Methods in enzymology, volume 553

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METHODS IN ENZYMOLOGY Editors-in-Chief JOHN N ABELSON and MELVIN I SIMON Division of Biology California Institute of Technology Pasadena, California ANNA MARIE PYLE Departments of Molecular, Cellular and Developmental Biology and Department of Chemistry Investigator Howard Hughes Medical Institute Yale University Founding Editors SIDNEY P COLOWICK and NATHAN O KAPLAN Academic Press is an imprint of Elsevier 225 Wyman Street, Waltham, MA 02451, USA 525 B Street, Suite 1800, San Diego, CA 92101-4495, USA 125 London Wall, London, EC2Y 5AS, UK The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK First edition 2015 Copyright © 2015 Elsevier Inc All rights reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein) Notices Knowledge and best practice in this field are constantly changing As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein ISBN: 978-0-12-801429-5 ISSN: 0076-6879 For information on all Academic Press publications visit our website at store.elsevier.com CONTRIBUTORS R.W Adamiak Department of Structural Chemistry and Biology of Nucleic Acids, Institute of Bioorganic Chemistry Polish Academy of Sciences, and European Center for Bioinformatics and Genomics, Institute of Computing Science, Poznan University of Technology, Poznan, Poland Kirill A Afonin Basic Research Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, USA M Antczak European Center for Bioinformatics and Genomics, Institute of Computing Science, Poznan University of Technology, Poznan, Poland Stefan Badelt Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria Eckart Bindewald Basic Science Program, Leidos Biomedical Research Inc., National Cancer Institute, National Institutes of Health, Frederick, Maryland, USA J Blazewicz Department of Structural Chemistry and Biology of Nucleic Acids, Institute of Bioorganic Chemistry Polish Academy of Sciences, and European Center for Bioinformatics and Genomics, Institute of Computing Science, Poznan University of Technology, Poznan, Poland Janusz M Bujnicki International Institute of Molecular and Cell Biology, Warsaw, and Faculty of Biology, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Poznan, Poland Giovanni Bussi Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy Haoyuan Chen Center for Integrative Proteomics Research, BioMaPS Institute and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey, USA Clarence Yu Cheng Department of Biochemistry, Stanford University, Stanford, California, USA Fang-Chieh Chou Department of Biochemistry, Stanford University, Stanford, California, USA P Clote Biology Department, Boston College, Boston, Massachusetts, USA xi xii Contributors Francesco Colizzi Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy Rhiju Das Department of Biochemistry, and Department of Physics, Stanford University, Stanford, California, USA Francesco Di Palma Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy Thakshila Dissanayake Center for Integrative Proteomics Research, BioMaPS Institute and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey, USA Nikolay V Dokholyan Department of Biochemistry and Biophysics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA Christoph Flamm Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria George M Giambas¸u Center for Integrative Proteomics Research, BioMaPS Institute and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey, USA Holger Gohlke Mathematisch-Naturwissenschaftliche Fakultaăt, Institut f ur Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universitaăt D usseldorf, D usseldorf, Germany Stefan Hammer Institute for Theoretical Chemistry, and Research Group Bioinformatics and Computational Biology, University of Vienna, Vienna, Austria Christian A Hanke Mathematisch-Naturwissenschaftliche Fakultaăt, Institut f ur Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universitaăt D usseldorf, D usseldorf, Germany Scott P Hennelly New Mexico Consortium, and Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA Ivo L Hofacker Institute for Theoretical Chemistry, and Research Group Bioinformatics and Computational Biology, University of Vienna, Vienna, Austria Changbong Hyeon School of Computational Sciences, Korea Institute for Advanced Study, Seoul Republic of Korea Namhee Kim Department of Chemistry and Courant Institute of Mathematical Sciences, New York University, New York, USA Maria Kireeva Gene Regulation and Chromosome Biology Laboratory, Center for Cancer Research, NCI, National Cancer Institute, Frederick, Maryland, USA Contributors xiii Serdal Kirmizialtin New Mexico Consortium, and Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA Andrey Krokhotin Department of Biochemistry and Biophysics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA Erich R Kuechler Center for Integrative Proteomics Research, BioMaPS Institute and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey, USA Grzegorz Łach International Institute of Molecular and Cell Biology, Warsaw, Poland Tai-Sung Lee Center for Integrative Proteomics Research, BioMaPS Institute and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey, USA Jong-Chin Lin Institute for Physical Science and Technology, and Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA P Lukasiak Department of Structural Chemistry and Biology of Nucleic Acids, Institute of Bioorganic Chemistry Polish Academy of Sciences, and European Center for Bioinformatics and Genomics, Institute of Computing Science, Poznan University of Technology, Poznan, Poland David H Mathews Department of Biochemistry & Biophysics, and Center for RNA Biology, University of Rochester Medical Center, Box 712, Rochester, New