S. Lusvarghi, J. Sztuba-Solinska, K.J. Purzycka, J.W. Rausch and S.F.J. Le Grice are supported by the Intramural Research Program of the National Cancer Institute, National Institutes of Health, USA.

<ol>
	<li>Scott, W. G., Martick, M., Chi, Y. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure+and+function+of+regulatory+RNA+elements:+ribozymes+that+regulate+gene+expression.">Structure and function of regulatory RNA elements: ribozymes that regulate gene expression.</a> Biochim. Biophys. Acta. 1789, 634-641 (2009).</li><li>Moore, P. B., Steitz, T. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+roles+of+RNA+in+the+synthesis+of+protein.">The roles of RNA in the synthesis of protein.</a> Cold Spring Harb. Perspect. Biol. 3, a003780 (2011).</li><li>Wilkinson, K. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+SHAPE+analysis+reveals+structures+in+HIV-1+genomic+RNA+strongly+conserved+across+distinct+biological+states.">High-throughput SHAPE analysis reveals structures in HIV-1 genomic RNA strongly conserved across distinct biological states.</a> Plos Biol. 6, 883-899 (2008).</li><li>Merino, E. J., Wilkinson, K. A., Coughlan, J. L., Weeks, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA+structure+analysis+at+single+nucleotide+resolution+by+selective+2+'-hydroxyl+acylation+and+primer+extension+(SHAPE).">RNA structure analysis at single nucleotide resolution by selective 2 '-hydroxyl acylation and primer extension (SHAPE).</a> J. Am. Chem. Soc. 127, 4223-4231 (2005).</li><li>Watts, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Architecture+and+secondary+structure+of+an+entire+HIV-1+RNA+genome.">Architecture and secondary structure of an entire HIV-1 RNA genome.</a> Nature. 460, 711-716 (2009).</li><li>Xu, W., Bolduc, F., Hong, N., Perreault, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+a+combination+of+computer-assisted+structure+prediction+and+SHAPE+probing+to+elucidate+the+secondary+structures+of+five+viroids.">The use of a combination of computer-assisted structure prediction and SHAPE probing to elucidate the secondary structures of five viroids.</a> Mol. Plant Pathol. , (2012).</li><li>Novikova, I. V., Hennelly, S. P., Sanbonmatsu, K. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+architecture+of+the+human+long+non-coding+RNA,+steroid+receptor+RNA+activator.">Structural architecture of the human long non-coding RNA, steroid receptor RNA activator.</a> Nucleic Acids Res. 40, 5034-5051 (2012).</li><li>Leshin, J. A., Heselpoth, R., Belew, A. T., Dinman, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+structural+analysis+of+yeast+ribosomes+using+hSHAPE.">High-throughput structural analysis of yeast ribosomes using hSHAPE.</a> RNA Biol. 8, 478-487 (2011).</li><li>Souliere, M. F., Haller, A., Rieder, R., Micura, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+powerful+approach+for+the+selection+of+2-aminopurine+substitution+sites+to+investigate+RNA+folding.">A powerful approach for the selection of 2-aminopurine substitution sites to investigate RNA folding.</a> J. Am. Chem. Soc. 133, 16161-16167 (2011).</li><li>Wilkinson, K. A., Merino, E. J., Weeks, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Selective+2+'-hydroxyl+acylation+analyzed+by+primer+extension+(SHAPE):+quantitative+RNA+structure+analysis+at+single+nucleotide+resolution.">Selective 2 '-hydroxyl acylation analyzed by primer extension (SHAPE): quantitative RNA structure analysis at single nucleotide resolution.</a> Nat. Protoc. 1, 1610-1616 (2006).</li><li>McGinnis, J. L., Duncan, C. D. S., Weeks, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-Throughput+Shape+and+Hydroxyl+Radical+Analysis+of+Rna+Structure+and+Ribonucleoprotein+Assembly.">High-Throughput Shape and Hydroxyl Radical Analysis of Rna Structure and Ribonucleoprotein Assembly.</a> Method Enzymol. 468, 67-89 (2009).</li><li>Low, J. T., Weeks, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SHAPE-directed+RNA+secondary+structure+prediction.">SHAPE-directed RNA secondary structure prediction.</a> Methods. 52, 150-158 (2010).</li><li>Das, R., Laederach, A., Pearlman, S. M., Herschlag, D., Altman, R. B. S. A. F. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Semi-automated+footprinting+analysis+software+for+high-throughput+quantification+of+nucleic+acid+footprinting+experiments.">Semi-automated footprinting analysis software for high-throughput quantification of nucleic acid footprinting experiments.</a> Rna-a Publication of the Rna Society. 11, 344-354 (2005).</li><li>Kertesz, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+measurement+of+RNA+secondary+structure+in+yeast.">Genome-wide measurement of RNA secondary structure in yeast.</a> Nature. 467, 103-107 (2010).