The kink-turn is a recurrent RNA structural motif that induces a sharp bend (kink) in the A-form RNA helix. It is defined by key structural features, including consecutive sheared AG base pairs, an A-minor interaction, and multiple base-sugar interactions. An accurate representation of these densely packed noncanonical interactions by molecular dynamics simulations poses a significant challenge for contemporary force fields (FFs). Here, we present extended simulations of the ribosomal kink-turn 7 (Kt-7) from H.m., the so-called "consensual" kink-turn, using a broad spectrum of pair-additive and polarizable RNA FFs. None of the tested FFs manage to flawlessly describe all of the structural features of the Kt-7 although several FFs provide rather acceptable results and should not cause problems in simulations of larger RNAs containing a kink-turn. On aggregate, the widely used OL3 (ff99bsc0χOL3) and polarizable AMOEBA FFs achieve the best performance for this motif. Interestingly, some more recently parametrized FF variants struggle to describe the Kt-7's tertiary A-minor interaction - a ubiquitous tertiary contact in RNA. This raises some concerns about the broader applicability of these FFs and suggests that they may be overfitted to small model systems, such as RNA tetranucleotides. In some cases, irreversible unkinking of the entire kink-turn motif can also be observed. The kink-turn motif is highly sensitive to variations in RNA FFs, and we strongly recommend its inclusion in training and benchmarking data sets as an important regression test to improve the robustness and accuracy of RNA FF parametrization.

Czech Advanced Technology and Research Institute CATRIN Palacký University Křížkovského 511 8 Olomouc 779 00 Czech Republic

Institute of Biophysics of the Czech Academy of Sciences Královopolská 135 Brno 612 00 Czech Republic

IT4Innovations VSB Technical University of Ostrava 17 listopadu 2172 15 Ostrava Poruba 708 00 Czech Republic

National Centre for Biomolecular Research Faculty of Science Masaryk University Kamenice 5 Brno 625 00 Czech Republic

Zobrazit více v PubMed

Huang L., Lilley D. M. J.. The Kink Turn, a Key Architectural Element in RNA Structure. J. Mol. Biol. 2016;428(5):790–801. doi: 10.1016/j.jmb.2015.09.026. PubMed DOI PMC

Huang L., Lilley D. M. J.. The kink-turn in the structural biology of RNA. Q. Rev. Biophys. 2018;51:e5. doi: 10.1017/S0033583518000033. PubMed DOI

Klein D. J., Schmeing T. M., Moore P. B., Steitz T. A.. The kink-turn: a new RNA secondary structure motif. EMBO J. 2001;20:4214. doi: 10.1093/emboj/20.15.4214. PubMed DOI PMC

Montange R. K., Batey R. T.. Structure of the S-adenosylmethionine riboswitch regulatory mRNA element. Nature. 2006;441(7097):1172–1175. doi: 10.1038/nature04819. PubMed DOI

Smith K. D., Shanahan C. A., Moore E. L., Simon A. C., Strobel S. A.. Structural basis of differential ligand recognition by two classes of bis-(3′-5′)-cyclic dimeric guanosine monophosphate-binding riboswitches. Proc. Natl. Acad. Sci. U. S. A. 2011;108(19):7757–7762. doi: 10.1073/pnas.1018857108. PubMed DOI PMC

Zhang J., Ferré-D’Amaré A. R.. Co-crystal structure of a T-box riboswitch stem I domain in complex with its cognate tRNA. Nature. 2013;500(7462):363–366. doi: 10.1038/nature12440. PubMed DOI PMC

Woźniak A. K., Nottrott S., Kühn-Hölsken E., Schröder G. F., Grubmüller H., Lührmann R., Seidel C. A., Oesterhelt F.. Detecting protein-induced folding of the U4 snRNA kink-turn by single-molecule multiparameter FRET measurements. RNA. 2005;11(10):1545–1554. doi: 10.1261/rna.2950605. PubMed DOI PMC

Vidovic I., Nottrott S., Hartmuth K., Lührmann R., Ficner R.. Crystal structure of the spliceosomal 15.5 kD protein bound to a U4 snRNA fragment. Mol. Cell. 2000;6(6):1331–1342. doi: 10.1016/S1097-2765(00)00131-3. PubMed DOI

Grabow W. W., Jaeger L.. RNA Self-Assembly and RNA Nanotechnology. Acc. Chem. Res. 2014;47(6):1871–1880. doi: 10.1021/ar500076k. PubMed DOI