York, USA Jose N Onuchic Center for Theoretical Biological Physics; Department of Physics and Astronomy; Department of Chemistry; Department of Biosciences, and Department of Biochemistry and Cell Biology, Rice University, Houston, Texas, USA Maria T Panteva Center for Integrative Proteomics Research, BioMaPS Institute and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey, USA Anna Philips European Center for Bioinformatics and Genomics, Institute of Bioorganic Chemistry, Polish Academy of Science, Poznan, Poland M Popenda Department of Structural Chemistry and Biology of Nucleic Acids, Institute of Bioorganic Chemistry Polish Academy of Sciences, Poznan, Poland K.J Purzycka Department of Structural Chemistry and Biology of Nucleic Acids, Institute of Bioorganic Chemistry Polish Academy of Sciences, Poznan, Poland xiv Contributors Brian K Radak Center for Integrative Proteomics Research, BioMaPS Institute and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey, USA Karissa Y Sanbonmatsu New Mexico Consortium, and Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA Tamar Schlick Department of Chemistry and Courant Institute of Mathematical Sciences, New York University, New York, USA Alexander Schug Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany Bruce A Shapiro Basic Research Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, Maryland, USA Michael F Sloma Department of Biochemistry & Biophysics, and Center for RNA Biology, University of Rochester Medical Center, Box 712, Rochester, New York, USA M Szachniuk Department of Structural Chemistry and Biology of Nucleic Acids, Institute of Bioorganic Chemistry Polish Academy of Sciences, and European Center for Bioinformatics and Genomics, Institute of Computing Science, Poznan University of Technology, Poznan, Poland D Thirumalai Institute for Physical Science and Technology, and Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA Jeseong Yoon School of Computational Sciences, Korea Institute for Advanced Study, Seoul Republic of Korea Darrin M York Center for Integrative Proteomics Research, BioMaPS Institute and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey, USA Mai Zahran Department of Chemistry and Courant Institute of Mathematical Sciences, New York University, New York, USA PREFACE It is clear from the Encyclopedia of DNA Elements (ENCODE) project that a significant portion in the transcriptome does not translate into protein sequences Many of these sequences function through the formation of specific RNA structures Riboswitches represent an important class of noncoding RNAs Riboswitches perform functions as genetic “switches” that regulate when and where genes are expressed To understand the structure and function of riboswitches requires computational models that can predict stable and metastable structures, their folding stabilities, kinetic pathways, transition states, and rate constants for the conformational switches, in addition to the effects of metal ions and ligand binding on RNA folding, all from the nucleotide sequence This book represents a state-of-the-art collection of advanced computational methods for modeling RNA riboswitch structure, thermal stability, dynamics, and kinetics Given the fact that riboswitches have been extensively studied experimentally, yet currently the accuracy of computational prediction for riboswitch remains generally inconsistent, this volume is particularly timely We believe that this volume will excite future more radical advances in computational modeling for riboswitches and other noncoding RNAs One of the major scientific challenges for RNA modeling relates to how we use the knowledge derived from limited information about known RNA structures Effective structure prediction methods often involve integration of knowledge-based algorithm and physics-based or experimental data-based models Several chapters present methods along this line: Purzycka et al and Cheng et al developed motif library-based and fragment-based methods, respectively; Sloma and Mathews developed methods that incorporate high-throughput structure probing data into secondary structure prediction; Kirmizialtin et al employed selective 20 -hydroxyl acylation by primer extension (SHAPE) data and developed a new force field for molecular dynamics simulation; and Krokhotin and Dokholyan incorporated SHAPE into discrete molecular dynamics to predict RNA structure Another major challenge for the computational modeling of riboswitches comes from conformational sampling due to the large conformational space of the molecule and the flexibility of RNA structure xv xvi Preface To enhance the quality of conformational sampling, Krokhotin and Dokholyan developed a three-bead coarse-grained structure model in molecular dynamics simulation and Kim et al developed a graph-theoretic approach to efficient sampling of RNA motif structures These methods offer very interesting new tools for modeling RNA folding Kinetics are intrinsic to RNA folding and conformational switching Riboswitches can fold cotranscriptionally into the conformations that correspond to ON and OFF states The kinetics of RNA folding and conformational switching are critical for understanding riboswitch structure and function This volume contains several chapters that address the methods to model riboswitch kinetics Badelt et al developed a cotranscriptional kinetics model by integrating RNA secondary structure with the dynamic energy landscape Lin et al developed a three-dimensional coarse-grained self-organized polymer model to predict riboswitch dynamics under force Using a steered molecular dynamics-based method, Di Palma et al developed an all-atom model to predict ligand-induced