POLYVIEW-MM: web-based platform for animation
and analysis of molecular simulations
Aleksey Porollo1,* and Jaroslaw Meller1,2,*
1Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA and2Department
of Informatics, Nicholas Copernicus University, Torun, Poland
Received February 17, 2010; Revised April 30, 2010; Accepted May 7, 2010
Molecular simulations offer important mechanistic
and functional clues in studies of proteins and
other macromolecules. However, interpreting the
results of such simulations increasingly requires
tools that can combine information from multiple
structural databases and other web resources, and
provide highly integrated and versatile analysis
tools. Here, we present a new web server that
motion (MM) with structural and functional ana-
lysis of macromolecules. The new tool, dubbed
POLYVIEW-MM, enables animation of trajectories
generated by molecular dynamics and related simu-
lation techniques, as well as visualization of alter-
native conformers, e.g. obtained as a result of
protein structure prediction
molecule docking. To facilitate structural analysis,
analysis of conformational changes using Jmol
and its tailored extensions, publication quality ani-
mation using PyMol, and customizable 2D summary
plots that provide an overview of MM, e.g. in terms
of changes in secondary structure states and
relative solvent accessibility of individual residues
in proteins. Furthermore, POLYVIEW-MM integrates
visualization with various structural annotations,
including automated mapping of known inter-
action sites from structural homologs, mapping of
cavities and ligand binding sites, transmembrane
methods or small
A growing number of tools are being developed for
visualization of macromolecules and animation of their
motion with the goal of facilitating experimental and com-
putational studies of macromolecules and their complexes.
Many of these tools are available as stand-alone packages,
such as VMD (1), Swiss-PDBViewer (2) or PyMol (3),
which combine impressive rendering capability with ver-
satile analyses, scripting languages and extensible architec-
ture. At the same time, web-based tools are being
developed to provide easy-to-use intuitive interfaces,
open-source Java viewer for chemical structures in 3D).
Several resources have specifically been developed to
provide animation of MM, including Molecules in
Motion (http://www.moleculesinmotion.com/), AISMIG
(4), POLYVIEW-3D (5) and Protein Movie Generator (6).
One of the major drawbacks of stand-alone tools is
their limited awareness of rich on-line resources and data-
bases. In addition, such tools are often rather difficult to
use for researchers with a limited technical expertise in
computing. On the other hand, existing on-line tools for
MM require further improvements both in terms of the
quality of visualization and integration with analysis and
annotation tools. Enhanced tools for visualization, anno-
tation and automated analyses that take advantage of ever
more complex network of web resources and databases is
an emerging need in the fields of structural biology, func-
tional genomics and molecular simulations.
Here, we present a new web-based tool, POLYVIEW-
MM, which integrates high-quality animation with struc-
tural annotation of MMs, and allows both qualitative
and quantitative analysis of the results of macromol-
ecular simulations. To facilitate structural analyses,
POLYVIEW-MM combines: (i) interactive view and
scripting-based conformational analysis using Jmol and
its tailored extensions; (ii) versatile annotation and publi-
cation quality animation using PyMol, which is available
through POLYVIEW-3D; and (iii) customizable 2D
summary plots that provide insights into MM in terms
of changes in secondary structure (SS) states, relative
*To whom correspondence should be addressed. Tel: +1 513 558 1945; Fax: +1 513 558 4397; Email: email@example.com
Correspondence may also be addressed to Jaroslaw Meller. Tel: +1 513 558 1958; Fax: +1 513 558 4397; Email: firstname.lastname@example.org
Nucleic Acids Research, 2010, Vol. 38, Web Server issuePublished online 26 May 2010
? The Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
in proteins, as well as distance plots and analysis of intra-
and inter-molecular contacts. The latter type of analysis is
facilitated by 2D summary plots highlighting residues
involved in complex formation, including two important
special cases of interaction interfaces in alternative
ligand complexes obtained from protein and small
molecule docking simulations, respectively.