</li><li>Underwood, J. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FragSeq:+transcriptome-wide+RNA+structure+probing+using+high-throughput+sequencing.">FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing.</a> Nat. Methods. 7, 995-1001 (2010).</li><li>Mauger, D. M., Weeks, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+global+RNA+structure+analysis.">Toward global RNA structure analysis.</a> Nat. Biotechnol. 28, 1178-1179 (2010).</li><li>Lucks, J. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplexed+RNA+structure+characterization+with+selective+2'-hydroxyl+acylation+analyzed+by+primer+extension+sequencing+(SHAPE-Seq).">Multiplexed RNA structure characterization with selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq).</a> Proc. Natl. Acad. Sci. USA. 108, 11063-11068 (2011).</li><li>Vasa, S. M., Guex, N., Wilkinson, K. A., Weeks, K. M., Giddings, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ShapeFinder:+a+software+system+for+high-throughput+quantitative+analysis+of+nucleic+acid+reactivity+information+resolved+by+capillary+electrophoresis.">ShapeFinder: a software system for high-throughput quantitative analysis of nucleic acid reactivity information resolved by capillary electrophoresis.</a> RNA. 14, 1979-1990 (2008).</li><li>Reuter, J. S., Mathews, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNAstructure:+software+for+RNA+secondary+structure+prediction+and+analysis.">RNAstructure: software for RNA secondary structure prediction and analysis.</a> BMC Bioinformatics. 11, 129 (2010).</li><li>Pang, P. S., Elazar, M., Pham, E. A., Glenn, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simplified+RNA+secondary+structure+mapping+by+automation+of+SHAPE+data+analysis.">Simplified RNA secondary structure mapping by automation of SHAPE data analysis.</a> Nucleic Acids Res. 39, e151 (2011).</li><li>Deigan, K. E., Li, T. W., Mathews, D. H., Weeks, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accurate+SHAPE-directed+RNA+structure+determination.">Accurate SHAPE-directed RNA structure determination.</a> Proc. Natl. Acad. Sci. USA. 106, 97-102 (2009).</li><li>Darty, K., Denise, A., Ponty, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=VARNA:+Interactive+drawing+and+editing+of+the+RNA+secondary+structure.">VARNA: Interactive drawing and editing of the RNA secondary structure.</a> Bioinformatics. 25, 1974-1975 (2009).</li><li>Byun, Y., Han, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PseudoViewer:+web+application+and+web+service+for+visualizing+RNA+pseudoknots+and+secondary+structures.">PseudoViewer: web application and web service for visualizing RNA pseudoknots and secondary structures.</a> Nucleic Acids Res. 34, 416-422 (2006).</li><li>Brown, T., Brown, D. J. S., Eckstein, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Oligonucleotides and Analogues - A Practical Approach. , 20 (1990).</li><li>Legiewicz, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+RNA+Transport+Element+of+the+Murine+musD+Retrotransposon+Requires+Long-range+Intramolecular+Interactions+for+Function.">The RNA Transport Element of the Murine musD Retrotransposon Requires Long-range Intramolecular Interactions for Function.</a> J. Biol. Chem. 285, 42097-42104 (2010).</li><li>Steen, K., Siegfried, N. A., Weeks, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Syntheis+of+1-methyl-8-nitroisatoic+anhydride+(1M7).">Syntheis of 1-methyl-8-nitroisatoic anhydride (1M7).</a> Protocol Exchange. , (2011).</li><li>Mortimer, S. A., Weeks, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+fast-acting+reagent+for+accurate+analysis+of+RNA+secondary+and+tertiary+structure+by+SHAPE+chemistry.">A fast-acting reagent for accurate analysis of RNA secondary and tertiary structure by SHAPE chemistry.</a> J. Am. Chem. Soc. 129, 4144-4145 (2007).</li><li>Mitra, S., Shcherbakova, I. V., Altman, R. B., Brenowitz, M., Laederach, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+single-nucleotide+structural+mapping+by+capillary+automated+footprinting+analysis.">High-throughput single-nucleotide structural mapping by capillary automated footprinting analysis.</a> Nucleic Acids Res. 36, e63 (2008).</li><li>Giddings, M. C., Severin, J., Westphall, M., Wu, J., Smith, L. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+software+system+for+data+analysis+in+automated+DNA+sequencing.">A software system for data analysis in automated DNA sequencing.</a> Genome Res. 8, 644-665 (1998).</li><li>Aviran, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+and+automation+of+sequencing-based+characterization+of+RNA+structure.">Modeling and automation of sequencing-based characterization of RNA structure.</a> Proc. Natl. Acad. Sci. USA. 108, 11069-11074 (2011).</li></ol>