riboswitch stability and structure changes at atomic resolution Hanke and Gohlke presented a critical analysis for the performances of the different force fields, including the Mg2+ ion effects, in riboswitch simulations In addition to the physical models, informatics-based approaches have been highly successful in predicting riboswitch structures and finding riboswitch genes Clote presented a comprehensive introduction for the different computational methods with particular emphasis on informatics-based approaches for riboswitch structure and kinetics Conformational changes in riboswitches can be induced by ligands or ions It is thus important to have a reliable tool to predict the binding sites and binding modes This volume contains two chapters on this issue Using knowledge-based scoring functions, Philips et al developed novel methods (“LigandRNA” and “MetalionRNA”) for the prediction of riboswitch binding Panteva et al presented a multiscale method for modeling conformational switching, metal ion binding, and enzymatic reactions In these chapters, the related computational methods are discussed, as well as their limitations and pitfalls RNA conformational switching can also be induced by the presence of other RNA or DNA molecules Afonin et al presented a thermodynamic model for predicting association and dissociation of RNA/DNA hybrids containing a novel split functionality The results demonstrate the great promise of using RNA structure and conformational switches in RNA nanotechnology Preface xvii Finally, we would like to thank the fabulous team of authors, who have put together high-quality chapters Without the phenomenal works of the authors, it is simply impossible to publish such a quality volume SHI-JIE CHEN DONALD H BURKE-AGUăERO CHAPTER ONE Automated 3D RNA Structure Prediction Using the RNAComposer Method for Riboswitches1 K.J Purzycka*, M Popenda*, M Szachniuk*,†, M Antczak†, P Lukasiak*,†, J Blazewicz*,†, R.W Adamiak*,†,2 *Department of Structural Chemistry and Biology of Nucleic Acids, Institute of Bioorganic Chemistry Polish Academy of Sciences, Poznan, Poland † European Center for Bioinformatics and Genomics, Institute of Computing Science, Poznan University of Technology, Poznan, Poland Corresponding author: e-mail address: adamiakr@ibch.poznan.pl Contents Introduction RNA FRABASE—Opening the Route to RNAComposer RNAComposer—From the RNA Secondary Structure to RNA 3D Structure 3.1 General description of the RNAComposer method 3.2 RNAComposer input data 3.3 Output data and quality control of the 3D models 3.4 RNAComposer web server Predicting the Tertiary Structure of Riboswitches with RNAComposer 4.1 RNAComposer accurately predicts 3D structure of several complex riboswitches 4.2 Application example 1: The c-di-GMP-II riboswitch 4.3 Application example 2: The c-di-GMP-II riboswitch relatives Conclusions and Perspectives Acknowledgments References 8 12 13 15 16 16 21 23 28 31 31 Abstract Understanding the numerous functions of RNAs depends critically on the knowledge of their three-dimensional (3D) structure In contrast to the protein field, a much smaller number of RNA 3D structures have been assessed using X-ray crystallography, NMR spectroscopy, and cryomicroscopy This has led to a great demand to obtain the RNA 3D This work is dedicated to Professor Colin B Reese (FRS) on the occasion of his 85th birthday anniversary Methods in Enzymology, Volume 553 ISSN 0076-6879 http://dx.doi.org/10.1016/bs.mie.2014.10.050 # 2015 Elsevier Inc All rights reserved Author Index Robertson, M P., 351–352 Robinson, D A., 140 Robinson, H., 167–169, 173–175 Rocca-Serra, P., 94–95 Rodionov, D A., 262–263, 290–292 Rodrigo, G., 195 Rodrı´guez-Go´mez, D., 358 Roe, D R., 170, 343–344 Roeder, R G., 327 Roh, J H., 230 Roitberg, A E., 347, 349–350 Roland, C., 358 Romanovski, V., 172–173 Romby, P., 95f Rose, P W., Rose, S D., 315 Rosenberg, J M., 73–74, 145, 358 Ross, W S., 172–173 Rossi, J J., 92 Rossi, P., 36 Roth, A., 255–256, 262–263, 295–296, 338 Rother, K M., 4–5, 108, 273, 274–275 Rother, M., 4–5, 108 Rouskin, S., 97–98 Roux, B., 266, 338, 340, 342, 343–344, 351–352, 358 Rueda, M., 344–345 Ruiz-Carmona, S., 268 Russell, A G., 287–288 Russell, R., 293–295 Ruzzo, W L., 92, 262–263, 291f, 295–296 Ryckaert, J P., 169 S Sabina, J., 71–72, 98, 99, 196–197, 318–319 Sagui, C., 358 Saini, J S., 172 Salmon, J K., 216 Salomon-Ferrer, R., 169, 351–352 Salsbury, F R Jr., 347–348 Salzberg, S L., 288 Sampath, R., 92 Sanbonmatsu, K Y., 164–166, 216–219, 222, 237 Sands, J., 92 Sanishvili, R., 167–169 Sanner, M F., 268 Sansom, M S P., 340 SantaLucia, J Jr., 93–94, 318–319 389 Santer, M., 238–239 Sarkar, K., 217 Sarver, M., 50 Sauerwine, B., 208 Saunders, J., 217–219, 237 Savvi, S., 262–263 Sawera, M., 92 Saxild, H H., 290–292, 291f Sayre, M H., 327 Scarpazza, D P., 153 Schafmeister, C E A F., 172–173 Scheuermann, G., 209 Schlick, T., 8, 119, 120–121, 120f, 237 Schmeing, T M., 336 Schmidtke, P., 268 Schnabl, J., 262–263 Schnare, M N., 75–76 Schnieders, M J., 342 Schr€ oder, G F., 216 Schrodinger, LLC, 42 Schroeder, R., 92 Schroeder, S J., 4, 93–94, 99, 155–157, 196–197 Schroth, G P., 97–98 Schug, A., 217–219, 220–221, 230, 237 Schulten, K., 67, 143 Schulz, R., 148 Schuster, I P., 195 Schuster, P., 12, 15, 92, 198, 206, 300 Schwalbe, H., 118t, 164–166, 167, 179, 183f, 238–239 Schwans, J P., 41 Schwieters, C D., 10 Scott, L G., 352–353 Scott, W G., 338–339, 351–354 Seeman, N C., 323–325 Seetin, M G., 4, 37–40, 39f, 41, 93–94, 97–98, 108 Seibold, S A., 327 Sengupta, S., 297 Senn, H M., 357 Senter, E., 299–301, 304 Serganov, A., 6–7, 66, 67–68, 92, 116–117, 118, 140–141, 146, 164, 178, 194, 236, 289–292, 291f, 302f Serra, M J., 102–105, 262–263 