Motivation: GSATools is a free software package to analyze conformational ensembles and to detect functional motions in proteins by means of a structural alphabet. The software integrates with the widely used GROMACS simulation package and can generate a range of graphical outputs. Three applications can be supported: (i) investigation of the conformational variability of local structures; (ii) detection of allosteric communication; and (iii) identification of local regions that are critical for global functional motions. These analyses provide insights into the dynamics of proteins and allow for targeted design of functional mutants in theoretical and experimental studies. Availability: The C source code of the GSATools, along with a set of pre-compiled binaries, is freely available under GNU General Public License from http://mathbio.nimr.mrc.ac.uk/wiki/GSATools. Contact: alessandro.pandini@kcl. ac.uk or jkleinj@nimr.mrc.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. © 2013 The Author 2013. Published by Oxford University Press

Fornili, A

Fraternali, F

Kleinjung, J

Pandini, A

Bioinformatics

English

PubMed

Pandini, Alessandro

Fornili, Arianna

Fraternali, Franca

Kleinjung, Jens

Elsevier - Publisher Connector 

648a Wednesday, February 19, 2014Posters
Correlated Motions in Proteins
3273-Pos Board B1
Distinct Dynamic Signatures of Amyloidogenic Insulin Revealed by
Neutron Spin Echo Spectroscopy
Claus Czeslik, Mirko Erlkamp, Roland Winter.
Technische Universita¨t Dortmund, Dortmund, Germany.
Insulin is a well-known model protein for studying the formation of amyloid
fibrils. At low pH-values, in the presence of sodium chloride (NaCl), and
at elevated temperatures, insulin readily aggregates and forms amyloid
fibrils. Without NaCl, but in the presence of ethanol, the lag time of this
temperature-induced aggregation is increased drastically. In this study, we
have analyzed the dynamical properties of bovine insulin following these
two aggregation pathways by using neutron spin echo (NSE) spectroscopy.
In addition, small-angle X-ray scattering (SAXS) and thioflavin T (ThT)
fluorescence experiments were carried out to track the concomitant structural
changes of insulin. Measurements have mainly been performed at 318 K,
where amyloid fibrils evolve over 25 h, when the insulin solution contains
0.1 mM of NaCl at pD = 2. In contrast, no amyloid fibrils are formed during
25 h at 318 K, when the insulin solution contains ethanol with a volume frac-
tion of 20 % at pD = 2. Remarkably, the NSE data reveal distinct dynamic
signatures of insulin under these two solvent conditions. Collective diffusion
of insulin molecules can be inferred from an increased diffusion coefficient
at low wavevector transfers in the non-fibrillating sample, whereas self-
diffusion is observed in the other case. The SAXS data confirm these dy-
namic behaviors, because a pronounced correlation peak is only observed
under conditions of collective diffusion. The dynamic responses of insulin,
as revealed here by NSE spectroscopy, are in agreement with intermolecular
interaction potentials derived recently from measurements of the static struc-
ture factors of insulin and lysozyme.
3274-Pos Board B2
Gsatools: Analysis of Allosteric Communication and Functional Local
Motions using a Structural Alphabet
Alessandro Pandini1, Arianna Fornili1, Franca Fraternali1, Jens Kleinjung2.
1King’s College London, London, United Kingdom, 2MRC - National
Institute for Medical Research, London, United Kingdom.
Background
Biomolecular motions play a key role in several biological functions: enzy-
matic activity, protein-protein interactions, ligand binding and allosteric
regulation. Computational approaches, such as Molecular Dynamics (MD),
are now routinely used to reproduce the intrinsic dynamics of proteins, but
effective tools are still required to gain functional insight from the simulated
data.
Methods and Results
We previously suggested a method aimed at recovering the role of local confor-
mational changes in functional motions. To this purpose we developed a Struc-
tural Alphabet (SA): a set of 25 canonical states of 4-residue protein fragments
(Ca atoms only) describing the most probable local conformations in high-
resolution protein structures [1]. The SA provides a mean for the coarse-
grained annotation and processing of local conformations in a string format,
which lends itself to a range of efficient sequence analysis algorithms. The
SA has been successfully used in analysing local changes and allosteric signal
transmission [2].
Here we present GSATools [3], a set of SA-related tools interfacing with GRO-
MACS for the analysis of conformational ensembles. GSATools is designed for
the investigation of the conformational dynamics of local structures, the func-
tional correlations between local and global motions, and the mechanisms of
allosteric communication.
Conclusions
GSATools is a free, easy-to-use and fully documented software for the analysis
of conformational ensembles of proteins. The GSATools complement the