Lescoute A., Leontis N. B., Massire C., Westhof E.. Recurrent structural RNA motifs, Isostericity Matrices and sequence alignments. Nucleic Acids Res. 2005;33(8):2395–2409. doi: 10.1093/nar/gki535. PubMed DOI PMC

Moore T., Zhang Y., Fenley M. O., Li H.. Molecular basis of box C/D RNA-protein interactions: cocrystal structure of archaeal L7Ae and a box C/D RNA. Structure. 2004;12(5):807–818. doi: 10.1016/S0969-2126(04)00116-9. PubMed DOI

Hamma T., Ferré-D’Amaré A. R.. Structure of protein L7Ae bound to a K-turn derived from an archaeal box H/ACA sRNA at 1.8 Å resolution. Structure. 2004;12(5):893–903. doi: 10.1016/S0969-2126(04)00096-6. PubMed DOI

Szewczak L. B. W., Gabrielsen J. S., Degregorio S. J., Strobel S. A., Steitz J. A.. Molecular basis for RNA kink-turn recognition by the h15. 5K small RNP protein. RNA. 2005;11(9):1407–1419. doi: 10.1261/rna.2830905. PubMed DOI PMC

Youssef O. A., Terns R. M., Terns M. P.. Dynamic interactions within sub-complexes of the H/ACA pseudouridylation guide RNP. Nucleic Acids Res. 2007;35(18):6196–6206. doi: 10.1093/nar/gkm673. PubMed DOI PMC

Huang L., Lilley D. M. J.. The molecular recognition of kink-turn structure by the L7Ae class of proteins. RNA. 2013;19(12):1703–1710. doi: 10.1261/rna.041517.113. PubMed DOI PMC

McPhee S. A., Huang L., Lilley D. M. J.. A critical base pair in k-turns that confers folding characteristics and correlates with biological function. Nat. Commun. 2014;5(1):5127. doi: 10.1038/ncomms6127. PubMed DOI PMC

Huang L., Wang J., Lilley D. M. J.. A critical base pair in k-turns determines the conformational class adopted, and correlates with biological function. Nucleic Acids Res. 2016;44(11):5390–5398. doi: 10.1093/nar/gkw201. PubMed DOI PMC

Leontis N. B., Westhof E.. Geometric nomenclature and classification of RNA base pairs. RNA. 2001;7(4):499–512. doi: 10.1017/S1355838201002515. PubMed DOI PMC

Xin Y., Laing C., Leontis N. B., Schlick T.. Annotation of tertiary interactions in RNA structures reveals variations and correlations. RNA. 2008;14(12):2465–2477. doi: 10.1261/rna.1249208. PubMed DOI PMC

Ban N., Nissen P., Hansen J., Moore P. B., Steitz T. A.. The Complete Atomic Structure of the Large Ribosomal Subunit at 2.4 Å Resolution. Science. 2000;289(5481):905–920. doi: 10.1126/science.289.5481.905. PubMed DOI

Nissen P., Ippolito J. A., Ban N., Moore P. B., Steitz T. A.. RNA tertiary interactions in the large ribosomal subunit: the A-minor motif. Proc. Natl. Acad. Sci. U. S. A. 2001;98(9):4899–4903. doi: 10.1073/pnas.081082398. PubMed DOI PMC

Chagot M.-E., Quinternet M., Rothé B., Charpentier B., Coutant J., Manival X., Lebars I.. The yeast C/D box snoRNA U14 adopts a “weak” K-turn like conformation recognized by the Snu13 core protein in solution. Biochimie. 2019;164:70–82. doi: 10.1016/j.biochi.2019.03.014. PubMed DOI

Falb M., Amata I., Gabel F., Simon B., Carlomagno T.. Structure of the K-turn U4 RNA: a combined NMR and SANS study. Nucleic Acids Res. 2010;38(18):6274–6285. doi: 10.1093/nar/gkq380. PubMed DOI PMC

Goody T. A., Melcher S. E., Norman D. G., Lilley D. M.. The kink-turn motif in RNA is dimorphic, and metal ion-dependent. RNA. 2004;10(2):254–264. doi: 10.1261/rna.5176604. PubMed DOI PMC

Zirbel C. L., Šponer J. E., Šponer J., Stombaugh J., Leontis N. B.. Classification and energetics of the base-phosphate interactions in RNA. Nucleic Acids Res. 2009;37(15):4898–4918. doi: 10.1093/nar/gkp468. PubMed DOI PMC

Liu J., Lilley D. M. J.. The role of specific 2′-hydroxyl groups in the stabilization of the folded conformation of kink-turn RNA. RNA. 2007;13(2):200–210. doi: 10.1261/rna.285707. PubMed DOI PMC