Sethi, A., 217 Severcan, I., 330 Severinov, K., 325–326, 328 390 Shafer, B., 327 Shajani, Z., 216–217 Shakhnovich, E I., 68 Shan, Y., 153 Shanahan, C A., 6–7, 118t Shapiro, B A., 4, 21, 301, 315–316, 323–326 Sharan, R., 296 Sharma, G., 92–93 Sharma, M., 179 Sharma, S., 4–5, 67–68, 70f, 86–87, 108 Sharp, P A., 336 Shatalin, K., 140, 262–263 Shaw, D E., 141, 153, 216, 217, 351–352, 356 Shaw, T I., 301 Sheets, J D., 297 Sheik, S., 299–301, 304 Shen, Y., 36 Sherman, M A., 4–5 Sherwood, P., 340 Shiffeldrim, N., 120–121 Shih, I., 338–339, 352–353 Shih, W M., 330 Shintani, D K., 289–290 Shirts, M R., 358 Shirvanyants, D., 69 Shortle, D., 48–49 Showalter, S A., 216–217 Shu, D., 330 Shu, Y., 330 Shukla, G C., 330 Shum, K T., 314 Sidorenkov, I., 328 Siederdissen, C., 197 Siegel, R B., 216–217 Siegfried, N A., 97–98 Sigel, R K., 262–263 Siggia, E D., 206 Sigurdsson, S T., 262–263 Silalahi, A R J., 343–344 Silva Lopez, C., 352–353 Sim, A Y., 67–68 Simmerling, C L., 167, 169, 343–344 Simon, A C., 6–7, 118t Simons, K T., 48–49 Sin, K., 237 Singh, P., 297 Author Index Sinitskiy, A V., 340 Sinner, C., 217–219, 220–221 Sippl, M J., 264 Sklenovsky, P., 178–179, 187–188 Smith, A G., 140 Smith, A M., 247 Smith, H., 345 Smith, K D., 6–7, 21, 118t Snedecor, G W., 172 Soares, C M., 347–348 Sobero´n, M., 290–292 Soifer, H S., 92 Sokol, F., 327 Song, J., 240–241 Sosa, C P., 351–353 Sosnick, T R., 208–209 Soto, A M., 166–167, 338–339, 345 Souaille, M., 358 Soukup, G A., 96, 216–217 Sousa, R., 325–326 Sˇpacˇkova´, N., 67, 173, 177, 336–337 Spitale, R C., 97–98 Sˇponer, J., 36, 67, 146, 166–167, 168np, 173, 178–179, 183f, 185–186, 217, 336–337, 342, 351–352 Sripakdeevong, P., 36, 37, 44–45, 47–48 Stadler, P F., 12, 92–94, 100, 195–196, 197, 201–202, 204f, 205, 207, 209, 262–263, 288–289, 295–296, 316 Stahl, D A., 216–217 Stamatoyannopoulos, J A., 287–288 Stanley, H E., 68, 70–71 Starosta, A L., 217–219 Steenberg, C D., 92, 316 Steffen, P., 92–93 Stefl, R., 173, 177 Steitz, T A., 92 Stiegler, P., 93–94 Stock, G., 179 Stockbridge, R B., 262–263 Stoddard, C D., 164–166, 165f, 167, 179, 186–187, 222, 229 Stombaugh, J., 50, 154 Stormo, G D., 75–76, 92–93 St-Pierre, P., 140–141, 262–263 Strathern, J., 327 Strobel, S A., 6–7, 37, 118t, 352–353 Strong, E., 362–363 391 Author Index Studitsky, V M., 328 Su, M., 330 Subramanian, S., 75–76 Sudarsan, N., 23, 92, 116, 140, 255–256, 262–263, 287–288, 289–292, 295–296 Suddala, K C., 216–217 Suess, B., 336 Sugimoto, N., 318–319 Sugita, Y., 73, 216, 358 Sugiyama, H., 330 Sugiyama, M E., 330 S€ uk€ osd, Z., 100 Sun, X., 238–239 Sund, J., 140 Sutton, J L., 217 Suzuki, Y., 330 Svozil, D., 67, 146, 183f, 342, 351–352 Svrcek-Seiler, W A., 207 Swails, J., 347, 349–350 Swanstrom, R., Swendsen, R H., 73–74, 145, 358 Swenson, M S., 100 Szabo, A., 145, 216 Szachniuk, M., 4–5, 6, Sztuba-Solinska, J., 12 T Tabei, Y., 92–93 Tacker, M., 92, 195 Tafer, H., 92, 93–94, 195–196, 316 Tahirov, T H., 325–326 Takada, S., 230 Tan, Z J., 266, 358 Tang, C., 254 Tang, M., 120–121 Tang, Y., 97 Tate, J., 23–26, 288–289, 295, 297 Tavan, P., 142 Tchernajenko, V., 328 Teixeira, V H., 347–348 Ten-no, S., 343–344 Thiel, W., 357 Thirumalai, D., 153, 237, 239, 240f, 242–243, 243f, 245, 246f, 248f Thomas, A S., 344–345 Thomas, B C., 92–93 Thomas, J R., 262–263 Thomas, V., 269 Thompson, J D., 41–42, 314 Thompson, J S., 297 Thore, S., 118t Thornton, J M., 172 Tian, S., 39f, 40–41, 51–52 Tinoco, I Jr., 36, 72, 92 Tirado-Rives, J., 342 Tjandra, N., 10 Tomao, S., 314 Tompa, M., 295–296 Tomsic, J., 222 Tonelli, M., 216–217 Topp, S., 255–256 Tor, Y., 330 Torarinsson, E., 92–93, 292, 295–296 Torelli, A T., 353–354 Torre, E A., 97 Torrie, G M., 142–143, 158–159, 358 Toulme´, J.-J., 330 Toulokhonov, I., 325–326 Trakhanov, S., 222 Trapnell, C., 97–98 Trausch, J J., 165f Travers, K J., 343–344, 345, 346f Tremblay, R., 229 Tribello, G A., 148 Trottier, M., 330 Truhlar, D G., 340, 356–357 Trusina, A., 254 Tsao, D., 69 Tschochner, H., 327 Tucker, B J., 164, 287–288, 289–290 Tuckerman, M E., 141 Tunc-Ozdemir, M., 289–290 Tung, C S., 222 Turner, D H., 4, 71–72, 93–94, 98, 99, 155–157, 196–197, 292, 293f, 318–319 Tuschl, T., 288–289, 314 Tuszynski, J., 343–344 Tuttle, N., 92 Tyka, M., 41–42 U Uhlenbeck, O C., 36, 116–117, 325–326 Ullmann, G M., 347 Underwood, J G., 97–98, 100 Uzilov, A V., 92, 97–98 392 V Vaiana, A C., 342 Valadkhan, S., 336 Valleau, J P., 142–143, 358 Van Bakel, H., 287–288 van der Graaf, M., 73–74 van der Kamp, M W., 340, 357 van Gunsteren, W F., 146–147, 342 Vanden-Eijnden, E., 338 VanderSpoel, D., 222–223 VanLang, C C., 37–40, 39f, 100 Vapnik, V., 288–289 Varani, G., 140–141, 145, 216–217 Vary, C P., 97 Vasa, S M., Vassylyev, D G., 325–326 Vassylyeva, M N., 325–326 Verma, A., 217–219, 220–221 Viard, M., 315–316, 321–322, 325–326, 328, 329–330 Vicens, Q., 101 Viladoms, J., 352–353 Villa, A., 175f, 179, 186–187, 237, 342, 343 Vitreschak, A G., 262–263, 290–292, 296 Vogel, J., 195–196 Vondrasek, J., 85–86 Vonrhein, C., 92–93 Vorobyov, I V., 342 Voß, B., 206 Voss, B., 92–93, 199, 299–300, 301–303, 302f, 306 Voss, N., 315–316, 325–326 Voth, G A., 340 Vournakis, J., 97 Vournakis, J N., 97 W Wachsmuth, M., 196, 202, 204f, 210 Wachter, A., 116, 287–288, 289–292, 295–296 Wacker, A., 118t, 167, 179, 183f Wadkins, T S., 338–339, 352–353 Wakeman, C A., 293–295 Walker, F M., 315–316 Walker, R C., 169, 351–352 Walter, K F., 216 Author Index Walter, N G., 36, 67, 166, 216–217, 237, 336–337 Walter, P., 353–354 Walter, W., 328 Wan, Y., 97–98 Wang, A H J., 167–169 Wang, F., 140–141, 243–244 Wang, J M., 37, 117–118, 118t, 146, 166, 167, 168np, 169, 342, 351–352 Wang, J X., 295–296 Wang, Y X., 216–217, 301 Warkentin, E., 118t, 167, 179, 183f Warner, D F., 262–263 Warner, K D., 