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Laurence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, Iowa 50011
Correspondence: Address reprint requests to Dr. Robert L Jernigan, L. H. Baker Center for Bioinformatics and Biological Statistics, 123 Office and Laboratory Bldg., Iowa State University, Ames, IA 50011. Tel.: 515-294-3833; Fax: 515-294-3841; E-mail: jernigan{at}iastate.edu.
| ABSTRACT |
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| INTRODUCTION |
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Even though the phenomenon has been observed in many proteins (Liu and Eisenberg, 2002
), the mechanism of domain swapping has been explored by only a few research groups (Hayes et al., 1999
; Kuhlman et al., 2001
; Rousseau et al., 2001
; Schymkowitz et al., 2001
; Xu et al., 1998
), and there remains much that is unknown. Among them, most of the studies to date were performed by crystallographers, and there have been only a few theoretical studies aimed at understanding the transition mechanism (Gouldson and Reynolds, 1997
; Gouldson et al., 1998
; Alonso et al., 2000
; Xu et al., 1998
). The hinge mechanism for this class of proteins is crucial for the manifestation of this phenomenon (Liu and Eisenberg, 2002
; Newcomer, 2002
) and has been engineered and studied by several groups (Murray et al., 1995
; Green et al., 1995
; Albright et al., 1996
).
Among the various domain swapped dimers and oligomers, diphtheria toxin (DT) is a good representative of this class of proteins, that are sufficiently complete structures for coarse grained analysis, where dimerization occurs through domain swapping in true sense, since in some other cases a segment rather than a domain is swapped and they should, truly, be designated as segment swapped proteins. The physiological relevance of domain swapping and its relation to protein function was described in Liu and Eisenberg (2002)
. DT is a model protein, which undergoes domain swapping to form dimers. The protein functions through its three distinct domains: catalytic domain (C, shown in red in Fig. 1), translocation domain (T, shown in green in Fig. 1), and the receptor domain (R, shown in blue in Fig. 1) (Bennett et al., 1994a
, 1994b
; Bennett and Eisenberg, 1994
).
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| MODEL AND METHODOLOGY |
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-carbons connected by harmonic springs where the modes of vibration of this elastic network represent the fluctuations of atoms about their mean positions using a highly cohesive model for proteins. It serves well to confirm the intrinsic flexibility of the structure, represented experimentally by crystallographic B-factors (Bahar et al., 1997
The model assumes an elastic network of harmonic springs, set up between each pair of nodes within a certain cutoff distance. The force constant is identical for all springs. The model provides a uniform elastic medium confined within the shape of the macromolecule. The theory accounts for slow cluster motions, which can aid in establishing functional mechanisms. The construction of the Kirchhoff or valence-adjacency matrix of such a structure is the first step as given in Eq. 1:
![]() | (1) |
-carbons and
is the cutoff distance, an adjustable parameter but not a very sensitive one.
The inverse of this Kirchhoff matrix is related to the magnitude of relative fluctuations of the ith and jth units in the network as shown in Eq. 2 and when i = j, this represents the mean-square fluctuation of each unit. The intrinsic flexibility of the structure, which is reported in the crystallographic B-factors, is also directly related to the mean-square fluctuations by Eq. 3:
![]() | (2) |
![]() | (3) |
The mean-square fluctuations of each unit and the cross-correlation fluctuations between different units are proportional to the diagonal and off-diagonal elements of the inverse of the Kirchhoff matrix, respectively. This inverse can also be expressed as
![]() | (4) |
are the eigenvalues and
are the eigenvectors of
and superscript T indicates transpose. The eigenvector with the lowest nonzero eigenvalue represents the slowest motion, which are usually domain or cluster motions. For this symmetric positive semidefinite matrix, the identical pseudo-inverse can be obtained using a singular value decomposition method. Although GNM provides the magnitudes of the displacements of atoms or chain units from their equilibrium positions for large scale motions, it does not provide any information on the directionality of the motions.
Anisotropic network model
The ANM is an extension of GNM, which adds directionalities to the motions. The directional displacements are essential for generating the specification of changed conformations. Atilgan et al. (2001)
gave a detailed theoretical development. Unlike GNM, where there is a single zero eigenvalue, ANM gives rise to six zero eigenvalues corresponding to the three overall translational and three overall rotational degrees of freedom. The eigenvector corresponding to the lowest nonzero eigenvalue corresponds to the largest scale motion (also the largest contribution to the total motion).
