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Chen X, Khan NS, Zhang S. LocalSTAR3D: a local stack-based RNA 3D structural alignment tool. Nucleic Acids Res 2020; 48:e77. [PMID: 32496533 PMCID: PMC7367197 DOI: 10.1093/nar/gkaa453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 05/15/2020] [Accepted: 05/27/2020] [Indexed: 11/29/2022] Open
Abstract
A fast-growing number of non-coding RNA structures have been resolved and deposited in Protein Data Bank (PDB). In contrast to the wide range of global alignment and motif search tools, there is still a lack of local alignment tools. Among all the global alignment tools for RNA 3D structures, STAR3D has become a valuable tool for its unprecedented speed and accuracy. STAR3D compares the 3D structures of RNA molecules using consecutive base-pairs (stacks) as anchors and generates an optimal global alignment. In this article, we developed a local RNA 3D structural alignment tool, named LocalSTAR3D, which was extended from STAR3D and designed to report multiple local alignments between two RNAs. The benchmarking results show that LocalSTAR3D has better accuracy and coverage than other local alignment tools. Furthermore, the utility of this tool has been demonstrated by rediscovering kink-turn motif instances, conserved domains in group II intron RNAs, and the tRNA mimicry of IRES RNAs.
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Affiliation(s)
- Xiaoli Chen
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Nabila Shahnaz Khan
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Shaojie Zhang
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
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Zheng J, Xie J, Hong X, Liu S. RMalign: an RNA structural alignment tool based on a novel scoring function RMscore. BMC Genomics 2019; 20:276. [PMID: 30961545 PMCID: PMC6454663 DOI: 10.1186/s12864-019-5631-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 03/20/2019] [Indexed: 01/30/2023] Open
Abstract
Background RNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein 3D complex structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases. Results Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore (0.88 and 0.78, respectively) with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structural similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Take the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME. Conclusion Based on a size-independent scoring function RMscore, a novel RNA 3D structural alignment approach RMalign is developed and integrated into PRIME2.0, which could be useful for the biological community in modeling protein-RNA interaction. Electronic supplementary material The online version of this article (10.1186/s12864-019-5631-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jinfang Zheng
- School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
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Piatkowski P, Jablonska J, Zyla A, Niedzialek D, Matelska D, Jankowska E, Walen T, Dawson WK, Bujnicki JM. SupeRNAlign: a new tool for flexible superposition of homologous RNA structures and inference of accurate structure-based sequence alignments. Nucleic Acids Res 2017; 45:e150. [PMID: 28934487 PMCID: PMC5766185 DOI: 10.1093/nar/gkx631] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Accepted: 07/12/2017] [Indexed: 01/28/2023] Open
Abstract
RNA has been found to play an ever-increasing role in a variety of biological processes. The function of most non-coding RNA molecules depends on their structure. Comparing and classifying macromolecular 3D structures is of crucial importance for structure-based function inference and it is used in the characterization of functional motifs and in structure prediction by comparative modeling. However, compared to the numerous methods for protein structure superposition, there are few tools dedicated to the superimposing of RNA 3D structures. Here, we present SupeRNAlign (v1.3.1), a new method for flexible superposition of RNA 3D structures, and SupeRNAlign-Coffee—a workflow that combines SupeRNAlign with T-Coffee for inferring structure-based sequence alignments. The methods have been benchmarked with eight other methods for RNA structural superposition and alignment. The benchmark included 151 structures from 32 RNA families (with a total of 1734 pairwise superpositions). The accuracy of superpositions was assessed by comparing structure-based sequence alignments to the reference alignments from the Rfam database. SupeRNAlign and SupeRNAlign-Coffee achieved significantly higher scores than most of the benchmarked methods: SupeRNAlign generated the most accurate sequence alignments among the structure superposition methods, and SupeRNAlign-Coffee performed best among the sequence alignment methods.