140 Warshel, A., 357 Washietl, S., 92, 97–98, 100, 288–289, 295–296 Wasik, S., Watanabe, M., 290–292 Watt, E D., 238–239 Watts, J M., Waugh, D S., 328 Weare, J., 340 Wedekind, J E., 178–179, 353–354 Weeks, K M., 4, 12, 37–40, 75–76, 77f, 82, 96, 97–98, 99, 101, 140, 216–217, 219–220 Wei, D., 92–93 Weigand, J E., 336 Weinberg, Z., 23, 92, 262–263, 295–296 Weinkam, P., 330 Weinreich, T M., 153 Weissheimer, N., 204f Weissig, H., 66–67 Weissman, J S., 97–98 Welch, B L., 172 Welz, R., 255–256 Westbrook, J., 66–67, 119 Westhof, E., 4, 14, 75–76, 102–105, 122–124, 148, 154, 176, 262–263, 266, 268, 353–354 Westover, K D., 325–326 Wheaton, V., 92–93 White, O., 288 Whitford, P C., 175–176, 217–219, 220–221, 222–223, 230, 237 Wichert, J M., 68 393 Author Index Wickiser, J K., 236, 246–247, 251–252, 254–255, 290 Wider, G., 28–31 Widmann, J., 165f Widom, M., 208 Wijmenga, S S., 73–74 Wiklund, E D., 295–296 Wilcox, J L., 340–341 Wilhelmsson, L M., 330 Wilkinson, K A., 4, 96, 101, 216–217 Will, S., 92–93 Williamson, J R., 336 Wilson, D N., 336 Wilson, T J., 336, 352–353 Wimberly, B T., 92–93 Winkler, W C., 92, 116–117, 140, 236, 262–263, 287–288, 289–292, 291f, 293–296, 293f Wirmer-Bartoschek, J., 118t, 167, 179, 183f Wise, S J., 164–166 Woese, C R., 92–93, 216–217 W€ ohnert, J., 118t, 164–166, 167, 179, 183f Wojtas-Niziurski, W., 338 Wolfe, B R., 92, 316 Wolfinger, M T., 201–202, 207, 209 Wolfson, H J., 23–26 Wolynes, P G., 230 Wong, K.-Y., 338–339, 351–353, 358, 361–362 Wong, T N., 208–209 Woods, D A., 12, 93–94 Woodside, M T., 140–141, 237, 243–244 Woodson, S A., 166–167, 172, 230 Wrede, P., 97 Wu, C., 342 Wu, J C., 301 Wu, L.-C., 297 Wu, P., 318–319 Wu, X., 358 Wuchty, S., 198, 201–202, 300 Wurst, R., 97 Wuthrich, K., 10 X Xia, T., 93–94 Xie, W., 340 Xiong, Y., 37, 328 Xu, H., 351–352, 356 Xu, S., 92, 314 Xu, X J., 4–5, 92–93, 119, 316 Xu, Z., 4, 92–93 Xue, C., 288–289 Y Yaghoubian, A J., 315–316, 325–326 Yajima, R., 338–339, 340–341, 354–355, 358–359, 360 Yamashita, N., 290–292 Yan, H., 330 Yang, H., 119 Yang, J., 295–296 Yang, W., 357–358 Yanofsky, C., 296–297 Yao, P., 293–295 Yao, Z., 92, 295–296 Yarus, M., 116–117 Ye, T., 330 Yeh, C.-T., 297 Yekta, S., 287–289 Yin, S., 85–86 Yingling, Y., 315–316 Yonetani, Y., 344–345 Yoo, J., 343–345 Yoon, J., 239, 240f, 241–242 Yoon, S., 37–40, 39f York, D M., 169, 336–337, 338–339, 340, 343–346, 345f, 347, 349–350, 351–354, 355, 357, 358, 361–362 Yoshida, N., 344–345 Yoshimura, S H., 330 Yoshiuchi, K., 290–292 Young, C., 141 Yu, H., 140–141 Yuan, Y R., 140–141, 178, 289–292, 291f, 302f Z Zadeh, J N., 92, 316 Zagrovic, B., 216 Zahran, M., 120f, 124 Zakrevsky, P., 315–316 Zalatan, J G., 361 Zarringhalam, K., 100, 108 Zaug, A J., 92, 97 Zgarbova´, M., 146, 166, 168np, 169, 181–182, 187–188, 217, 342, 351–352 394 Zhang, C., 328, 330 Zhang, J Z H., 352–353 Zhang, Q C., 97–98, 216–217, 238–239 Zhang, S., 296, 361–363 Zhang, X., 288–289 Zhang, Y., 97, 357–358 Zhang, Z C., 316 Zhao, P N., 4–5, 119 Zhao, Q., 37 Zheng, H., 265 Zheng, Z., 120–121 Zhou, J., 253–254, 314 Zhou, K., 340–341, 354 Author Index Zhou, Y., 68 Zhou, Z H., 120–121 Ziegeler, M., 37 Zimniak, L., 92 Zirbel, C L., 36, 50, 119 Zok, T., 6, 119 Zorn, J., 120–121 Zou, X., 92, 140 Zubradt, M., 97–98 Zuckerman, D M., 358 Zuker, M., 4, 71–72, 73, 93–94, 98, 99, 119, 155–157, 196–197, 198, 317–319 Zuo, X., 216–217 Zwier, M C., 356 SUBJECT INDEX Note: Page numbers followed by “f ” indicate figures and “t ” indicate tables A All-atom model DMD simulations, 85–86 RAG-3D, 124 template initial structure, 85–86 Amber force field, 166, 170, 176–177 apo-RNase A, 348–350 Aptamer domain atomic RMSF calculation, 179 B subtilis, 186 ff99 and ff99+parmbsc0, 181–182 Gswapt and Gswloop, 181 MD simulations, 179, 180f, 181, 182, 184 per-nucleotide frequency, 182 structural deviations, 179 torsion angles α, γ, and χ, 181–182, 183f Atomistic simulations dynamic feature of water, 241–242 folding landscapes, purine riboswitches (see Purine riboswitches) gene regulation, 251–255 heterogeneity, water dynamics, 241 hydration, RNA, 238–239 SAM riboswitch, 247–251 water hydrogen bond kinetics, 239–241 A9–U63 bp pulling, riboswitches ad hoc modified system, 154 apo and holo results, 155–157 error estimation, free-energy changes, 157, 157f free-energy profiles, 155, 155f hydrogen bonds, 155, 156f Jarzynski equality, 154 rupture, 154, 154f C c-di-GMP-II riboswitch ARTS pairwise alignment parameters, 26–28, 28t CyloFold program, 21, 22t 3D structure prediction, 26–28, 29t pairwise superimposition, 3D models, 23–26, 27f 3Q3Z structure topology, 23–28, 24t RF01786 family, 23–26 RNAlyzer visualization, 23, 26f U-turn/S-turn architecture, 21 web-accessible tools, 21, 22t Coarse-grained model See also Three-bead model energy landscape, 67–68 RNA simulation, 87 Cobalamin riboswitch structure, 275, 276t, 277f Computational experiments conformational switch, 304–306 riboswitch structure, 304 RNAshapes, 306 XPT genes, 303 Computational methods RNA-ligand complex structures, 263–269 RNA-small molecule complex structures aminoglycoside antibiotics, 268 Dock6, 269 drugScoreRNA, 268–269 isotropic spheres, 267–268 MORDOR, 269 organic molecules, 267 RNA-ion complexes, 267–268 scoring function, 268 Conformational switches FFTbor, 300–301 hok/sok system, 301 machine-learning riboswitch, 299–300 paRNAss, 300 RNAbor, 300–301 RNAshapes, 300 shrep, 300 Constraint methods DMS, 100 enzymatic cleavage, 99 enzymatic probing, 98 395 396 Constraint methods (Continued ) free energy minimization, 98 PPV, 98–99 pseudo free energy, 100 quantitative probing data, 100 RNAstructure software package, 100 SHAPE chemistry, 99 20 30 -Cyclic phosphate (cCMP) complex, 348–350 D Dimethyl sulfate (DMS) advantages, 97 