截至論文撰寫之時，世界上超過 50% 的人口已在一年內第三次接種疫苗，以抵禦由冠狀病毒（SARS-CoV2）所引起的百年以來最大的流行病。與每季均能逃避疫苗識別而不斷突變的棘蛋白相比，-1 程序性核醣體移碼 (-1 PRF) 與觸發此PRF機制的 mRNA 假結 (PK)在演化上相對保守許多，包含冠狀病毒在內的病毒屬。因此，科學家們非常感興趣的是可以開發調節PRF的藥物來干擾病毒蛋白的轉譯，進而干擾病毒的複製。儘管科學家已了解PK 的平衡結構及其在核醣體上的位置，但人們仍不清楚 PK 如何的誘導 PRF 產生以及藥物應被設計結合在 PK 中的什麼位置才能增強或減少病毒的移碼效率（FE%）。在本論文中，我們展示了通過整合小分子對接、分子動力學（MD）模擬和少量藥物的體外篩選，來實現抗 PK 藥物的設計。我們開發了一種新方法：接觸分佈匹配，以有效及省時地找到讓FE%上升或下降的調節劑。這些調節劑是從 2000 多種 FDA 批准的藥物庫中挑選出來的，所以可以讓醫師有機會來off-label使用。接觸分佈由分析一個藥物前十名與SARS-CoV2 PK結合的低能量構型而來，此PK的結構是由冷凍電鏡解析出後，以分子動力學模擬(MD simulations)來鬆弛 (relax) 平衡。通過小分子對接的概率分佈匹配及根據雙螢光素酶測定 (DLA) 所確定現有 FE% 調節劑的證據，我們有效地擴大了可以篩選的藥物數量。為了支持此平台，我們也開發了化學相似性搜索引擎 SmChem，找出少數可有效調控FE%的 FDA 藥物（老藥新用），並以生化和病毒抑制試驗中來驗證。我們使用此新平台僅對十種藥物進行了篩選，就發現了兩種新的 FE% 變化調節劑，並且該藥在化學結構上不同於已知的調節劑。通過使用光鑷 (OT) 在有或無FE% 調節劑的情況下進一步測試 PK 的機械穩定性和解旋力(unfolding forces)中間體的數量，我們對 PK 的構象可塑性和 PRF 效率之間的相關性提供了新的見解，主要在於FE%增加劑比FE%抑制劑更能減少解旋力中間體的生成但解旋力卻變化不大。這些結果對我們如何能合理地設計藥物以靶向有翻譯調節功能的 RNA結構提供了一個明確的證據，並提供了一種進化上保守且及時的途徑來使用FDA 已核准的藥物以應對病毒感染。

As of when the thesis is written, more than 50% of the world population has been
vaccinated for the third time in a year to arm themselves against the biggest pandemic in a
century caused by a coronavirus, named SARS-CoV2. Contrasting to constantly mutating
capsid proteins that escape the recognition of vaccines in a seasonal basis, the mechanism of -
1 programmed ribosomal frameshifting (-1PRF) and the mRNA pseudoknot (PK) that triggers
the PRF have been found evolutionarily conserved across a wide range of virus genus including
coronaviruses. As a result, it has been of scientists’ high interest to develop PRF-modulating
drugs to interfere with the translation of viral proteins and therefore viral replication. Despite
the good progress made in structural biology to visualize the PK and its position on the
ribosome, however, it remains unclear how a PK induces PRF as well as where in the PK
should be chemically perturbed by a drug in order to boost or reduce the frameshifting
efficiency (FE%) of the virus. In this thesis, we show that the anti-PK drug design can be made
possible by integrating small-molecule docking, molecular dynamics (MD) simulations and in
vitro screening of a small number of drugs. A new method, contact-distribution-matching, was
developed to effectively and efficiently discover FE%-increasing and FE%-decreasing
modulators culled from a library of 2000+ FDA-approved drugs. The contact distribution is
derived from top poses from drugs docked onto a simulation-relaxed SARS-CoV2 PK structure
determined by cryo-EM. The match of probability distributions via small molecule docking
efficiently expanded the number of drugs that can be screened in accord with existing FE%-
modulation evidence collected by dual-luciferase assays (DLA). To support the platform, a
chemical similarity search engine, SmChem, is also developed as an accurate and efficient tool
to repurpose FDA drugs into FE% modulators that were later verified biochemically and in
virus suppression assays. Two new FE%-changing modulators, chemically different from
known modulators, were found by this new platform with a screening effort of only 15 drugs.
