RNA Nanostructures Design Characterization and Applications Methods in Molecular Biology 2709 Kirill A. Afonin (Editor) RNA Nanostructures Design Characterization and Applications Methods in Molecular Biology 2709 Kirill A. Afonin (Editor) RNA Nanostructures Design Characterization and Applications Methods in Molecular Biology 2709 Kirill A. Afonin (Editor)

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3. Volume 2709
Methods in Molecular Biology
Series Editor
John M. Walker
School of Life and Medical Sciences, University of Hertfordshire, Hatfield,
Hertfordshire, UK
For further volumes: http://​
www.​
springer.​
com/​
series/​
7651
For over 35 years, biological scientists have come to rely on the
research protocols and methodologies in the critically acclaimed
Methods in Molecular Biology series. The series was the first to
introduce the step-by-step protocols approach that has become the
standard in all biomedical protocol publishing. Each protocol is
provided in readily-reproducible step-by-step fashion, opening with an
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trade as well as troubleshooting advice. These hallmark features were
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protocols from the series are indexed in PubMed.

4. Editor
Kirill A. Afonin
RNA Nanostructures
Design, Characterization, and Applications

5. Editor
Kirill A. Afonin
Department of Chemistry, UNC Charlotte, Charlotte, NC, USA
ISSN 1064-3745 e-ISSN 1940-6029
Methods in Molecular Biology
ISBN 978-1-0716-3416-5 e-ISBN 978-1-0716-3417-2
https://doi.org/10.1007/978-1-0716-3417-2
© The Editor(s) (if applicable) and The Author(s), under exclusive
license to Springer Science+Business Media, LLC, part of Springer
Nature 2023
This work is subject to copyright. All rights are solely and exclusively
licensed by the Publisher, whether the whole or part of the material is
concerned, specifically the rights of translation, reprinting, reuse of
illustrations, recitation, broadcasting, reproduction on microfilms or in
any other physical way, and transmission or information storage and
retrieval, electronic adaptation, computer software, or by similar or
dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks,
service marks, etc. in this publication does not imply, even in the
absence of a specific statement, that such names are exempt from the
relevant protective laws and regulations and therefore free for general
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The publisher, the authors, and the editors are safe to assume that the
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6. been made. The publisher remains neutral with regard to jurisdictional
claims in published maps and institutional affiliations.
This Humana imprint is published by the registered company Springer
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The registered company address is: 1 New York Plaza, New York, NY
10004, U.S.A.

7. Preface
The study of RNA improves our understanding of cellular processes and
the origin of various diseases. Rationally designed functional RNA
nanostructures benefit from the inherent biological properties of RNA
and its capacity to assemble from a diverse set of structural and
interacting motifs. RNA nanostructures are attractive for a broad range
of biomedical applications and clinical use because of their controllable
architectures and proficiency in responding readily to biological
environment changes.
This book is an extensive resource of detailed protocols that
renowned experts in computer-assisted design and characterization of
RNA nanostructures, assessment of immunology of nanomaterials,
biosensing, RNA nanotechnology, and drug delivery have organized.
This collection of in-depth chapters addresses this field’s dimensions,
covering RNA nanostructures’ design and characterization, which
outlines their production, storage, and immunorecognition assessment
protocols. This book also highlights a diverse set of biomedical
applications and delivery approaches for therapeutic RNA
nanoparticles.
This collection will interest a broad audience due to its
interdisciplinary nature aiming to address essential topics and
concerns in the growing field of RNA nanotechnology.
Kirill A. Afonin
Charlotte, NC, USA

8. Contents
Part I Computational Design and In Silico Studies of RNA
Nanostructures
1 Molecular Dynamics Simulations of RNA Motifs to Guide the
Architectural Parameters and Design Principles of RNA
Nanostructures
Valentina Abondano Perdomo and Taejin Kim
2 Computer-Assisted Design and Characterization​of RNA
Nanostructures
Christina J. Bayard and Yaroslava G. Yingling
3 Combining Experimental Restraints and RNA 3D Structure
Prediction in RNA Nanotechnology
Jian Wang, Congzhou M. Sha and Nikolay V. Dokholyan
4 Structural Characterization​of Nucleic Acid Nanoparticles Using
SAXS and SAXS-Driven MD
James Byrnes, Kriti Chopra, Lewis A. Rolband, Leyla Danai,
Shirish Chodankar, Lin Yang and Kirill A. Afonin
Part II Production and Storage of Functional RNA Nanostructures
5 Metalated Nucleic Acid Nanostructures
Douglas Zhang and Thomas Hermann
6 Bioconjugation of Functionalized Oligodeoxynucleo​
tides with
Fluorescence Reporters for Nanoparticle Assembly
Erwin Doe, Hannah L. Hayth and Emil F. Khisamutdinov
7 Light-Assisted Drying for the Thermal Stabilization of Nucleic
Acid Nanoparticles and Other Biologics
Susan R. Trammell
8 Preparation of Nucleic Acid Aptamer Functionalized Silver/​
Gold
Nanoparticle Conjugates Using Thiol-Substituted Oligonucleotides​
Joshua D. Quarles, Allen T. Livingston, Ashley E. Wood and
Timea Gerczei Fernandez
Part III Characterization of RNA Nanostructures

9. 9 Thermodynamic Characterization​of Nucleic Acid Nanoparticles
Hybridization by UV Melting
Megan Teter, Ross Brumett, Abigail Coffman and
Emil F. Khisamutdinov
10 Structural Characterization​of DNA-Templated Silver
Nanoclusters by Energy Dispersive Spectroscopy
Damian Beasock and Kirill A. Afonin
11 Small Volume Microrheology to Evaluate Viscoelastic
Properties of Nucleic Acid-Based Supra-Assemblies
Akhilesh Kumar Gupta, Joel Petersen, Elizabeth Skelly,
Kirill A. Afonin and Alexey V. Krasnoslobodtsev
12 Characterization​of RNA Nanoparticles and Their Dynamic
Properties Using Atomic Force Microscopy
Alexander J. Lushnikov, Yelixza I. Avila, Kirill A. Afonin and
Alexey V. Krasnoslobodtsev
Part IV Intracellular Delivery and Immunorecognition of RNA
Nanostructures
13 Synthesis of Mesoporous Silica Nanoparticles for the Delivery
of Nucleic Acid Nanostructures
Tamanna Binte Huq and Juan L. Vivero-Escoto
14 Assessment of Intracellular Compartmentaliza​
tion of RNA
Nanostructures
Yasmine Radwan, Kirill A. Afonin and M. Brittany Johnson
15 Discriminating Immunorecognitio​
n Pathways Activated by RNA
Nanostructures
Leyla Danai, M. Brittany Johnson and Kirill A. Afonin
16 Detection of Nanoparticles’ Ability to Stimulate Toll-Like
Receptors Using HEK-Blue Reporter Cell Lines
Edward Cedrone and Marina A. Dobrovolskaia
17 Characterization​of PAMAM Dendrimers for the Delivery of
Nucleic Acid Nanoparticles
Yelixza I. Avila, Laura Rebolledo, Melanie Andrade-Muñoz and
Kirill A. Afonin

10. Part V RNA and DNA Nanostructures Designed for Biomedical
Applications
18 Reverse Transfection of Functional RNA Rings into Cancer Cells
Followed by in Vitro Irradiation
Renata de Freitas Saito, Isabella Nevoni Ferreira,
Maria Cristina Rangel and Roger Chammas
19 Aptamer Conjugated RNA/​
DNA Hybrid Nanostructures
Designed for Efficient Regulation of Blood Coagulation
Lewis A. Rolband, Weina Ke and Kirill A. Afonin
20 Detection of Multiplex NASBA RNA Products Using Colorimetric
Split G Quadruplex Probes
Maria S. Rubel, Liubov A. Shkodenko, Daria A. Gorbenko,
Valeria V. Solyanikova, Yulia I. Maltzeva, Aleksandr A. Rubel,
Elena I. Koshel and Dmitry M. Kolpashchikov
21 Synthesis of DNA-Templated Silver Nanoclusters and the
Characterization​of Their Optical Properties and Biological
Activity
Elizabeth Skelly, Lewis A. Rolband, Damian Beasock and
Kirill A. Afonin
22 Dynamic Nanostructures for Conditional Activation and
Deactivation of Biological Pathways
Yasmine Radwan, Laura P. Rebolledo, Martin Panigaj and
Kirill A. Afonin
23 Anticoagulant Activity of Nucleic Acid Nanoparticles (NANPs)
Assessed by Thrombin Generation Dynamics on a Fully Automated
System
Renata de Freitas Saito, Bárbara Gomes Barion,
Tania Rubia Flores da Rocha, Alex Rolband, Kirill A. Afonin and
Roger Chammas
Index