quantitative probing data, 100 and SHAPE, 99–100 Discrete molecular dynamics (DMD) collision approach, 69 Newton’s equations, 68 particle’s kinetic energy, 68 potential force fields, 68, 69f time-dependent motions, 68 DMD See Discrete molecular dynamics (DMD) DMS See Dimethyl sulfate (DMS) Dock6, 269 DrugScore method, 268–269 F FARFAR See Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) Flavine mononucleotide (FMN), 262–263 Force field dependence, riboswitch aptamer domain, 179–184 circular variances, dihedral angles, 172 ff99+parmbsc0+parmχOL3, 166 hydrogen bonds, L2/L3 loop region, 170, 171t ladder-like structures, 178–179 loop–loop interactions, 184–187 Mg2+ ions, 170, 172–178 RMSD calculation, 170, 171t SEM, 172 simulations set up, 167–170 Forster resonance energy transfer (FRET), 315–316, 326f, 329–330 Fragment Assembly of RNA with FullAtom Refinement (FARFAR) command line, 55, 59 Subject Index file output, 49 low-resolution models, 48 nucleotide-resolution, 37 Rosetta framework, 37 Fragment assembly, RNA, 37, 44–47 FRET See Forster resonance energy transfer (FRET) G Guanine-sensing riboswitch (Gsw) B subtilis, 186 force fields simulation, 171t G37A/C61U mutation, 185–186 high-anti state, 184 hypoxanthine, 164–166 ligand bound crystal structures, 183f L2/L3 loop region, 171t MD simulations, 166, 167, 168t, 181 secondary structure and sequence, 165f structural deviations, 179 H Hepatitis delta virus ribozyme (HDVr) application, 354–356 free energy profiles, 358–360 hepatitis D virus, 340–341 physiological conditions, 352–353 prototype protein enzyme and RNase A, 340–341 Hidden Markov models (HMMs), 288 Hierarchical graph folding approach all-atom models, RAG-3D, 124 graphs assessment, 123–124 junction prediction, RNAJAG, 122–123 MC/SA graph sampling, 123 RNA 2D and 3D graph representation, 122 Hill equation, 348–349 I iFoldRNA Web server, 86–87 Infernal B subtilis genome, 297–298 parentheses/brackets, 298 purine riboswitches, 298 K Kinetic folding, RNA barriers/treekin, 206–208 E coli polymerase transcription rate, 210 397 Subject Index Gillespie-type simulation algorithms, 205–206 hybrid-simulation framework BarMap, 209, 210f mapping process, 209f Markov process, 205–206 RNA elongation step, 209 RS10 riboswitch, 210 stochastic simulation, 206 terminator hairpin, 210 Kinetic isotope effects (KIEs) challenges, 363 experiments, 361 light and heavy isotope, 361 RNase A and Zn2+ catalytic mechanisms, 361–363 transition state, 361 M Machine learning, 295, 299–300, 307 Magnesium ion Amber force fields, 166–167 Amber software, 172–173 aptamer domain, 178 Aqvist parameters, 177–178 atomic RMSF, 177 harmonic restraints, 173–175 hexahydrated ion, 175–176 inner-coordination sphere, 172 ion–phosphate oxygen interactions, 173 Lennard–Jones parameters, 176–177 MD simulations, 166–167 placement, 173–175, 174f purine-binding riboswitches, 178 structural dynamics, Gswapt, 188 water molecules, 173–175, 175f MD simulations χ-anti region, 187–188 Mg2+ ion placement, 166–167, 168t Metal ion-binding sites, riboswitches cobalamin riboswitch structure, 275, 276t K+ ions, SAM-I riboswitch structure, 275–276, 278t MetalionRNA, 274–275 Mg2+ cations, 275, 277f riboswitch-ligand complexes, 275–276, 280t SAM-I riboswitch structure, 275–276 MetalionRNA and LigandRNA bond–atom interaction, 271 Dock6 program, 273–274 geometries, 270 grid cells, 273 metal ion-binding sites, riboswitches, 274–276 PDB files, 274 RNA-ligand complexes, 271 statistical approach, 270 statistical potential-practical information, 270, 271f, 272–273 structure preparation, 272 third-party docking program, 269–270 Web servers, 272–274 Molecular dynamics simulations atomic resolution, 216 chemical probing reactions analysis, 224 computation, SHAPE reactivity, 219–220 crystallographic structure, 217–219 detecting nucleotide mobility, 216–217, 218f in-line chemical probing, 224, 225 integrating experiment and atomistic simulation, 230 P4 domain, 229 potential energy function, 220–222 ribosome studies, 216–217 RNA systems, 223 SHAPE, 216–217, 223–224 SHAPE-FIT, 229–230 T tengcongensis metF SAM-I riboswitch, 219, 219f, 222 Molecular modeling Software, 268–269 N Noncoding RNA (ncRNA), 287–288 Nonlinear Poisson–Boltzmann (NLPB) equation, 343–345, 345f, 346f O Organic ligand-binding sites, riboswitches Dock6, 276–279 LigandRNA, 279 RiboDock, 279 riboswitch-ligand complexes, 281–282, 281t s-adenosylmethionine (SAM), 281–282, 282f 398 P pH replica exchange molecular dynamics (pH-REMD), 347–348, 349–350, 364 Positive predictive value (PPV), 98–99 Probing methods chemical, 95–96 comparison, 97 DMS, 97 enzymatic cleavage, 97 high-throughput technologies, 97–98 inline probing, 96 RNA secondary structure, 94–95 SHAPE, 94–95, 95f, 96 Profile hidden Markov model (pHMM), 297 P1 stem pulling, riboswitches apo and holo forms, 153 coarse-grain models, 153 definitive disruption, 150–152, 152t mechanical work, 152–153, 152f rupture, 150–152, 151f Purine riboswitches aptamer domain, 242 Brownian dynamics simulations, 244 force-induced dynamics, 243f free energy profiles, 246 helices determines, 245 ligand binding, 242–243 optical tweezer experiments, 244 P2 and P3 hairpin loops, 245 pbuE A-riboswitch, 244, 246f P1, P2 and P3 helices, 243–244 P2/P3 tertiary interactions, 244 SOP model, 242–243 stability hypothesis, 245 triple-helix junction, 244–245 unfolding transition, 246–247 Q QM/MM models, 357–358, 360–361 R RAGTOP 2D to 3D graphs, 121 pseudoknots, 122 riboswitch tertiary structure, 130–131 Subject Index RNAJAG, 122–123 Riboswitches add adenine, 140–141, 236 apo state, 164–166 aptamer domain, 194, 290 atomistic simulations (see Atomistic simulations) A9–U63 bp pulling, 154–157 bacterial, 289–290 c-di-GMP-II