SCOR: Structural Classification of RNA, version 2.0
http://www.100md.com
《核酸研究医学期刊》
1 Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA and 2 Department of Plant and Microbial Biology, 111 Koshland Hall #3102, University of California, Berkeley, CA 94720-3102, USA
*To whom correspondence should be addressed at Department of Structural Biology, Physical Biosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 64–123, Berkeley, CA 94720, USA. Tel: +1 510 486 4304; Fax: +1 510 486 6059; Email: srholbrook@lbl.gov
ABSTRACT
SCOR, the Structural Classification of RNA (http://scor.lbl.gov), is a database designed to provide a comprehensive perspective and understanding of RNA motif three-dimensional structure, function, tertiary interactions and their relationships. SCOR 2.0 represents a major expansion and introduces a new classification organization. The new version represents the classification as a Directed Acyclic Graph (DAG), which allows a classification node to have multiple parents, in contrast to the strictly hierarchical classification used in SCOR 1.2. SCOR 2.0 supports three types of query terms in the updated search engine: PDB or NDB identifier, nucleotide sequence and keyword. We also provide parseable XML files for all information. This new release contains 511 RNA entries from the PDB as of 15 May 2003. A total of 5880 secondary structural elements are classified: 2104 hairpin loops and 3776 internal loops. RNA motifs reported in the literature, such as ‘Kink turn’ and ‘GNRA loops’, are now incorporated into the structural classification along with definitions and descriptions.
INTRODUCTION
The SCOR database was first released in September 2001 (1). Version 1.2, released in July 2002, added the structural classification of small and large ribosomal RNA, a classification of the ribose zipper tertiary interaction (2) and enhancement of the search engine. These first versions of the SCOR database provided a hierarchical classification of structural elements; however, one RNA structural element can have several distinct features and may belong to multiple classes. For example, the subclass GNRA tetraloops (3,4) should be found under both the class U-Turn (5,6) and the class Tetraloops. That is, a classified node can have multiple parents, and thus different paths from the root can be used to reach the same subclass. Such expressivity is not supported by a hierarchical structure. We therefore reviewed the feature inheritance between classes and subclasses in SCOR 1.2 and mapped them into a Directed Acyclic Graph (DAG) structure to allow multiple parents. Such a DAG representation has been used in the Gene Ontology to represent terms and their relationships (7).
The number of RNA structures in the Protein Databank (8) and Nucleic Acid Database (9) has dramatically increased in the past few years, from 259 in October 2000 to >500 in May 2003, emphasizing the need for an organizational classification of the observed data. A unified terminology to describe the RNA structural elements is also becoming increasingly important for structural research. In the SCOR database, a description for each class is provided to help create a common terminology or glossary for RNA structural motifs.
For the new database, a total of 511 PDB entries were classified. These entries contain more than two RNA residues, are solved by NMR spectroscopy or X-ray crystallography (with resolution better than 4.0 ?), and were released before 15 May 2003. Structures of DNA–RNA hybrids and chimeras were included in the classification. If a structure was updated in the PDB and its previous entry obsolete, then the new structure was included and references to the previous entry were removed from the classification. The list of structures is available on the website, including PDB and NDB identifiers and original literature references. Structural motifs were identified visually with the assistance of computational tools (10–12). A comparison of the different versions of the SCOR database is given in Table 1. A parseable XML format of the database can be downloaded from the website.
Table 1. Growth of the SCOR database
DATABASE STRUCTURE
SCOR 2.0 has been fundamentally revised to use a DAG organization for the structural motif classification (see Fig. 1). In the DAG, each class has a parent–child relationship, where the child class, or subclass, contains all the features of its parent. There is no limit on the number of levels from the root to the motif, and there is no special nomenclature for each level of classification. In the previous release of SCOR, we used special terminology according to the depth of level such as ‘Class’, ‘Subclass’ and ‘Motif’. In the current version, we follow the model from modern phylogenetics, which has largely abandoned the kingdom/phylum/class/order nomenclature and allows arbitrary numbers of levels. Indeed, in SCOR one can be at different numbers of nodes from the root depending on the path.
Figure 1. Example of the DAG structure in SCOR. The directed acyclic graph allows multiple paths to a class. In this example, ‘Loops with dinucleotide platforms’ is a class of ‘Internal loops’ with the subclass ‘Loops with dinucleotide platforms in a triple’. This subclass is also a subclass of ‘Loops with base triples’.