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Affiliation(s)
- Pawel Piatkowski
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Trojdena 4, 02-109 Warsaw, Poland
| | - Jagoda Jablonska
- Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznan, Poland
| | - Adriana Zyla
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Trojdena 4, 02-109 Warsaw, Poland
| | - Dorota Niedzialek
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Trojdena 4, 02-109 Warsaw, Poland
| | - Dorota Matelska
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Trojdena 4, 02-109 Warsaw, Poland
| | - Elzbieta Jankowska
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Trojdena 4, 02-109 Warsaw, Poland
| | - Tomasz Walen
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Trojdena 4, 02-109 Warsaw, Poland.,Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland
| | - Wayne K Dawson
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Trojdena 4, 02-109 Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, ul. Trojdena 4, 02-109 Warsaw, Poland.,Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, 61-614 Poznań, Poland
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Srivastava S, Lal SB, Mishra DC, Angadi UB, Chaturvedi KK, Rai SN, Rai A. An efficient algorithm for protein structure comparison using elastic shape analysis. Algorithms Mol Biol 2016; 11:27. [PMID: 27708689 PMCID: PMC5041553 DOI: 10.1186/s13015-016-0089-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Accepted: 09/21/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Protein structure comparison play important role in in silico functional prediction of a new protein. It is also used for understanding the evolutionary relationships among proteins. A variety of methods have been proposed in literature for comparing protein structures but they have their own limitations in terms of accuracy and complexity with respect to computational time and space. There is a need to improve the computational complexity in comparison/alignment of proteins through incorporation of important biological and structural properties in the existing techniques. RESULTS An efficient algorithm has been developed for comparing protein structures using elastic shape analysis in which the sequence of 3D coordinates atoms of protein structures supplemented by additional auxiliary information from side-chain properties are incorporated. The protein structure is represented by a special function called square-root velocity function. Furthermore, singular value decomposition and dynamic programming have been employed for optimal rotation and optimal matching of the proteins, respectively. Also, geodesic distance has been calculated and used as the dissimilarity score between two protein structures. The performance of the developed algorithm is tested and found to be more efficient, i.e., running time reduced by 80-90 % without compromising accuracy of comparison when compared with the existing methods. Source codes for different functions have been developed in R. Also, user friendly web-based application called ProtSComp has been developed using above algorithm for comparing protein 3D structures and is accessible free. CONCLUSIONS The methodology and algorithm developed in this study is taking considerably less computational time without loss of accuracy (Table 2). The proposed algorithm is considering different criteria of representing protein structures using 3D coordinates of atoms and inclusion of residue wise molecular properties as auxiliary information.
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Abstract
Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes. Structures of protein-RNA complexes are important for characterization of biological processes. The number of experimentally determined protein-RNA complexes is limited. Thus modeling of these complexes is important. Reliable structural predictions of proteins and their complexes are provided by comparative modeling, which takes advantage of similar complexes with experimentally determined structures. Thus, in the case of protein-RNA complexes, it is important to determine if similar proteins and RNAs bind in a similar way. We show that, similarly to the earlier published results on protein-protein complexes, such correlation of the protein-RNA binding mode and the monomers similarity indeed exists, and is stronger when the similarity is determined by structure rather than sequence alignment. The data shows clear transition from random to similar binding mode with the increase of the structural similarity of the monomers. On the basis of the results we designed and implemented a predictive tool, which should be useful for the biological community interested in modeling of protein-RNA interactions.