No conflicts of interest declared.

We present here a detailed protocol for high-throughput SHAPE, a technique that allows secondary structure determination to single-nucleotide resolution for RNAs of any size. Moreover, coupling experimental SHAPE data with secondary structure prediction algorithms facilitates generation of RNA 2D models with a higher degree of accuracy than is possible with either method alone. The combination of fluorescently-labeled primers and automated CE provides significant advantages over the traditional gel-based SHAPE, facilitating resolution of long RNA sequences in a single experiment, as well as substantially higher speed and throughput for multiple experiments. The expediency of this method and availability of suitable data analysis tools make SHAPE ideally-suited for structural analysis of previously intractable viral, intact messenger, and noncoding RNAs. As the 2D structures of these intriguing RNAs become clearer, the use of hydroxyl radical probing, through-space cleavage methodologies and molecular modeling should help elucidate complex tertiary interactions and eventually allow researchers to determine the structures of these RNAs in three dimensions.

To understand the functions of catalytic and non-coding RNAs involved in regulation of splicing, translation, virus replication and cancer, a detailed knowledge of RNA structure is required1,2. Unfortunately, accurate prediction of RNA folding presents a formidable challenge. Classical probing agents suffer from many disadvantages such as toxicity, incomplete nucleotide coverage and/or throughput limited to 100-150 nucleotides per experiment. Unaided secondary structure prediction algorithms are similarly disadvantageous, owing to inaccuracies resulting from their inability to effectively distinguish among energetically similar structures. Large RNAs in particular are also often refractory to methods of 3D structure determination such as X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy, due to their conformational flexibility and large quantities of highly pure samples required for these techniques.
High-throughput SHAPE solves many of these problems by providing an effective, simple approach to probing the structures of large RNAs at single-nucleotide resolution. Moreover, the reagents used for SHAPE are safe, easy to handle and, in contrast to most other chemical probing reagents, react with all four ribonucleotides. These reagents can also penetrate cellular membranes, making it possible to probe RNAs in their in vivo context(s)3. Originally developed in the Weeks laboratory4, SHAPE has been used to analyze a wide variety of RNAs, the most notable example being determination of the complete secondary structure of the ~9 kb HIV-1 RNA genome5. Other notable achievements using SHAPE include elucidation of the structures of infectious viroids6, human long non-coding RNAs7, yeast ribosomes8, and riboswitches9 as well as to identify protein binding sites in virion-associated HIV-1 RNA3. While the original and high-throughput variations of the SHAPE protocol have been published elsewhere10-12, the present work provides a detailed description of RNA secondary structure determination by high-throughput SHAPE using fluorescent oligonucleotides, the Beckman Coulter CEQ 8000 Genetic Analyzer, and SHAPEfinder and RNAStructure (v5.3) software. Previously unpublished technical details and troubleshooting advice are also included.
Variations of SHAPE
The essence of SHAPE and its variations is exposure of RNA in aqueous solution to electrophilic anhydrides that selectively acylate 2'-hydroxyl (2'-OH) ribose groups, producing bulky adducts at the sites of modification. This chemical reaction serves as a means of interrogating local RNA structural dynamics, as single-stranded nucleotides are more prone to adopt conformations conducive to electrophilic attack by these reagents, while base paired or architecturally constrained nucleotides are less or unreactive10. Sites of adduct formation are detected by reverse transcription initiating from fluorescently or radiolabeled primers hybridized to a specific site on the modified RNA (the "(+)" primer extension reaction). When reverse transcriptase (RT) fails to traverse the acylated ribonucleotides, a pool of cDNA products is produced whose lengths coincide with sites of modification. A control, "(-)" primer extension reaction utilizing RNA that has not been exposed to reagent is also performed so that premature termination of DNA synthesis (i.e. "stops") due to RNA structure, nonspecific RNA strand breakage, etc., may be distinguished from pausing produced by chemical modification. Finally, two dideoxy-sequencing reactions initiating from the same primers are used as markers to correlate reactive nucleotides with the RNA primary sequence following electrophoresis.
In the original application of SHAPE, the same 32P-end-labeled primer is utilized for the (+), (-), and two sequencing reactions. Products of these reactions are loaded into adjacent wells in a 5-8% polyacrylamide slab gel, and fractionated by denaturing polyacrylamide gel electrophoresis (PAGE; Figure 1). Quantitative analysis of the gel images produced by conventional SHAPE can be performed using SAFA, a semi-automated footprinting analysis software13.
In contrast, high-throughput SHAPE employs fluorescently labeled primers and automated capillary electrophoresis. Specifically, for each region of RNA under investigation, a set of four DNA primers having a common sequence but different 5' fluorescent labels must be synthesized or purchased. These differently-labeled oligonucleotides serve to prime two SHAPE reactions and two sequencing reactions, the products of which are pooled and fractionated/detected by automated capillary electrophoresis (CE). Whereas the reactivity profile of 100-150 nt of RNA can be obtained from a set of four reactions using the original approach, high-throughput SHAPE allows resolution of 300-600 nt from a single pooled sample3. Up to 8 sets of reactions may be fractionated simultaneously, while as many as 96 samples can prepared for fractionation over the course of 12 consecutive CE runs (Figure 2). Moreover, the SHAPEfinder software, developed to process and analyze data emerging from the CEQ and other genetic analyzers, is more automated and require much less user intervention than SAFA13 or other gel-analysis packages.
More advanced high-throughput methodologies have recently emerged such as PARS (parallel analysis of RNA structure)14 and Frag-Seq (fragment-sequencing)15, which use structure-specific enzymes rather than alkylation reagents in conjunction with next generation sequencing techniques to obtain information about RNA structure. Despite the attractiveness of these techniques, the many limitations inherent to nuclease probing still remain16. These problems can be circumvented in the SHAPE sequencing (SHAPE-Seq)17 protocol, where next generation sequencing is preceded by chemical modification and reverse transcription of RNAs in a manner similar to that performed in conventional SHAPE. While these methods may represent the future of RNA structure determination, it is important to remember that next generation sequencing is very expensive, and remains unavailable to many laboratories.
SHAPE Data Analysis
Data produced in the genetic analyzer is presented in the form of an electropherogram, wherein the fluorescence intensity of the sample(s) flowing through the capillary detector is plotted against an index of migration time. This plot takes the form of overlapping traces corresponding to the four fluorescence channels used to detect the different fluorophores, and where each trace is comprised of peaks corresponding to individual cDNA or sequencing products. Electropherogram data is exported from the genetic analyzer as a tab-delimited text file and imported into ShapeFinder transformation and analysis software18.
ShapeFinder is initially used to perform a series of mathematical transformations on the data to ensure that migration times and peak volumes accurately reflect the identities and quantities of the reaction products, respectively. Peaks are then aligned and integrated, and the results tabulated together with the primary RNA sequence. A "reactivity profile" for the pertinent segment of RNA is obtained by subtracting control values from the (+) values associated with each RNA nucleotide, and normalizing the data as described below. This profile is imported into RNAstructure (v5.3) software19,20 , which converts the normalized reactivity values into pseudo-energy constraints that are incorporated into the RNA secondary structure folding algorithm. Combining chemical probing and folding algorithms in this way significantly improves the accuracy of structure prediction compared to either method alone12,21. The output of RNAstructure (v5.3) includes images of the lowest energy RNA secondary structures color-coded with the SHAPE reactivity profile(s), as well as the same structures in textual dot-bracket notation. The latter may subsequently be exported to software dedicated to the graphical display of RNA secondary structure such as Varna22 and PseudoViewer23.
<img alt="RNA structure prediction diagram; in vitro transcription, chemical modification, gel, HT analysis." fo:content-width="4.8in" fo:src="/files/ftp_upload/50243/50243fig1highres.jpg" src="/files/ftp_upload/50243/50243fig1.jpg" data-altupdatedat="1747911167000" data-alt="Figure 1"> 
Figure 1. Flowchart of RNA structure determination via SHAPE4,10. (A) RNA may be obtained from biological samples or by in vitro transcription. (B) Depending on the source, RNA is folded or otherwise processed and modified with SHAPE reagent. (C) Reverse transcription using fluorescently or radioactively labeled primers. (D) cDNA products are fractionated via either capillary or slab gel-based electrophoresis. (E) Fragment analysis. (F) RNA structure prediction. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50243/50243fig1large.jpg" target="_blank">Click here to view larger figure.</a>
<img alt="RNA secondary structure diagram with 3kb linear sequence and microplate analysis layout." fo:content-width="6in" fo:src="/files/ftp_upload/50243/50243fig2highres.jpg" src="/files/ftp_upload/50243/50243fig2.jpg" data-altupdatedat="1747911167000" data-alt="Figure 2"> 
Figure 2. The high-throughput character of CE-based SHAPE allows rapid analysis of multiple RNAs, and/or multiple segments of the same RNA. (A) Represents how an RNA may be divided into 300-600 nt sections (color coded in green, blue and red) (B) Sections of the RNA are probed independently using different sets of fluorescent primers (black arrows) (C) Sets of reactions are pooled and loaded into wells A1, B1, C1, etc, respectively, providing complete coverage for the ~3 kb RNA1. Reaction products from RNAs 2, 3, 4, etc. may be similarly prepared for fractionation in consecutive electrophoretic runs. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50243/50243fig2large.jpg" target="_blank">Click here to view larger figure.</a>