11. Contributors
Kirill A. Afonin
Nanoscale Science Program, Department of Chemistry, University of
North Carolina at Charlotte, Charlotte, NC, USA
Melanie Andrade-Muñoz
Nanoscale Science Program, Department of Chemistry, University of
North Carolina at Charlotte, Charlotte, NC, USA
Yelixza I. Avila
Nanoscale Science Program, Department of Chemistry, University of
North Carolina at Charlotte, Charlotte, NC, USA
Bárbara Gomes Barion
Laboratório de Hemostasia do Hospital das Clínicas da Faculdade de
Medicina da Universidade de São Paulo, São Paulo, SP, Brazil
Christina J. Bayard
Department of Materials Science and Engineering, North Carolina State
University, Raleigh, NC, USA
Damian Beasock
University of North Carolina at Charlotte, Charlotte, NC, USA
Ross Brumett
Chemistry Department, Ball State University, Muncie, IN, USA
James Byrnes
Brookhaven National Laboratory, Upton, NY, USA
Edward Cedrone
Nanotechnology Characterization Laboratory, Cancer Research
Technology Program, Frederick National Laboratory for Cancer
Research, Frederick, MD, USA
Roger Chammas

12. Comprehensive Center for Precision Oncology, Centro de Investigação
Translacional em Oncologia (LIM24), Departamento de Radiologia e
Oncologia, Faculdade de Medicina da Universidade de São Paulo and
Instituto do Câncer do Estado de São Paulo, São Paulo, SP, Brazil
Shirish Chodankar
Brookhaven National Laboratory, Upton, NY, USA
Kriti Chopra
Brookhaven National Laboratory, Upton, NY, USA
Abigail Coffman
Chemistry Department, Ball State University, Muncie, IN, USA
Leyla Danai
Nanoscale Science Program, Department of Chemistry, University of
North Carolina at Charlotte, Charlotte, NC, USA
Marina A. Dobrovolskaia
Nanotechnology Characterization Laboratory, Cancer Research
Technology Program, Frederick National Laboratory for Cancer
Research, Frederick, MD, USA
Erwin Doe
Department of Chemistry, Ball State University, Muncie, IN, USA
Nikolay V. Dokholyan
Department of Pharmacology, Penn State College of Medicine, Hershey,
PA, USA
Department of Engineering Science and Mechanics, Penn State
University, State College, PA, USA
Department of Biochemistry and Molecular Biology, Penn State College
of Medicine, Hershey, PA, USA
Department of Chemistry, Penn State University, State College, PA, USA
Department of Biomedical Engineering, Penn State University, State
College, PA, USA
Timea Gerczei Fernandez

13. Department of Chemistry, Physics, Geology and the Environment, Sims
Building, Winthrop University, Rock Hill, SC, USA
Isabella Nevoni Ferreira
Comprehensive Center for Precision Oncology, Centro de Investigação
Translacional em Oncologia (LIM24), Departamento de Radiologia e
Oncologia, Faculdade de Medicina da Universidade de São Paulo and
Instituto do Câncer do Estado de São Paulo, São Paulo, SP, Brazil
Renata de Freitas Saito
Comprehensive Center for Precision Oncology, Centro de Investigação
Translacional em Oncologia (LIM24), Departamento de Radiologia e
Oncologia, Faculdade de Medicina da Universidade de São Paulo and
Instituto do Câncer do Estado de São Paulo, São Paulo, SP, Brazil
Daria A. Gorbenko
Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint
Petersburg, Russia
Akhilesh Kumar Gupta
Department of Physics, University of Nebraska Omaha, Omaha, NE, USA
Hannah L. Hayth
Department of Chemistry, Ball State University, Muncie, IN, USA
Thomas Hermann
Department of Chemistry and Biochemistry, University of California,
San Diego, CA, USA
Center for Drug Discovery Innovation, University of California, San
Diego, CA, USA
Program in Materials Science and Engineering, University of California,
San Diego, CA, USA
Tamanna Binte Huq
Department of Chemistry, Nanoscale Science Program, University of
North Carolina, Charlotte, NC, USA
M. Brittany Johnson

14. Department of Biological Sciences, University of North Carolina at
Charlotte, Charlotte, NC, USA
Weina Ke
University of North Carolina at Charlotte, Charlotte, NC, USA
Emil F. Khisamutdinov
Department of Chemistry, Ball State University, Muncie, IN, USA
Taejin Kim
Physical Sciences Department, West Virginia University Institute of
Technology, Beckley, WV, USA
Dmitry M. Kolpashchikov
Department of Chemistry, University of Central Florida, Orlando, FL,
USA
Burnett School of Biomedical Sciences, University of Central Florida,
Orlando, FL, USA
Center for Forensic Science, University of Central Florida, Orlando, FL,
USA
Elena I. Koshel
Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint
Petersburg, Russia
Alexey V. Krasnoslobodtsev
Department of Physics, University of Nebraska Omaha, Omaha, NE, USA
Allen T. Livingston
Department of Chemistry, Physics, Geology and the Environment, Sims
Building, Winthrop University, Rock Hill, SC, USA
Alexander J. Lushnikov
Nanoimaging Core Facility at the University of Nebraska Medical Center,
Omaha, NE, USA
Yulia I. Maltzeva

15. Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint
Petersburg, Russia
Martin Panigaj
Department of Chemistry, University of North Carolina, Charlotte, NC,
USA
Valentina Abondano Perdomo
Physical Sciences Department, West Virginia University Institute of
Technology, Beckley, WV, USA
Joel Petersen
Department of Physics, University of Nebraska Omaha, Omaha, NE, USA
Joshua D. Quarles
Department of Chemistry, Physics, Geology and the Environment, Sims
Building, Winthrop University, Rock Hill, SC, USA
Yasmine Radwan
Department of Chemistry, University of North Carolina at Charlotte,
Charlotte, NC, USA
Maria Cristina Rangel
Comprehensive Center for Precision Oncology, Centro de Investigação
Translacional em Oncologia (LIM24), Departamento de Radiologia e
Oncologia, Faculdade de Medicina da Universidade de São Paulo and
Instituto do Câncer do Estado de São Paulo, São Paulo, SP, Brazil
Laura Rebolledo
Nanoscale Science Program, Department of Chemistry, University of
North Carolina at Charlotte, Charlotte, NC, USA
Laura P. Rebolledo
Department of Chemistry, University of North Carolina, Charlotte, NC,
USA
Department of Biological Sciences, University of North Carolina,
Charlotte, NC, USA

16. Tania Rubia Flores da Rocha
Laboratório de Hemostasia do Hospital das Clínicas da Faculdade de
Medicina da Universidade de São Paulo, São Paulo, SP, Brazil
Alex Rolband
University of North Carolina, Charlotte, NC, USA
Lewis A. Rolband
University of North Carolina at Charlotte, Charlotte, NC, USA
Aleksandr A. Rubel
Laboratory of Amyloid Biology, Saint-Petersburg State University, Saint
Petersburg, Russia
Maria S. Rubel
Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint
Petersburg, Russia
Congzhou M. Sha
Department of Pharmacology, Penn State College of Medicine, Hershey,
PA, USA
Department of Engineering Science and Mechanics, Penn State
University, State College, PA, USA
Liubov A. Shkodenko
Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint
Petersburg, Russia
Elizabeth Skelly
Nanoscale Science Program, Department of Chemistry, University of
North Carolina at Charlotte, Charlotte, NC, USA
University of North Carolina, Charlotte, NC, USA
Valeria V. Solyanikova
Laboratory of DNA-Nanosensor Diagnostics, ITMO University, Saint
Petersburg, Russia
Megan Teter

17. Chemistry Department, Ball State University, Muncie, IN, USA
Susan R. Trammell
Department of Physics and Optical Science, University of North Carolina
at Charlotte, Charlotte, NC, USA
Juan L. Vivero-Escoto
Department of Chemistry, Nanoscale Science Program, University of
North Carolina, Charlotte, NC, USA
Jian Wang
Department of Pharmacology, Penn State College of Medicine, Hershey,
PA, USA
Ashley E. Wood
Department of Chemistry, Physics, Geology and the Environment, Sims
Building, Winthrop University, Rock Hill, SC, USA
Lin Yang
Brookhaven National Laboratory, Upton, NY, USA
Yaroslava G. Yingling
Department of Materials Science and Engineering, North Carolina State
University, Raleigh, NC, USA
Douglas Zhang
Department of Chemistry and Biochemistry, University of California,
San Diego, CA, USA