3
With further testing of the mechanical stability of PK and population of unfolding-force
intermediates in the presence and absence of identified FE% modulators using optical tweezers
(OT), we provided new insights into the correlation between conformational plasticity of a PK
and PRF efficiency. These results provide clear evidence on how we can rationally design drugs
to target translation-modulating RNA structures and provide an evolutionarily conserved and
timely pathway to tackle viral infection using repurposed FDA drugs.

Abstract..............................................................................................................2
中文摘.................................................................................................................4
Dedication ..........................................................................................................5
Acknowledgments...............................................................................................6
Table of Contents ................................................................................................7
List of Figures .....................................................................................................8
List of Tables .......................................................................................................8
1. Introduction .....................................................................................................10
2. Methods ..........................................................................................................15
2.1 Chemical similarity search engine – SmChem and its algorithm ...................15
Molecular fingerprints ..........................................................................................15
Extended Connectivity Fingerprints (ECFP) .........................................................16
Tanimoto similarity ................................................................................................20
Hierarchical search algorithm ...............................................................................20
2.2 Computational screening of 2000+ FDA-approved drugs with SARS-CoV-2 Pseudoknot 25
2.2.1 SARS-CoV-2-PK Molecular Dynamics (MD) simulations in the absence and presence of a drug and
MM/GBSA free energy calculations ................................................................................................................................25
2.2.2 Molecular Docking using Autodock Vina....................................................... 27
2.2.3 Contact-distribution-matching method to select “good” drugs to modulate frameshifting efficiency of the SARS-CoV2 PK .............................................................................28
2.3 Dual-luciferase assay and measurement of -1 ribosomal frameshifting efficiency 29
2.3.1 Plasmids construction and mRNA preparation ...............................................29
2.3.2 Dual-Luciferase Reporter Assay and Frameshifting Efficiency Measurement. 30
2.4 PK unfolding force measurements using Optical Tweezers and Data Analysis ...31
2.5 Viral Plaque Formation Assay...............................................................................32
3. Results....................................................................................................................32
3.1 SmChem identifies repurposed FDA drugs as modulators of SARS-CoV2 -1PRF..32
3.2 Anti-frameshifting drugs reduce the conformation plasticity of SARS-CoV2 PK..42
3.3 Suppression of SARS-CoV2 replication is correlated with -1PRF modulation.......46
3.4 Matching the contact patterns of drug-PK effectively and efficiently identifies new
modulatory drugs of -1 PRF..........................................................................................48
4. Discussion ................................................................................................................59
5. References.................................................................................................................62

Bajusz D., R cz A., H berger K. (2015) Why is Tanimoto index an appropriate choice for
fingerprint-based similarity calculations? J Cheminform; 7:20
Ban š, P., Hollas, D., Zgarbov , M., Jurecka, P., Orozco, M., Cheatham III, T.E., Šponer, J.,
Otyepka, M. Performance of molecular mechanics force fields for RNA simulations:
Stability of UUCG and GNRAhairpins.J. Chem. Theory. Comput.,2010,6, 3836–3849.
Bender A, Glen RC: Molecular similarity: a key technique in molecular informatics. Org
Biomol Chem.2004; 2(22): 3204–3218.
Bhatt, P.R., Scaiola, A., Loughran, G., Leibundgut, M., Kratzel, A. et al. Structural basis of
ribosomal frameshifting during translation of the SARS-CoV-2 RNA genome. Science
372.6548 (2021): 1306-1313.
Brierley-f, I., Jenner, A. J., &Inglis, S. C. (1992). Mutational Analysis of the “Slipperysequence”
Component of a Coronavirus Ribosomal Frameshifting Signal, 463–479.
63
Bryant, Andrew MSc1,*; Lawrie, Theresa A. MBBCh, PhD2; Dowswell, Therese PhD2;
Fordham, Edmund J. PhD2; Mitchell, Scott MBChB, MRCS3; Hill, Sarah R. PhD1;
Tham, Tony C. MD, FRCP4 Ivermectin for Prevention and Treatment of COVID-19
Infection: A Systematic Review, Meta-analysis, and Trial Sequential Analysis to Inform
Clinical Guidelines, American Journal of Therapeutics: July/August 2021 - Volume 28 -
Issue 4 - p e434-e460 doi: 10.1097/MJT.0000000000001402
Case, D.A., Cheatham III, T.E., Darden, T., Gohlke, H., Luo, R., Merz Jr, K.M., Onufriev, A.,
Simmerling, C., Wang, B. and Woods, R.J., (2005). The Amber biomolecular simulation
programs. Journal of computational chemistry, 26(16), pp.1668-1688.