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Affiliation(s)
- Jinfang Zheng
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Petras J. Kundrotas
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
| | - Ilya A. Vakser
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (IAV); (SL)
| | - Shiyong Liu
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail: (IAV); (SL)
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Yang CH, Shih CT, Chen KT, Lee PH, Tsai PH, Lin JC, Yen CY, Lin TY, Lu CL. iPARTS2: an improved tool for pairwise alignment of RNA tertiary structures, version 2. Nucleic Acids Res 2016; 44:W328-32. [PMID: 27185896 PMCID: PMC4987943 DOI: 10.1093/nar/gkw412] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2016] [Accepted: 05/04/2016] [Indexed: 02/02/2023] Open
Abstract
Since its first release in 2010, iPARTS has become a valuable tool for globally or locally aligning two RNA 3D structures. It was implemented by a structural alphabet (SA)-based approach, which uses an SA of 23 letters to reduce RNA 3D structures into 1D sequences of SA letters and applies traditional sequence alignment to these SA-encoded sequences for determining their global or local similarity. In this version, we have re-implemented iPARTS into a new web server iPARTS2 by constructing a totally new SA, which consists of 92 elements with each carrying both information of base and backbone geometry for a representative nucleotide. This SA is significantly different from the one used in iPARTS, because the latter consists of only 23 elements with each carrying only the backbone geometry information of a representative nucleotide. Our experimental results have shown that iPARTS2 outperforms its previous version iPARTS and also achieves better accuracy than other popular tools, such as SARA, SETTER and RASS, in RNA alignment quality and function prediction. iPARTS2 takes as input two RNA 3D structures in the PDB format and outputs their global or local alignments with graphical display. iPARTS2 is now available online at http://genome.cs.nthu.edu.tw/iPARTS2/.
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Affiliation(s)
- Chung-Han Yang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 30050, Taiwan Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 30050, Taiwan
| | - Cheng-Ting Shih
- Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Kun-Tze Chen
- Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Po-Han Lee
- Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Ping-Han Tsai
- Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Jian-Cheng Lin
- Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Ching-Yu Yen
- Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Tiao-Yin Lin
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 30050, Taiwan
| | - Chin Lung Lu
- Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
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Abstract
The various roles of versatile non-coding RNAs typically require the attainment of complex high-order structures. Therefore, comparing the 3D structures of RNA molecules can yield in-depth understanding of their functional conservation and evolutionary history. Recently, many powerful tools have been developed to align RNA 3D structures. Although some methods rely on both backbone conformations and base pairing interactions, none of them consider the entire hierarchical formation of the RNA secondary structure. One of the major issues is that directly applying the algorithms of matching 2D structures to the 3D coordinates is particularly time-consuming. In this article, we propose a novel RNA 3D structural alignment tool, STAR3D, to take into full account the 2D relations between stacks without the complicated comparison of secondary structures. First, the 3D conserved stacks in the inputs are identified and then combined into a tree-like consensus. Afterward, the loop regions are compared one-to-one in accordance with their relative positions in the consensus tree. The experimental results show that the prediction of STAR3D is more accurate for both non-homologous and homologous RNAs than other state-of-the-art tools with shorter running time.
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Affiliation(s)
- Ping Ge
- Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Shaojie Zhang
- Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA
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Abstract
Comparison of ribonucleic acid (RNA) molecules is important for revealing their
evolutionary relationships, predicting their functions and predicting their
structures. Many methods have been developed for comparing RNAs using either
sequence or three-dimensional (3D) structure (backbone geometry) information.
Sequences and 3D structures contain non-overlapping sets of information that
both determine RNA functions. When comparing RNA 3D structures, both types of
information need to be taken into account. However, few methods compare RNA
structures using both sequence and 3D structure information. Recently, we have
developed a new method based on elastic shape analysis (ESA) that compares RNA
molecules by combining both sequence and 3D structure information. ESA treats
RNA structures as 3D curves with sequence information encoded on additional
coordinates so that the alignment can be performed in the joint
sequence-structure space. The similarity between two RNA molecules is quantified
by a formal distance, geodesic distance. In this study, we implement a web
server for the method, called RASS, to make it publicly available to research
community. The web server is located at http://cloud.stat.fsu.edu/RASS/.
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Affiliation(s)
- Gewen He
- Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA
| | - Albert Steppi
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Jose Laborde
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Anuj Srivastava
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Peixiang Zhao
- Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
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