Černý J., Božíková P., Svoboda J., Schneider B.. A unified dinucleotide alphabet describing both RNA and DNA structures. Nucleic Acids Res. 2020;48(11):6367–6381. doi: 10.1093/nar/gkaa383. PubMed DOI PMC

Richardson J. S., Schneider B., Murray L. W., Kapral G. J., Immormino R. M., Headd J. J., Richardson D. C., Ham D., Hershkovits E., Williams L. D.. et al. RNA backbone: consensus all-angle conformers and modular string nomenclature (an RNA Ontology Consortium contribution) RNA. 2008;14(3):465–481. doi: 10.1261/rna.657708. PubMed DOI PMC

Hollingsworth S. A., Dror R. O.. Molecular dynamics simulation for all. Neuron. 2018;99(6):1129–1143. doi: 10.1016/j.neuron.2018.08.011. PubMed DOI PMC

Šponer J., Bussi G., Krepl M., Banáš P., Bottaro S., Cunha R. A., Gil-Ley A., Pinamonti G., Poblete S., Jurečka P., Walter N. G., Otyepka M.. RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview. Chem. Rev. 2018;118(8):4177–4338. doi: 10.1021/acs.chemrev.7b00427. PubMed DOI PMC

Yildirim I., Stern H. A., Tubbs J. D., Kennedy S. D., Turner D. H.. Benchmarking AMBER force fields for RNA: comparisons to NMR spectra for single-stranded r­(GACC) are improved by revised χ torsions. J. Phys. Chem. B. 2011;115(29):9261–9270. doi: 10.1021/jp2016006. PubMed DOI PMC

Tubbs J. D., Condon D. E., Kennedy S. D., Hauser M., Bevilacqua P. C., Turner D. H.. The nuclear magnetic resonance of CCCC RNA reveals a right-handed helix, and revised parameters for AMBER force field torsions improve structural predictions from molecular dynamics. Biochemistry. 2013;52(6):996–1010. doi: 10.1021/bi3010347. PubMed DOI PMC

Bergonzo C., Henriksen N. M., Roe D. R., Cheatham T. E. III. Highly sampled tetranucleotide and tetraloop motifs enable evaluation of common RNA force fields. RNA. 2015;21(9):1578–1590. doi: 10.1261/rna.051102.115. PubMed DOI PMC

Cheatham T. E., Bergonzo C. III. Improved Force Field Parameters Lead to a Better Description of RNA Structure. J. Chem. Theory Comput. 2015;11(9):3969–3972. doi: 10.1021/acs.jctc.5b00444. PubMed DOI

Condon D. E., Kennedy S. D., Mort B. C., Kierzek R., Yildirim I., Turner D. H.. Stacking in RNA: NMR of Four Tetramers Benchmark Molecular Dynamics. J. Chem. Theory Comput. 2015;11(6):2729–2742. doi: 10.1021/ct501025q. PubMed DOI PMC

Bottaro S., Banáš P., Šponer J., Bussi G.. Free Energy Landscape of GAGA and UUCG RNA Tetraloops. J. Phys. Chem. Lett. 2016;7(20):4032–4038. doi: 10.1021/acs.jpclett.6b01905. PubMed DOI

Cesari A., Gil-Ley A., Bussi G.. Combining Simulations and Solution Experiments as a Paradigm for RNA Force Field Refinement. J. Chem. Theory Comput. 2016;12(12):6192–6200. doi: 10.1021/acs.jctc.6b00944. PubMed DOI

Kührová P., Best R. B., Bottaro S., Bussi G., Šponer J., Otyepka M., Banáš P.. Computer Folding of RNA Tetraloops: Identification of Key Force Field Deficiencies. J. Chem. Theory Comput. 2016;12(9):4534–4548. doi: 10.1021/acs.jctc.6b00300. PubMed DOI PMC

Bottaro S., Bussi G., Kennedy S. D., Turner D. H., Lindorff-Larsen K.. Conformational ensembles of RNA oligonucleotides from integrating NMR and molecular simulations. Sci. Adv. 2018;4(5):eaar8521. doi: 10.1126/sciadv.aar8521. PubMed DOI PMC

Tan D., Piana S., Dirks R. M., Shaw D. E.. RNA force field with accuracy comparable to state-of-the-art protein force fields. Proc. Natl. Acad. Sci. U. S. A. 2018;115(7):E1346–e1355. doi: 10.1073/pnas.1713027115. PubMed DOI PMC