Chang, K.-C., Salawu, E.O., Chang, Y.-Y., Wen, J.-D., Yang, L.-W., 2019. Resolutionexchanged
structural modeling and simulations jointly unravel that subunit rolling
underlies the mechanism of programmed ribosomal frameshifting. Bioinformatics 35, 945–
952. doi:10.1093/bioinformatics/bty762
Chen G, Chang KY, Chou MY, Bustamante C, Tinoco I, Jr (2009) Triplex structures in an
RNA pseudoknot enhance mechanical stability and increase efficiency of −1 ribosomal
frameshifting. Proc Natl Acad Sci USA 106:12706–12711
Chen, G.; Chang, K. Y.; Chou, M. Y.; Bustamante, C.; Tinoco, I., Jr. Proc. Natl. Acad. Sci.
U.S.A. 2009, 106, 12706. (b) Hansen, T. M.; Reihani, S. N.; Oddershede, L. B.; S rensen,
M. A. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 5830.
Christie, B. D., Leland, B. A., Nourse, J. G. Structure Searching in Chemical Databases by
Direct Lookup Methods. J. Chem. Inf. Comput. Sci. 1993, 33, 545–547.
D.A. Case, H.M. Aktulga, K. Belfon, I.Y. Ben-Shalom, S.R. Brozell, D.S. Cerutti, T.E.
Cheatham, III, G.A. Cisneros, V.W.D. Cruzeiro, T.A. Darden, R.E. Duke, G. Giambasu,
M.K. Gilson, H. Gohlke, A.W. Goetz, R. Harris, S. Izadi, S.A. Izmailov, C. Jin, K.
64
Kasavajhala, M.C. Kaymak, E. King, A. Kovalenko, T. Kurtzman, T.S. Lee, S. LeGrand, P.
Li, C. Lin, J. Liu, T. Luchko, R. Luo, M. Machado, V. Man, M. Manathunga, K.M. Merz,
Y. Miao, O. Mikhailovskii, G. Monard, H. Nguyen, K.A. O’Hearn, A. Onufriev, F. Pan, S.
Pantano, R. Qi, A. Rahnamoun, D.R. Roe, A. Roitberg, C. Sagui, S. Schott-Verdugo, J. Shen,
C.L. Simmerling, N.R. Skrynnikov, J. Smith, J. Swails, R.C. Walker, J. Wang, H. Wei, R.M.
Wolf, X. Wu, Y. Xue, D.M. York, S. Zhao, and P.A. Kollman (2021), Amber 2021,
University of California, San Francisco.
Dinman, J.D.; Wickner, R.B. Ribosomal frameshifting efficiency and gag/gag-pol ratio are
critical for yeast M1 double-stranded RNA virus propagation. J. Virol. 1992, 66, 3669–
3676.
Dolinsky, T.J., Czodrowski, P., Li, H., Nielsen, J. E., Jensen, J. H., Klebe, G., & Baker, N. A.
(2007). PDB2PQR: expanding and upgrading automated preparation of biomolecular
structures for molecular simulations. Nucleic acids research, 35(suppl_2), W522-W525.
Dongwan Kim, Joo-Yeon Lee, Jeong-Sun Yang, Jun Won Kim, V. Narry Kim, Hyeshik
Chang, The Architecture of SARS-CoV-2 Transcriptome, Cell, Volume 181, Issue 4,
2020, Pages 914-921.e10, ISSN 0092-8674, https://doi.org/10.1016/j.cell.2020.04.011.
Durant JL, Leland BA, Henry DR, Nourse JG: Reoptimization of MDL keys for use in drug
discovery. J Chem Inf Comput Sci 2002, 42:1273-1280.
Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding
affinities. Expert Opin Drug Discov. 2015;10:449-61
Green L, Kim CH, Bustamante C, Tinoco I, Jr (2008) Characterization of the mechanical
unfolding of RNA pseudoknots. J Mol Biol 375:511–528.
Grentzmann, G., Ingram, J.A., Kelly, P.J., Gesteland, R.F., Atkins, J.F., 1998. A dual-luciferase
reporter system for studying recoding signals. RNA 4 (4), 479–486.