STRUCTURAL CLASSIFICATION
RNA loop elements are diverse structures that provide a scaffold for folding and recognition; thus, in SCOR, the structural classification is done at the RNA motif level for hairpin loops, internal loops and tertiary interactions. We provide a definition and schematic diagram for each class, as well as a mapping of the parent–child relationships. Motifs are the leaves of the DAG, i.e. they have parents and no children. Structural motifs are classified manually by base pairing, base stacking and backbone conformation. Familiar RNA motifs such as ‘S Turn’ (13–15) and ‘GNRA tetraloops’ (3,4) are specifically highlighted.
A total of 5880 secondary structural elements are classified in SCOR 2.0. This is nearly a 10-fold increase from SCOR 1.2. While the number of available RNA structures has nearly doubled, the increase in the number of loops is due primarily to the ribosome structures 1jj2 (16) and 1j5e (17) and their associated structures with bound ligands.
FUNCTIONAL CLASSIFICATION
SCOR now classifies function at the molecular level and the motif level. In the Molecular Function classification, we place RNAs into two classes: Naturally occurring RNAs and Evolved (SELEX) RNAs. Naturally occurring RNAs includes tRNA, rRNA and SRP RNAs, among other examples of molecules found in nature.
Motif Function is a new classification in SCOR 2.0 and represents functions specific to structural motifs. Under Motif Function are four classes: Protein binding, Small molecule binding, Replicase recognition and Helical packing. These functions are related to local RNA structure rather than global RNA structure. The most specific level of this classification is linked to the structural motif classification.
Structural models that have been created to study specific aspects of RNA function and structure are also classified. Structural Models include RNA–DNA chimeric duplexes, RNA–DNA four-way junctions, Self-complementary duplexes and Self-complementary quadruplexes.
In the Functional Classification, we display the chain, or fragment of the chain, from the PDB entry as the motif, at the lowest level of the hierarchy. We provide a new feature in SCOR 2.0 that allows the user to follow a link at this motif level to the structural motif classification or the tertiary interaction classification of the element.
TERTIARY INTERACTIONS
SCOR classifies the tertiary interactions responsible for the folding, stability and maintenance of RNA 3D structure into a small number of general classes including Coaxial Helices, Kissing Hairpins, Tetraloop-Receptor, A-Minor Motif, Pseudoknot, tRNA D-Loop:T-Loop and Ribose Zipper. Of these, we have comprehensively examined the ribose zipper interaction, and made a detailed set of subclassifications. This type of classification is currently only partially implemented for other tertiary interactions. At the most detailed level, we display the elements of tertiary interactions as groups of structural motifs and provide links to the structural motifs.
UPDATED WEB INTERFACE
The web interface provides a straightforward but flexible means to explore the multi-layered classification of SCOR 2.0. Users are given a standard set of icons and buttons to navigate the classes. The interface allows the user to view multiple parallel classes, subclasses (i.e. child classes) and paths simultaneously. The user may also choose to focus on a single class or path along a branch of the hierarchy—or may choose a combination of views.
The database entries are presented in a tree view showing the path to the class from a root. By clicking + adjacent to a class, the view is expanded to display the subclasses, and by selecting – adjacent to a class, the tree is collapsed. These options expand the tree in place to show the subclasses; the parent class and all classes at the same level remain in view. Two links are provided next to each class: the link ‘subclass’ displays the children, and the option ‘list all’ displays all elements of the class organized by the sequence’s RNA type and species of origin. Each of these links displays a new page with all paths to the class displayed at the top of the page.
If a class has more than one parent, a designation (o) is displayed before +/–, and clicking on this designation expands all paths to the class, with the target class emphasized in bold type. Selecting ‘alternate path’ after the class name expands and highlights alternative paths to the class.
A textual description of each class appears when a user clicks the Description icon. Literature references are displayed by selecting the Reference icon. Selecting the Sketch icon displays or hides two-dimensional schematic sketches for each class.
SEARCH ENGINE
SCOR may be searched with PDB (or NDB) identifier, PDB residue number, sequence or keyword. The result of a search by PDB identifier includes a summary of the PDB entry, including links to the motif structure, function and tertiary interactions classifications. Combinations of queries, e.g. PDB identifier and sequence, can further limit a search. Additionally, regular expressions, linked by Boolean operators, may be used with sequence and keyword searches. This is especially useful for sequence expressions with common abbreviations such as R for purine or Y for pyrimidine. The result of a sequence search returns all paths to motifs containing the sequence, and the result of a keyword search returns all paths to classes containing the keyword.