GROMACS toolkit with a powerful set of analyses to detect, annotate and
interpret local motions of functional relevance.
1. Pandini A., Fornili A. & Kleinjung J. BMC Bioinformatics 11, 97 (2010).
2. Pandini A., Fornili A., Fraternali F. & Kleinjung J., FASEB J., 26, 868
(2012).
3. Pandini A., Fornili A., Fraternali F. & Kleinjung J., Bioinformatics, 29, 2053
(2013).3275-Pos Board B3
Recapturing the Correlated Motions of Protein by using Coarse Grained
Models
Yan Lu, Freddie R. Salsbury, Jr.
Wake Forest University, Winston Salem, NC, USA.
Long range interaction and allosteric behavior is important for many biological
process, such as: metabolic mechanisms, signal transduction pathways.
Increasing evidence show that internal dynamics may play an important role
in such behavior. Investigating the dynamical effects of proteins is a chal-
lenging problem by using all atom molecular dynamics, because these proteins
are usually large. Here we use three well-defined coarse grained model: Go,
Martini and Cafemol model to examine if these coarse grained model can
explore the dynamics effects efficiently, also to explore the effects of mutation.
Eglin C is used as a model system. We found that both Go and Cafemol model
can recapture the dynamical characteristic of proteins very well, especially for
the defined secondary structures, while Martini model explore the dynamics
characteristic fairly well. For coil part, three coarse grained models have higher
fluctuation and correlated motions than all-atom model. Mutation effects are
also discussed.
3276-Pos Board B4
Modulation of Active Site Picosecond Dynamics in Mutant Forms of a
Thermophilic Alcohol Dehydrogenase (HT-ADH)
Corey W. Meadows, Judith P. Klinman.
Chemistry, UC Berkeley, Berkeley, CA, USA.
Previous kinetic and solvent-exchange data have suggested that an Arrhenius
break observed at 30oC is attributed to a shift in the equilibrium populations
of inactive and active microstates accompanied with a change in protein flex-
ibility [1,2]. We employed time-correlated single photon counting to investi-
gate the statistical nature of the fluorescence dynamics as a function of
temperature in ht-ADH. Two single-tryptophan variants were used as reporter
probes; Trp87 is fortuitously located at the active site and Trp167 is a surface
tryptophan>25A˚ from the catalytic center. The temperature dependence of the
time-resolved Stokes shifts reveal that the active site Trp contains highly
temperature-dependent relaxation dynamics while the surface Trp is virtually
temperature-independent. Though no break is observed that correlates to catal-
ysis, such results are discussed in the context of slower, more global dynamics
as a major contributor to catalytic behavior. Using both single-Trp variants, Ala
was then introduced to Tyr25 at the dimer interface and Val260 within the
cofactor binding site. Such alterations were previously shown to either elimi-
nate or exacerbate the magnitude of the temperature break, respectively
[3,4]. The fluorescence dynamics reveal that the temperature-dependent dy-
namics at Trp87 are drastically altered relative to its wild-type counterpart,
while Trp167 shows minor differences. Such results are discussed in the
context of an altered conformational landscape that further corroborates an
equilibration process occurring more slowly than the ps-timescale.
[1] Kohen, A., et al (1999) Nature, 399, 496-499.
[2] Liang, Z. X., et al (2004) PNAS, 101, 9556-9561.
[3] Nagel, Z. D., et al (2011) PNAS, 108, 10520-10525.
[4] Nagel, Z. D., et al (2013) JBC, 288, 14807-14097.
3277-Pos Board B5
ATP Binding Induced Conformational Change in RecQ Helicase
Maria Mills, Keir Neuman.
Lab of Single Molecule Biophysics, NIH/NHLBI, Bethesda, MD, USA.
RecQ helicases are a family of DNA helicases critical for DNA replication,
repair, and recombination. These enzymes catalyze DNA strand separation
via the binding and hydrolysis of ATP and are essential for resolving complex
DNA structures such as double Holliday junctions and D-loops. Mutations in
three human RecQ family proteins have been identified in several disorders
linked to premature aging and a high predisposition to cancer. Studies of the
structural dynamics of RecQ helicases are necessary to fully understand the
mechanism of this important class of proteins.
The RecQ family contains a highly conserved core consisting of two RecA-like
helicase domains, a zinc binding domain, and a dsDNA-binding winged helix
domain. Crystal structures of E. coli RecQ and human RecQ1 indicate that the
enzyme binds ATP in the cleft between the two RecA-like domains. This struc-
tural detail is consistent with the nucleotide binding motif of other SF2 family
helicases, in which ATP hydrolysis drives translocation along ssDNA. Howev-
er, it is not currently known how the binding and hydrolysis of ATP is coupled
to ssDNA translocation in RecQ. Using single molecule fluorescence and mo-
lecular dynamics simulations, we have investigated the ATP-dependent dy-
namics of the core of E. coli RecQ. We show that binding of ATP induces a


