pepcat?a new tool for conformational analysis of peptides
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PEPCATA New Tool for ConformationalAnalysis of Peptides
M. F. ODONOHUE,1 E. MINASIAN,2 S. J. LEACH,2 A. W. BURGESS,1
H. R. TREUTLEIN11Ludwig Institute for Cancer Research and Cooperative Research Center for Cellular Growth Factors,P.O. Royal Melbourne Hospital, Parkville, Victoria 3050, Australia2Department of Biochemistry & Molecular Biology, Melbourne University, Parkville, Victoria 3052,Australia
Received 9 August 1999; accepted 15 November 1999
ABSTRACT: We report a new technique for the efficient analysis andvisualization of peptide and protein conformations and conformationalrelationships, which we have implemented in a computer program calledPEPCAT. PEPCAT (an abbreviation for Peptide Conformational Analysis Tool)provides a simple, graphical, and flexible framework that allows the user todefine a specific structural feature or juxtaposition of amino acids and to followthe fate of the motif during a molecular dynamics simulation. Here we describethe PEPCAT analysis of the effects of environmental and chemical modificationson conformational preferences of a regulator of hemopoiesis, namely thepentapeptide pyro-EEDCK, and of a conformational transition in theimmunosuppressant drug cyclosporin A. PEPCAT, however, can be applied tothe conformational analysis of peptides and proteins in general. c 2000 JohnWiley & Sons, Inc. J Comput Chem 21: 446461, 2000
Keywords: cyclosporin A; molecular modeling; transitions; dynamics;conformational search; conformational analysis; hemopoietic peptides
C onformations of biological macromoleculesare determined by the properties of theirmolecular building blocks and by their surround-ing environment.1, 2 Macromolecular systems, suchas peptides and proteins, often display multiple
Correspondence to: H. R. Treutlein; e-mail: Herbert.Treutlein@ludwig.edu.au
conformations dependent on temperature and envi-ronment. A description and classification of possibleconformations is important for the understandingof molecular properties. Methods for the analysisand display of conformational types and energieshave been developed for small molecules,3 but untilnow simple methods have not been readily avail-able for macromolecules. In this work we describePEPCAT, a new method for evaluation, comparisonof conformational properties, and conformational
Journal of Computational Chemistry, Vol. 21, No. 6, 446461 (2000)c 2000 John Wiley & Sons, Inc.
PEPCATA NEW TOOL FOR CONFORMATIONAL ANALYSIS OF PEPTIDES
interrelationships, which can be applied to macro-molecules.
This article is organized as follows: the remain-der of the Introduction section will discuss prob-lems with current conformational analysis tools.The Method section describes our PEPCAT methodin detail, and this is followed in the Results sec-tion by a description of two examples of a PEP-CAT analysis. Finally, in the Discussion section, ourmethod is summarized, and possible extensions arediscussed.
Some of the crucial questions for peptide struc-tural analysis are how to describe peptide structuresand how to determine when they are similar enoughthat they can be considered to share the same iden-tity or conformation. The most popular method forpeptide conformational analysis calculates a sim-ilarity measure between each pair of structures,using a root-mean-square difference (RMSD) tech-nique (see below). The structures are then groupedinto distinct conformations based on the relativedeviation to other observed structures.4 There areproblems with these techniques that can be illus-trated by their inability to generate classificationhierarchies similar to the three-dimensional folddatabases CATH5 and SCOP6 without manual in-spection as an essential contribution.
Molecular dynamics simulations provide an ex-cellent source of information about the details ofpathways between different molecular conforma-tions. The results of molecular dynamics simula-tions are often saved in a trajectory file, whichcontains coordinate sets (or frames) of the peptidestructure saved at regular intervals during the sim-ulation. Each frame consists of the set of all atomicCartesian coordinates in the peptide at that time-step in the simulation. Because of the large size oftrajectory files, and the fact that many separate cal-culations are needed to ensure statistically relevantresults, the data used for conformational analysiscan easily occupy large amounts of storage space.