FUTURE DIRECTIONS
In the immediate future we plan to complete the classification of RNA tertiary interactions, expand the functional classification to include metal-ion binding regions, junction loops and helical regions, and include new classes as they are discovered. We will also explore additional database search criteria, such as a secondary structure-based search. Our goal is to provide a comprehensive up-to-date classification of RNA structural motifs and tertiary interactions.
ACKNOWLEDGEMENTS
The authors are grateful to NIGMS of the NIH for support of this project through grant GM 66199, to S.R.H. and S.E.B., and to the NHGRI of the NIH for grant K22 HG00056, to S.E.B., who is a Searle Scholar.
REFERENCES
Klosterman,P.S., Tamura,M., Holbrook,S.R. and Brenner,S.E. (2002) SCOR: a Structural Classification of RNA database. Nucleic Acids Res., 30, 392–394.
Tamura,M. and Holbrook,S.R. (2002) Sequence and structural conservation in RNA ribose zippers. J. Mol. Biol., 320, 455–474.
Legault,P., Li,J., Mogridge,J., Kay,L.E. and Greenblatt,J. (1998) NMR structure of the bacteriophage N peptide/boxB RNA complex: recognition of a GNRA fold by an arginine-rich motif. Cell, 93, 289–299.
Heus,H.A. and Pardi,A. (1991) Structural features that give rise to the unusual stability of RNA hairpins containing GNRA loops. Science, 253, 191–194.
Holbrook,S.R., Sussman,J.L., Warrant,R.W. and Kim,S.H. (1978) Crystal structure of yeast phenylalanine transfer RNA. II. Structural features and functional implications. J. Mol. Biol., 123, 631–660.
Jucker,F.M. and Pardi,A. (1995) GNRA tetraloops make a U-turn. RNA, 1, 219–222.
Ashburner,M., Ball,C.A., Blake,J.A., Botstein,D., Butler,H., Cherry,J.M., Davis,A.P., Dolinski,K., Dwight,S.S., Eppig,J.T. et al. The Gene Ontology Consortium (2000) Gene Ontology: tool for the unification of biology. Nature Genet., 25, 25–29.
Berman,H.M., Westbrook,J., Feng,Z., Gilliland,G., Bhat,T.N., Weissig,H., Shindyalov,I.N. and Bourne,P.E. (2000) The Protein Data Bank. Nucleic Acids Res., 28, 235–242.
Berman,H.M., Olson,W.K., Beveridge,D.L., Westbrook,J., Gelbin,A., Demeny,T., Hsieh,S.H., Srinivasan,A.R. and Schneider,B. (1992) The nucleic acid database. A comprehensive relational database of three-dimensional structures of nucleic acids. Biophys. J., 63, 751–759.
Duarte,C.M. and Pyle,A.M. (1998) Stepping through an RNA structure: A novel approach to conformational analysis. J. Mol. Biol., 284, 1465–1478.
Gendron,P., Lemieux,S. and Major,F. (2001) Quantitative analysis of nucleic acid three-dimensional structures. J. Mol. Biol., 308, 919–936.
Guex,N. and Peitsch,M.C. (1997) SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis, 18, 2714–2723.
Szewczak,A.A. and Moore,P.B. (1995) The sarcin/ricin loop, a modular RNA. J. Mol. Biol., 247, 81–98.
Leontis,N.B. and Westhof,E. (1998) A common motif organizes the structure of multi-helix loops in 16S and 23S ribosomal RNAs. J. Mol. Biol., 283, 571–583.
Klinck,R., Westhof,E., Walker,S., Afshar,M., Collier,A. and Aboul-Ela,F. (2000) A potential RNA drug target in the hepatitis C virus internal ribosomal entry site. RNA, 6, 1423–1431.
Klein,D.J., Schmeing,T.M., Moore,P.B. and Steitz,T.A. (2001) The kink-turn: a new RNA secondary structure motif. EMBO J., 20, 4214–4221.