<table><tbody><tr><td colspan="4">&lt;strong&gt;REAGENTS&lt;/strong&gt;</td></tr><tr><td>N-methylisatoic anhydride (NMIA)</td><td>Life technologies</td><td>M25</td><td>Dissolve in anhydrous DMSO</td></tr><tr><td>1-methyl-t-nitroisatoic anhydride (1M7)</td><td>see ref. 22</td><td></td><td></td></tr><tr><td>Superscript III Reverse Transcriptase</td><td>Life technologies</td><td>18080044</td><td>10,000 units</td></tr><tr><td>Thermo sequenase cycle sequencing kit</td><td>Affymetrix</td><td>78500</td><td></td></tr><tr><td colspan="4">&lt;strong&gt;Materials provided by the user&lt;/strong&gt;</td></tr><tr><td>RNA of interest</td><td></td><td></td><td>6 pmol per reaction (the limit of detection will be determined by the instrument)</td></tr><tr><td>Sets of four 5&#039; labeled primers (Cy5, Cy5.5, WellRed D2 and WellRed D1/Licor IR800)</td><td></td><td></td><td>Primers are complementary to the RNA and are used in reverse transcription and sequencing reactions. The listed fluorophores are optimal for the Beckman Coulter 8000 CEQ. Primers may be purchased or synthesized in house.</td></tr><tr><td>DNA template</td><td></td><td></td><td>DNA is used for sequencing reactions, and must contain the sequence of the RNA being studied - including any 3&#039;terminal extension, if present. Where applicable, it is often convenient to use the RNA transcription template.</td></tr><tr><td colspan="4">&lt;strong&gt;Buffers&lt;/strong&gt;</td></tr><tr><td>10x RNA renaturation buffer</td><td></td><td></td><td>100 mM Tris-HCl pH 8.0, 1 M KCl, 1 mM EDTA</td></tr><tr><td>5X RNA folding buffer</td><td></td><td></td><td>200 mM Tris-HCl pH 8.0, 25 mM MgCl&lt;sub&gt;2&lt;/sub&gt;, 2.5 mM EDTA, 650 mM KCl. (This buffer might be changed depending on the case (&lt;em&gt;e.g.&lt;/em&gt; pH, EDTA, Mg, RNase inhibitor)</td></tr><tr><td>2.5X RT mix</td><td></td><td></td><td>4 &amp;mu;l 5X buffer, 1 &amp;mu;l 100 mM DTT, 1.5 &amp;mu;l water,1 &amp;mu;l 10 mM dNTPs, 0.5 &amp;mu;l SuperScript III. Note that the 5X buffer and 100 mM DTT are provided with purchase of SuperScript III (Invitrogen).</td></tr><tr><td>GenomeLab Sample Loading Solution (Beckman Coulter)</td><td></td><td></td><td>&lt;strong&gt;Attention:&lt;/strong&gt; Avoid multiple freeze-thaw cycles</td></tr><tr><td colspan="4">&lt;strong&gt;EQUIPMENT&lt;/strong&gt;</td></tr><tr><td>Capillary electrophoresis</td><td>Beckman</td><td>CEQ8000</td><td></td></tr><tr><td>Thermocycler</td><td>varies</td><td></td><td></td></tr></tbody></table>