65
Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graph. 1996;
14:33-8, 27-8.
Irwin, John J, and Brian K Shoichet. “ZINC--a free database of commercially available
compounds for virtual screening.” Journal of chemical information and modeling vol. 45,1
(2005): 177-82. doi:10.1021/ci049714+
J. Han, M. Kamber, J. Pei. "2 - Getting to Know Your Data". Data Mining (Third Edition),
Editor(s): Jiawei Han, Micheline Kamber, Jian Pei, Morgan Kaufmann, 2012, Pages 39-82.
ISBN 9780123814791. https://doi.org/10.1016/B978-0-12-381479-1.00002-2.
(https://www.sciencedirect.com/science/article/pii/B9780123814791000022)
Jaccard, P.: The distribution of the flora in the alpine zone. New Phytologist 1912, 11(2), 37–
50. doi:10.1111/j.1469-8137. 1912.tb05611.x
Jacks, T., Madhani, H.D., Masiraz, F.R., Varmus, H.E., 1988. Signals for ribosomal
frameshifting in the Rous Sarcoma Virus gag-pol region. Cell 55, 447–458.
Jakalian, A., Jack, D.B., Bayly, C.I., 2002. Fast, efficient generation of high-quality atomic
charges. AM1-BCC model: II. Parameterization and validation. Journal of Computational
Chemistry 23, 1623–1641. doi:10.1002/jcc.10128
Johnson M, Maggiora GM: Concepts and applications of molecular similarity. Wiley, 1990.
Joung, I.S., & Cheatham III, T. E. (2009). Molecular dynamics simulations of the dynamic and
energetic properties of alkali and halide ions using water-model-specific ion parameters.
The Journal of Physical Chemistry B, 113(40), 13279-13290.
K.L. Tsai, S.Y. Chang, L.W. Yang (2021). DRDOCK: A Drug Repurposing platform
integrating automated docking, simulations and a log-odds-based drug ranking scheme.
bioRxiv 2021.01.31.429052; doi: https://doi.org/10.1101/2021.01.31.429052
66
Khailany, R. A., Safdar, M., & Ozaslan, M. (2020). Genomic characterization of a novel
SARS-CoV-2. Gene reports, 19, 100682. https://doi.org/10.1016/j.genrep.2020.100682
Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B,
Zaslavsky L, Zhang J, Bolton EE. PubChem in 2021: new data content and improved web
interfaces. Nucleic Acids Res. 2019 Jan 8;47(D1):D1388-D1395. doi:
10.1093/nar/gkaa971. PMID: 33151290.
L. Caly, J. D. Druce, M. G. Catton, D. A. Jans, K. M. Wagstaff, The FDA-approved drug
ivermectin inhibits the replication of SARS-CoV-2 in vitro. Antiviral Res. 178, 104787
(2020).
L. Caly, J. D. Durce, M. G. Gatton, D. A. Jans, K. M. Wagstaff, The FDA-approved drugs
ivermectin inhibits the replication of SARS-CoV2 in viitro. Antivirals Res. 178, 104787
(2020).
Massova, I., Kollman, P.A., 2000. Combined molecular mechanical and continuum solvent
approach (MM-PBSA/GBSA) to predict ligand binding. Perspectives in Drug Discovery
and Design 18, 113–135. doi:10.1023/A:1008763014207
Miller, B.R., Mcgee, T.D., Swails, J.M., Homeyer, N., Gohlke, H., Roitberg, A.E., 2012.
MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. Journal of
Chemical Theory and Computation 8, 3314–3321. doi:10.1021/ct300418h
Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: Automated
docking with selective receptor flexibility. J Comput Chem. 2009; 30:2785-91.
Munshi, S.; Neupane, K.; Ileperuma, S.M.; Halma, M.T.J.; Kelly, J.A.; Halpern, C.F.; Dinman,
J.D.; Loerch, S.; Woodside, M.T. Identifying Inhibitors of −1 Programmed Ribosomal
Frameshifting in a Broad Spectrum of Coronaviruses. Viruses 2022, 14, 177.
https://doi.org/10.3390/v14020177
67
Neupane, K.; Munshi, S.; Zhao, M.; Ritchie, D.B.; Ileperuma, S.M.; Woodside, M.T. Antiframeshifting
ligand active against SARS coronavirus-2 is resistant to natural mutations of
the frameshift-stimulatory pseudoknot. J. Mol. Biol. 2020, 432, 5843–5847
Park, S.-J.; Kim, Y.-G.; Park, H.-J. Identification of RNA pseudoknot-binding ligand that
inhibits the −1 ribosomal frameshifting of SARS-coronavirus by structure-based virtual
screening. J. Am. Chem. Soc. 2011, 133, 10094–10100.