Cesari A., Bottaro S., Lindorff-Larsen K., Banáš P., Šponer J., Bussi G.. Fitting Corrections to an RNA Force Field Using Experimental Data. J. Chem. Theory Comput. 2019;15(6):3425–3431. doi: 10.1021/acs.jctc.9b00206. PubMed DOI

Kührová P., Mlýnský V., Zgarbová M., Krepl M., Bussi G., Best R. B., Otyepka M., Šponer J., Banáš P.. Improving the Performance of the Amber RNA Force Field by Tuning the Hydrogen-Bonding Interactions. J. Chem. Theory Comput. 2019;15(5):3288–3305. doi: 10.1021/acs.jctc.8b00955. PubMed DOI PMC

Mlýnský V., Kührová P., Kühr T., Otyepka M., Bussi G., Banáš P., Šponer J.. Fine-Tuning of the AMBER RNA Force Field with a New Term Adjusting Interactions of Terminal Nucleotides. J. Chem. Theory Comput. 2020;16(6):3936–3946. doi: 10.1021/acs.jctc.0c00228. PubMed DOI

Mráziková K., Mlýnský V., Kührová P., Pokorná P., Kruse H., Krepl M., Otyepka M., Banáš P., Šponer J.. UUCG RNA Tetraloop as a Formidable Force-Field Challenge for MD Simulations. J. Chem. Theory Comput. 2020;16(12):7601–7617. doi: 10.1021/acs.jctc.0c00801. PubMed DOI

Zhao J., Kennedy S. D., Berger K. D., Turner D. H.. Nuclear Magnetic Resonance of Single-Stranded RNAs and DNAs of CAAU and UCAAUC as Benchmarks for Molecular Dynamics Simulations. J. Chem. Theory Comput. 2020;16(3):1968–1984. doi: 10.1021/acs.jctc.9b00912. PubMed DOI

Bergonzo C., Grishaev A., Bottaro S.. Conformational heterogeneity of UCAAUC RNA oligonucleotide from molecular dynamics simulations, SAXS, and NMR experiments. RNA. 2022;28(7):937–946. doi: 10.1261/rna.078888.121. PubMed DOI PMC

Fröhlking T., Mlýnský V., Janeček M., Kührová P., Krepl M., Banáš P., Šponer J., Bussi G.. Automatic Learning of Hydrogen-Bond Fixes in the AMBER RNA Force Field. J. Chem. Theory Comput. 2022;18(7):4490–4502. doi: 10.1021/acs.jctc.2c00200. PubMed DOI PMC

Zhao J., Kennedy S. D., Turner D. H.. Nuclear Magnetic Resonance Spectra and AMBER OL3 and ROC-RNA Simulations of UCUCGU Reveal Force Field Strengths and Weaknesses for Single-Stranded RNA. J. Chem. Theory Comput. 2022;18(2):1241–1254. doi: 10.1021/acs.jctc.1c00643. PubMed DOI

Mlýnský V., Kührová P., Stadlbauer P., Krepl M., Otyepka M., Banáš P., Šponer J.. Simple Adjustment of Intranucleotide Base-Phosphate Interaction in the OL3 AMBER Force Field Improves RNA Simulations. J. Chem. Theory Comput. 2023;19(22):8423–8433. doi: 10.1021/acs.jctc.3c00990. PubMed DOI PMC

Krepl M., Pokorná P., Mlýnský V., Stadlbauer P., Šponer J.. Spontaneous binding of single-stranded RNAs to RRM proteins visualized by unbiased atomistic simulations with a rescaled RNA force field. Nucleic Acids Res. 2022;50(21):12480–12496. doi: 10.1093/nar/gkac1106. PubMed DOI PMC

Muscat S., Martino G., Manigrasso J., Marcia M., De Vivo M.. On the Power and Challenges of Atomistic Molecular Dynamics to Investigate RNA Molecules. J. Chem. Theory Comput. 2024;20(16):6992–7008. doi: 10.1021/acs.jctc.4c00773. PubMed DOI

Kührová P., Mlýnský V., Zgarbová M., Krepl M., Bussi G., Best R. B., Otyepka M., Šponer J., Banáš P.. Correction to “Improving the Performance of the Amber RNA Force Field by Tuning the Hydrogen-Bonding Interactions”. J. Chem. Theory Comput. 2020;16(1):818–819. doi: 10.1021/acs.jctc.9b01189. PubMed DOI PMC

