome sequence analysis. J Vet Diagn Invest 2017; 29:721-724. [DOI: 10.1177/1040638717700689] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
We assessed the ability of a commercial DNA microarray to characterize bovine Shiga toxin–producing Escherichia coli (STEC) isolates and evaluated the results using in silico hybridization of the microarray probes within whole genome sequencing scaffolds. From a total of 69,954 reactions (393 probes with 178 isolates), 68,706 (98.2%) gave identical results by DNA microarray and in silico probe hybridization. Results were more congruent when detecting the genoserotype (209 differing results from 19,758 in total; 1.1%) or antimicrobial resistance genes (AMRGs; 141 of 26,878; 0.5%) than when detecting virulence-associated genes (VAGs; 876 of 22,072; 4.0%). Owing to the limited coverage of O-antigens by the microarray, only 37.2% of the isolates could be genoserotyped. However, the microarray proved suitable to rapidly screen bovine STEC strains for the occurrence of high numbers of VAGs and AMRGs and is suitable for molecular surveillance workflows.
Collapse
Affiliation(s)
- Stefanie A. Barth
- Friedrich-Loeffler-Institut/Federal Research Institute for Animal Health, Institute of Molecular Pathogenesis, Jena, Germany (Barth, Menge, Geue)
- Free University Berlin, Institute of Microbiology and Epizootics, Berlin, Germany (Eichhorn, Semmler)
- Robert Koch Institute, Berlin, Germany (Semmler)
- The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge, UK (Pickard)
| | - Christian Menge
- Friedrich-Loeffler-Institut/Federal Research Institute for Animal Health, Institute of Molecular Pathogenesis, Jena, Germany (Barth, Menge, Geue)
- Free University Berlin, Institute of Microbiology and Epizootics, Berlin, Germany (Eichhorn, Semmler)
- Robert Koch Institute, Berlin, Germany (Semmler)
- The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge, UK (Pickard)
| | - Inga Eichhorn
- Friedrich-Loeffler-Institut/Federal Research Institute for Animal Health, Institute of Molecular Pathogenesis, Jena, Germany (Barth, Menge, Geue)
- Free University Berlin, Institute of Microbiology and Epizootics, Berlin, Germany (Eichhorn, Semmler)
- Robert Koch Institute, Berlin, Germany (Semmler)
- The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge, UK (Pickard)
| | - Torsten Semmler
- Friedrich-Loeffler-Institut/Federal Research Institute for Animal Health, Institute of Molecular Pathogenesis, Jena, Germany (Barth, Menge, Geue)
- Free University Berlin, Institute of Microbiology and Epizootics, Berlin, Germany (Eichhorn, Semmler)
- Robert Koch Institute, Berlin, Germany (Semmler)
- The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge, UK (Pickard)
| | - Derek Pickard
- Friedrich-Loeffler-Institut/Federal Research Institute for Animal Health, Institute of Molecular Pathogenesis, Jena, Germany (Barth, Menge, Geue)
- Free University Berlin, Institute of Microbiology and Epizootics, Berlin, Germany (Eichhorn, Semmler)
- Robert Koch Institute, Berlin, Germany (Semmler)
- The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge, UK (Pickard)
| | - Lutz Geue
- Friedrich-Loeffler-Institut/Federal Research Institute for Animal Health, Institute of Molecular Pathogenesis, Jena, Germany (Barth, Menge, Geue)
- Free University Berlin, Institute of Microbiology and Epizootics, Berlin, Germany (Eichhorn, Semmler)
- Robert Koch Institute, Berlin, Germany (Semmler)
- The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge, UK (Pickard)
| |
Collapse
|
39
|
Abstract
In order to carry out biological functions, RNA molecules must fold into specific three-dimensional (3D) structures. Current experimental methods to determine RNA 3D structures are expensive and time consuming. With the recent advances in computational biology, RNA structure prediction is becoming increasingly reliable. This chapter describes a recently developed RNA structure prediction software, Vfold, a virtual bond-based RNA folding model. The main features of Vfold are the physics-based loop free energy calculations for various RNA structure motifs and a template-based assembly method for RNA 3D structure prediction. For illustration, we use the yybP-ykoY Orphan riboswitch as an example to show the implementation of the Vfold model in RNA structure prediction from the sequence.