18. Part I
Computational Design and In Silico
Studies of RNA Nanostructures

19. (1)
© The Author(s), under exclusive license to Springer Science+Business Media, LLC,
part of Springer Nature 2023
K. A. Afonin (ed.), RNA Nanostructures, Methods in Molecular Biology 2709
https://doi.org/10.1007/978-1-0716-3417-2_1
1. Molecular Dynamics Simulations of
RNA Motifs to Guide the Architectural
Parameters and Design Principles of
RNA Nanostructures
Valentina Abondano Perdomo1
and Taejin Kim1
Physical Sciences Department, West Virginia University Institute of
Technology, Beckley, WV, USA
Taejin Kim
Email: taejin.kim@mail.wvu.edu
Abstract
Molecular dynamics (MD) simulations can be used to investigate the
stability and conformational characteristics of RNA nanostructures.
However, MD simulations of an RNA nanostructure is computationally
expensive due to the size of nanostructure and the number of atoms.
Alternatively, MD simulations of RNA motifs can be used to estimate the
conformational stability of constructed RNA nanostructure due to their
small sizes. In this chapter, we introduce the preparation and MD
simulations of two RNA kissing loop (KL) motifs, a linear KL complex
and a bent KL complex, and an RNA nanoring. The initial solvated
system and topology files of each system will be prepared by two major
force fields, AMBER and CHARMM force fields. MD simulations will be
performed by NAMD simulation package, which can accept both force
fields. In addition, we will introduce the use of the AMBER cpptraj
program and visual molecular dynamics (VMD) for data analysis. We

20. will also discuss how MD simulations of two KL motifs can be used to
estimate the conformation and stability of RNA nanoring as well as to
explain the vibrational characteristics of RNA nanoring.
Key words Molecular dynamics simulations – RNA motif – RNA
nanostructure – AMBER – CHARMM – NAMD
1 Introduction
Since RNA tectoRNA [1–5] was built in the early 2000s, RNA
nanotechnology has rapidly developed computationally and
experimentally. The various shapes of RNA nanostructures have been
built using numerous RNA motifs. The examples of RNA motifs are kink-
turn motif, junction motif, pseudoknot, kissing loop hairpins, GNRA
loop-receptor, triple helical scaffold, and G- quadruplex. Examples of
RNA nanostructure shapes, which are constructed by RNA motifs, are
triangle [6–8], square [9], hexagonal ring [10–12], cubes [13–16], and
polyhedron [17, 18]. The various shapes of RNA nanostructures also
have been investigated to develop diverse biomedical applications, such
as drug delivery [19–21], gene therapy [14, 22], and molecular beacon
[23–31].
Several computational methods have been developed to design RNA
nanostructure. Examples of computational design tools are RNA2D3D
[32], NanoTiler [33], Assemble2 [34], INFO-RNA [35], and NUPACK
[36]. Once an initial RNA nanostructure is computationally generated,
molecular dynamics (MD) simulations can be used to fix steric crashes
in the nanostructure as well as to investigate the stability and
conformational changes of nanostructure. Most commonly used MD
simulation packages are Assisted Model Building with Energy
Refinement (AMBER, https://​
ambermd.​
org/​
) [37], Nanoscale
Molecular Dynamics (NAMD, https://​
www.​
ks.​
uiuc.​
edu/​
) [38],
Chemistry at Harvard Macromolecular Mechanics (CHARMM, https://​
www.​
charmm.​
org/​
) [39], and GROningen MAchine for Chemical
Simulations (GROMACS, https://​
www.​
gromacs.​
org/​
) [40]. Each MD
simulation package uses specific atomic interaction parameters and
topological information, which are called force fields (FF). AMBER,
CHARMM, and GROMACS have their own FF, and the NAMD platform

21. can accept these FF to run MD simulations. In this chapter, we will
introduce the use of AMBER and CHARMM FF to run MD simulations
using NAMD. The atomic interactions in AMBER MD simulation are
described by the below equation.
The first three terms describe short-range interactions in two
(bonded), three (angle), and four (dihedral) atoms. The last two terms
calculate long-range van der Waals and electrostatic interactions,
respectively. Atomic potentials in CHARMM are described by additional
potential components. Besides short- and long-range interactions,
additional terms such as improper interactions, two-body Urey-Bradley
term, and CMAP term are included as below.
The above atomic potentials are applied to describe the molecular
behavior of all types of biomolecules, such as DNA, RNA, proteins, and
lipids. However, the unique behavior of each biomolecule is
characterized by specific FF. Thus, when users prepare MD simulations,
it is necessary to choose proper FF for biomolecules as well as the most
recent FF.
In this chapter, we introduce how to prepare initial structure and
topology files of DNA and RNA motifs as well as an RNA nanostructure.
To provide a wide range of selecting FF for user’s research, we
introduce how to prepare initial systems and run MD simulations using

22. both AMBER and CHARMM FFs. In Subheading 3.1, it will be explained
how to apply AMBER FF to prepare the initial solvated system and
topology file for a linear RNA motif. In Subheading 3.2, it will be
explained how CHARMM FF can be applied to a bent RNA motif and an
RNA nanostructure. The MD simulations of these systems will be
performed by NAMD package. For post MD simulation data analysis, we
will introduce the use of AMBER cpptraj program [41] and visual
molecular dynamics (VMD) [42]. The cpptraj is a very convenient and
powerful text command for data analysis, while VMD provides basic
data analysis tools based on a graphic interface. The detailed
explanations of cpptraj and VMD are described in Subheading 3.3.
In Note 1, we will introduce how MD simulation can be failed due to
the small periodic boundary box, especially when the biomolecule
experiences a large conformational change during MD simulations. In
Notes 2 and 3, we will also briefly describe how MD simulations of RNA
motifs can be used to estimate the conformational stability of RNA
nanostructure as well as to understand its vibrational mode.
2 Materials
In this section, we briefly introduce AMBER, CHARMM, and NAMD. We
will also briefly introduce VMD and Discovery Studio Visualizer (DSV),
which can be used for the visualization of molecular structure and MD
trajectory, molecular editing, and basic data analysis.
2.1 AMBER
AMBER (https://​
ambermd.​
org/​
) is a comprehensive biomolecular MD
simulation package. The most recent AMBER FFs can be found in the
AMBER website (https://​
ambermd.​
org/​
AmberModels.​
php). However,
it is strongly advised that users check the details of FF in the most
recent AMBER reference manual (https://​
ambermd.​
org/​
Manuals.​
php)
before preparing MD simulation. AMBER MD simulation can be
performed by one of two commands, sander (simulated annealing with
NMR-derived energy restraints) or pmemd (particle mesh Ewald
molecular dynamics). The pmemd command shows better performance
in terms of computation speed. In addition, the performance of MD
simulation can be further improved when the graphics processing unit

23. (GPU) is used by pmemd.CUDA command. More detailed information
about sander, pmemd, and pmemd.CUDA can be found in the most
recent AMBER reference manual. Besides MD simulations, AMBER
package also provides a comprehensive data analysis program, which is
called cpptraj. The cpptraj can perform various data analysis. Examples
of fundamental data analysis by cpptraj are molecular geometry
measurements, such as distance, angle, dihedral angle, root-mean-
square deviation (rmsd) calculations, and hydrogen bond (HB)
interactions. The functionality and data file treatments by cpptraj
command can be found in the most recent AMBER reference manual.
2.2 CHARMM and NAMD
Most recent CHARMM FF and detailed information can be found from
the MacKerell lab website (http://​
mackerell.​
umaryland.​
edu/​
charmm_​
ff.​
shtml). NAMD is an MD simulation platform, which can run MD
simulation of a biomolecular system prepared by AMBER, CHARMM, or
GROMACS FF. In the Method section, it will be explained how to run MD
simulations using NAMD when the initial structure and topology file are
prepared by AMBER FF (Subheading 3.1) or CHARMM FF (Subheading
3.2). Most recent NAMD package can be downloaded from NAMD
website (https://​
www.​
ks.​
uiuc.​
edu/​
Development/​
Download/​
download.​
cgi?​
PackageName=​
NAMD). This website provides two
different versions of NAMD. One version is for using the central
processing unit (CPU) system and the other version is for NVIDIA
CUDA-based GPU system. The basic data analysis can be performed by
either AMBER cpptraj or VMD.
2.3 VMD (Visual Molecular Dynamics)
VMD provides 3D visualization of biomolecules. It can visualize static
structure or MD trajectory of AMBER, CHARMM, and GROMACS. VMD
also provides basic data analysis tools, such as atomic distance, angle
along three atoms, dihedral angle along four atoms, RMSD, HB
interactions, and salt bridge. VMD also can be used to generate initial
solvated structure and topology files using CHARMM FF. More details of
generating initial structure and topology are explained in Subheading
3.2. The most recent VMD version can be downloaded from VMD

