1Koc University, Center of Computational Biology and Bioinformatics, and College of Engineering, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey, 2Basic Research Program, SAIC-Frederick, Inc., Laboratory of Experimental and Computational Biology, NCI-Frederick, Frederick, MD 21702, USA and 3Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
Correspondence should be addressed to R.Nussinov at SAIC-Frederick, Inc. or to O.Keskin. E-mail: ruthn{at}ncifcrf.gov or okeskin{at}ku.edu.tr
![]() |
Abstract |
---|
![]() ![]() ![]() ![]() ![]() |
---|
Keywords: interface motifs/protein architecture/proteinprotein binding/proteinprotein interaction/proteinprotein interfaces
![]() |
Introduction |
---|
![]() ![]() ![]() ![]() ![]() |
---|
Proteinprotein interfaces have been characterized in terms of their structural and physical properties (size, shape, complementarity and packing) and their chemical nature (amino acid composition, chemical group distributions, hydrophobicity/hydrophilicity, electrostatic interactions, hydrogen bonding and interactions with water) (Katchalski-Katzir et al., 1992; Jones and Thornton, 1996
; Wallis et al., 1998
; Todd et al., 2002
; Arkin et al., 2003
; Nooren and Thornton, 2003
). Most of the physical and biochemical data are derived from either enzymes (mostly proteases, interacting with protein inhibitors) and antibodies interacting with their cognate antigens. Evolutionary perspectives of interfaces have also been studied. Subunit interfaces in proteins are generally hydrophobic and aromatic residues are frequently conserved. Conserved functional residues across interfaces between the two chains and on one side of the interface and experimental hot spot residues, were observed to be organized in cooperative hot regions (Keskin et al., 2005
).
Recently, we have extracted all interfaces between two protein chains obtained from higher complexes of proteins (Keskin et al., 2004). Interfaces sharing similar architectures were clustered. At the end of the iterative procedure, we obtained 3799 interface clusters. These structurally similar interface clusters were further filtered to eliminate redundancy. A remaining cluster should contain at least five members to be a valid cluster. These criteria have decreased the number of clusters from 3799 to 103. The final set of clusters contains member proteins as diverse as enzymes, antibodies, viral capsids, etc. We divide the 103 clusters into three categories as summarized in Table I. Type I: two chain interface clusters with unique functions. Members of these clusters have similar chains and similar interfaces. Thus, the entire complexes are well aligned. Within a cluster, the single chains from which the interfaces were derived have similar functions. Type II: two chain interfaces with multi-functions. The interfaces of members of these clusters have both of their chains aligned. However, the functions of the proteins whose interfaces belong to the cluster differ. Type III: interfaces with multi-functions. However, unlike Type II, members of these clusters have only one side of their interface aligned. Within a cluster, all proteins whose interfaces belong to the cluster have dissimilar functions. Schematic representations of Type I, II and III interfaces are given in Figure 1. Table II provides the listing of the Type II interfaces. Clusters containing similar interfaces with dissimilar global protein folds are good candidates for detailed structural/functional studies. Since the overall structures of the proteins are different, these proteins mostly have different functions.
|
|
|
![]() |
Results and discussion |
---|
![]() ![]() ![]() ![]() ![]() |
---|
A proteinprotein interface consists of two polypeptide chains forming the two sides of the interface. Residues on both sides interact with each other. Several criteria may define an interface. Here, two residues are defined to be interacting if the distance between any two atoms of the two residues from the different chains is less than the sum of their corresponding van der Waals radii plus 0.5 Å (Tsai et al., 1996). A residue is defined to be a nearby residue if the distance between its C
atom and a C
atom of any interacting residue is <6 Å. Nearby residues are important in clustering, since they provide the interface scaffold.