At the same time, using easy to generate animations
and other graphical representations, POLYVIEW-MM
enables visualization of trajectories generated by molecu-
lar dynamics (MD) and related simulation techniques.
Moreover, alternative conformers of small molecules,
proteins and other macromolecules can be visualized
using molecular movies. As an input, POLYVIEW-MM
accepts files in the standard Protein Data Bank (PDB)
format with multiple models. This format has been
widely adopted by servers and programs that aim at
Examples of the latter include tools for analysis of dis-
tortions and slow coordinated motions in proteins, as ob-
tained using, e.g. AD-ENM (7) or MolMovDB (8). To
enable direct analysis of MD trajectories, POLYVIEW-
MM accepts trajectory files in the DCD format, which is
supported by widely used simulation packages NAMD (9)
and CHARMM (10), as well as the TRR format sup-
ported by GROMACS (11). For the analysis of small
molecule docking, the DLG format used by the popular
AutoDock program (12) is accepted as well.
POLYVIEW-MM incorporates into the analysis of
macromolecular simulations many structural and func-
tional annotations, including automated mapping of
protein domains from the Pfam database of protein
families (13), membrane domains from the PDB_TM
database (14) and protein–protein interaction interfaces
from the PDB database (15). The latter option can
greatly facilitate structural and functional analyses in the
context of constantly growing number of resolved protein
complexes. The mapping is conducted using sequence
homology search. The results of several annotation
tools, including CASTp for the identification of structural
cavities (16), and SPPIDER for predicting putative novel
interaction sites (17) can be automatically retrieved and
mapped into POLYVIEW-MM output as well. Addi-
tionally, the server accepts requests for calculation and
mapping of evolutionary conservation scores derived
from multiple sequence alignment using PSI-BLAST
(18). This option enables analysis of conserved functional
hotspots in the context of MM.
and analysis ofMM.
Input and output
As the primary input format, POLYVIEW-MM uses 3D
coordinate files in the PDB (15) format that can contain
multiple structural models of the same molecule, or a
complex of molecules, including proteins, nucleic acids
and small molecules. This format can be used with each
of the basic types of queries that include: (i) NMR-based
ensembles of models (these queries can also be specified
by the PDB entry ID); (ii) trajectories obtained using
MD simulations; (iii) molecular morphs or snapshots rep-
resenting slow coordinated motions obtained, e.g. using
elastic network models; and (iv) protein–protein or pro-
simulations. Other specific input formats include the
DCD and TRR formats for MD trajectories and the
DLG (AutoDock) format for ligand docking simulations.
For other MD formats, utilities similar to CatDCD (1) or
MDanalysis (http://code.google.com/p/mdanalysis/) may
need to be used to convert MD trajectories to the
multiple model PDB or DCD formats. For faster upload
of the data, MD trajectories can be submitted using com-
pressed files in ZIP, GZIP and BZIP2 formats.
As part of the output, POLYVIEW-MM allows to
display trajectories and multiple conformers using inter-
active animations and static views in Jmol, coupled with
tailored selection and annotation options. These can be
subsequently imported into POLYVIEW-3D in order to
generate publication quality static pictures and animations
using PyMol rendering. POLYVIEW-MM also generates
a number of customizable 2D plots that provide simple
yet informative summaries of conformational changes
and differences between individual models. All movies
and plots generated by the server can be deposited with
user-defined annotations to an image library as a mechan-
ism to document and share data with colleagues.
Motion at a glance
To provide an overview of conformation changes in
proteins, POLYVIEW-MM generates 2D plots that
display SS and RSA states for individual residues in
each snapshot of a trajectory, or each conformer
submitted as a multiple model PDB file. SS and RSA
are calculated using the DSSP program (19), using either
eight (G, H, I, T, E, B S, and C, as defined in DSSP) or
three SS states (H—a-helix, E—b-strand, and C—loop).