24. website (https://​
www.​
ks.​
uiuc.​
edu/​
Development/​
Download/​
download.​
cgi?​
PackageName=​
VMD).
2.4 Discovery Studio Visualizer (DSV)
One of the unique functionalities of DSV is that users can build or edit
nucleic acids, proteins, or small molecules. DSV also provides cleaning
atomic clashes, which is a similar process of energy minimization of MD
simulation. Besides these functionalities, DSV provides atomic
geometry measurements, HB interactions, and visualization of
CHARMM MD trajectory. Recent DSV can be downloaded from https://​
discover.​
3ds.​
com/​
discovery-studio-visualizer-download.
3 Methods
MD simulation of a large RNA nanostructure, which is built by self-
assembly of multiple RNA motifs, is computationally very expensive
because of the size of the system and number of atoms. Alternatively,
MD simulation of an RNA motif can be used to predict the
conformational stability of the RNA nanostructure with a small cost of
computational power. In this section, we introduce how to prepare the
initial solvated system and topology files of RNA motifs and an RNA
nanostructure using AMBER (Subheading 3.1) and CHARMM
(Subheading 3.2) FFs. We also introduce NAMD protocols for MD
simulations. The basic data analysis using AMBER cpptraj commands
and VMD will be explained at Subheading 3.3.
3.1 MD Simulation of a Linear RNA Motif
To demonstrate how to determine the stability of a linear RNA motif by
MD simulations, we use the dimerization initiation site (DIS) of HIV-1
RNA (PDB ID: 2FCX.pdb), which forms a kissing loop (KL) interaction.
We modify the sequence of HIV-1 DIS to prepare two mutated kissing
loop complexes. The bases of the KL-1 complex form full Watson-Crick
base pairings, while the KL-2 complex forms non-Watson-Crick base
pairings. The sequence of two DIS and initial crystal structure are
plotted in Fig. 1. The bases of the DIS are replaced by DSV. In this
section, the preparation of the solvated KL complexes and topology files

25. using AMBER FF as well as NAMD protocols for MD simulations will be
discussed.
Fig. 1 (a) The initial structure of the linear RNA motif (PDB ID: 2FCX.pdb). (b) The
DIS sequence and the final MD structure of the KL-1 complex. (c) The DIS sequence
and the final MD structure of the KL-2 complex
3.1.1 Preparation of Initial Structure and Topology
Using AMBER FF
Two RNA KL complexes may have steric crashes due to base
modifications by DSV. The steric crashes can be repaired by energy
minimization. To minimize the structure, it is necessary to generate the
initial structure and topology files which MD simulation can accept. The
initial structure and corresponding topology files using AMBER FF can
be generated by the AMBER tleap command as below.
tleap
source leaprc.RNA.OL3
mol = loadpdb KL.pdb
saveamberparm mol KL.prmtop KL.inpcrd

26. Besides tleap, xleap, which is a window interface, can be also used.
The command source loads RNA.OL3 force field for RNA. The command
loadpdb loads the input pdb file, KL.pdb. The saveamberparm command
saves the result coordinates to inpcrd format and topological
information to prmtop format.
The energy minimization can be performed by the below AMBER
command:
sander -i min.in -o min.out -p KL.prmtop -c KL.inpcrd -r KL-EMIN.rst -x
KL-EMIN.x
Sander is one of main MD simulation programs in AMBER. Min.in is
an input file which details are listed below.
&cntrl
imin=1, ntx=1, drms=0.01,
irest=0, ntxo=1, cut=15.0,
ntpr=100, ntwx=100, ntwe=100,
nsnb=20,
maxcyc=30000, ncyc=1000,
igb=1,saltcon=1.0,
ntt=0,offset=0.13,
gbsa=1, ntb=0,
&end
The detailed explanations of each command in the input file can be
found in the most recent AMBER manual. KL-EMIN.rst is the minimized
structure, and KL-EMIN.x is the trajectory file. Trajectory file can be
visualized by VMD. In VMD, select File → New Molecule → Browse →
select KL.prmtop → select AMBER 7 Parm → Load → select KL-EMIN.x →
select AMBER Coordinates → Load.
The minimized structure, KL-EMIN.rst, can be converted to pdb
format by the AMBER command, ambpdb, as below.
ambpdb -p KL.prmtop -c KL-EMIN.rst > KL-EMIN.pdb
The converted pdb file (KL-EMIN.pdb) is used for solvation and
ionization for explicit MD simulations. The solvated system can be
generated by tleap as below.
tleap,

27. source leaprc.RNA.OL3
source leaprc.water.tip3p
loadAmberParams frcmod.ions234lm_1264_tip3p
molEMIN = loadpdb KL-EMIN.pdb
solvateBox molEMIN TIP3PBOX 20.0 0.8
addions molEMIN K 0
addionsRAND molEMIN K 22 CL 22
addionsRAND molEMIN MG 1 CL 2
saveamberparm molEMIN KL-Solvated.prmtop KL-Solvated.inpcrd
savepdb molEMIN KL-Solvated.pdb
quit
The command source loads RNA.OL3 force field and water.tip3p
force field for KL complex and water, respectively. The command
loadAmberParams loads ion force field, frcmod.ions234lm_1264_tip3p.
The command solvateBox places water molecules around the KL
complex with 0.8 Å gap and the thickness of 20 Å water layer from the
KL complex. The significance of water box size is discussed in Note
1. The command addions adds K+
ions to neutralize the KL complex.
Extra ions can be added to increase salt concentrations. In this example,
extra K+
, Cl−
, and Mg2+
are added to set 50 mM of KCl and 2 mM of
MgCl2. The number of extra ions can be calculated using formula below
based on the volume of the water box.
AMBER tleap command generates a log file to record detailed
information of tleap. The volume of the water box can be also found in
the log file after the solvateBox command is executed. The solvated
structure and topology files are saved by saveamberparm command.
The resultant structure is also saved by pdb format using savepdb
command.
3.1.2 Initial Minimization and Equilibration
The first step of MD simulation is the energy minimization of the entire
system (KL complex, water, and ions). After minimization, equilibration
is applied to water and ions for a given temperature, while the KL
complex is fixed. VMD can be used to generate a pdb file, which

28. identifies fixed atoms. To generate the pdb file for fixed atoms, in VMD,
select File → New Molecule → Browse → select KL-Solvated.prmtop →
select AMBER7 Parm → Browse → select KL-Solvated.inpcrd → select
AMBER7 Restart → Load. Then, type below commands to VMD console
window.
vmd > set all [atomselect top all]
vmd > set Fixatom [atomselect top "resname A C G U G5 C3"]
vmd > $all set beta 0
vmd > $Fixatom set beta 1
vmd > $all writepdb KL-Fixed.pdb
vmd > set center [measure center $all]
The above commands assign index 1 to the beta column of atoms
that belong to the residue names, A, C, G, U, G5, and C3. Atoms with
index 1 in the beta column are recognized as fixed atoms during NAMD
simulations. The result is saved to pdb format (KL-Fixed.pdb). The
command set center detects the center of the water box in (x, y, z)
coordinates. This coordinate will be used for the periodic boundary
conditions in the NAMD config file (line 48 in the EminEQ-I.conf in Table
1).
Table 1 The list of input files for minimization, equilibration, and MD simulations.
These files can be used to run NAMD simulations with AMBER FF. If the system is
prepared by CHARMM FF, use the config files listed in Table 2

32. Initial minimization and equilibration are performed using NAMD
by the below command with the config file, EminEQ-I.conf.
namd2 +p number of cores EminEQ-I.conf > EminEQ-I.ene
The above command is for using a CPU system to run MD
simulation. Depending on the available computational resource, users
can specify the number of CPU cores to run MD simulation after the flag
+p. EminEQ-I.conf is an input config file (see Table 1 for the details).
Note that the commands for AMBER in EminEQ-I.conf are specified in
bold text. If a system is prepared by CHARMM FF, # AMBER Input
section (line 6–9) must be removed. In addition, switching (line 17)
must be on and switchdist (line 18), and pairlistdist (line 19) in the #
Force-Field Parameters must be activated by removing # symbol. There
are a few more sections that should be noted. The fixed atoms in the KL
complex are activated (line 32). The initial periodic box is defined (line
49), while the pressure control is deactivated (line 65). Under this
condition, NVT simulation will be performed. To control temperature,
Langevin dynamics (line 83–87) is employed. EminEQ-I.ene is an output
file, which contains the molecular mechanics information, such as
pressure, temperature, energy terms in bonding, angular, dihedral, van
der Waals, and electrostatic interactions. The initial minimization and
equilibration generate the following output files.