Wimberly,B.T., Brodersen,D.E., Clemons,W.M.,Jr, Morgan-Warren,R.J., Carter,A.P., Vonrhein,C., Hartsch,T. and Ramakrishnan,V. (2000) Structure of the 30S ribosomal subunit. Nature, 407, 327–339.(Makio Tamura1, Donna K. Hendrix2, Peter )
*To whom correspondence should be addressed at Department of Structural Biology, Physical Biosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 64–123, Berkeley, CA 94720, USA. Tel: +1 510 486 4304; Fax: +1 510 486 6059; Email: srholbrook@lbl.gov
ABSTRACT
SCOR, the Structural Classification of RNA (http://scor.lbl.gov), is a database designed to provide a comprehensive perspective and understanding of RNA motif three-dimensional structure, function, tertiary interactions and their relationships. SCOR 2.0 represents a major expansion and introduces a new classification organization. The new version represents the classification as a Directed Acyclic Graph (DAG), which allows a classification node to have multiple parents, in contrast to the strictly hierarchical classification used in SCOR 1.2. SCOR 2.0 supports three types of query terms in the updated search engine: PDB or NDB identifier, nucleotide sequence and keyword. We also provide parseable XML files for all information. This new release contains 511 RNA entries from the PDB as of 15 May 2003. A total of 5880 secondary structural elements are classified: 2104 hairpin loops and 3776 internal loops. RNA motifs reported in the literature, such as ‘Kink turn’ and ‘GNRA loops’, are now incorporated into the structural classification along with definitions and descriptions.
INTRODUCTION
The SCOR database was first released in September 2001 (1). Version 1.2, released in July 2002, added the structural classification of small and large ribosomal RNA, a classification of the ribose zipper tertiary interaction (2) and enhancement of the search engine. These first versions of the SCOR database provided a hierarchical classification of structural elements; however, one RNA structural element can have several distinct features and may belong to multiple classes. For example, the subclass GNRA tetraloops (3,4) should be found under both the class U-Turn (5,6) and the class Tetraloops. That is, a classified node can have multiple parents, and thus different paths from the root can be used to reach the same subclass. Such expressivity is not supported by a hierarchical structure. We therefore reviewed the feature inheritance between classes and subclasses in SCOR 1.2 and mapped them into a Directed Acyclic Graph (DAG) structure to allow multiple parents. Such a DAG representation has been used in the Gene Ontology to represent terms and their relationships (7).
The number of RNA structures in the Protein Databank (8) and Nucleic Acid Database (9) has dramatically increased in the past few years, from 259 in October 2000 to >500 in May 2003, emphasizing the need for an organizational classification of the observed data. A unified terminology to describe the RNA structural elements is also becoming increasingly important for structural research. In the SCOR database, a description for each class is provided to help create a common terminology or glossary for RNA structural motifs.
For the new database, a total of 511 PDB entries were classified. These entries contain more than two RNA residues, are solved by NMR spectroscopy or X-ray crystallography (with resolution better than 4.0 ?), and were released before 15 May 2003. Structures of DNA–RNA hybrids and chimeras were included in the classification. If a structure was updated in the PDB and its previous entry obsolete, then the new structure was included and references to the previous entry were removed from the classification. The list of structures is available on the website, including PDB and NDB identifiers and original literature references. Structural motifs were identified visually with the assistance of computational tools (10–12). A comparison of the different versions of the SCOR database is given in Table 1. A parseable XML format of the database can be downloaded from the website.
Table 1. Growth of the SCOR database
DATABASE STRUCTURE
SCOR 2.0 has been fundamentally revised to use a DAG organization for the structural motif classification (see Fig. 1). In the DAG, each class has a parent–child relationship, where the child class, or subclass, contains all the features of its parent. There is no limit on the number of levels from the root to the motif, and there is no special nomenclature for each level of classification. In the previous release of SCOR, we used special terminology according to the depth of level such as ‘Class’, ‘Subclass’ and ‘Motif’. In the current version, we follow the model from modern phylogenetics, which has largely abandoned the kingdom/phylum/class/order nomenclature and allows arbitrary numbers of levels. Indeed, in SCOR one can be at different numbers of nodes from the root depending on the path.
Figure 1. Example of the DAG structure in SCOR. The directed acyclic graph allows multiple paths to a class. In this example, ‘Loops with dinucleotide platforms’ is a class of ‘Internal loops’ with the subclass ‘Loops with dinucleotide platforms in a triple’. This subclass is also a subclass of ‘Loops with base triples’.