Surface-exposed area computed using DSSP is normalized
by the maximum value of the solvent exposed surface
area for a given type of amino acid as determined
in (20), resulting in RSA values between 0% and 100%,
with the latter corresponding to a fully exposed residue.
Real-valued RSA are subsequently projected into 10
discrete states for the purpose of displaying them as
shaded boxes, with black boxes corresponding to fully
buried and white boxes to fully exposed residues, respect-
ively. At each position, entropy in terms of the SS and
RSA states is computed in order to capture conformation-
al variability at that position in a trajectory or a set of
conformers. In addition, simple distance plots between
interactively selected pairs of atoms or residues can be
generated for analysis of molecular trajectories.
Protein–protein and protein–ligand interfaces
Following previous studies (17,21,22), we define protein–
protein interaction sites based on the RSA change upon
complex formation, i.e. RSA difference between an
unbound and bound (complex) structure of an individual
chain. The procedure and thresholds used to assign an
amino acid residue as an interaction site can be found
Nucleic AcidsResearch, 2010, Vol.38, WebServer issueW663
in (17). Protein–protein interaction interfaces can be
characterized in terms of the surface area buried upon
complex formation, amino acid properties and the
presence of conserved hot spots, facilitating analysis of
protein docking simulations, for instance. Protein–ligand
contacts are determined using the respective procedure
adopted in Protein Explorer (23) and subsequently in
the FirstGlance in Jmol server (FGiJ, http://firstglance.
jmol.org/) that accounts for hydrogen bonds, water and
salt bridges, hydrophobic and aromatic ring interaction
and different types of metals binding. For the correspond-
ing bond distance definitions, the reader is referred to the
FGiJ documentation. Mapping specific residues in contact
with the ligand in alternative poses of protein–ligand
complexes can be used to assess the results of docking
simulations. Consistency of interacting sites observed in
protein–protein or protein–ligand docking models is
measured by computing frequency of being in contact
with the interacting co-factor.
POLYVIEW-MM provides the ability to automatically
annotate images and movies with structural and function-
al characteristics derived using annotation and prediction
tools. The following structural annotations can be
retrieved and mapped at present (more to be included in
future): (i) structural cavities determined using the CASTp
server (16); (ii) protein domains and motifs annotated in
the Pfam database (13); (iii) transmembrane domains
identified using the PDB_TM database (14); (iv) known
protein–protein interaction interfaces found in complexes
deposited to PDB; and (v) putative interaction sites pre-
dicted using SPPIDER (17); position-specific evolutionary
scores derived from multiple sequence alignments using
PSI-BLAST (18). Retrieval and mapping of available
structural annotations is conducted using sequence-based
homology search using BLAST (24). Homology hits with
E-value equal to or lower than 0.001 with sufficient level of
sequence identity (70%) are considered when performing
searches against PDB, Pfam and PDB_TM databases,
and residues within structural and functional hot spots
(e.g. interaction interfaces) are mapped into the query
structure based on the sequence alignment. Specifically,
all homology hits, as defined above, from Pfam and
PDB are retrieved for annotation, whereas only the best
homology hit is used to map transmembrane domains.
Multiple sequence alignment is performed against nr
database with E=0.001 cutoff. Conservation scores are
computed from the position-specific scoring matrix
obtained after three PSI-BLAST iterations.
To illustrate the capabilities of POLYVIEW-MM, we
present two examples. Since molecular movies cannot be
directly displayed here, these examples focus on static
pictures and are supplemented by animations in the docu-
mentation and image gallery available from the home
page. The first example presented here concerns visualiza-
tion and analysis of MD trajectories. In Figure 1, an
NAMD generated trajectory for an idealized resilin-like
peptide, AN16 (25), is shown using a 2D plot that char-
acterizes conformational changes in terms of SSs of indi-
vidual snapshots in the trajectory. The number of times a
given residue changes the SS state, which is a measure
of conformational transitions at that position throughout
the trajectory is represented by white through pink to red
bars shown below the amino acid sequence. In addition,
one particular 3D snapshot is shown in Figure 1B for
illustration of persistent disorder observed in this case
(only transient helices and short beta strands are
observed in some repeats). This persistent disorder can
also be easily discerned using a 2D plot with RSA
profiles of each structural snapshot (data not shown).