33. KL-EminEQ-I.coor, KL-EminEQ-I.vel, KL-EminEQ-I.xst, KL-EminEQ-
I.restart.xsc.
KL-EminEQ-I.restart.coor, KL-EminEQ-I.restart.vel, KL-EminEQ-
I.restart.xsc, and KL-EminEQ-I.dcd.
The first group of files are the final coordinates, velocities, and
periodic boundary information (xst and xsc files), respectively. The
second group of files are restart files of coordinates, velocities, periodic
boundary information, and NAMD trajectory files (KL-EminEQ-I.dcd.),
respectively. Restart files are periodically updated by NAMD command,
restartfreq. More detailed information about config file and output file
can be found in the recent NAMD manual (http://​
www.​
ks.​
uiuc.​
edu/​
Research/​
namd/​
).
3.1.3 Constrained MD Simulation
Once water and ion molecules are equilibrated at the target
temperature, it is necessary to apply another minimization and then
equilibrate the entire system while holding the KL complex with a weak
constraint. To generate a constraint file, load initial solvated system and
topology files (KL-Solvated.prmtop and KL-Solvated.inpcrd) to VMD as
described in Subheading 3.1.2. To assign constraints to the KL complex,
type the below commands to the VMD console.
vmd > set all [atomselect top all]
vmd > set ConstraintAtom [atomselect top "resname A C G U G5 C3"]
vmd > $all set beta 0
vmd > $ConstraintAtom set beta 0.5
vmd > $all writepdb KL-Constraint.pdb
Here, the beta column is set to zero, while atoms which belong to
the residue names, A, C, G, U, G5, and C3, are set to 0.5 kcal/(mol∙Å ). If a
biomolecular behavior is sensitive to equilibration, it may need to apply
strong initial constraint to the molecule and gradually reduce
constraints during multiple constrained MD simulations. The
constrained MD simulation can be performed by the below command.
namd2 +p number of cores EminEQ-II.conf > EminEQ-II.ene
The details of EminEQ-II.conf are listed in Table 1. Note that
coordinate, velocity, and periodic boundary files from the previous

34. simulation are specified as input files (line 2–6) in the EminEQ-II.conf.
In addition, the fixed KL complex is turned off (line 37), while the
constrained KL complex is activated (line 45). For the constrained MD
simulation, NVP is changed to NPT by turning off the initial boundary
box in line 54 and activating Langevin pressure control in line 71–81.
3.1.4 Final Equilibration and Product MD
Simulations
Once constrained MD simulation is completed, release constraints by
turning it off (line 45) in the EQ-III.conf file (see Table 1) to run the final
equilibration MD. This time, all components in the system, KL complex,
ions, and water, will be equilibrated at the target temperature. The
NAMD command for the final equilibration is below.
namd2 +p number of cores EQ-III.conf > EQ-III.ene
The details of the EQ-III.conf file are listed in Table 1. After the final
equilibration MD simulation is completed, the product MD simulation
can be performed using the below NAMD command.
namd2 +p number of cores MD.conf > MD.ene
The details of the MD.conf file are listed in Table 1. The MD.conf will
run to produce a 6 ns-long MD trajectory (3,000,000 step ×
2 fs = 6,000,000 fs = 6 ns). Longer MD trajectory can be produced by
running consecutive MD simulations using coordinate, velocity, and
periodic boundary files of the previous MD run. The results of MD
simulations are discussed in Note 2.
3.2 MD Simulation of a Bent RNA Motif and RNA
Nanoring
In this section, we introduce MD simulations of a bent RNA and an RNA
nanoring (Fig. 2). The bent RNA is composed by kissing loop
interactions between two dumbbell-shaped RNA motifs [11]. The RNA
nanostructure has a ring shape, which is constructed by six dumbbell-
shaped RNA motifs. Each dumbbell-shaped RNA has slightly different
sequences in the stem. The initial solvated structure and topology will

35. be prepared with CHARMM FF using VMD. Load the initial pdb
structure (bentRNA.pdb) to VMD by the below procedure.
File → New Molecule → Browse → select bentRNA.pdb → select file
type as pdb → Load.
Fig. 2 (a) The initial structure of the bent RNA, which is built by two dumbbell-
shaped RNA motifs. The red dotted arrow indicates the length of the dumbbell-
shaped RNA motif, and the blue dotted lines indicate the bending angle of the bent
RNA. (b) The final MD structure of the bent RNA. (c) Top and side views of the initial
structure of RNA nanoring. (d) Top and side views of the final structure of RNA
nanoring
Generate structure and topology files with CHARMM FF as below.
Extensions → Modeling → Automatic PSF Builder → define Output
basename as bentRNA. Then, follow below steps in the Automatic PSF
Builder window.
Step 1: Load default CHARMM FF and structure files. If newer
CHARMM FF and structure files are available, delete default FF and

36. structure files, and click the Add button to load newer versions of
FF. Click the Load input files button to complete Step 1.
Step 2: Select the type of molecules. In this case, select Nucleic Acid.
Then, click the Guess and split chains using current selections
button.
Step 3: Detailed information of the loaded structure will show up
in the window box in Step 3. Select the strand, and click the Edit
chain button to confirm the First Atom and the Last Atom index are
correct as well as the 5′ (5TER) and the 3′ (3TER) ends being
properly defined. If everything is okay, click the Create Chain
button. Now, Automatic PSF Builder will generate bentRNA.pdb
(structure) and bentRNA.psf (topology) files.
To run explicit MD simulation, it is necessary to solvate the system
with ions. To solvate the KL complex, in the VMD menu, select
Extensions → Add Solvation Box → make sure that the previously
generated structure and topology files (bentRNA.pdb and bentRNA.psf)
are loaded under Input → Define the name of solvated system in the
Output (e.g., bentRNA-sol) → Check Use Molecule Dimensions option.
Enter the water box padding size in the Box Padding → Click the Solvate
button. As discussed in Note 1, the size of water box padding should be
carefully defined to avoid the violation of periodic boundary
conditions. Now, the solvated structure and topology will be generated
by pdb (bentRNA-sol.pdb) and psf (bentRNA-sol.psf) file formats,
respectively.
To ionize the solvated system, load the solvated system (bentRNA-
sol.pdb and bentRNA-sol.psf) to VMD. In the VMD menu, select
Extensions → Modeling → Add Ions → Define the name of the ionized
system in the Output prefix (e.g., bentRNA-solion). VMD provides six
default salt types, NaCl, KCl, CsCl, MgCl2, CaCl2, and ZnCl2. Choose the
proper salt type for your simulation. In the section of Ion placement
mode, users can select Only neutralize system with select salt type,
Neutralize and set salt concentration to used defined salt concentration
in mol/L, or User-defined number of ions. Once the preferred salt
conditions are determined, click the Autoionize button. The ionized
structure and topology will be generated as pdb and psf file formats,
respectively.

37. When the solvated system with ionization is prepared, the explicit
MD simulations can be performed with the same MD protocols
described in Subheadings 3.1.2, 3.1.3 and 3.1.4. However, the AMBER
part in NAMD config files must be removed. In Table 2, NAMD input
files, EminEQ-I.conf, EminEQ-II.conf, EQ-III.conf, and MD.conf, are listed.
Notice that # AMBER Input is removed. In addition, commands for
CHARMM FF are included in bold text. For example, in EminEQ-I.conf
file, paraTypeCharmm is on (line 10); CHARMM parameter,
par_all36_na.prm, is defined (line 11), switching is on (line 18); and
switchdist (line 19) and pairlistdist (line 20) are defined. The results of
MD simulation of the bent RNA will be discussed in Note 3.
Table 2 The list of input files for minimization, equilibration, and MD simulations.
These files for NAMD run with CHARMM FF. If the system is prepared by AMBER FF,
use the config files listed in Table 1

41. The same procedure can be applied to generate the initial solvated
system and corresponding topology files of RNA nanoring. The MD
simulation of RNA nanoring can be performed by the same protocols in
Subheadings 3.1.2, 3.1.3 and 3.1.4. with NAMD config files in Table 2. A
brief discussion about MD simulation of RNA nanostructure will be
discussed in Note 3.
3.3 Data Analysis Using cpptraj and VMD
3.3.1 cpptraj
Cpptraj is one of the AMBER sub-programs that provides a wide range
of data analysis tools. The basic command to run cpptraj is
cpptraj -p topology file <cpptraj-input file> cpptraj-output file
Cpptraj-input file can contain multiple data analysis action
commands. Below is an example of a cpptraj-input file.
trajin MD-trajectory.dcd
center : 9-16,34-41
strip :TIP3
strip :POT
strip :CLA
distance dist1 :2@N3 :20@N3 out DataOutPut.dat
angle ang1 :4@N1 :5@N1 :6@N1 out DataOutPut.dat
dihedral dihe1 :4@N1 :5@N1 :6@N1 :7@N1 out DataOutPut.dat
hbond :9-16,34-41 avgout DataOutPut-hbond.dat

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43. OUR COUNTRY AND OUR HOME
There is a land, of every land the pride,
Beloved by Heaven o’er all the world beside;
Where brighter suns dispense serener light,
And milder moons emparadise the night:
A land of beauty, virtue, valor, truth,
Time-tutored age, and love-exalted youth:
The wandering mariner whose eye explores
The wealthiest isles, the most enchanting shores,
Views not a realm so bountiful and fair,
Nor breathes the spirit of a purer air.
For in this land of Heaven’s peculiar grace,
The heritage of Nature’s noblest race,
There is a spot of earth supremely blest—
A dearer, sweeter spot than all the rest:
Here woman reigns; the mother, daughter, wife,
Strew with fresh flowers the narrow way of life;
In the clear heaven of her delightful eye,
An angel-guard of loves and graces lie;
Around her knees domestic duties meet,
And fireside pleasures gambol at her feet.
“Where shall that land, that spot of earth be found?”
Art thou a man?—a patriot?—look around;
Oh, thou shalt find, howe’er thy footsteps roam,
That land thy Country, and that spot thy Home.
—Montgomery.