riboswitch (see c-di-GMPII riboswitch) coarse grained (CG) model, 237 collective variable (CV), 142 computational approaches, 211 cotranscriptional folding, 211 description, 140 3D models characteristics and accuracy, 16, 18t energy value calculation, 3D models, 19–21, 20t error estimate, 145–146 experiments comparison, 157–158 expression platform, 290 force field dependence (see Force field dependence, riboswitch) G37A/C61U double mutation, 166 gene finders bacteria, 293–295 computational approaches, 295 covariance models (CMs), 295–296 dynamic programming algorithm, 297 HMMER, 299 human immunodeficiency virus type 1, 293–295 metagenomics techniques, 296 pHMM, 297 purine riboswitches, 299 RibEx, 296–297 SCFG, 295 Gsw, 164–166, 165f guanine riboswitch yxjA, 290–292, 291f HMMs, 288 hydration dynamics, 237 in vitro studies, 237 Jarzynski equality, 143–144 junctional, 6–7 kinetic folding, 205–208 ligand-binding, 140, 195–196, 289–290 Subject Index ligand-induced stabilization, terminal helix, 142 ligand recognition sites, 194 loop–loop interactions, 164–166 methodological improvements, 158–159 molecular dynamics (MD), 141 ncRNA, 287–288 noncoding mRNA regions, ON and OFF, 237 pbuE adenine, 236 protocols, 146–148 pseudoknots, 6–7 P1 stem pulling, 150–153 purine-binding and S-adenosylmethionine (SAM), 237 reweighting scheme, 144–145 RNA landscape computations, 196 sample input files, 148–150 SCFGs, 288 secondary structure conservation, 292, 294f sequence and secondary structure topology, 16, 17t steered MD, 143 structural alignment, 292, 293f structural analysis, 148 superimposition, 3D models, 16, 19f SVM, 288–289 temperature-dependent, 195, 196f tertiary interactions, 164–166 thermodynamic (see Thermodynamic RNA folding) TPP, 290–292 transcriptional regulation, 290–292 transcriptional/translational level, 194 transcription and translation regulation, OFF riboswitches, 237, 238f translational regulation, 290–292 umbrella sampling method, 142–143 Riboswitch tertiary structures artificial genetic circuits, 132 coarse-grained graph sampling approach, 130–131 coaxial stacking, 127 3D tree graphs, 129–130 fluoride riboswitch, 129–130 graph results, junction prediction, 124–127, 125t, 126f 399 helical arrangements, 130 junction region, 118, 130–131 k-turn motifs, 119, 131–132 lowest-scored graph, 129 Monte Carlo sampling, 124–127 PDB database, 117–118, 118t pseudoknot, 129–130 RNA structure prediction, 119–121 SAM-I and fluoride, 118 structure and function, 116–117 thiamine pyrophosphate (TPP) riboswitch, 116–117 vertex-to-vertex distance measurement, 124–127, 128f, 130 Ribozyme catalytic strategies, 352–353 complexity, 339f density-functional methods, 360–361 divalent metal ions, 338–339 nucleolytic, 340–341 RNA-based technologies, 336 RNA catalysis biochemical reactions, 336 catalytic riboswitches, 351–352 challenges, 345–346, 356 chemical reaction challenges, 360–361 free energy analysis, 357, 358 HDVr catalysis, 358–360 mechanistic pathways, 356–357 multistate Bennett acceptance ratio, 358 pH and ionic conditions, 356–357 precatalytic “reactant,” 356–357 QM/MM methods, 357–358 semi-empirical quantum model, 358 vFEP method, 358 classicalMDsimulations, 364 components, 363 computational RNA enzymology, 337, 338–339 conformational landscape, 351–352 experiments, 336–337 free energy pathways, 364 HDVr, 354–356 ion atmosphere around nucleic acids, 343–345 ion models, 342–343 400 RNA catalysis (Continued ) KIEs (see Kinetic isotope effects (KIEs)) MD simulations, 353–354 multiscale modeling strategy, 340 mutagenesis, 351–352 phosphodiester backbone, 340–341 pH-rate apo and cCMP-bound RNase A, 348–350 challenges, 350–351 CpHMD and pH-REMD, 347–348 kinetic data, 347 pKa values, 347 protonation states, 346–347 pH-REMD, 364 ribozyme engineering, 336 ribozymes, 352–353 structure and function, 341–342 RNAComposer cyclic di-GMP-II riboswitch structure, 8–10, 9f description, 3D models, 13–15 3D structure elements search and preparation, 10 3D structure refinement, 10 input data, 12–13 log.txt file, 11 machine translation, 11–12 NMR-derived 3D elements, 10–11 riboswitches, 16–21 RNA secondary structure fragmentation, RNA structure building, 10 web server, 15–16 RNA2Dfold, 199–201 RNA 3D models chemical mapping data, 37–40, 39f 1D chemical mapping experiments, 40–41 MAPseeker software, 41 MOHCA-seq, 41 “mutate-and-map” (M2) approach, 40–41 mutation-rescue experiments, 40–41 nonparametric bootstrapping, 37–40 phylogenetic analysis, 37–40 reverse transcription, 37–40 subhelix-resolution, 36 Subject Index RNA/DNA hybrids cotranscriptional production, 325–326 Dicer Substrate RNA, 315 experimental testing, 329–330 FRET, 315–316 in vitro transcription systems, 327, 328 multiple split functionalities, 314f, 315 NanoFolder, 316 nanotechnologies, 330 12-nucleotide toeholds, 315 NUPACK software, 316 promoter-dependent transcription, 327, 327f RNAcofold, 316 RNAi, 314 RNA polymerase II, 325–326, 326f, 327, 328 sequence design, 323–325 split functionalities, 325, 326f thermodynamic prediction (see Thermodynamic of RNA/DNA hybrids reassociation) T7 RNA polymerase, 325–326 RNA 3D structures automated prediction, 4–5 description, industrial applications, 5–6 output data and quality control RNAComposer action, 13 RNAlyzer, 14–15 RNApdbee, 13 riboswitches, 6–7 web-accessible tools, 4–5 RNA FRABASE, 7–8 RNA interactions atomic details, 263 FMN, 262–263 free energies, 263 functions, 262–263 metal ions and organic molecules, 263 RNA-Junction-As-Graphs (RNAJAG) data-mining program, 121 junction prediction, 122–123, 127 RNA–ligand complex structures binding constant, 264 intermolecular interaction potential, 264 metal ions and small organic molecules, 263 401 Subject Index