rna secondary structure prediction using high-throughput shape

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed "high-throughput selective 2' hydroxyl acylation analyzed by primer extension", or SHAPE, allows prediction of RNA secondary structure with single nucleotide resolution. This approach utilizes chemical probing agents that preferentially acylate single stranded or flexible regions of RNA in aqueous solution. Sites of chemical modification are detected by reverse transcription of the modified RNA, and the products of this reaction are fractionated by automated capillary electrophoresis (CE). Since reverse transcriptase pauses at those RNA nucleotides modified by the SHAPE reagents, the resulting cDNA library indirectly maps those ribonucleotides that are single stranded in the context of the folded RNA. Using ShapeFinder software, the electropherograms produced by automated CE are processed and converted into nucleotide reactivity tables that are themselves converted into pseudo-energy constraints used in the RNAStructure (v5.3) prediction algorithm. The two-dimensional RNA structures obtained by combining SHAPE probing with in silico RNA secondary structure prediction have been found to be far more accurate than structures obtained using either method alone.

RNA containing the HIV-1 rev response element (RRE) and a 3' terminal structure cassette4 was prepared from a linearized plasmid by in vitro transcription, after which it was folded by heating, cooling, and incubation at 37 °C in the presence of MgCl2. RNA was exposed to NMIA and then reverse transcribed from a 5'-end-labeled DNA primer hybridized to the 3' terminal structure cassette. The resulting SHAPE cDNA library, together with control and sequencing reactions, was then fractionated using the Beckman Coulter CEQ 8000 automated capillary electrophoresis system to produce the electropherogram depicted in Figure 4. The four overlapping, color coded traces are produced by migration of the four sets of reaction products through the capillary, as follows: Blue (Cy5-labeled RT products generated from reverse transcription of NMIA-modified RNAs), green (Cy5.5-labeled RT products from folded, but otherwise unmodified RNAs), black (WellRed D2-labeled DNA sequencing ladder generated using ddG) and red (Lycor800-labeled sequencing ladder, ddT).
The raw CE traces were separated, processed aligned and integrated using ShapeFinder software18. The region of the trace(s) corresponding to the RRE SLII region is depicted in Figure 5, the data having been processed to the point immediately prior to peak integration (i.e., traces have been aligned, subjected to background subtraction, correction for signal decay, etc.). Reactivity values for each nucleotide are calculated by integrating the corresponding peaks in the NMIA(+) and NMIA(-) reactions (in the blue and green traces, respectively), and subtracting the latter from the former. These reactivity values indicate the extent in which RT terminates at each nucleotide during reverse transcription, which is a reflection of the degree to which each nucleotide has been modified by NMIA and therefore its propensity to be single stranded in solution.
The HIV-1 RRE SHAPE reactivity profile generated in ShapeFinder was normalized and converted into a text document suitable for import into RNAStructure (v5.3)19. In the latter software, reactivity values were incorporated into the secondary structure prediction algorithm as pseudo-energy constraints, thereby influencing which structures are predicted to have the lowest free energy. Program output is comprised of two dimensional RNA secondary structure maps depicting the lowest energy structures generated by the algorithm as well as text files containing these structures expressed in dot-bracket notation. The latter of these outputs may be exported into RNA visualization software such as VARNA22. Figure 6 shows the 2D structure of the HIV RRE SLII region generated in RNAStructure (v5.3) using the SHAPE-derived reactivity profiles and visualized using VARNA. Color coded SHAPE reactivity values are superimposed.
<img alt="DNA sequencing chromatogram analysis chart; electrophoresis data showing base peaks and activity levels." fo:content-width="5in" fo:src="/files/ftp_upload/50243/50243fig4highres.jpg" src="/files/ftp_upload/50243/50243fig4.jpg" data-altupdatedat="1747911167000" data-alt="Figure 4"> 
Figure 4. Representative electropherogram of an RNA sample viewed in the CEQ 8000 Genetic Analysis System (Beckman). The software displays a trace where each channel is shown as a colored line: Blue and green channels represent the (+) reagent and (-) reagent experiments, and black and red represent sequencing ladders. X-and Y-axes denote resolution time and fluorescence intensity, respectively. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50243/50243fig4large.jpg" target="_blank">Click here to view larger figure.</a>
<img alt="Chromatography analysis graph detailing nucleotide sequences for DNA sequencing experiments." fo:content-width="6.4in" fo:src="/files/ftp_upload/50243/50243fig5highres.jpg" src="/files/ftp_upload/50243/50243fig5.jpg" data-altupdatedat="1747911167000" data-alt="Figure 5"> 
Figure 5. Electropherogram analysis using ShapeFinder tools18. The Data View Window (center) provides graphical feedback on each data processing step. The Tool Inspector window (bottom left) shows parameters for the tool selected in the Scripting Inspector. The Scripting Inspector (upper right) displays those tools that have been applied to the data. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50243/50243fig5large.jpg" target="_blank">Click here to view larger figure.</a>
<img alt="RNA secondary structure diagram; base pairing regions, colorful nucleotides, stability analysis." fo:content-width="3.5in" fo:src="/files/ftp_upload/50243/50243fig6highres.jpg" src="/files/ftp_upload/50243/50243fig6.jpg" data-altupdatedat="1747911167000" data-alt="Figure 6"> 
Figure 6. Two-dimensional RNA structure color coded for SHAPE reactivity generated using the VARNA visualization applet22. Nucleotides are color-coded to indicate the degree(s) of SHAPE reactivity. In the spectrum shown, blue and red circles indicate nucleotides having low and high reactivity, respectively.