Collapse
Affiliation(s)
- Chenhan Zhao
- Department of Physics, Informatics Institute, University of Missouri, Columbia, MO 65211, USA
| | - Xiaojun Xu
- Department of Physics, Informatics Institute, University of Missouri, Columbia, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics, Informatics Institute, University of Missouri, Columbia, MO 65211, USA
| |
Collapse
|
40
|
Shi YZ, Jin L, Wang FH, Zhu XL, Tan ZJ. Predicting 3D Structure, Flexibility, and Stability of RNA Hairpins in Monovalent and Divalent Ion Solutions. Biophys J 2016; 109:2654-2665. [PMID: 26682822 DOI: 10.1016/j.bpj.2015.11.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 10/09/2015] [Accepted: 11/06/2015] [Indexed: 10/24/2022] Open
Abstract
A full understanding of RNA-mediated biology would require the knowledge of three-dimensional (3D) structures, structural flexibility, and stability of RNAs. To predict RNA 3D structures and stability, we have previously proposed a three-bead coarse-grained predictive model with implicit salt/solvent potentials. In this study, we further develop the model by improving the implicit-salt electrostatic potential and including a sequence-dependent coaxial stacking potential to enable the model to simulate RNA 3D structure folding in divalent/monovalent ion solutions. The model presented here can predict 3D structures of RNA hairpins with bulges/internal loops (<77 nucleotides) from their sequences at the corresponding experimental ion conditions with an overall improved accuracy compared to the experimental data; the model also makes reliable predictions for the flexibility of RNA hairpins with bulge loops of different lengths at several divalent/monovalent ion conditions. In addition, the model successfully predicts the stability of RNA hairpins with various loops/stems in divalent/monovalent ion solutions.
Collapse
Affiliation(s)
- Ya-Zhou Shi
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of the Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Lei Jin
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of the Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Feng-Hua Wang
- Engineering Training Center, Jianghan University, Wuhan, China
| | - Xiao-Long Zhu
- Department of Physics, School of Physics and Information Engineering, Jianghan University, Wuhan, China
| | - Zhi-Jie Tan
- Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of the Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China.
| |
Collapse
|
41
|
Sun LZ, Chen SJ. Monte Carlo Tightly Bound Ion Model: Predicting Ion-Binding Properties of RNA with Ion Correlations and Fluctuations. J Chem Theory Comput 2016; 12:3370-81. [PMID: 27311366 PMCID: PMC5520805 DOI: 10.1021/acs.jctc.6b00028] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Experiments have suggested that ion correlation and fluctuation effects can be potentially important for multivalent ions in RNA folding. However, most existing computational methods for the ion electrostatics in RNA folding tend to ignore these effects. The previously reported tightly bound ion (TBI) model can treat ion correlation and fluctuation but its applicability to biologically important RNAs is severely limited by the low computational efficiency. Here, on the basis of Monte Carlo sampling for the many-body ion distribution, we develop a new computational model, the Monte Carlo tightly bound ion (MCTBI) model, for ion-binding properties around an RNA. Because of an enhanced sampling algorithm for ion distribution, the model leads to a significant improvement in computational efficiency. For example, for a 160-nt RNA, the model causes a more than 10-fold increase in the computational efficiency, and the improvement in computational efficiency is more pronounced for larger systems. Furthermore, unlike the earlier model that describes ion distribution using the number of bound ions around each nucleotide, the current MCTBI model is based on the three-dimensional coordinates of the ions. The higher efficiency of the model allows us to treat the ion effects for medium to large RNA molecules, RNA-ligand complexes, and RNA-protein complexes. This new model together with proper RNA conformational sampling and the energetics model may serve as a starting point for further development for the ion effects in RNA folding and conformational changes and for large nucleic acid systems.