statistical potential, 264–265 RNA–ligand interactions inverse Boltzmann Ansatz, 264–265 metal ion-binding sites, 265–267 metal ions and organic molecules, 263 molar concentration, 264 statistical potentials, 264–265 RNA 20 -O-transesterification, 341f RNA secondary structure prediction automated methods, 92–93 biological molecule, 92 CircleCompare web server, 102–105, 106f CMCT, 105 command line tool, 102 comparative sequence analysis, 92–93 computational methods, 93–94 constraint methods, 98–99 electrophoresis, 94 Escherichia coli 5S rRNA, 102–105, 103f Gibbs free energy, 93–94 inline probing, 108 predicted structure, 102–105, 103f probing methods, 94–98 riboswitches, 92 SHAPE, 102 ShapeKnots, 101–102 step-by-step protocols, 102 RNA structure computational methods, 263 prediction, 263 coarse-grained models, 67–68 noncoding RNAs, 66 Protein Data Bank (PDB), 66–67 riboswitch tertiary structures, 119–121 three-dimensional (3D) structures (see Three-dimensional (3D) structures) preparation, 272 riboswitches, 262–263 RNA–ligand interactions, 263 statistical potential, 274 RNAsubopt, barriers barrier trees, 202, 203f Basin hopping graph approach, 205 energy landscape, 201 ligand-binding riboswitches, 202 limitation, 202–205 RS10, 202 temperature sensitive, 202 terminator hairpin, 202 RNA tertiary structure advanced strategies, 50–51 clustering model, 48–49 command lines and files, 53–62 de novo modeling, 37 3D models (see RNA 3D models) evaluation, 51–52 FARFAR, 37, 47–48 global fold, fragment assembly FARNA, 44–45 low-resolution models, 46f, 47 MOHCA-seq data, 45–46 Monte Carlo algorithm, 44–45 PDB-formatted models, 47 README_SETUP file, 45–47 RMSD values, 45 installing software and accessing computation resources, 41–43 MINIMIZE, 48 preassembling helices, 43–44 RNA-Puzzles trials, 36 Rosetta framework, 37, 38f Rosetta full-atom energy function, 47–48 Rosetta computation resources, 41–42 documentation, 53–62 FARFAR, 37 FASTA file, 53 pseudoenergy constraint file, 56 revised FASTA file, 61 revised secondary structure file, 62 RosettaCommons Web site, 41–42 ROSIE, 43 secondary structure file, 53 software package, 53 Rosetta online server that includes everyone (ROSIE), 43 RS10 riboswitch, 202, 207, 208f, 210 S S-adenosylmethionine (SAM) riboswitch folding landscapes, 247–249 thermodynamic control, 249–251 SAM riboswitch See S-adenosylmethionine (SAM) riboswitch 402 Selective 20 -hydroxyl acylation by primer extension (SHAPE) acylation reaction rate, 219–220 base stability and backbone mobility, 220 chemical probing, 223–224 comparison, SAM-I riboswitch aptamer domain, 226f, 227f, 228f, 229 description, 216–217 distance function calculation, 224–225 electrophoresis data, 101 laser-induced fluorescence detector, 224 Lennard–Jones potential, 220–221 mapping data, 100–101 native interactions, 220–221 nonbonding native interactions, 220–221 parameters, 221 P4 domain, 229 probing and in-line probing, 223 pseudo-energy function, 219–220 SHAPE-FIT, 229–230 steepest-descent minimization algorithm, 221–222 structure-based potential, 220–221 X-ray/NMR studies, 220–221 Self-organized polymer (SOP) model, 242–243, 244 SHAPE See Selective 20 -hydroxyl acylation by primer extension (SHAPE) Single nucleotide resolution nucleic acid structure mapping experiments (SNRNASM), 94–95 siRNA delivery, 323 Small RNA structure folding kinetics, pseudoknot RNA., 73–74, 75f pseudoknot structure, 73–74 Q-values, 73, 74f reconstructed structures, 73 RMSD calculation, 73, 74f SOP model See Self-organized polymer (SOP) model Specific gene silencing, 329–330 Split functionalities, 315, 325, 326f Statistical potential distance and angle, 270 LigandRNA, 273–274 PDB file, 274 Subject Index performance, 282 radial, 264–265 RNA receptor structure, 274 RNA’s, 272 Steered molecular dynamics, 143, 158–159 Stochastic context-free grammars (SCFGs), 288–289, 301 Structure prediction, 263 Support vector machines (SVM), 288–289 T Thermodynamic of RNA/DNA hybrids reassociation computer program, 322–323 partition function, 317–319 program output, 323, 324f search algorithm, 319–321 secondary structure predictions, 321–322, 322f Thermodynamic RNA folding definition, 196–197 energy model, 196–197 RNA2Dfold, 199–201 RNA structure prediction, 197–199 RNAsubopt, barriers, 201–205 Thermodynamics-based algorithm, 292 Thiamine pyrophosphate (TPP) riboswitch, 116–117 Three-bead model base-pairing, 70–71 base stacking, 71 chain connectivity and local geometry, 69–70, 70f hydrophobic interactions, 71 loop entropy, 72 nonbonded interactions, 69–70 parameterization, 71–72 phosphate repulsion, 71 small RNA structure, 73–74 Three-dimensional reference interaction site model (3D-RISM), 343–346, 345f, 346f Three-dimensional (3D) structures coarse-grained three-bead model, 67–68 403 Subject Index folding RNA, experimental constraints, 75–76 HRP bias potential, 77–80 HRP experiments, 80–82 MD simulations, 67 Monte Carlo-based methods, 67 simulation protocol, 82, 83f through-space contacts, 76–77, 77f, 78t traditional analytical methods, 66–67 training set, 83–85, 84f W Water hydrogen bond kinetics, 239–241 ... experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein In using such information or methods they should be mindful of their own safety... modeling RNA folding Kinetics are intrinsic to RNA folding and conformational switching Riboswitches can fold cotranscriptionally into the conformations that correspond to ON and OFF states The kinetics... kinetics Conformational changes in riboswitches can be induced by ligands or ions It is thus important to have a reliable tool to predict the binding sites and binding modes This volume contains
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