Watch this Scientific Journal Video about RNA Secondary Structure Prediction Using High-throughput SHAPE at JoVE.com

RNA Secondary Structure Prediction Using High-throughput SHAPE

High-throughput selective 2' hydroxyl acylation analyzed by primer extension (SHAPE) utilizes a novel chemical probing technology, reverse transcription, capillary electrophoresis and secondary structure prediction software to determine the structures of RNAs from several hundred to several thousand nucleotides at single nucleotide resolution.

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed ...

rna-secondary-structure-prediction-using-high-throughput-shape

Nanyang Technological University

Research

Education

JoVE Journal

JoVE Core

Molecular Biology

Biology

Unit 1

Transcription: DNA to RNA

SCIENTISTS IN ACTION

31.3K Views.  Frederick National Laboratory for Cancer Research. High-throughput selectieve 2-hydroxyl acylering geanalyseerd door primerverlenging (SHAPE) gebruikt een nieuwe chemische indringende techniek, reverse transcriptie, capillaire elektroforese en secundaire structuur predictie software om de structuren van RNA bepalen van enkele honderden tot enkele duizenden nucleotiden aan nucleotide resolutie.

Video: RNA Secondary Structure Prediction Using High-throughput SHAPE

Assessment of Immunologically Relevant Dynamic Tertiary Structural Features of the HIV-1 V3 Loop Crown R2 Sequence by ab initio Folding

The antigenic diversity of HIV-1 has long been an obstacle to vaccine design, and this variability is especially pronounced in the V3 loop of the virus' surface envelope glycoprotein. We previously proposed that the crown of the V3 loop, although dynamic and sequence variable, is constrained throughout the population of HIV-1 viruses to an immunologically relevant &beta;-hairpin tertiary structure. Importantly, there are thousands of different V3 loop crown sequences in circulating HIV-1 viruses, making 3D structural characterization of trends across the diversity of viruses difficult or impossible by crystallography or NMR. Our previous successful studies with folding of the V3 crown1, 2 used the ab initio algorithm 3 accessible in the ICM-Pro molecular modeling software package (Molsoft LLC, La Jolla, CA) and suggested that the crown of the V3 loop, specifically from positions 10 to 22, benefits sufficiently from the flexibility and length of its flanking stems to behave to a large degree as if it were an unconstrained peptide freely folding in solution. As such, rapid ab initio folding of just this portion of the V3 loop of any individual strain of the 60,000+ circulating HIV-1 strains can be informative. Here, we folded the V3 loop of the R2 strain to gain insight into the structural basis of its unique properties. R2 bears a rare V3 loop sequence thought to be responsible for the exquisite sensitivity of this strain to neutralization by patient sera and monoclonal antibodies4, 5. The strain mediates CD4-independent infection and appears to elicit broadly neutralizing antibodies. We demonstrate how evaluation of the results of the folding can be informative for associating observed structures in the folding with the immunological activities observed for R2.

Assessment of Immunologically Relevant Dynamic Tertiary Structural Features of the HIV-1 V3 Loop Crown R2 Sequence by ab initio Folding

The crown region of different V3 loop sequences of the surface envelope glycoprotein (gp120) of HIV-1 can be structurally characterized in many cases by in silico folding of positions 10 to 22 of the loop using a state-of-the-art ab initio folding algorithm. Here we demonstrate the folding and evaluation of this region of the V3 loop from the R2 strain of HIV-1, a uniquely neutralization sensitive strain with puzzling functional properties.

The antigenic diversity of HIV-1 has long been an obstacle to vaccine design, and this variability is especially pronounced in the V3 loop of the virus' ...

Immunology and Infection

Combining Wet and Dry Lab Techniques to Guide the Crystallization of Large Coiled-coil Containing Proteins

Obtaining crystals for structure determination can be a difficult and time consuming proposition for any protein. Coiled-coil proteins and domains are found throughout nature, however, because of their physical properties and tendency to aggregate, they are traditionally viewed as being especially difficult to crystallize. Here, we utilize a variety of quick and simple techniques designed to identify a series of possible domain boundaries for a given coiled-coil protein, and then quickly characterize the behavior of these proteins in solution. With the addition of a strongly fluorescent tag (mRuby2), protein characterization is simple and straightforward. The target protein can be readily visualized under normal lighting and can be quantified with the use of an appropriate imager. The goal is to quickly identify candidates that can be removed from the crystallization pipeline because they are unlikely to succeed, affording more time for the best candidates and fewer funds expended on proteins that do not produce crystals. This process can be iterated to incorporate information gained from initial screening efforts, can be adapted for high-throughput expression and purification procedures, and is augmented by robotic screening for crystallization.