Plant, E. P., Rakauskaite, R., Taylor, D. R., Dinman, J. D. Achieving a golden mean:
Mechanisms by which corona viruses ensure synthesis of the correct stoichiometric ratios
of viral proteins. J. Virol. 2010, 84, 4330–4340.
Plant, E.P., Dinman, J.D., 2005. Torsional restraint: a new twist on frameshifting pseudoknots.
Nucleic Acids Res. 33, 1825–1833. https://doi.org/10.1093/nar/gki329.
Qu X, et al. (2011) The ribosome uses two active mechanisms to unwind messenger RNA
during translation. Nature 475:118–121.
Ritchie, D.B.; Foster, D.A.; Woodside, M.T. Programmed −1 frameshifting efficiency
correlates with RNA pseudoknot conformational plasticity, not resistance to mechanical
unfolding. Proc. Natl. Acad. Sci. USA 2012, 109, 16167–16172
Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. 2010, 50,
742−754.
Somogyi, P., Jenner, A.J., Brierley, I.A., Inglis, S.C., 1993. Ribosomal pausing during
translation of an RNA pseudoknot. Mol. Cell Biol. 13, 6931–6940.
Sun, Y.; Abriola, L.; Niederer, R.O.; Pedersen, S.F.; Alfajaro, M.M.; Monteiro, V.S.; Wilen,
C.B.; Ho, Y.-C.; Gilbert, W.V.; Surovtseva, Y.V.; et al. Restriction of SARS-CoV-2
replication by targeting programmed −1 ribosomal frameshifting. Proc. Natl. Acad. Sci.
USA 2021, 118, e2023051118.
68
Tanimoto, T.: An elementary mathematical theory of classification and prediction. Technical
report, International Business Machines Corporation, New York (1958).
Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors, Wiley-VCH: Weinheim,
Germany, 2000.
Trott, O., Olson, A.J., 2009. AutoDock Vina: Improving the speed and accuracy of docking
with a new scoring function, efficient optimization, and multithreading. Journal of
Computational Chemistry. doi:10.1002/jcc.21334
Wang, J., Wang, W., Kollman, P.A., Case, D.A., 2006. Automatic atom type and bond type
perception in molecular mechanical calculations. Journal of Molecular Graphics and
Modelling 25, 247–260. doi:10.1016/j.jmgm.2005.12.005
Wang, J., Wolf, R.M., Caldwell, J.W., Kollman, P.A., Case, D.A., 2004. Development and
testing of a general amber force field. Journal of Computational Chemistry 25, 1157–1174.
Wen JD, et al. (2008) Following translation by single ribosomes one codon at a time. Nature
452:598–603.
Wen,J.D., Lancaster,L., Hodges,C., Zeri,A.C., Yoshimura,S.H., Noller,H.F., Bustamante,C.
and Tinoco,I. Jr (2008) Following translation by single ribosomes one codon at a time.
Nature, 452, 598–603
Wen,J.D., Manosas,M., Li,P.T., Smith,S.B., Bustamante,C., Ritort,F. and Tinoco,I. Jr (2007)
Force unfolding kinetics of RNA using optical tweezers. I. Effects of experimental variables
on measured results. Biophys. J., 92, 2996–3009.
White KH, Orzechowski M, Fourmy D, Visscher K (2011) Mechanical unfolding of the beet
western yellow virus −1 frameshift signal. J Am Chem Soc 133:9775–9782.
69
Wu, Y. J., Wu, C. H., Yeh, A. Y., & Wen, J. D. (2014). Folding a stable RNA pseudoknot
through rearrangement of two hairpin structures. Nucleic acids research, 42(7), 4505–4515.
https://doi.org/10.1093/nar/gkt1396
Xue L, Bajorath J. Molecular descriptors in chemoinformatics, computational combinatorial
chemistry, and virtual screening. Comb Chem High Throughput Screen. 2000
Oct;3(5):363-72. doi: 10.2174/1386207003331454. PMID: 11032954.