PEPCAT?A new tool for conformational analysis of peptides

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<ul><li><p>PEPCATA New Tool for ConformationalAnalysis of Peptides</p><p>M. F. ODONOHUE,1 E. MINASIAN,2 S. J. LEACH,2 A. W. BURGESS,1</p><p>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 &amp; Molecular Biology, Melbourne University, Parkville, Victoria 3052,Australia</p><p>Received 9 August 1999; accepted 15 November 1999</p><p>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 &amp; Sons, Inc. J Comput Chem 21: 446461, 2000</p><p>Keywords: cyclosporin A; molecular modeling; transitions; dynamics;conformational search; conformational analysis; hemopoietic peptides</p><p>Introduction</p><p>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</p><p>Correspondence to: H. R. Treutlein; e-mail: Herbert.Treutlein@ludwig.edu.au</p><p>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</p><p>Journal of Computational Chemistry, Vol. 21, No. 6, 446461 (2000)c 2000 John Wiley &amp; Sons, Inc.</p></li><li><p>PEPCATA NEW TOOL FOR CONFORMATIONAL ANALYSIS OF PEPTIDES</p><p>interrelationships, which can be applied to macro-molecules.</p><p>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.</p><p>CONFORMATION IDENTIFICATION</p><p>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.</p><p>TRAJECTORY FILES</p><p>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.</p><p>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</p><p>more subtle conformational features and changesthat occur during a dynamics simulation.</p><p>CARTESIAN RMSD MEASUREMENT</p><p>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</p><p>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.</p><p>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.</p><p>DIHEDRAL RMSD MEASUREMENT</p><p>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</p><p>JOURNAL OF COMPUTATIONAL CHEMISTRY 447</p></li><li><p>ODONOHUE ET AL.</p><p>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.</p><p>RMSD AVERAGING EFFECT</p><p>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.</p><p>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.</p><p>GROUPING STRUCTURES USING RMSD</p><p>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.</p><p>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.</p><p>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 reassess-ment of all existing data.</p><p>Much work has been done in applying cluster-ing techniques to dynamics simulations, and themethod is a subject of current research interest.11 15</p><p>GROUPING STRUCTURES USING ACLASSIFICATION SCHEME</p><p>An alternative method for grouping peptidestructures is the use of a classification scheme inwhich an abstract set of rules is applied to iden-tify similar peptide structures. A commonly usedclassification scheme is a residueresidue contactmap.16, 17 Two residues are considered to be in con-tact if any atom of one residue comes within apredetermined cutoff radius of an atom of the otherresidue. Different conformations of peptide struc-tures will have different patterns of residueresiduecontacts. This classification scheme does not use acontinuous solution space, as the peptide Cartesiancoordinates does, but instead, maps these structuresinto a discrete solution space. This new coordinatesystem has a number of advantages. A conforma-tion can be simply represented as a K K matrix,where K is the number of residues in the peptide,with cell values containing a Boolean true or falsevalue for contact between residues. The size of thesolution space, which is infinite in atomic Cartesiancoordinate space and is still infinite (but with lessdimensions) in dihedral angle space, is reduced to2KK possible contact matrices. A comparison be-tween two different contact maps can be calculatedeasily by comparing the two matrices. And finally,classification is computationally less expensive, as itdoes not require a comparison between all possiblepairs of structures, but only a calculation performedonce on each structure. The identification of a dis-crete conformational state in this manner opensthe way for use of traditional state space searchand analysis techniques commonly used in artificialintelligence applications.18 Contact map representa-tions have been used, for example, to analyze resultsfrom the series of CASP experimental predictiontrials.19</p><p>More general classification schemes not basedon a contact matrix, but on the application of anumber of independent descriptors can be used toidentify a conformation. Lambert et al.20 have useddescriptors based on regions in maps to ana-lyze and predict the conformation of small peptide</p><p>448 VOL. 21, NO. 6</p></li><li><p>PEPCATA NEW TOOL FOR CONFORMATIONAL ANALYSIS OF PEPTIDES</p><p>sequences. The map for a peptide was di-vided into four regions, and a peptide conformationwas classified as the sequence of residue descrip-tor values. Bravi et al.21 used a mixture of distanceand dihedral descriptor measurements, each withan individual cutoff comparison threshold value todevelop a procedure to define and compare mole-cular conformations. A nonhierarchical clusteringtechnique is then employed to group the observedconformations.</p><p>SUBJECTIVE NATURE OF CLASSIFICATION</p><p>The definition of a peptide conformation de-pends upon the needs of the investigator and thequestion being asked. What constitutes a confor-mation in a ligand docking problem may differsubstantially from that required for evaluating pro-tein flexibility. This subjective nature of classifica-tion makes it difficult to devise a single structuralclassification scheme. However, it does allow forthe development of a tool kit that can be used todefine the structural criteria that will allow the re-searcher to discriminate between different proteinor peptide conformations. For instance, in a ligand-docking problem, only residues near the bindingpocket need to be considered in detail; changes inother residues can either be ignored or monitored ata lower level of accuracy.</p><p>It is worth noting that grouping of structuresusing only the three dimensional coordinates asthe basis for similarity, may also be misleading insome cases. Two conformations, which may be clus-tered together based on their structural similarity,may have significantly different potential energies,and/or be separated by great energy barriers on thepotential energy surface of the molecule.</p><p>THE PEPCAT METHOD</p><p>To address many of the problems encounteredin conventional conformational analysis methodswe have developed a method that allows a flexi-ble definition of conformational states and an effi-cient comparison of a large set of structures. Ourmethod is called PEPCAT (for Peptide Conforma-tional Analysis Tool), and consists of five steps:conformation definition, classification, comparison,trajectory analysis, and visualization of the results.Emphasis was also put on an intuitive display of theresults, which facilitates the recognition of impor-tant conformational features present in moleculardynamics trajectory files. The PEPCAT methodol-ogy is first defined in the next section. This is fol-</p><p>lowed by a detailed description of two applicationsof PEPCAT in the Results section.</p><p>Methods</p><p>There are five steps in the PEPCAT analysis tech-nique: first, the user defines the geometric proper-ties of the peptide relevant to the particular prob-lem; second, structures are processed using theclassification scheme; third, the similarities betweenstructures are calculated using a generalized dis-tance measure; forth, trajectory files are reexpressedas a compact sequence of conformational statesrather than coordinates; and th...</p></li></ul>