Gsatools: Analysis of Allosteric Communication and Functional Local Motions using a Structural Alphabet 

MOTIVATION: GSATools is a free software package to analyse conformational ensembles and to detect functional motions in proteins by means of a Structural Alphabet. The software integrates with the widely used GROMACS simulation package and can generate a range of graphical outputs. Three applications can be supported: (i) investigation of the conformational variability of local structures; (ii) detection of allosteric communication; (iii) identification of local regions that are critical for global functional motions. These analyses provide insights into the dynamics of proteins and allow for targeted design of functional mutants in theoretical and experimental studies. AVAILABILITY: The C source code of the GSATools along with a set of pre-compiled binaries is freely available under GNU General Public License from http://mathbio.nimr.mrc.ac.uk/wiki/GSATools. CONTACT: alessandro.pandini@kcl.ac.uk and jkleinj@nimr.mrc.ac.uk. SUPPLEMENTARY INFORMATION: A step-by-step tutorial is available in the Supplementary data at Bioinformatics online

King's Research Portal

GSATools:analysis of allosteric communication and functional local motions using a Structural Alphabet

Abstract
               Motivation: GSATools is a free software package to analyze conformational ensembles and to detect functional motions in proteins by means of a structural alphabet. The software integrates with the widely used GROMACS simulation package and can generate a range of graphical outputs. Three applications can be supported: (i) investigation of the conformational variability of local structures; (ii) detection of allosteric communication; and (iii) identification of local regions that are critical for global functional motions. These analyses provide insights into the dynamics of proteins and allow for targeted design of functional mutants in theoretical and experimental studies.
               Availability: The C source code of the GSATools, along with a set of pre-compiled binaries, is freely available under GNU General Public License from http://mathbio.nimr.mrc.ac.uk/wiki/GSATools.
               Contact:  alessandro.pandini@kcl.ac.uk or jkleinj@nimr.mrc.ac.uk
               Supplementary information:  Supplementary data are available at Bioinformatics online.</jats:p

Alessandro Pandini

Arianna Fornili

Franca Fraternali

Jens Kleinjung

Crossref

GSATools: analysis of allosteric communication and functional local motions using a structural alphabet