AN16 comprises 16 repeats of an 11-mer derived from
elastomeric insect protein called resilin (26), which is
Figure 1. Visualization of MD trajectory for resilin-like peptide with changes in SS states along the trajectory shown in (A). Red colors correspond
to different helices, green to beta states and blue to unstructured regions, bends and magenta to turns, respectively (see POLYVIEW-MM docu-
mentation for details). Each row corresponds to an individual snapshot of the structure, an example of which is shown in (B) with tyrosine side
chains shown in colors corresponding to initial SS states and using stick models. Note the lack of regular SSs.
W664Nucleic Acids Research, 2010,Vol.38, Web Server issue
characterized by the highest known resilience of all known
elastic materials. Upon stretching, entropy of the dis-
ordered relaxed state is lost, giving rise to an entropic
force(27).In nature, multiple
cross-linked at tyrosine residues to form an elastic fiber,
which signifies the importance of the distribution of
tyrosine pairwise distances that can be generated in
POLYVIEW-MM together with movies and other repre-
sentations of the trajectory.
The second example presented here illustrates how
POLYVIEW-MM can be used to visualize and analyze
docking simulations (Figure 2). Specifically, the widely
used package for small molecule docking, AutoDock4
(12), has been used to generate alternative poses (models
of the receptor–ligand complexes) for fucose bound to the
capsid protein of a specific strain (VA387) of norovirus
that causes large outbreaks of gastroenteritis (28).
Noroviruses recognize histoblood type antigens that are
presented by host cells in the gut (28), and binding of the
fucose ring in these polysaccharides to norovirus capsid
proteins plays an important role (29). Examples of alter-
native fucose poses superimposed together are shown in
the right panel using a static 3D picture (the correspond-
ing animation is available from the POLYVIEW-MM
gallery). The corresponding interaction interfaces are
shown in the left upper panel, with residues in contact
with the ligand indicated by magenta (each row represents
one docking model). In the lower left panel, evolutionary
conservation of amino acid residues is indicated by the
background of the amino acid letter (red corresponds to
highly conserved, and blue to variable positions), chemical
profile (with yellow indicating hydrophobic and red and
brown hydrophilic positions) is shown below the sequence
row, and the SSs and solvent accessibilities (indicated by
shaded boxes with black for buried and white for exposed
positions) are shown in rows 3 and 4, respectively. As can
be seen, fucose binding site(s), majority of which corres-
ponds to the trisaccharide binding site in the resolved
structure (PDB ID: 2obs), are located within variable,
largely hydrophilic loops.
In summary, POLYVIEW-MM provides a versatile
Jmol-based interactive view of molecular trajectories
and multiple conformers that can be generated by
variety of simulation and modeling techniques. In
addition, high-quality movies and static pictures can be
generated to complement the interactive assessment of
MM. These 3D representations are supplemented by cus-
tomizable, intuitive 2D plots that capture at a glance the
essence of conformational changes or differences between
individual models to be assessed. POLYVIEW-MM
combines visualization with structural and functional an-
notation by automatically mapping functional hot spots
and structural features into analyzed models. The corres-
ponding web server is publicly available, and it utilizes the
same communication protocol and data submission/pro-
cessing/retrieval technology as previously developed for
POLYVIEW-2D (30) and POLYVIEW-3D (5). These
servers are being widely used, logging more than 100000
submissions from over 80 countries to date. Therefore,
POLYVIEW-MM is expected to provide a fast and
robust execution while processing significant number of
requests from the users.