Although automated trajectory analysis tech-niques exist,4, 7 10 direct viewing of the trajectoryfile in the form of a playback movie of molecularmovement still remains the most common analy-sis technique for the visualization of conformationalchanges. However, more elaborate and automatedtools are necessary to analyze and represent the
more subtle conformational features and changesthat occur during a dynamics simulation.
CARTESIAN RMSD MEASUREMENT
Peptide conformational analysis relies heavily onthe comparison between structures, and this placesa great responsibility on the comparison operator tocorrectly discriminate between structures. The mostpopular method to calculate a measure of structuralsimilarity is to overlay the structures using a least-squares fit algorithm, and to compare the Cartesiancoordinates of the backbone C atoms only. Thesimilarity can be expressed in terms of an averagedeviation of C atoms from their average position,the root mean square deviation (RMSD).4
The RMSD value often gives a good measureof similarity, and is insensitive to small changes inatomic Cartesian coordinates. However, the changeof a single backbone dihedral value can lead to alarge overall RMSD value. Consider, for example,a backbone dihedral angle change in a residue inthe middle of a peptide with an extended -strandconformation. Under these circumstances, the singleRMSD value correctly identifies the quite signifi-cant change in the shape of the peptide and thevast number of atoms that have suffered dramaticchanges in their Cartesian coordinates. However, itgives no clue that a comparatively simple structuralchange (rotation of a single dihedral bond angle) isresponsible for the difference, and that the two sub-structures on either side of the changed residue arestill identical to the original peptide structure.
Calculation of the RMSD value requires the com-putation of the optimal overlay of the two peptidestructures. This is computationally expensive whencompared to other equivalent low level operationssuch as comparing two numerical values. The highcosts of the Cartesian RMSD operation make it anundesirable candidate for comparing large numbersof structures.
DIHEDRAL RMSD MEASUREMENT
A peptide structure can also be represented asa sequence of dihedral bond angle values ratherthan as a set of Cartesian coordinates. This inter-nal coordinate system better represents the localconformations and the degrees of movements thatare available to the molecule. Bond length and an-gle stretching are ignored, as they usually does notsignificantly contribute to the overall peptide con-formation. A backbone dihedral RMSD value canthen be calculated between two structures using the
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difference in peptide backbone dihedral angle val-ues. This has the advantage of not requiring theexpensive overlay operation necessary for Carte-sian RMSD comparisons. Structures that differ inonly one dihedral angle (such as our previouslymentioned example) will, when compared to theoriginal structure, generate a relatively small back-bone dihedral RMSD value.
RMSD AVERAGING EFFECT
The RMSD comparison measures the average dif-ference over all coordinate values (this applies inboth Cartesian and backbone dihedral angle com-parisons). Unfortunately, this destroys the ability todistinguish between large changes in a small num-ber of coordinates, which can be observed in someconformational transitions, from small changes in alarge number of coordinate values.
This problem is often accentuated in molecu-lar dynamics simulations (particularly those at el-evated temperatures) where the thermal motioninduces all coordinate values to undergo small fluc-tuations and produce a large number of structuralvariations of each peptide conformation. A struc-tural change, for example, the radical change of asingle amide peptide dihedral from cis to a trans,may go unnoticed against the background struc-tural fluctuations. This averaging effect obviouslyreduces the effectiveness of an RMSD-based classi-fication scheme to discriminate between groups ofstructures based on large changes in a small numberof residues.
GROUPING STRUCTURES USING RMSD
One popular method for grouping related pep-tide structures used in the analysis of trajectoryfiles is the calculation of a difference measure be-tween all possible pairs of structures. A clusteringalgorithm can then sort the structures into a hier-archy of groups and subgroups. A cutoff value canbe used to partition the structures into conforma-tional families.4 This clustering method does notscale up well for the processing of large volumesof data because the number of RMSD comparisonoperations required increases quadratically with in-creasing numbers of structures.
This restricts the application of this type ofanalysis to conformational data sets with hundredsor thousands of frames. However, current mole-cular dynamics calculations often produce muchlarger conformational data sets where the use ofRMSD-based techniques become computationallyprohibitive.
It is also difficult to extend an already concludedanalysis with the current method because the addi-tion of new conformational data to an existing set ofpreviously analyzed data set requires the re