STRUCTURAL CLASSIFICATION
RNA loop elements are diverse structures that provide a scaffold for folding and recognition; thus, in SCOR, the structural classification is done at the RNA motif level for hairpin loops, internal loops and tertiary interactions. We provide a definition and schematic diagram for each class, as well as a mapping of the parent–child relationships. Motifs are the leaves of the DAG, i.e. they have parents and no children. Structural motifs are classified manually by base pairing, base stacking and backbone conformation. Familiar RNA motifs such as ‘S Turn’ (13–15) and ‘GNRA tetraloops’ (3,4) are specifically highlighted.
A total of 5880 secondary structural elements are classified in SCOR 2.0. This is nearly a 10-fold increase from SCOR 1.2. While the number of available RNA structures has nearly doubled, the increase in the number of loops is due primarily to the ribosome structures 1jj2 (16) and 1j5e (17) and their associated structures with bound ligands.
FUNCTIONAL CLASSIFICATION
SCOR now classifies function at the molecular level and the motif level. In the Molecular Function classification, we place RNAs into two classes: Naturally occurring RNAs and Evolved (SELEX) RNAs. Naturally occurring RNAs includes tRNA, rRNA and SRP RNAs, among other examples of molecules found in nature.
Motif Function is a new classification in SCOR 2.0 and represents functions specific to structural motifs. Under Motif Function are four classes: Protein binding, Small molecule binding, Replicase recognition and Helical packing. These functions are related to local RNA structure rather than global RNA structure. The most specific level of this classification is linked to the structural motif classification.
Structural models that have been created to study specific aspects of RNA function and structure are also classified. Structural Models include RNA–DNA chimeric duplexes, RNA–DNA four-way junctions, Self-complementary duplexes and Self-complementary quadruplexes.
In the Functional Classification, we display the chain, or fragment of the chain, from the PDB entry as the motif, at the lowest level of the hierarchy. We provide a new feature in SCOR 2.0 that allows the user to follow a link at this motif level to the structural motif classification or the tertiary interaction classification of the element.
TERTIARY INTERACTIONS
SCOR classifies the tertiary interactions responsible for the folding, stability and maintenance of RNA 3D structure into a small number of general classes including Coaxial Helices, Kissing Hairpins, Tetraloop-Receptor, A-Minor Motif, Pseudoknot, tRNA D-Loop:T-Loop and Ribose Zipper. Of these, we have comprehensively examined the ribose zipper interaction, and made a detailed set of subclassifications. This type of classification is currently only partially implemented for other tertiary interactions. At the most detailed level, we display the elements of tertiary interactions as groups of structural motifs and provide links to the structural motifs.
UPDATED WEB INTERFACE
The web interface provides a straightforward but flexible means to explore the multi-layered classification of SCOR 2.0. Users are given a standard set of icons and buttons to navigate the classes. The interface allows the user to view multiple parallel classes, subclasses (i.e. child classes) and paths simultaneously. The user may also choose to focus on a single class or path along a branch of the hierarchy—or may choose a combination of views.
The database entries are presented in a tree view showing the path to the class from a root. By clicking + adjacent to a class, the view is expanded to display the subclasses, and by selecting – adjacent to a class, the tree is collapsed. These options expand the tree in place to show the subclasses; the parent class and all classes at the same level remain in view. Two links are provided next to each class: the link ‘subclass’ displays the children, and the option ‘list all’ displays all elements of the class organized by the sequence’s RNA type and species of origin. Each of these links displays a new page with all paths to the class displayed at the top of the page.
If a class has more than one parent, a designation (o) is displayed before +/–, and clicking on this designation expands all paths to the class, with the target class emphasized in bold type. Selecting ‘alternate path’ after the class name expands and highlights alternative paths to the class.
A textual description of each class appears when a user clicks the Description icon. Literature references are displayed by selecting the Reference icon. Selecting the Sketch icon displays or hides two-dimensional schematic sketches for each class.
SEARCH ENGINE
SCOR may be searched with PDB (or NDB) identifier, PDB residue number, sequence or keyword. The result of a search by PDB identifier includes a summary of the PDB entry, including links to the motif structure, function and tertiary interactions classifications. Combinations of queries, e.g. PDB identifier and sequence, can further limit a search. Additionally, regular expressions, linked by Boolean operators, may be used with sequence and keyword searches. This is especially useful for sequence expressions with common abbreviations such as R for purine or Y for pyrimidine. The result of a sequence search returns all paths to motifs containing the sequence, and the result of a keyword search returns all paths to classes containing the keyword.