Change in internal distances for residues
The above quantities are appropriate for monitoring changes in the displacements that are large, but another measure is required to follow the relatively small-scale motions at hinge sites. The change in the sum of internal distances for each residue is an appropriate parameter for identifying the locations and motions of hinges during transitions (Hinsen, 1998
; Hinsen et al., 1999
).
![]() | (5) |
represents the differences in the sum of internal distances of the ith residue to all j residues to which it is directly connected and the subscripts 1 and 2 identify conformation 1 and conformation 2. We call this quantity the "relative displacement" between two structures. This quantity plays an important role in determining the details of hinge motions in the process of transformation. This is a simple and powerful means for locating and studying the hinges in any structural transformation.
Overlap coefficient
The overlap between the conformational change vector and the ANM vector is described by Tama and Sanejouand (2001)
and is expressed as
![]() | (6) |
is the conformational displacement of the ith residue and
is the displacement of the ith residue in the jth ANM mode. The overlap represents a measure of the extent to which a particular mode is in the direction of the displacement of the swapped cluster toward its final "closed" state. The conformational change vector is defined as the difference of the two conformational vectors (Tama and Sanejouand, 2001
atoms.
Correlation coefficient
This quantity measures the correlation between the magnitudes of displacements between the conformational change vector and the ANM vector, and indicates whether the less displaced and more displaced C
atoms are coherent between two vectors:
![]() | (7) |
is the correlation coefficient between the two vectors,
and
are the means of the corresponding
and
Method of calculation
To calculate the slowest modes, the Kirchhoff matrix has first been constructed, according to Eq. 1 by using a cutoff of 7.0 Å, which is then decomposed into eigenmodes by the standard singular value decomposition method given in Eq. 3. The crystal coordinate data of the monomeric (1MDT) and the dimeric (1DDT) DT were obtained from the Protein Data Bank (Berman et al., 2000
). The directional motions were obtained by ANM calculations with a cutoff of 15.0 Å, which is described in detail by Atilgan et al. (2001)
. The dimer in Fig. 1 E was visualized using the CNS system program (Brunger et al., 1998
).
| RESULTS AND DISCUSSION |
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A systematic way to determine the mechanism of domain swapping is to locate the hinges associated with motions for different timescales or modes. The opening of the monomer occurs about a hinge and the R domain rotates almost 180° about this hinge axis (shown as z axis, the C2 axis). There is a slight twist about an axis perpendicular to the previous one (shown in Fig. 1 as x axis). In this work, we mainly stress the closing of the dimer with the GNM and ANM approach. As shown and discussed by Tama and Sanejouand (2001)
, studying the open state is more effective and logical since the domains and clusters are more separated in that form.
The contact maps of the closed monomeric state, open monomeric state, and coupled dimeric state are shown in Fig. 2. The monomers have, obviously, only intramolecular contacts, whereas the dimers have both intra- and intermolecular contacts. This figure shows how the intra- and intermolecular contacts in the coupled dimeric state are mutually exclusive to each other, and when combined how the contact map actually looks, nearly identical to that of the monomeric state. The contact map of the closed monomeric state is shown in Fig. 2 A. The intramolecular contacts of the open monomer are shown in Fig. 2 B and intermolecular contacts of the open monomer are shown in Fig. 2 C. The combination of intra- and intermolecular contact maps (shown in Fig. 2 D) of the dimer perfectly matches with the contact map of the monomer, which shows that the dimer actually looks like the monomer in the crystal environment.
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We also explore the local sum of all intramolecular distances centered about each C
atom and use the differences between the corresponding values for the two forms (according to Eq. 5) to identify the locations of the hinge for the transition. This difference is shown in Fig. 5 for each residue. This shows a clear hinge around residue 381. The global minimum in this curve represents the most dominant hinge for domain swapping between the monomer and the dimer. The next levels of less dominant hinges are located at 265271, 318320, and 400403, which are also obvious from the first and second modes. The other set of less dominant hinges have smaller effects on the transformation and are shown in Fig. 5. The minima are considered to be the hinge points and the maxima are the most mobile regions. This method is also applied to another set of 12 proteins and the hinge locations are given in Table 1. The hinge locations are taken as the minima in the plots of the type of Fig. 5. The hinge locations determined in this method are in full agreement with the literature values (Liu and Eisenberg, 2002
).
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| CONCLUSION |
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Submitted on September 16, 2003; accepted for publication February 6, 2004.
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