Tucker M. R., Piana S., Tan D., LeVine M. V., Shaw D. E.. Development of force field parameters for the simulation of single-and double-stranded DNA molecules and DNA–protein complexes. J. Phys. Chem. B. 2022;126(24):4442–4457. doi: 10.1021/acs.jpcb.1c10971. PubMed DOI PMC

Zgarbová M., Otyepka M., Sponer J., Mladek A., Banas P., Cheatham T. E. III, Jurecka P.. Refinement of the Cornell et al. nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles. J. Chem. Theory Comput. 2011;7(9):2886–2902. doi: 10.1021/ct200162x. PubMed DOI PMC

Pérez A., Marchán I., Svozil D., Sponer J., Cheatham T. E., Laughton C. A., Orozco M.. Refinement of the AMBER force field for nucleic acids: improving the description of α/γ conformers. Biophys. J. 2007;92(11):3817–3829. doi: 10.1529/biophysj.106.097782. PubMed DOI PMC

Cornell W. D., Cieplak P., Bayly C. I., Gould I. R., Merz K. M., Ferguson D. M., Spellmeyer D. C., Fox T., Caldwell J. W., Kollman P. A.. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc. 1996;118(9):2309. doi: 10.1021/ja955032e. DOI

Aytenfisu A. H., Spasic A., Grossfield A., Stern H. A., Mathews D. H.. Revised RNA dihedral parameters for the amber force field improve RNA molecular dynamics. J. Chem. Theory Comput. 2017;13(2):900–915. doi: 10.1021/acs.jctc.6b00870. PubMed DOI PMC

Yang C., Lim M., Kim E., Pak Y.. Predicting RNA structures via a simple van der Waals correction to an all-atom force field. J. Chem. Theory Comput. 2017;13(2):395–399. doi: 10.1021/acs.jctc.6b00808. PubMed DOI

Li Z., Mu J., Chen J., Chen H.-F.. Base-specific RNA force field improving the dynamics conformation of nucleotide. Int. J. Biol. Macromol. 2022;222:680–690. doi: 10.1016/j.ijbiomac.2022.09.183. PubMed DOI

Chen A. A., García A. E.. High-resolution reversible folding of hyperstable RNA tetraloops using molecular dynamics simulations. Proc. Natl. Acad. Sci. U. S. A. 2013;110(42):16820–16825. doi: 10.1073/pnas.1309392110. PubMed DOI PMC

Denning E. J., Priyakumar U. D., Nilsson L., Mackerell A. D. Jr.. Impact of 2′-hydroxyl sampling on the conformational properties of RNA: update of the CHARMM all-atom additive force field for RNA. J. Comput. Chem. 2011;32(9):1929–1943. doi: 10.1002/jcc.21777. PubMed DOI PMC

Lemkul J. A., MacKerell A. D. Jr.. Polarizable force field for RNA based on the classical drude oscillator. J. Comput. Chem. 2018;39(32):2624–2646. doi: 10.1002/jcc.25709. PubMed DOI PMC

MacKerell A. D. Jr., Lemkul J. A.. Polarizable force field for DNA based on the classical Drude oscillator: II. Microsecond molecular dynamics simulations of duplex DNA. J. Chem. Theory Comput. 2017;13(5):2072–2085. doi: 10.1021/acs.jctc.7b00068. PubMed DOI PMC

Lemkul J. A., MacKerell A. D. Jr.. Polarizable force field for DNA based on the classical Drude oscillator: I. Refinement using quantum mechanical base stacking and conformational energetics. J. Chem. Theory Comput. 2017;13(5):2053–2071. doi: 10.1021/acs.jctc.7b00067. PubMed DOI PMC

Savelyev A., MacKerell A. D. Jr.. All-atom polarizable force field for DNA based on the classical drude oscillator model. J. Comput. Chem. 2014;35(16):1219–1239. doi: 10.1002/jcc.23611. PubMed DOI PMC

Zhang C., Lu C., Jing Z., Wu C., Piquemal J.-P., Ponder J. W., Ren P.. AMOEBA polarizable atomic multipole force field for nucleic acids. J. Chem. Theory Comput. 2018;14(4):2084–2108. doi: 10.1021/acs.jctc.7b01169. PubMed DOI PMC

Zhang C., Bell D., Harger M., Ren P.. Polarizable multipole-based force field for aromatic molecules and nucleobases. J. Chem. Theory Comput. 2017;13(2):666–678. doi: 10.1021/acs.jctc.6b00918. PubMed DOI PMC