FUTURE DIRECTIONS
In the immediate future we plan to complete the classification of RNA tertiary interactions, expand the functional classification to include metal-ion binding regions, junction loops and helical regions, and include new classes as they are discovered. We will also explore additional database search criteria, such as a secondary structure-based search. Our goal is to provide a comprehensive up-to-date classification of RNA structural motifs and tertiary interactions.
ACKNOWLEDGEMENTS
The authors are grateful to NIGMS of the NIH for support of this project through grant GM 66199, to S.R.H. and S.E.B., and to the NHGRI of the NIH for grant K22 HG00056, to S.E.B., who is a Searle Scholar.
REFERENCES
Klosterman,P.S., Tamura,M., Holbrook,S.R. and Brenner,S.E. (2002) SCOR: a Structural Classification of RNA database. Nucleic Acids Res., 30, 392–394.
Tamura,M. and Holbrook,S.R. (2002) Sequence and structural conservation in RNA ribose zippers. J. Mol. Biol., 320, 455–474.
Legault,P., Li,J., Mogridge,J., Kay,L.E. and Greenblatt,J. (1998) NMR structure of the bacteriophage N peptide/boxB RNA complex: recognition of a GNRA fold by an arginine-rich motif. Cell, 93, 289–299.
Heus,H.A. and Pardi,A. (1991) Structural features that give rise to the unusual stability of RNA hairpins containing GNRA loops. Science, 253, 191–194.
Holbrook,S.R., Sussman,J.L., Warrant,R.W. and Kim,S.H. (1978) Crystal structure of yeast phenylalanine transfer RNA. II. Structural features and functional implications. J. Mol. Biol., 123, 631–660.
Jucker,F.M. and Pardi,A. (1995) GNRA tetraloops make a U-turn. RNA, 1, 219–222.
Ashburner,M., Ball,C.A., Blake,J.A., Botstein,D., Butler,H., Cherry,J.M., Davis,A.P., Dolinski,K., Dwight,S.S., Eppig,J.T. et al. The Gene Ontology Consortium (2000) Gene Ontology: tool for the unification of biology. Nature Genet., 25, 25–29.
Berman,H.M., Westbrook,J., Feng,Z., Gilliland,G., Bhat,T.N., Weissig,H., Shindyalov,I.N. and Bourne,P.E. (2000) The Protein Data Bank. Nucleic Acids Res., 28, 235–242.
Berman,H.M., Olson,W.K., Beveridge,D.L., Westbrook,J., Gelbin,A., Demeny,T., Hsieh,S.H., Srinivasan,A.R. and Schneider,B. (1992) The nucleic acid database. A comprehensive relational database of three-dimensional structures of nucleic acids. Biophys. J., 63, 751–759.
Duarte,C.M. and Pyle,A.M. (1998) Stepping through an RNA structure: A novel approach to conformational analysis. J. Mol. Biol., 284, 1465–1478.
Gendron,P., Lemieux,S. and Major,F. (2001) Quantitative analysis of nucleic acid three-dimensional structures. J. Mol. Biol., 308, 919–936.
Guex,N. and Peitsch,M.C. (1997) SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis, 18, 2714–2723.
Szewczak,A.A. and Moore,P.B. (1995) The sarcin/ricin loop, a modular RNA. J. Mol. Biol., 247, 81–98.
Leontis,N.B. and Westhof,E. (1998) A common motif organizes the structure of multi-helix loops in 16S and 23S ribosomal RNAs. J. Mol. Biol., 283, 571–583.
Klinck,R., Westhof,E., Walker,S., Afshar,M., Collier,A. and Aboul-Ela,F. (2000) A potential RNA drug target in the hepatitis C virus internal ribosomal entry site. RNA, 6, 1423–1431.
Klein,D.J., Schmeing,T.M., Moore,P.B. and Steitz,T.A. (2001) The kink-turn: a new RNA secondary structure motif. EMBO J., 20, 4214–4221.
Wimberly,B.T., Brodersen,D.E., Clemons,W.M.,Jr, Morgan-Warren,R.J., Carter,A.P., Vonrhein,C., Hartsch,T. and Ramakrishnan,V. (2000) Structure of the 30S ribosomal subunit. Nature, 407, 327–339.(Makio Tamura1, Donna K. Hendrix2, Peter )