Collapse
Affiliation(s)
- Li-Zhen Sun
- Department of Physics, Department of Biochemistry, and Informatics Institute, University of Missouri, Columbia, MO 65211
- Department of Applied Physics, Zhejiang University of Technology, Hangzhou 310023, China
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Informatics Institute, University of Missouri, Columbia, MO 65211
| |
Collapse
|
42
|
Venditti V, Egner TK, Clore GM. Hybrid Approaches to Structural Characterization of Conformational Ensembles of Complex Macromolecular Systems Combining NMR Residual Dipolar Couplings and Solution X-ray Scattering. Chem Rev 2016; 116:6305-22. [PMID: 26739383 PMCID: PMC5590664 DOI: 10.1021/acs.chemrev.5b00592] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Solving structures or structural ensembles of large macromolecular systems in solution poses a challenging problem. While NMR provides structural information at atomic resolution, increased spectral complexity, chemical shift overlap, and short transverse relaxation times (associated with slow tumbling) render application of the usual techniques that have been so successful for medium sized systems (<50 kDa) difficult. Solution X-ray scattering, on the other hand, is not limited by molecular weight but only provides low resolution structural information related to the overall shape and size of the system under investigation. Here we review how combining atomic resolution structures of smaller domains with sparse experimental data afforded by NMR residual dipolar couplings (which yield both orientational and shape information) and solution X-ray scattering data in rigid-body simulated annealing calculations provides a powerful approach for investigating the structural aspects of conformational dynamics in large multidomain proteins. The application of this hybrid methodology is illustrated for the 128 kDa dimer of bacterial Enzyme I which exists in a variety of open and closed states that are sampled at various points in the catalytic cycles, and for the capsid protein of the human immunodeficiency virus.
Collapse
Affiliation(s)
- Vincenzo Venditti
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
| | - Timothy K. Egner
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, United States
| | - G. Marius Clore
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892-0520, United States
| |
Collapse
|
43
|
Coarse-grained modeling of RNA 3D structure. Methods 2016; 103:138-56. [PMID: 27125734 DOI: 10.1016/j.ymeth.2016.04.026] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Revised: 04/21/2016] [Accepted: 04/22/2016] [Indexed: 12/21/2022] Open
Abstract
Functional RNA molecules depend on three-dimensional (3D) structures to carry out their tasks within the cell. Understanding how these molecules interact to carry out their biological roles requires a detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as a host of other interactions within the cellular environment. Experimental determination of these properties is difficult, and various computational methods have been developed to model the folding of RNA 3D structures and their interactions with other molecules. However, computational methods also have their limitations, especially when the biological effects demand computation of the dynamics beyond a few hundred nanoseconds. For the researcher confronted with such challenges, a more amenable approach is to resort to coarse-grained modeling to reduce the number of data points and computational demand to a more tractable size, while sacrificing as little critical information as possible. This review presents an introduction to the topic of coarse-grained modeling of RNA 3D structures and dynamics, covering both high- and low-resolution strategies. We discuss how physics-based approaches compare with knowledge based methods that rely on databases of information. In the course of this review, we discuss important aspects in the reasoning process behind building different models and the goals and pitfalls that can result.
Collapse
|
44
|
Henke PS, Mak CH. An implicit divalent counterion force field for RNA molecular dynamics. J Chem Phys 2016; 144:105104. [DOI: 10.1063/1.4943387] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Affiliation(s)
- Paul S. Henke
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
| | - Chi H. Mak
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA
- Center of Applied Mathematical Sciences, University of Southern California, Los Angeles, California 90089, USA
| |
Collapse
|
45
|
Holliday Junction Thermodynamics and Structure: Coarse-Grained Simulations and Experiments. Sci Rep 2016; 6:22863. [PMID: 26971574 PMCID: PMC4789735 DOI: 10.1038/srep22863] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 02/22/2016] [Indexed: 02/08/2023] Open
Abstract
Holliday junctions play a central role in genetic recombination, DNA repair and other cellular processes. We combine simulations and experiments to evaluate the ability of the 3SPN.2 model, a coarse-grained representation designed to mimic B-DNA, to predict the properties of DNA Holliday junctions. The model reproduces many experimentally determined aspects of junction structure and stability, including the temperature dependence of melting on salt concentration, the bias between open and stacked conformations, the relative populations of conformers at high salt concentration, and the inter-duplex angle (IDA) between arms. We also obtain a close correspondence between the junction structure evaluated by all-atom and coarse-grained simulations. We predict that, for salt concentrations at physiological and higher levels, the populations of the stacked conformers are independent of salt concentration, and directly observe proposed tetrahedral intermediate sub-states implicated in conformational transitions. Our findings demonstrate that the 3SPN.2 model captures junction properties that are inaccessible to all-atom studies, opening the possibility to simulate complex aspects of junction behavior.