Frohlking T., Mlýnský V. c., Janeček M., Kührová P., Krepl M., Banáš P., Šponer J. í., Bussi G.. Automatic learning of hydrogen-bond fixes in the AMBER RNA force field. J. Chem. Theory Comput. 2022;18(7):4490–4502. doi: 10.1021/acs.jctc.2c00200. PubMed DOI PMC

Kuhrova P., Mlynsky V., Zgarbová M., Krepl M., Bussi G., Best R. B., Otyepka M., Sponer J., Banas P.. Improving the performance of the amber RNA force field by tuning the hydrogen-bonding interactions. J. Chem. Theory Comput. 2019;15(5):3288–3305. doi: 10.1021/acs.jctc.8b00955. PubMed DOI PMC

Grotz K. K., Schwierz N.. Magnesium force fields for OPC water with accurate solvation, ion-binding, and water-exchange properties: Successful transfer from SPC/E. J. Chem. Phys. 2022;156(11):114501. doi: 10.1063/5.0087292. PubMed DOI

Steinbrecher T., Latzer J., Case D. A.. Revised AMBER Parameters for Bioorganic Phosphates. J. Chem. Theory Comput. 2012;8(11):4405–4412. doi: 10.1021/ct300613v. PubMed DOI PMC

Mlýnský V., Kührová P., Zgarbová M., Jurečka P., Walter N. G., Otyepka M., Šponer J., Banáš P.. Reactive conformation of the active site in the hairpin ribozyme achieved by molecular dynamics simulations with ε/ζ force field reparametrizations. J. Phys. Chem. B. 2015;119(11):4220–4229. doi: 10.1021/jp512069n. PubMed DOI

Raguette L. E., Gunasekera S. S., Diaz Ventura R. I., Aminov E., Linzer J. T., Parwana D., Wu Q., Simmerling C., Nagan M. C.. Adjusting the Energy Profile for CH–O Interactions Leads to Improved Stability of RNA Stem-Loop Structures in MD Simulations. J. Phys. Chem. B. 2024;128(33):7921–7933. doi: 10.1021/acs.jpcb.4c01910. PubMed DOI

Mlýnský V., Kührová P., Pykal M., Krepl M., Stadlbauer P., Otyepka M., Banáš P., Šponer J.. Can We Ever Develop an Ideal RNA Force Field? Lessons Learned from Simulations of the UUCG RNA Tetraloop and Other Systems. J. Chem. Theory Comput. 2025;21(8):4183–4202. doi: 10.1021/acs.jctc.4c01357. PubMed DOI PMC

Klein D. J., Moore P. B., Steitz T. A.. The Roles of Ribosomal Proteins in the Structure Assembly, and Evolution of the Large Ribosomal Subunit. J. Mol. Biol. 2004;340(1):141–177. doi: 10.1016/j.jmb.2004.03.076. PubMed DOI

Izadi S., Anandakrishnan R., Onufriev A. V.. Building water models: a different approach. J. Phys. Chem. Lett. 2014;5(21):3863–3871. doi: 10.1021/jz501780a. PubMed DOI PMC

Piana S., Donchev A. G., Robustelli P., Shaw D. E.. Water dispersion interactions strongly influence simulated structural properties of disordered protein states. J. Phys. Chem. B. 2015;119(16):5113–5123. doi: 10.1021/jp508971m. PubMed DOI

Berendsen H. J. C., Grigera J. R., Straatsma T. P.. The missing term in effective pair potentials. J. Phys. Chem. 1987;91(24):6269–6271. doi: 10.1021/j100308a038. DOI

Lamoureux G., Harder E., Vorobyov I. V., Roux B., MacKerell A. D. Jr. A polarizable model of water for molecular dynamics simulations of biomolecules. Chem. Phys. Lett. 2006;418(1–3):245–249. doi: 10.1016/j.cplett.2005.10.135. DOI

Joung I. S., Cheatham T. E. III. Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J. Phys. Chem. B. 2008;112(30):9020–9041. doi: 10.1021/jp8001614. PubMed DOI PMC

Sengupta A., Li Z., Song L. F., Li P., Merz K. M. Jr.. Parameterization of Monovalent Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models. J. Chem. Inf. Model. 2021;61(2):869–880. doi: 10.1021/acs.jcim.0c01390. PubMed DOI PMC

MacKerell A. D. Jr., Bashford D., Bellott M., Dunbrack R. L. Jr., Evanseck J. D., Field M. J., Fischer S., Gao J., Guo H., Ha S.. et al. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B. 1998;102(18):3586–3616. doi: 10.1021/jp973084f. PubMed DOI