44. NOTES ABOUT AUTHORS
Page 7.—François Coppée, a noted French writer, was born at
Paris in 1842. Although he was the writer of good French poetry and
some successful plays, he is best known to American readers by his
charming short stories, in which he depicts the life and aspirations of
the common people. In his later life he was an ardent Catholic, and
as such wrote fearlessly in defense of the rights of the Church in
France. He died in 1908.
Page 14.—John James Audubon, a noted American ornithologist
of French descent, was born at New Orleans in 1780. Perhaps no
other person has done so much for the birds of America, or has
described them so well, as he. His drawings of birds are particularly
famous. He died at New York in 1851.
Page 16.—J. R. Marre, is a contemporary Catholic writer whose
poems are well known to readers of The Ave Maria and other
religious periodicals.
Page 17.—Rev. John Banister Tabb was born in Virginia, March
22, 1845. He studied for the priesthood and was ordained in 1884.
He is an instructor in St. Charles College, Maryland. His poems are
exquisite in movement and diction no less than in richness of
thought.
Page 18.—Horace Binney Wallace, a noted American lawyer and
prose writer, was born at Philadelphia, 1817; died at Paris, 1852. His
best known work, Literary Criticisms, was published after his death.
Page 23.—Henry Coyle is a contemporary Catholic poet residing
at Boston, Massachusetts. He is well known as a contributor to
Catholic periodicals. His first volume of poetry, entitled The Promise
of Morning, was published in 1899. His writings are characterized by
deep religious feeling no less than by rare poetic charm.

45. Page 24.—Miguel de Saavedra Cervantes, a celebrated Spanish
poet and novelist, was born near Madrid, 1547; died, 1616. His most
famous work is the romance entitled Don Quixote, which was first
printed in 1605. It has been translated into every language of
Europe.
Page 43.—John Henry, Cardinal Newman was born at London
in 1801. He was educated at a private school until he entered
Oxford, where he took his degree before he was twenty. In 1822 he
was elected Fellow in Oriel College. In 1845 he left the Church of
England for the Roman Catholic Church. He wrote many sermons,
treatises, and poems. In literary merit his work ranks very high. He
died in 1890.
Rev. Thomas Edward Bridgett, a noted priest and author, was
born at Derby, England, in 1829. He was the founder of the
Confraternity of the Holy Family for men, and much of his life was
devoted to missionary work. He was the author of numerous
religious and historical works, among which may be named, The
History of the Holy Eucharist, Life of the Blessed John Fisher,
Blunders and Forgeries, etc. Father Bridgett died at St. Mary’s
Clapham, England, in 1899.
Page 56.—William Cowper, a celebrated English poet, was born
in 1731. He attended Westminster school and afterwards studied
law. His most famous poems are The Task and the ballad John
Gilpin’s Ride. He died in 1800.
Page 58.—Rev. Frederick William Faber was born in Yorkshire,
England, in 1814. He was an eloquent preacher, a brilliant talker, and
had an unsurpassed power of gaining the love of all with whom he
came in contact. His hymns are well known, and sung throughout
the world. He founded a religious community which was afterwards
merged in the oratory of St. Philip Neri. He died in 1863.
Page 75.—John Greenleaf Whittier was born at Haverhill,
Massachusetts, 1807. At the age of eighteen he studied for two
years at an academy near his home. In 1829 he became the editor

46. of a paper established at Boston to advocate protective tariff. He
was active in the cause of antislavery. He died in 1892.
Page 82.—Mary Lydia Bolles Branch was born at New London,
Connecticut, in 1840. She is best known as a writer of stories for
children.
Page 84.—John Burroughs was born in Roxbury, New York, in
1837. He was the son of a farmer, but received a good college
education. For eight or nine years he taught school, and then
became a journalist in New York city. From 1861 till 1873 he was a
clerk in the Treasury Department at Washington. He finally settled
on a farm at West Park, New York, giving his time to literature and
the observation of nature. His love of nature has inspired most of
what he has contributed to the literature of the world.
Page 96.—Aubrey de Vere, an Irish Catholic poet, was born in
1788. He belonged to a good family, and always had leisure to
cultivate a naturally refined taste. At first he wrote dramas, but later,
poems, especially sonnets. He was a true patriot, and pays many
tributes of love to his country in his historical themes. He died in
1846.
Page 97.—Sir Walter Scott was born at Edinburgh in 1771. His
delightful art of story telling, both in prose and poetry, has been
excelled by few. Among his most popular poems are The Lady of the
Lake and Marmion; among his most popular novels are Kenilworth,
Ivanhoe, The Talisman, and Old Mortality. He died in 1832.
Page 106.—Thomas Moore was born at Dublin, Ireland, in 1779;
died in 1852. He entered Trinity College, Dublin, at fifteen years of
age. He studied law, and in 1799 entered the Middle Temple,
London. In 1803 he received a government appointment to the
Bermuda Islands and traveled quite extensively in the United States.
Among English Catholic poets he holds a high rank.
Page 107.—Andrew Lang was born in Scotland in 1844; died at
London in 1912. He pursued many different lines of literary work,

47. and was one of the most versatile writers of modern times. The
number of volumes bearing his name as author is surprisingly large.
Page 114.—Lady Gregory is the daughter of Dudley Presse,
Deputy Lieutenant of Roxborough, County Galway, Ireland. She has
done very valuable service to literature in preserving and editing
many of the early Celtic legends. Some of her publications are: Poets
and Dreamers, Cuchullain of Muerthemme, and Gods and Fighting
Men.
Page 118.—Helen Hunt Jackson was born in 1831 at Amherst,
Massachusetts. In 1867 she wrote her first stories, and from that
time until her death books from the pen of H. H. were published
with frequency. She wrote verses, essays, sketches of travel,
children’s stories, novels, and tracts on questions of the day.
Page 120.—St. Ambrose or Ambrosius, one of the fathers of the
Latin Church, was born at Treves, A.D. 340; died, 397. He was the
champion of the Catholics against Arians and pagans; he became
Bishop of Milan in 374. He was the author of numerous hymns and
other religious works.
Page 121.—James Sheridan Knowles was born at Dublin,
Ireland, 1784. For a time he held a commission in the militia, but
became attracted to the stage and entered the dramatic profession.
He died in 1862.
Page 132.—Washington Irving was born in New York city, April
3, 1783; died, 1859. His early schooling was not very systematic.
When a young man he began the study of law, but never followed
the profession very steadily. He is the most popular of the American
writers of the early part of the nineteenth century.
Page 152.—Alfred Tennyson was born at Somersby, England, in
1809. He was educated at Cambridge, where he gained the
Chancellor’s medal for his poem Timbuctoo in blank verse. In 1830
he published his first volume of poems. Other poems followed
quickly and soon became popularly known. Tennyson’s poetry is