Collapse
|
46
|
Xu X, Yu T, Chen SJ. Understanding the kinetic mechanism of RNA single base pair formation. Proc Natl Acad Sci U S A 2016; 113:116-21. [PMID: 26699466 PMCID: PMC4711849 DOI: 10.1073/pnas.1517511113] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
RNA functions are intrinsically tied to folding kinetics. The most elementary step in RNA folding is the closing and opening of a base pair. Understanding this elementary rate process is the basis for RNA folding kinetics studies. Previous studies mostly focused on the unfolding of base pairs. Here, based on a hybrid approach, we investigate the folding process at level of single base pairing/stacking. The study, which integrates molecular dynamics simulation, kinetic Monte Carlo simulation, and master equation methods, uncovers two alternative dominant pathways: Starting from the unfolded state, the nucleotide backbone first folds to the native conformation, followed by subsequent adjustment of the base conformation. During the base conformational rearrangement, the backbone either retains the native conformation or switches to nonnative conformations in order to lower the kinetic barrier for base rearrangement. The method enables quantification of kinetic partitioning among the different pathways. Moreover, the simulation reveals several intriguing ion binding/dissociation signatures for the conformational changes. Our approach may be useful for developing a base pair opening/closing rate model.
Collapse
Affiliation(s)
- Xiaojun Xu
- Department of Physics, University of Missouri, Columbia, MO 65211; Department of Biochemistry, University of Missouri, Columbia, MO 65211; Informatics Institute, University of Missouri, Columbia, MO 65211
| | - Tao Yu
- Department of Physics, University of Missouri, Columbia, MO 65211; Department of Biochemistry, University of Missouri, Columbia, MO 65211; Informatics Institute, University of Missouri, Columbia, MO 65211; Department of Physics, Jianghan University, Wuhan, Hubei 430056, China
| | - Shi-Jie Chen
- Department of Physics, University of Missouri, Columbia, MO 65211; Department of Biochemistry, University of Missouri, Columbia, MO 65211; Informatics Institute, University of Missouri, Columbia, MO 65211;
| |
Collapse
|
47
|
Boniecki MJ, Lach G, Dawson WK, Tomala K, Lukasz P, Soltysinski T, Rother KM, Bujnicki JM. SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Res 2015; 44:e63. [PMID: 26687716 PMCID: PMC4838351 DOI: 10.1093/nar/gkv1479] [Citation(s) in RCA: 234] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 12/05/2015] [Indexed: 01/08/2023] Open
Abstract
RNA molecules play fundamental roles in cellular processes. Their function and interactions with other biomolecules are dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. Here, we present SimRNA: a new method for computational RNA 3D structure prediction, which uses a coarse-grained representation, relies on the Monte Carlo method for sampling the conformational space, and employs a statistical potential to approximate the energy and identify conformations that correspond to biologically relevant structures. SimRNA can fold RNA molecules using only sequence information, and, on established test sequences, it recapitulates secondary structure with high accuracy, including correct prediction of pseudoknots. For modeling of complex 3D structures, it can use additional restraints, derived from experimental or computational analyses, including information about secondary structure and/or long-range contacts. SimRNA also can be used to analyze conformational landscapes and identify potential alternative structures.
Collapse
Affiliation(s)
- Michal J Boniecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland
| | - Grzegorz Lach
- 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
| | - Konrad Tomala
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland
| | - Pawel Lukasz
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland
| | - Tomasz Soltysinski
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland
| | - Kristian M Rother
- 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
| |
Collapse
|
48
|
Dawson WK, Bujnicki JM. Computational modeling of RNA 3D structures and interactions. Curr Opin Struct Biol 2015; 37:22-8. [PMID: 26689764 DOI: 10.1016/j.sbi.2015.11.007] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 11/11/2015] [Accepted: 11/12/2015] [Indexed: 11/25/2022]
Abstract
RNA molecules have key functions in cellular processes beyond being carriers of protein-coding information. These functions are often dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is difficult, which has prompted the development of computational methods for structure prediction from sequence. Recent progress in 3D structure modeling of RNA and emerging approaches for predicting RNA interactions with ions, ligands and proteins have been stimulated by successes in protein 3D structure modeling.