Chen A. A., Pappu R. V.. Parameters of monovalent ions in the AMBER-99 forcefield: Assessment of inaccuracies and proposed improvements. J. Phys. Chem. B. 2007;111(41):11884–11887. doi: 10.1021/jp0765392. PubMed DOI

Brooks B. R., Brooks C. L. III, Mackerell A. D. Jr., Nilsson L., Petrella R. J., Roux B., Won Y., Archontis G., Bartels C., Boresch S.. et al. CHARMM: the biomolecular simulation program. J. Comput. Chem. 2009;30(10):1545–1614. doi: 10.1002/jcc.21287. PubMed DOI PMC

Case, D. A. ; Walker, R. C. ; Cheatham, T. E., III ; Simmerling, C. ; Roitberg, A. ; Merz, K. M. ; Li, P. ; Luo, R. ; Darden, T. ; Sagui, C. . et al. Amber 2020; University of California: San Francisco: San Francisco, 2020. (accessed.

Le Grand S., Götz A. W., Walker R. C.. SPFP: Speed without compromiseA mixed precision model for GPU accelerated molecular dynamics simulations. Comput. Phys. Commun. 2013;184(2):374–380. doi: 10.1016/j.cpc.2012.09.022. DOI

Hopkins C. W., Le Grand S., Walker R. C., Roitberg A. E.. Long-time-step molecular dynamics through hydrogen mass repartitioning. J. Chem. Theory Comput. 2015;11(4):1864–1874. doi: 10.1021/ct5010406. PubMed DOI

Essmann U., Perera L., Berkowitz M. L., Darden T., Lee H., Pedersen L. G.. A smooth particle mesh Ewald method. J. Chem. Phys. 1995;103(19):8577–8593. doi: 10.1063/1.470117. DOI

Parisi G.. Correlation functions and computer simulations. Nucl. Phys. B. 1981;180(3):378–384. doi: 10.1016/0550-3213(81)90056-0. DOI

Abraham M. J., Murtola T., Schulz R., Páll S., Smith J. C., Hess B., Lindahl E.. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1:19–25. doi: 10.1016/j.softx.2015.06.001. DOI

Hess B., Bekker H., Berendsen H. J. C., Fraaije J. G. E. M.. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 1997;18(12):1463–1472. doi: 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H. DOI

Bussi G., Donadio D., Parrinello M.. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007;126(1):014101. doi: 10.1063/1.2408420. PubMed DOI

Phillips J. C., Hardy D. J., Maia J. D., Stone J. E., Ribeiro J. V., Bernardi R. C., Buch R., Fiorin G., Hénin J., Jiang W.. et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J. Chem. Phys. 2020;153(4):044130. doi: 10.1063/5.0014475. PubMed DOI PMC

Knappeová B., Mlýnský V., Pykal M., Šponer J., Banáš P., Otyepka M., Krepl M.. Comprehensive Assessment of Force-Field Performance in Molecular Dynamics Simulations of DNA/RNA Hybrid Duplexes. J. Chem. Theory Comput. 2024;20(15):6917–6929. doi: 10.1021/acs.jctc.4c00601. PubMed DOI PMC

Eastman P., Swails J., Chodera J. D., McGibbon R. T., Zhao Y., Beauchamp K. A., Wang L.-P., Simmonett A. C., Harrigan M. P., Stern C. D.. et al. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. Plos Comput. Biol. 2017;13(7):e1005659. doi: 10.1371/journal.pcbi.1005659. PubMed DOI PMC

Jiang W., Hardy D. J., Phillips J. C., MacKerell A. D. Jr., Schulten K., Roux B.. High-performance scalable molecular dynamics simulations of a polarizable force field based on classical Drude oscillators in NAMD. J. Phys. Chem. Lett. 2011;2(2):87–92. doi: 10.1021/jz101461d. PubMed DOI PMC

Huang, J. ; Lemkul, J. A. ; Eastman, P. K. ; MacKerell, A. D., Jr. . Molecular dynamics simulations using the drude polarizable force field on GPUs with OpenMM: Implementation, validation, and benchmarks. Wiley Online Library: 2018. PubMed PMC

Ferguson D. M., Chow K.-H.. Isothermal-isobaric molecular dynamics simulations with Monte Carlo volume sampling. Comput. Phys. Commun. 1995;91(1–3):283–289. doi: 10.1016/0010-4655(95)00059-O. DOI

Ryckaert J. P., Ciccotti G., Berendsen H. J. C.. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 1977;23(3):327–341. doi: 10.1016/0021-9991(77)90098-5. DOI

Miyamoto S., Kollman P. A.. Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. J. Comput. Chem. 1992;13(8):952–962. doi: 10.1002/jcc.540130805. DOI