Brunel University Research Archive

Vol. 29 no. 16 2013, pages 2053–2055BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btt326Structural bioinformatics Advance Access publication June 5, 2013GSATools: analysis of allosteric communication and functionallocal motions using a structural alphabetAlessandro Pandini1,*, Arianna Fornili1, Franca Fraternali1,2 and Jens Kleinjung3,*1Randall Division of Cell and Molecular Biophysics, King’s College London, London SE1 1UL, 2The Thomas YoungCentre for Theory and Simulation of Materials, London WC2R 2LS and 3Division of Mathematical Biology, MRC NationalInstitute for Medical Research, London NW7 1AA, UKAssociate Editor: Anna TramontanoABSTRACTMotivation: GSATools is a free software package to analyze conform-ational ensembles and to detect functional motions in proteins bymeans of a structural alphabet. The software integrates with thewidely used GROMACS simulation package and can generate arange of graphical outputs. Three applications can be supported:(i) investigation of the conformational variability of local structures;(ii) detection of allosteric communication; and (iii) identification oflocal regions that are critical for global functional motions. These ana-lyses provide insights into the dynamics of proteins and allow fortargeted design of functional mutants in theoretical and experimentalstudies.Availability: The C source code of the GSATools, along with a set ofpre-compiled binaries, is freely available under GNU General PublicLicense from http://mathbio.nimr.mrc.ac.uk/wiki/GSATools.Contact: alessandro.pandini@kcl.ac.uk or jkleinj@nimr.mrc.ac.ukSupplementary information: Supplementary data are available atBioinformatics online.Received on April 24, 2013; revised on May 24, 2013; accepted onMay 30, 20131 INTRODUCTIONBiomolecular motions play a key role in several biological func-tions: enzymatic activity, protein–protein interactions, ligandbinding and allosteric regulation. Computational approaches,such as molecular dynamics (MD), are now routinely used toreproduce the intrinsic dynamics of proteins, but effective toolsare still required to gain functional insight from the simulateddata.Global collective motions are often associated with biologicalfunctions, and it was demonstrated that these motions can beextracted from conformational ensembles (Amadei et al., 1993).In a previous study, we suggested a method aimed at recover-ing the role of local conformational changes in functional mo-tions (Pandini et al., 2010). To this purpose, we developed astructural alphabet (SA): a set of 25 canonical states of four-residue protein fragments (C atoms only) describing the mostprobable local conformations in high-resolution protein struc-tures. Therefore, the SA provides a means for the coarse-grainedannotation and processing of local conformations in a stringformat, which lends itself to a range of efficient sequence analysisalgorithms.The SA has been used successfully in analyzing local changesin implicit solvent simulations (Kleinjung et al., 2012), allostericsignal transmission (Pandini et al., 2012) and conformationalchanges on effector binding (Baussand and Kleinjung, 2012).Here, we present GSATools, a set of SA-related tools inter-facing with GROMACS (Pronk et al., 2013) for the analysis ofconformational ensembles. GSATools is a software package de-signed for the investigation of the conformational dynamics oflocal structures, the functional correlations between local andglobal motions and the mechanisms of allosteric communication.2 IMPLEMENTATION AND FUNCTIONALITYGSATools was implemented in C as a set of analysis programsfor GROMACS 4.0.x (Van Der Spoel et al., 2005) and 4.5.x(Pronk et al., 2013) with a user-friendly and familiar interface.The required input is a trajectory file with a reference structure ofthe protein in PDB format. The trajectory can be composed ofconformers derived by MD or other simulation methods. Theanalysis is performed at the C level so that ensembles from one-bead coarse-grained methods are also suitable input.The requirement to install GSATools is a working installationof GROMACS. GSATools comprises the g_sa_encode pro-gram to encode a protein trajectory into an alignment of struc-tural strings and to perform basic statistical analysis, and theg_sa_analyze program to perform correlation analysis. The soft-ware distribution includes exemplary R (R-Development CoreTeam, 2010) scripts to directly generate figures and plots fromthe output files. In addition to standard R libraries, some scriptsrequire the Bio3D package (Grant et al., 2006).2.1 SA encoding and analysis of local motionsThe dynamics of local structures is captured by comparison ofthe sampled conformations with a set of representative backbonefragments (Pandini et al., 2010). The conformation of a proteinof n residues is condensed in a structural string of length n-3(Pandini et al., 2007); therefore, an MD ensemble containing mconformers can be encoded into a set of m aligned structuralstrings. This structural alphabet (SA) alignment can be savedin FASTA format for further sequence-oriented analyses.The g_sa_encode program provides two modes of encoding[local and global (Park and Levitt, 1995)] and yields several*To whom correspondence should be addressed. The Author(s) 2013. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), whichpermits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. by guest on January 15, 2015http://bioinformatics.oxfordjournals.org/Downloaded from statistics metrics about the encoded trajectory. The user can visu-alize the time evolution of structural changes by a color-codedrepresentation of the alignment (Fig. 1A). Additionally, theaccuracy of the encoding can be measured and plotted for in-spection. The extent of conformational variability at each pos-ition can be estimated by the Shannon entropy, and the relativefrequency of the representative fragments at each position can besaved as a sequence profile of the alignment. Example graphs ofthese statistics are included in the Supplementary Data.The condensed string representation and the sequence statis-tics are particularly suitable to detect subtle conformationalchanges often hidden by analyses of global structure anddynamics (Pandini et al., 2012).2.2 Analysis of correlated local motions and allosteryThe correlation between local conformational changes of