We describe a framework incorporating straightforward biochemical and computational analysis to guide the characterization and crystallization of large coiled-coil domains. This framework can be adapted for globular proteins or extended to incorporate a variety of high-throughput techniques.

Obtaining crystals for structure determination can be a difficult and time consuming proposition for any protein. Coiled-coil proteins and domains are ...

Biochemistry

Self-assembly of Complex Two-dimensional Shapes from Single-stranded DNA Tiles

Current methods in DNA nano-architecture have successfully engineered a variety of 2D and 3D structures using principles of self-assembly. In this article, we describe detailed protocols on how to fabricate sophisticated 2D shapes through the self-assembly of uniquely addressable single-stranded DNA tiles which act as molecular pixels on a molecular canvas. Each single-stranded tile (SST) is a 42-nucleotide DNA strand composed of four concatenated modular domains which bind to four neighbors during self-assembly. The molecular canvas is a rectangle structure self-assembled from SSTs. A prescribed complex 2D shape is formed by selecting the constituent molecular pixels (SSTs) from a 310-pixel molecular canvas and then subjecting the corresponding strands to one-pot annealing. Due to the modular nature of the SST approach we demonstrate the scalability, versatility and robustness of this method. Compared with alternative methods, the SST method enables a wider selection of information polymers and sequences through the use of de novo designed and synthesized short DNA strands.

DNA tiling is an effective approach to make programmable nanostructures. We describe the protocols to construct complex two-dimensional shapes by the self-assembly of single-stranded DNA tiles.

Current methods in DNA nano-architecture have successfully engineered a variety of 2D and 3D structures using principles of self-assembly. In this ...

Chemistry

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Supramolecular protein assemblies play fundamental roles in biological processes ranging from host-pathogen interaction, viral infection to the propagation of neurodegenerative disorders. Such assemblies consist in multiple protein subunits organized in a non-covalent way to form large macromolecular objects that can execute a variety of cellular functions or cause detrimental consequences. Atomic insights into the assembly mechanisms and the functioning of those macromolecular assemblies remain often scarce since their inherent insolubility and non-crystallinity often drastically reduces the quality of the data obtained from most techniques used in structural biology, such as X-ray crystallography and solution Nuclear Magnetic Resonance (NMR). We here present magic-angle spinning solid-state NMR spectroscopy (SSNMR) as a powerful method to investigate structures of macromolecular assemblies at atomic resolution. SSNMR can reveal atomic details on the assembled complex without size and solubility limitations. The protocol presented here describes the essential steps from the production of 13C/15N isotope-labeled macromolecular protein assemblies to the acquisition of standard SSNMR spectra and their analysis and interpretation. As an example, we show the pipeline of a SSNMR structural analysis of a filamentous protein assembly.

Structures of supramolecular protein assemblies at atomic resolution are of high relevance because of their crucial roles in a variety of biological phenomena. Herein, we present a protocol to perform high-resolution structural studies on insoluble and non-crystalline macromolecular protein assemblies by magic-angle spinning solid-state nuclear magnetic resonance spectroscopy (MAS SSNMR).

Supramolecular protein assemblies play fundamental roles in biological processes ranging from host-pathogen interaction, viral infection to the ...

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences is desired. We herein provide a detailed in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) protocol to study the RNA structural change in the presence of an experimental drug for spinal muscular atrophy (SMA), survival of motor neuron (SMN)-C2, and in exon 7 of the pre-mRNA of the SMN2 gene. In in vitro SHAPE, an RNA sequence of 140 nucleotides containing SMN2 exon 7 is transcribed by T7 RNA polymerase, folded in the presence of SMN-C2, and subsequently modified by a mild 2&#39;-OH acylation reagent, 2-methylnicotinic acid imidazolide (NAI). This 2&#39;-OH-NAI adduct is further probed by a 32P-labeled primer extension and resolved by polyacrylamide gel electrophoresis (PAGE). Conversely, 2&#39;-OH acylation in in-cell SHAPE takes place in situ with SMN-C2 bound cellular RNA in living cells. The pre-mRNA sequence of exon 7 in the SMN2 gene, along with SHAPE-induced mutations in the primer extension, was then amplified by PCR and subject to next-generation sequencing. Comparing the two methodologies, in vitro SHAPE is a more cost-effective method and does not require computational power to visualize results. However, the in vitro SHAPE-derived RNA model sometimes deviates from the secondary structure in a cellular context, likely due to the loss of all interactions with RNA-binding proteins. In-cell SHAPE does not need a radioactive material workplace and yields a more accurate RNA secondary structure in the cellular context. Furthermore, in-cell SHAPE is usually applicable for a larger range of RNA sequences (~1,000 nucleotides) by utilizing next-generation sequencing, compared to in vitro SHAPE (~200 nucleotides) that usually relies on PAGE analysis. In case of exon 7 in SMN2 pre-mRNA, the in vitro and in-cell SHAPE derived RNA models are similar to each other.

Detailed protocols for both in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) experiments to determine the secondary structure of pre-mRNA sequences of interest in the presence of an RNA-targeting small molecule are presented in this article.

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences ...