Åqvist J., Wennerström P., Nervall M., Bjelic S., Brandsdal B. O.. Molecular dynamics simulations of water and biomolecules with a Monte Carlo constant pressure algorithm. Chem. Phys. Lett. 2004;384(4–6):288–294. doi: 10.1016/j.cplett.2003.12.039. DOI

Tuckerman M., Berne B. J., Martyna G. J.. Reversible multiple time scale molecular dynamics. J. Chem. Phys. 1992;97(3):1990–2001. doi: 10.1063/1.463137. DOI

Adjoua O., Lagardère L., Jolly L. H., Durocher A., Very T., Dupays I., Wang Z., Inizan T. J., Célerse F., Ren P., Ponder J. W., Piquemal J. P.. Tinker-HP: Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields Using GPUs and Multi-GPU Systems. J. Chem. Theory Comput. 2021;17(4):2034–2053. doi: 10.1021/acs.jctc.0c01164. PubMed DOI PMC

Maier J. A., Martinez C., Kasavajhala K., Wickstrom L., Hauser K. E., Simmerling C.. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J. Chem. Theory Comput. 2015;11(8):3696–3713. doi: 10.1021/acs.jctc.5b00255. PubMed DOI PMC

Rázga F., Zacharias M., Réblová K., Koča J., Šponer J.. RNA kink-turns as molecular elbows: hydration, cation binding, and large-scale dynamics. Structure. 2006;14(5):825–835. doi: 10.1016/j.str.2006.02.012. PubMed DOI

Rázga F., Koca J., Sponer J., Leontis N. B.. Hinge-like motions in RNA kink-turns: the role of the second a-minor motif and nominally unpaired bases. Biophys. J. 2005;88(5):3466–3485. doi: 10.1529/biophysj.104.054916. PubMed DOI PMC

Altona C., Sundaralingam M.. Conformational analysis of the sugar ring in nucleosides and nucleotides. New description using the concept of pseudorotation. J. Am. Chem. Soc. 1972;94(23):8205–8212. doi: 10.1021/ja00778a043. PubMed DOI

Wlodawer A., Minor W., Dauter Z., Jaskolski M.. Protein crystallography for non-crystallographers, or how to get the best (but not more) from published macromolecular structures. FEBS J. 2008;275(1):1–21. doi: 10.1111/j.1742-4658.2007.06178.x. PubMed DOI PMC

Krepl M., Réblová K., Koca J., Sponer J.. Bioinformatics and molecular dynamics simulation study of L1 stalk non-canonical rRNA elements: kink-turns, loops, and tetraloops. J. Phys. Chem. B. 2013;117(18):5540–5555. doi: 10.1021/jp401482m. PubMed DOI

Réblová K., Sponer J. E., Špačková N., Beššeová I., Sponer J.. A-minor tertiary interactions in RNA kink-turns. Molecular dynamics and quantum chemical analysis. J. Phys. Chem. B. 2011;115(47):13897–13910. doi: 10.1021/jp2065584. PubMed DOI

Lilley D. M. J.. The structure and folding of kink turns in RNA. Wiley Interdiscip. Rev.: RNA. 2012;3(6):797–805. doi: 10.1002/wrna.1136. PubMed DOI

Zgarbová M., Jurečka P., Banáš P., Havrila M., Šponer J., Otyepka M.. Noncanonical α/γ Backbone Conformations in RNA and the Accuracy of Their Description by the AMBER Force Field. J. Phys. Chem. B. 2017;121(11):2420–2433. doi: 10.1021/acs.jpcb.7b00262. PubMed DOI

Matoušková E., Dršata T., Pfeifferová L., Šponer J., Réblová K., Lankaš F.. RNA kink-turns are highly anisotropic with respect to lateral displacement of the flanking stems. Biophys. J. 2022;121(5):705–714. doi: 10.1016/j.bpj.2022.01.025. PubMed DOI PMC

Winkler L., Galindo-Murillo R., Cheatham T. E. III. Assessment of A- to B- DNA Transitions Utilizing the Drude Polarizable Force Field. J. Chem. Theory Comput. 2023;19(23):8955–8966. doi: 10.1021/acs.jctc.3c01002. PubMed DOI PMC

Winkler L., Cheatham T. E. III. Benchmarking the Drude Polarizable Force Field Using the r­(GACC) Tetranucleotide. J. Chem. Inf. Model. 2023;63(8):2505–2511. doi: 10.1021/acs.jcim.3c00250. PubMed DOI PMC