We would like to thank Roman Petrenko and Jacek
Biesiada for their help with generating examples of MD
and docking simulations. The authors also acknowledge
the support of the Cincinnati Children’s Hospital Medical
Center (CCHMC) and University of Cincinnati Medical
College. We would like to dedicate this work to the author
of PyMol, Warren DeLano, who passed away recently.
R01A1055649; National Science Foundation GOALI:
081163; Next Generation Biomedical Investigator Award
(to A.P.) by the Center for Environmental Genetics
funded by National Institute of Environmental Health
Institutesof Health R01GM067823,
Figure 2. Analysis of docking models for the complex of fucose and norovirus capsid protein. Right panel shows alternative fucose poses
superimposed together. The corresponding interaction interfaces are shown in the left upper panel, with residues in contact with the ligand indicated
by magenta. In the lower left panel, amino acid residues are aligned with the corresponding profiles: evolutionary conservation, physico-chemical
properties, SS and RSA states.
Nucleic AcidsResearch, 2010, Vol.38, WebServer issue W665
Sciences (P30ES006096). Funding for open access charge:
Grants from National Institutes of Health; Personal
faculty member departmental accounts.
Conflict of interest statement. None declared.
1. Humphrey,W., Dalke,A. and Schulten,K. (1996) VMD: visual
molecular dynamics. J. Mol. Graph., 14, 27–38.
2. Guex,N. and Peitsch,M.C. (1997) SWISS-MODEL and the
Swiss-PdbViewer: an environment for comparative protein
modeling. Electrophoresis, 18, 2714–2723.
3. DeLano,W.L. (2008) The PyMOL Molecular Graphics System.
DeLano Scientific LLC, Palo Alto, CA.
4. Bohne-Lang,A., Groch,W.D. and Ranzinger,R. (2005)
AISMIG—an interactive server-side molecule image generator.
Nucleic Acids Res., 33, W705–709.
5. Porollo,A. and Meller,J. (2007) Versatile annotation and
publication quality visualization of protein complexes using
POLYVIEW-3D. BMC bioinformatics, 8, 316.
6. Autin,L. and Tuffery,P. (2007) PMG: online generation of
high-quality molecular pictures and storyboarded animations.
Nucleic Acids Res., 35, W483–W488.
7. Zheng,W., Brooks,B.R. and Thirumalai,D. (2006) Low-frequency
normal modes that describe allosteric transitions in biological
nanomachines are robust to sequence variations. Proc. Natl Acad.
Sci. USA, 103, 7664–7669.
8. Gerstein,M. and Krebs,W. (1998) A database of macromolecular
motions. Nucleic Acids Res., 26, 4280–4290.
9. Phillips,J.C., Braun,R., Wang,W., Gumbart,J., Tajkhorshid,E.,
Villa,E., Chipot,C., Skeel,R.D., Kale,L. and Schulten,K. (2005)
Scalable molecular dynamics with NAMD. J. Comput. Chem., 26,
10. Brooks,B.R., Bruccoleri,R.E., Olafson,B.D., States,D.J.,
Swaminathan,S. and Karplus,M. (1983) CHARMM—a program
for macromolecular energy, minimization, and dynamics
calculations. J. Comput. Chem., 4, 187–217.
11. Lindahl,E., Hess,B. and van der Spoel,D. (2001) GROMACS 3.0:
a package for molecular simulation and trajectory analysis.
J. Mol. Model., 7, 306–317.
12. Goodsell,D.S., Morris,G.M. and Olson,A.J. (1996) Automated
docking of flexible ligands: applications of AutoDock. J. Mol.
recognit., 9, 1–5.
13. Finn,R.D., Tate,J., Mistry,J., Coggill,P.C., Sammut,S.J.,
Hotz,H.R., Ceric,G., Forslund,K., Eddy,S.R., Sonnhammer,E.L.
et al. (2008) The Pfam protein families database. Nucleic Acids
Res., 36, D281–D288.
14. Tusnady,G.E., Dosztanyi,Z. and Simon,I. (2005) PDB_TM:
selection and membrane localization of transmembrane proteins
in the protein data bank. Nucleic Acids Res., 33, D275–D278.