48. distinguished by its rare quality and delicate choice of language. He
was for many years poet laureate. He died in 1892.
Page 158.—Sister Mary Antonia is an occasional and highly
esteemed contributor of verse to current Catholic periodicals.
Page 161.—Miriam Coles Harris is a contemporary Catholic
writer whose works have attracted considerable attention. The
extract is from A Corner of Spain, published in 1896.
Page 166.—William Cullen Bryant, a famous American poet, was
born at Cummington, Massachusetts, November 3, 1794. He entered
Williams College at the age of sixteen, but at the end of two years
took honorable dismission and engaged in the study of law. He was
admitted to the bar in 1815; removed to New York in 1825; was
editor of the New York Review in the same year; and in 1826
became connected with the Evening Post, with which he continued
until his death, which occurred in 1878.
Page 170.—Conrad Von Bolanden is the pseudonym of a
contemporary German Catholic writer, Monsignor Joseph Bischoff,
who was born in August, 1828. He was made a Papal Chamberlain
to Pope Pius IX in recognition of the merits of his efforts in the field
of Catholic literature. He has written much, finding the motives of his
books in history and in the problems of social life.
Page 174.—Henry Wadsworth Longfellow is often called the
children’s poet, partly because of his love for children and partly
because of some poems written for children. He was born in
Portland, Maine, in 1807. From 1835 to 1854 he was professor of
modern languages at Harvard University. He died in 1882.
Page 178.—John Gilmary Shea, a brilliant Catholic writer, was
born at New York city, July 1824; died, 1892. He devoted most of his
time to literature instead of to the law, for which he was educated.
Perhaps no one has done more to preserve the history and language
of the aborigines of this country. History of the Catholic Missions
among the Indian Tribes of the United States, Early Voyages up and

49. down the Mississippi, History of the Catholic Church in Colonial
Times, are some of his most popular works.
Page 186.—James Russell Lowell was born at Cambridge,
Massachusetts, February 22, 1819. He died in the same house in
which he was born, August 12, 1891. For many years he held the
chair of modern languages in Harvard University. He was a man who
represented American culture and letters at their best.
Page 188.—Mother Mary Loyola of the Bar Convent, York,
England, is a writer of more than ordinary power on the subjects
dearest to every true Catholic. Her book, Jesus of Nazareth, from
which our selection is taken, was written especially for American
children and is dedicated to them.
Page 196.—Francis Scott Key, author of “The Star-spangled
Banner,” was born in Frederick County, Maryland, in 1780. It was
during the British invasion in 1814, while he was detained on a
British man-of-war within sight of the bombardment of Fort
McHenry, that Key wrote this beautiful lyrical poem. He died at
Baltimore in 1843.
Page 214.—James Montgomery was a Scottish poet, born in
1776; died in 1854. His poems, once very popular, are now almost
forgotten.

50. WORD LIST
GUIDE TO PRONUNCIATION
ā, as in māte.
ā̇ , as in sen´ā̇ te.
â, as in câre.
ă, as in ăt.
ä, as in ärm.
ȧ, as in ȧsk.
a̤ , as in a̤ ll.
ạ = ŏ, as in whạt.
ç = s, as in çell.
ch = k, as in chorus.
çh = sh, as in maçhine.
ē, as in hē, mēte.
ē̇ , as in ē̇ vent.
ĕ, as in mĕt.
ẽ, as in hẽr.
e̱ = ā̱ , as in e̱ ight.
ê, = â, as in whêre.
ḡ, as in ḡet.
ġ = j, as in ġem.
ī, as in mīne.

51. i̇, as in i̇dea.
ĭ, as in ĭt.
ĩ = ẽ, as in sĩr, bĩrd.
ï = ē, as in machïne.
ṉ = ng, as in baṉk, liṉger.
ō, as in ōld.
ō̇ , as in ō̇ bey.
ô, as in ôr.
ŏ, as in nŏt.
o̤ = o̅ o̅ , as in do̤ , ro̅ o̅ m.
ọ = o͝ o or ụ, as in wọlf, fo͝ ot.
ȯ = ŭ, as in sȯn.
s̱ = z, as in his̱ .
th, as in thin.
t͞ h, as in t͞ hen.
ū, as in mūte.
ŭ, as in thŭs.
ṳ, as in rṳde.
ụ= o͝ o, as in fụll.
û, as in bûrn.
x̱ = gz, as in ex̱ ist.
ȳ = ī, as in bȳ.
y̆ = ĭ, as in hy̆ mn.
ỹ = ẽ, as in mỹrtle.

52. Certain vowels, as a and e, when obscure are marked thus, a̯ , e̯ .
Silent letters are italicized. In the following word list only accented
syllables and syllables of doubtful pronunciation are marked.
a băn´don
ab hôr´
a bŏm´i nā´tion
a bŭn´dạnçe
ăc´çi dent
ăc côrd´
āch´ing
ac quāint´ed
ä dieū´
ad jā´çent
ăd´mĭ rā´tion
ad mĭt´tançe
al lē vĭ ā´tion
a māz´ing
a māze´ment
am´mu nĭ´tion
ăn´chor
ăṉ´guĭsh
ăn´ĭ māt ed
ăn´tĭ quāt ed
ăṉx´ious (-yŭs)
a pŏs´tle
ap pa̤ ll´ing

53. ap păr´el
ap pâr´ent ly
ap´pa rĭ´tion
ăp´pe tīte
ap pla̤ us̱ e´
ap´plĭ cā´tion
ap prōached´
ăp´pro bā´tion
ärch´er y
är´mor
as săs´sĭn
as sa̤ ult´
as sĕm´ble
at tĕnd´a̯ nt
a̤ u tŭm´nal
ăv´ȧ lănche
a vĕnġe´
a wa̤ rd´
bä nä´nȧ
băṉ´quet
băr´rĭ er
bē̇ ăt´ĭ tude
be hāv´ior (-yer)
be hĕst´
be liēf´

54. bĕn´e fit
brĭl´liançe (-ya̯ ns)
brĭl´liant
bŭg´ȧ boo
cälm´
căl´u met
cam pāign´
cā̇ prïçe´
cär´di nal
ca̤ u´tious ly
cav ȧ liēr´
căv´i ty
çel´e brā´tion
chā´ŏs
chăr´ĭ ot
chef (shĕf)
çhĕv´a liēr´
chiēf´ta̯ in
çhĭv´al ry
çĭv´il ly
clēave
cŏm´ic
cŏm´mȧn dänt´
com mŏd´ĭ ty
cȯm´pa ny

55. com´plē mĕnt´a ry
cŏm´plĭ ment
com pōs̱ ´er
com po s̱ ĭ´tion
con çēal´
con çĕp´tion
con fū´s̱ ion
cŏn gre gā´tion
cŏṉ´quer (-kẽr)
cŏṉn´quer or
con sĕnt´
con sẽrv´a to ry
con sĭd´er a ble
con tĕnt´
con trĭ bu´tion
coun´çil
coun´te na̯ nçe
couple (kŭp´l)
coûr´aġe
coûr´te ous ly
coûr´te sy
cōurt´ĭer
cȯv´ert
cre ā´tor
crĕv´ĭçe

56. crĭm´s̱ on
crṳ´çĭ fȳ
crṳa sāde´
cū´bit
cū´rĭ ous
cŭs´tom
çy´press
dān´ġer ous
de çēive´
dĕl´ĭ cā̇ çy̆
dĕl´ĭ cate
de pūt´ed
de rānġe´
de s̱ ẽrve´
dĕs´ic cāt ed
de s̱ īgn´
des´o lā´tion
dĕs´per ate
des per ā´tion
dev´ăs tat ing
de vĕl´op ment
de vīçe´
de vout´
dĭs̱ ´ma̯ l
dis māy´

57. dis´o bē´di ĕnçe
dis pẽrse´
dĭs´trict
do mĕs´tic
dŏṉ´key̆
dȯz´en
dūnes̱
ēa´ger
ēa´ger ly
ẽar´nest ly
ĕd´u cā´tion
ĕl´e ment
ĕl´o quent
ĕm´er ald
en dēar´
en dūr´a̯ nçe
ĕn´ē̇ my
en´ter tāin´
en thū´s̱ ĭ asm
ĕn´vy
e rĕct´
es pĕ´çĭal ly
ĕv´ĭ dent ly
ĕx´çel lent
ex ha̤ ust´

58. ex pănse´
ex pe dĭ´tion
ex plō´s̱ ion
ex pō´s̱ ure
ex prĕss´ive
ex traôr´dĭ na ry
fa̤ l´con ry
fath´om
fā´vor ĭte
fẽr´vor
fĕs´tĭ val
fī´nal ly
fĭs´sūre
fŏre´hĕad
fra̤ ud
frĕs´co
frṳit´age
fū´ġĭ tĭve
fûr´nish
gär´land
ġĕn er oŭs
ġĕn´e sĭs
ġĕn´ū ĭne
ġī´ant
ġī găn´tic

59. gnärled
grăd´u al ly
grăn´deûr
griēv´ing
hab´ĭ tā´tion
hȧ răngue´
ha̤ ugh´ty
häunt
heīght
hĕr´it age
hẽr´mit
hīre´ling
hŏl´ĭ day
hŏn´ŏr
ho rī´zon
hȯv´er ing
hū´man
hu mĭl ĭ ā´tion
hū´mor
hûr´rĭ cā̇ ne
īdē´ȧ
ī dŏl´a try
ĭm ăġ´ĭne
im mĕnse´
in crēase´