twofragments in a protein can be calculated as the mutual informa-tion (MI) between two columns (positions) in the SA alignment.The MI matrix (Fig. 1B) of all pairwise correlations is a conciserepresentation of a protein’s local motions (Pandini et al., 2012).The correlation analysis is performed using the g_sa_analyze onthe SA alignment. The program calculates the positional MImatrix and estimates the statistical significance of each correl-ation. Additionally, a transition probability matrix can be calcu-lated to estimate the relative frequency of specific fragmenttransitions.A network model of the local motions can be derived from thenormalized MI matrix. The software distribution includes an Rscript to output a GML file for visualization in Cytoscape(Shannon et al., 2003) and a PyMOL (Schro¨dinger, 2009) plug-in to project the network onto the protein structure (Fig. 1C).Key protein fragments can be identified by eigenvector cen-trality (Newman, 2010) within the correlation network using aprovided R script. Nodes with higher network centrality repre-sent fragments that show correlated motions preferentially withother highly correlated fragments (Fig. 1D).If the protein of interest has an allosteric function, communi-cation pathways between the allosteric and orthosteric sites canbe extracted from the network model as shown in Figure 1E(Pandini et al., 2012).2.3 Detection of local motions correlated to functionFunctionally relevant motions can be identified if a function-related structural property is known. Generally, global or collect-ive motions are considered for this type of analysis (Hub and DeGroot, 2009). In the GSATools approach, the g_sa_analyzescans the protein to detect contributions to a functional changeby local motions.Any time-dependent index of a function-related property canbe provided. Then the g_sa_analyze program can calculate thecorrelation (as MI) between the functional index and each pro-tein fragment (Fig. 1F). The MI value is eventually used as ascore to predict putative regions of the protein for further ana-lysis, e.g. site-directed mutagenesis (Fig. 1G).The functional index may be represented by a collectivemotion known to be associated with a biological mechanism(Pandini et al., 2012). In this special case, the analysis can iden-tify local structures (e.g. hinges) whose motion is propagated totrigger a functional change on a global scale.2.4 TutorialThe software distribution includes a step-by-step tutorial(Supplementary Data) with input files, output files, shell scriptsand R scripts. The scripts can easily be modified to process user-provided input data.3 CONCLUSIONSGSATools is a free, easy-to-use and fully documented softwarefor the analysis of conformational ensembles of proteins. TheGSATools complements the GROMACS toolkit with a power-ful set of analyses to detect, annotate and interpret local motionsof functional relevance.Dynamics of local structures, functional correlations of localand global motions and mechanisms of allosteric communicationcan be extracted from ensembles of conformations. An exampleof a comprehensive analysis is provided in the software distribu-tion; each analysis can be performed independently if desired.Results are generated in standard formats for easy comparisonwith other analyses aimed at identification of global motions, i.e.principal component analysis (Amadei et al., 1993).GSATools addresses the need for automated functional ana-lysis emerging from the wealth of molecular simulationscurrently available in the scientific community.Funding: This research was supported by the Medical ResearchCouncil (U117581331 to JK); the British Heart Foundation(FS/12/41/29724 to AF); the Biotechnology and BiologicalFig. 1. Overview of the major analysis steps for the NtrC protein(Pandini et al., 2012). A high-resolution image is given in theSupplementary Data2054A.Pandini et al. by guest on January 15, 2015http://bioinformatics.oxfordjournals.org/Downloaded from Sciences Research Council (BB/I023291/1 and BB/H018409/1 toAP and FF).Conflict of Interest: none declared.REFERENCESAmadei,A. et al. (1993) Essential dynamics of proteins. Proteins, 17, 412–425.Baussand,J. and Kleinjung,J. (2012) Specific conformational states of Ras GTPaseupon effector binding. J. Chem. Theory Comput., 9, 738–749.Grant,B.J. et al. (2006) Bio3d: an R package for the comparative analysis of proteinstructures. Bioinformatics, 22, 2695–2696.Hub,J.S. and De Groot,B.L. (2009) Detection of functional modes in proteindynamics. PLoS Comput. Biol., 5, e1000480.Kleinjung,J. et al. (2012) Implicit solvation parameters derived from explicit waterforces in large-scale molecular dynamics simulations. J. Chem. Theory Comput.,8, 2391–2403.Newman,M. (2010) Networks: An Introduction. Oxford University Press, UK.Pandini,A. et al. (2007) MinSet: a general approach to derive maximally represen-tative database subsets by using fragment dictionaries and its application to theSCOP database. Bioinformatics, 23, 515–516.Pandini,A. et al. (2010) Structural alphabets derived from attractors in conform-ational space. BMC Bioinformatics, 11, 97.Pandini,A. et al. (2012) Detection of allosteric signal transmission by information-theoretic analysis of protein dynamics. FASEB J., 26, 868–881.Park,B.H. and Levitt,M. (1995) The complexity and accuracy of discrete statemodels of protein structure. J. Mol. Biol., 249, 493–507.Pronk,S. et al. (2013) GROMACS 4.5: a high-throughput and highly parallel opensource molecular simulation toolkit. Bioinformatics, 29, 845–854.R-Development Core Team. (2010) R: A Language and Environment for StatisticalComputing. Vienna, Austria.Schro¨dinger,L.L.C. (2009) The PyMOL Molecular Graphics System, Version 1.2r0.Shannon,P. et al. (2003) Cytoscape: a software environment for integrated modelsof biomolecular interaction networks. Genome Res., 13, 2498–2504.Van Der Spoel,D. et al. (2005) GROMACS: fast, flexible, and free, J. Comput.Chem., 26, 1701–1718.2055GSATools by guest on January 15, 2015http://bioinformatics.oxfordjournals.org/Downloaded from 

GSATools: Analysis of allosteric communication and functional local motions using a structural alphabet

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GSATools: Analysis of allosteric communication and functional local motions using a structural alphabet

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