15. Bernstein,F.C., Koetzle,T.F., Williams,G.J., Meyer,E.F. Jr,
Brice,M.D., Rodgers,J.R., Kennard,O., Shimanouchi,T. and
Tasumi,M. (1977) The Protein Data Bank: a computer-based
archival file for macromolecular structures. J. Mol. Biol., 112,
16. Dundas,J., Ouyang,Z., Tseng,J., Binkowski,A., Turpaz,Y. and
Liang,J. (2006) CASTp: computed atlas of surface topography
of proteins with structural and topographical mapping of
functionally annotated residues. Nucleic Acids Res., 34,
17. Porollo,A. and Meller,J. (2007) Prediction-based fingerprints of
protein-protein interactions. Proteins, 66, 630–645.
18. Altschul,S.F., Madden,T.L., Schaffer,A.A., Zhang,J., Zhang,Z.,
Miller,W. and Lipman,D.J. (1997) Gapped BLAST and
PSI-BLAST: a new generation of protein database search
programs. Nucleic Acids Res., 25, 3389–3402.
19. Kabsch,W. and Sander,C. (1983) Dictionary of protein secondary
structure: pattern recognition of hydrogen-bonded and
geometrical features. Biopolymers, 22, 2577–2637.
20. Chothia,C. (1976) The nature of the accessible and buried
surfaces in proteins. J. Mol. Biol., 105, 1–12.
21. Jones,S. and Thornton,J.M. (1997) Analysis of protein-protein
interaction sites using surface patches. J. Mol. Biol., 272,
22. Chakrabarti,P. and Janin,J. (2002) Dissecting protein-protein
recognition sites. Proteins, 47, 334–343.
23. Martz,E. (2002) Protein Explorer: easy yet powerful
macromolecular visualization. Trends Biochem. Sci., 27, 107–109.
24. Altschul,S.F., Gish,W., Miller,W., Myers,E.W. and Lipman,D.J.
(1990) Basic local alignment search tool. J. Mol. Biol., 215,
25. Nairn,K.M., Lyons,R.E., Mulder,R.J., Mudie,S.T., Cookson,D.J.,
Lesieur,E., Kim,M., Lau,D., Scholes,F.H. and Elvin,C.M. (2008)
A synthetic resilin is largely unstructured. Biophys. J., 95,
26. Elvin,C.M., Carr,A.G., Huson,M.G., Maxwell,J.M., Pearson,R.D.,
Vuocolo,T., Liyou,N.E., Wong,D.C., Merritt,D.J. and Dixon,N.E.
(2005) Synthesis and properties of crosslinked recombinant
pro-resilin. Nature, 437, 999–1002.
27. Petrenko,R., Dickerson,M., Naik,R., Patnaik,S., Beck,T. and
Meller,J. (2009) In Arabnia,H.R. and Yang,M.Q. (eds),
Proceedings of the 2009 International Conference on Bioinformatics
& Computational Biology, BIOCOMP 2009, Vol. II. CSREA
Press. pp. 598–603.
28. Tan,M., Huang,P., Meller,J., Zhong,W., Farkas,T. and Jiang,X.
(2003) Mutations within the P2 domain of norovirus capsid affect
binding to human histo-blood group antigens: evidence for a
binding pocket. J. Virol., 77, 12562–12571.
29. Cao,S., Lou,Z., Tan,M., Chen,Y., Liu,Y., Zhang,Z., Zhang,X.C.,
Jiang,X., Li,X. and Rao,Z. (2007) Structural basis for the
recognition of blood group trisaccharides by norovirus. J. Virol.,
30. Porollo,A.A., Adamczak,R. and Meller,J. (2004) POLYVIEW:
a flexible visualization tool for structural and functional
annotations of proteins. Bioinformatics, 20, 2460–2462.
W666Nucleic Acids Research, 2010,Vol.38, Web Server issue