Genetics

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

The role of RNA structure in virtually any biological process has become increasingly evident, especially in the past decade. However, classical approaches to solving RNA structure, such as RNA crystallography or cryo-EM, have failed to keep up with the rapidly evolving field and the need for high-throughput solutions. Mutational profiling with sequencing using dimethyl sulfate (DMS) MaPseq is a sequencing-based approach to infer the RNA structure from a base&#39;s reactivity with DMS. DMS methylates the N1 nitrogen in adenosines and the N3 in cytosines at their Watson-Crick face when the base is unpaired. Reverse-transcribing the modified RNA with the thermostable group II intron reverse transcriptase (TGIRT-III) leads to the methylated bases being incorporated as mutations into the cDNA. When sequencing the resulting cDNA and mapping it back to a reference transcript, the relative mutation rates for each base are indicative of the base&#39;s &#34;status&#34; as paired or unpaired. Even though DMS reactivities have a high signal-to-noise ratio both in vitro and in cells, this method is sensitive to bias in the handling procedures. To reduce this bias, this paper provides a protocol for RNA treatment with DMS in cells and with in vitro transcribed RNA.

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

The protocol provides instruction for modifying RNA with dimethyl sulfate for mutational profiling experiments. It includes in vitro and in vivo probing with two alternative library preparation methods.

The role of RNA structure in virtually any biological process has become increasingly evident, especially in the past decade. However, classical ...

A Protocol for Computer-Based Protein Structure and Function Prediction

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently &gt;20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the ...

The ITS2 Database

The internal transcribed spacer 2 (ITS2) has been used as a phylogenetic marker for more than two decades. As ITS2 research mainly focused on the very variable ITS2 sequence, it confined this marker to low-level phylogenetics only. However, the combination of the ITS2 sequence and its highly conserved secondary structure improves the phylogenetic resolution1 and allows phylogenetic inference at multiple taxonomic ranks, including species delimitation2-8.
The ITS2 Database9 presents an exhaustive dataset of internal transcribed spacer 2 sequences from NCBI GenBank11 accurately reannotated10. Following an annotation by profile Hidden Markov Models (HMMs), the secondary structure of each sequence is predicted. First, it is tested whether a minimum energy based fold12 (direct fold) results in a correct, four helix conformation. If this is not the case, the structure is predicted by homology modeling13. In homology modeling, an already known secondary structure is transferred to another ITS2 sequence, whose secondary structure was not able to fold correctly in a direct fold.
The ITS2 Database is not only a database for storage and retrieval of ITS2 sequence-structures. It also provides several tools to process your own ITS2 sequences, including annotation, structural prediction, motif detection and BLAST14 search on the combined sequence-structure information. Moreover, it integrates trimmed versions of 4SALE15,16 and ProfDistS17 for multiple sequence-structure alignment calculation and Neighbor Joining18 tree reconstruction. Together they form a coherent analysis pipeline from an initial set of sequences to a phylogeny based on sequence and secondary structure.
In a nutshell, this workbench simplifies first phylogenetic analyses to only a few mouse-clicks, while additionally providing tools and data for comprehensive large-scale analyses.

The ITS2 Database is a workbench for phylogenetic inference simultaneously considering sequence and secondary structure of the internal transcribed spacer 2. This includes data collection with accurate annotation, structure prediction, multiple sequence-structure alignment and fast tree calculation. In a nutshell, this workbench simplifies first phylogenetic analyses to a few clicks.

The internal transcribed spacer 2 (ITS2) has been used as a phylogenetic marker for more than two decades.  As ITS2 research mainly focused on the very ...

Analyzing and Building Nucleic Acid Structures with 3DNA

The 3DNA software package is a popular and versatile bioinformatics tool with capabilities to analyze, construct, and visualize three-dimensional nucleic acid structures. This article presents detailed protocols for a subset of new and popular features available in 3DNA, applicable to both individual structures and ensembles of related structures. Protocol 1 lists the set of instructions needed to download and install the software. This is followed, in Protocol 2, by the analysis of a nucleic acid structure, including the assignment of base pairs and the determination of rigid-body parameters that describe the structure and, in Protocol 3, by a description of the reconstruction of an atomic model of a structure from its rigid-body parameters. The most recent version of 3DNA, version 2.1, has new features for the analysis and manipulation of ensembles of structures, such as those deduced from nuclear magnetic resonance (NMR) measurements and molecular dynamic (MD) simulations; these features are presented in Protocols 4 and 5. In addition to the 3DNA stand-alone software package, the w3DNA web server, located at <a href="http://w3dna.rutgers.edu" target="_blank">http://w3dna.rutgers.edu</a>, provides a user-friendly interface to selected features of the software. Protocol 6 demonstrates a novel feature of the site for building models of long DNA molecules decorated with bound proteins at user-specified locations.

The 3DNA software package is a popular and versatile bioinformatics tool with capabilities to analyze, construct, and visualize three-dimensional nucleic acid structures. This article presents detailed protocols for a subset of new and popular features available in 3DNA, applicable to both individual structures and ensembles of related structures.

The 3DNA software package is a popular and versatile bioinformatics tool with capabilities to analyze, construct, and visualize three-dimensional nucleic ...

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.
To disseminate these methods for broader use we present Protein WISDOM (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

We developed computational de novo protein design methods capable of tackling several important areas of protein design. To disseminate these methods we present Protein WISDOM, an online tool for protein design (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>). Starting from a structural template, design of monomeric proteins for increased stability and complexes for increased binding affinity can be performed.

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific ...

RNA Secondary Structure Prediction Using High-throughput SHAPE

Samenvatting

Meer video's verkennen

Hoofdstukken in deze video