Collapse
Affiliation(s)
- Wayne K Dawson
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Ks. Trojdena 4, 02-109 Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, 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.
| |
Collapse
|
49
|
Jabbari H, Aminpour M, Montemagno C. Computational Approaches to Nucleic Acid Origami. ACS COMBINATORIAL SCIENCE 2015; 17:535-47. [PMID: 26348196 DOI: 10.1021/acscombsci.5b00079] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Recent advances in experimental DNA origami have dramatically expanded the horizon of DNA nanotechnology. Complex 3D suprastructures have been designed and developed using DNA origami with applications in biomaterial science, nanomedicine, nanorobotics, and molecular computation. Ribonucleic acid (RNA) origami has recently been realized as a new approach. Similar to DNA, RNA molecules can be designed to form complex 3D structures through complementary base pairings. RNA origami structures are, however, more compact and more thermodynamically stable due to RNA's non-canonical base pairing and tertiary interactions. With all these advantages, the development of RNA origami lags behind DNA origami by a large gap. Furthermore, although computational methods have proven to be effective in designing DNA and RNA origami structures and in their evaluation, advances in computational nucleic acid origami is even more limited. In this paper, we review major milestones in experimental and computational DNA and RNA origami and present current challenges in these fields. We believe collaboration between experimental nanotechnologists and computer scientists are critical for advancing these new research paradigms.
Collapse
Affiliation(s)
- Hosna Jabbari
- Ingenuity Lab, 11421 Saskatchewan
Drive, Edmonton, Alberta T6G 2M9, Canada
- Department
of Chemical and Materials Engineering, University of Alberta, Edmonton T6G 2V4, Canada
| | - Maral Aminpour
- Ingenuity Lab, 11421 Saskatchewan
Drive, Edmonton, Alberta T6G 2M9, Canada
- Department
of Chemical and Materials Engineering, University of Alberta, Edmonton T6G 2V4, Canada
| | - Carlo Montemagno
- Ingenuity Lab, 11421 Saskatchewan
Drive, Edmonton, Alberta T6G 2M9, Canada
- Department
of Chemical and Materials Engineering, University of Alberta, Edmonton T6G 2V4, Canada
| |
Collapse
|
50
|
Pinamonti G, Bottaro S, Micheletti C, Bussi G. Elastic network models for RNA: a comparative assessment with molecular dynamics and SHAPE experiments. Nucleic Acids Res 2015; 43:7260-9. [PMID: 26187990 PMCID: PMC4551938 DOI: 10.1093/nar/gkv708] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Accepted: 06/30/2015] [Indexed: 11/23/2022] Open
Abstract
Elastic network models (ENMs) are valuable and efficient tools for characterizing the collective internal dynamics of proteins based on the knowledge of their native structures. The increasing evidence that the biological functionality of RNAs is often linked to their innate internal motions poses the question of whether ENM approaches can be successfully extended to this class of biomolecules. This issue is tackled here by considering various families of elastic networks of increasing complexity applied to a representative set of RNAs. The fluctuations predicted by the alternative ENMs are stringently validated by comparison against extensive molecular dynamics simulations and SHAPE experiments. We find that simulations and experimental data are systematically best reproduced by either an all-atom or a three-beads-per-nucleotide representation (sugar-base-phosphate), with the latter arguably providing the best balance of accuracy and computational complexity.
Collapse
Affiliation(s)
- Giovanni Pinamonti
- Scuola Internazionale Superiore di Studi Avanzati, International School for Advanced Studies, 265, Via Bonomea I-34136 Trieste, Italy
| | - Sandro Bottaro
- Scuola Internazionale Superiore di Studi Avanzati, International School for Advanced Studies, 265, Via Bonomea I-34136 Trieste, Italy
| | - Cristian Micheletti
- Scuola Internazionale Superiore di Studi Avanzati, International School for Advanced Studies, 265, Via Bonomea I-34136 Trieste, Italy
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, International School for Advanced Studies, 265, Via Bonomea I-34136 Trieste, Italy
| |
Collapse
|