60. in´dĭg nā´tion
in fē´rĭ or
ĭn´fĭ nĭte
ĭn´fĩrm´i ty
ĭn´flu ençe
in grăt´i tude
in hăb´it ant
ĭn´no çent
in´no vā´tion
in quī´ry
in sĭst´ed
ĭn´ter val
in tŏl´er a ble
in vĕs´ti gate
in vĭ tā´tion
jew´el
joŭr´ney̆
joŭst
jŭs´tĭce
kĭn´dred
lēa
lēague
lieū tĕn´ant
lux ū´rious
măm´moth

61. mär’tyr dom
mär´veled
ma tē´rĭ al
mēa´ger ly
mĕl´an chol y
mĕn´tion
mẽr´çi ful
mĕs´saġe
mĕs´sen ġer
mĭl´i ta ry
mĭn´strel sy
mĭr´a cle
mĭs hăp´
mĭs´sĭle
mod´es ty
mōld
mŏn´ster
mo̅ o̅ red
mŏt´to
mŭl´tĭ tūde
mûr´mur
my̆ s´tē rĭ ous
my̆ s´ter y
my̆ th
noŭr´ish ing

62. o bē´di ençe
ŏb´sti nate
oc cā´s̱ ion
ō´çean (-sha̯ n)
ŏp´e rȧ
ŏp´po s̱ ĭte
op prĕssed´
or´acle
o rā´tion
pā´gan
pälms
par tĭc´u lar
pā´tiençe (-shens)
pa trōlled´
pĕas̱ ´ant
pe cūl´iar
pĕn´ançe
pĕn´sĭve
pĕr´il ous
per plĕx´i ty
per se cū´tion
pẽr´son age
per suāde´
per suā´sion
pĕt´ri fied

63. phĭ lŏs´o pher
phy̆ s̱ ´ic al
pĭ ăz´zȧ
pĭl´grim age
pĭt´y
plä´zȧ
plūm´age
pō´em
pō´et ry
pŏl´i cy
pol lū´tion
pȯm´mel
pŏp´u lar
pôr´ridge
pos̱ s̱ ĕss´
pŏv´er ty
prĕ´cious
pre s̱ erve´
prĭs̱ ´on er
prŏb´a bly
pro çĕs´sion
pro tĕct´or
prŏv´ĭ dençe
pûr´pose
pûr sūit´

64. rāi´ment
răm´parts
răp´tur ous
rē´al ly
rĕck´on ing
rĕc´og nize
re cȯv´er y
rĕf´uge
re lā´tion
re liēf´
re nowned´
re pos̱ e´
rĕs´cūe
re s̱ ŏlve´
rĕs´ŭr rĕc´tion
re tôrts´
re trēat´
re vēal´
re vĕnġe´
rĕv´er ent
rhȳme
rīght´eous (-chŭs)
rĭv´et ed
rō´s̱ ē̇ āte
rŭf´fĭ an

65. săl´u ta´tion
sal vā´tion
sănc´tion
săt´is fy
săv´aġe
scăf´fold
scăr´çĭ ty
scâre´crow
scär´let
scēne
scĕnt´ed
sẽarch
sĕm´i cĩr´cle
sĕn´si tive
sĕp´a rat ed
shrewd
siēġe
sĭg´nal
sĭg´ni fy
sĭn´ew
skĕl´e ton
sleeve
snĭv´el ing
sō´cia ble
so´cia bĭl´ĭ ty

66. sō´cial (-shal)
so ç´īe ty
so joûrn´er
so lĕm´nĭ ty
sŏl´emn ly
sŏl´ī tude
spĕ´cial
spē´cies (-shē̇ z)
spĕç´i men
spĕc´ter
sphēre
spĭr´it
spĭr´it u al
spŏn´sor
stĕad´ĭ ly
sŭb´stance
subtle (sŭt´l)
sŭd´den ly
sŭf fi´cien cy
sŭm´mit
sŭmp´tu ous
sŭs pĕct´
sy̆ m´pa thy̆
tăl´ent
tĕn´der ly

67. tĕr´rā̇ çe
tĕr´ri fied
ter´ror
thē´a ter
thē´o ry
thĩrst
thrŭsh
tŏr´rent
tôr´ture
to̤ ur´na ment
to̤ ur´ney
trăġ´e dy
trăġ´ic
trăṉ´quil
trăns pâr´ent
trĭ´bute
trĭp´le
tri´umph
tri ŭm´phant
tȳ´rant
un cĭv´il
un co̤ uth´
ûr´chin
ū´s̱ ū al
ŭt´ter ançe

68. văn´ish
ve̱ in´ing
vĕn´ture
vẽr´dur ous
vẽr´min
vĕs´per
vĭ çĭn´ĭ ty
vĭc´tor
vĭc´to ry
vĭg´or
vĭg´or ous
vĭl´ lain
vī´o lençe
vĭs̱ ´ion
wäm´pum
wĕap´on
whĕlp
wrēath
zĕal´ous
PROPER NAMES
Ad mē´tus
Af´rĭ cȧ
A̤ l´ba ny
Al ex ăn´der

69. Am´brōs̱ e
An´ġe lo
An ï´ta´
An´tĭ och
Ap´en nīnes̱
A rā´bĭ a̯
A´sĭȧ
As sĭ´sĭ
A̤ u gŭs´tĭne
A̤ u gŭs´tu̯ s
Āy´mer
Ben e dĭct´ĭne
Bẽr lĭn´
Blĕn´heim
Bo´he mond
Bŏn´ĭ fāçe
Bouillon (bo̅ o̅ yōṉ´)
Brĭt´ain
Brṳçe
Căl´va ry
Ca pẽr´na um
Cär rä´rä
Căth´bad
Çhĕv ȧ liēr´
Çhĕv´ĭ ot

70. Clẽr´mont
Comyn (kŭm´in)
Cŏn´ehū bär
Cŏn´na̤ ught
Cŏn´stan tĭ nō´ple
Cor o nä´rï
Cū´bȧ
Cuchulain (ko̅ o̅ ho̅ o̅ ´lin)
Cṳlāin
Da kō´tȧ
Da măs´cus
De troit´
Don Quixote (dŏn kehō´te)
Doŭg´las
Drĕs̱ ´den
Drṳ´ĭd
Dul çĭn´e a
E´bro
E´ġy̆ pt
E māin´
E´rin
Es´the̯ r
Eū´rope
Fẽr´gus
Flŏr´ence

71. Fon tĭ nĕl´lȧ
Frăn´cis
Frĕd´er ick
Frï´s̱ ĭ ȧ
Gā´brĭ el
Ġĕn´ō̇ ȧ
Ġĕn o ēs̱ e´
Gĕs´ler
Ghï bẽr´tï
Ġiō chï´no
Gŏd´frey̆
Grĕg´o ry
Häl´le̯
Han´del
Hel vĕl´ly̆ n
Hŭṉ´ gȧ ry
Ich´ȧ bŏd
In´dĭes̱
It´a ly
Je rṳ´sa lem
Joliet (zhō lyā´)
Jôr´da̯ n
Lē o närd´ō̇
Lē´vīte
Măç´e don

72. Măl´a gȧ
Mär quette´ (-kĕt)
Mĕc´cȧ
Me dï´nȧ
Mĕd´ĭ ter rā´ne an
Me nŏm´o nĭe
Mī´cha el
Mĭl´an
Mis´sis sĭp´pĭ
Mo hăm´med
Mŏs̱ lem
Mus tȧ´phȧ
Nĭch´o las
Nï´ña
Păl´es tīne
Pä´lōs
Păn´the on
Pe̱ ´rez (-āth)
Persia (pēr´shĭȧ)
Pe̱ ´sä rō
Piacenza (pē ä chĕn´zä)
Pil är´
Pĭn´ta
Po nē´mäh
Que bĕc´

73. Rāph´a el
Rat bō´do
Ros sï´nï
Ro´zĭ năn te
Sa măr´ĭ tan
Săn´cho
Sän Săl´va dor
Sän Sïs´to
Sän´tȧ Crō´ce (-chā)
Sän´ta Ma rï´a
Săr´a çen
Săx´o ny
Se tăn´ta
Seville (sĕv´ĭl)
Sĭs´tïne
Spăn´ĭard
Stä´bat Mä´ter
Tăn´cred
Thames (tĕmz)
Ul´ster
Ur´ban
Ur bï´no
Valence (vä lŏṉs´)
Văt´ĭ can
Vĕn´ĭçe

74. Vẽr´ner
Vï´ȧ Cŏr o nä´rĭ
Vï ĕn´nȧ
Wis cŏn´sin
Wọlff
Wu̇ lf´ram

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