Dataset
We applied these definitions to all multi-chain PDB entries (Berman et al., 2000) in the database. On 18 July 2002, there were 18 687 entries in the PDB, which included 35 112 single chains. PDB entries that contain more than two chains were used to obtain all two-chain combinations. Thus, interfaces between any two chains were extracted (Keskin et al., 2004
). These included all two-chain interfaces from dimers, trimers and higher complexes. As a result, 21 686 two-chain interfaces were obtained. Dimers are included in the dataset. The interfaces were renamed as follows: if the PDB code of a protein is 1fq3
[PDB]
and it has two chains A and B, the interface is named 1fq3AB, indicating that there is an interface between chains A and B of protein 1fq3
[PDB]
. All the interfaces were structurally compared by the Geometric Hashing sequence order-independent structural algorithm (Nussinov and Wolfson, 1991
; Tsai et al., 1996
). We used a heuristic iterative clustering procedure that assigned an interface into a cluster if its similarity to the cluster representative was below predefined thresholds, otherwise it was assigned as a new cluster representative. Six clustering cycles were performed, gradually relaxing the thresholds. Overall, 3799 clusters were obtained. Sequences within each cluster were compared using CLUSTALW (Higgins et al., 1994
) and the BLOSSUM90 substitution matrix (Henikoff and Henikoff, 1992
). Any one of two entries in the same cluster sharing more than 50% similarity was eliminated. Clusters with less than five members were removed, leading to 103 clusters. Crystal interfaces are included in the dataset. Since crystal interfaces may be structurally similar to biological interfaces, it is often the case that a given cluster contains both biological and crystal interfaces. Three of the case studies described below are examples of such clusters. Although we have not segregated the dataset into transient and permanent complexes, Type II (and Type III) interfaces may be expected to fall largely into the transient category.
Structural alignment algorithm: MultiProt
MultiProt is fully automated software to align multiple protein structures simultaneously. It finds the common geometric cores among the input structures. It does not require that all members participate in the alignments and detects high scoring partial multiple alignments of the input structures. It is a residue-sequence order and directionality independent algorithm, which makes it applicable to proteinprotein interfaces (Shatsky et al., 2004). Using MultiProt, we structurally aligned cluster members. The parameters used in the alignments are as follows: maximal r.m.s.d. for matching = 3.5 Å; minimal size of rigidly matched fragments = 3; maximal shift in indices of two matched fragments = 20; overlap ratio = 0.8; OnlyRefMol = 0; and FullSet = 1. MultiProt chooses one of the structures as the representative of the multiple alignment. This representative is the one most similar to all members, hence not necessarily the same one as in the previous, hierarchical clustering. We analyzed the residue identities at specific spatial positions. If a residue type is found at a specific position in more than half of the interfaces, it is labeled as a computational hot spot or a structurally conserved residue.
Interface family types: similar interfaces, similar global folds (Type I); similar interfaces, dissimilar folds (Type II)
In most cases, if the interfaces are similar, the overall protein folds are also similar. Such similar interface, similar fold clusters (Type I) contain a single family. However, some clusters (listed in Table II), belong to a different, particularly interesting category. In these cases the interfaces are structurally similar; however the global protein folds are different (Type II). These similar interfaces, dissimilar protein folds fall into different families [see the SCOP classification (Murzin et al., 1995), also provided in Table II, first column]. However, since they have similar interfaces they are nevertheless members of the same interface clusters. The parent proteins of these interfaces belong to families that have different functions. Hence interface similarity does not ensure global structural similarity. Furthermore, it has been shown previously that globally similar structures may have different functions in proteins, although there is usually a correspondence between fold and function (Orengo et al., 1999
; Moult and Melamud, 2000
; Thornton et al., 2000
; Nagano et al., 2002
). Cases such as those listed here illustrate that this paradigm can be taken further: similar interfaces do not imply similar functions of the parent proteins from which the interfaces were derived.
These similar interfaces, different functions clusters may aid in illuminating aspects of protein binding and function. Below, we discuss some cases in detail. Note that there are three clusters for Type II in Table II where the representative of the cluster does not appear in the list of family members. These cases are cellulose-binding domain family III, MHC antigen-recognition domain and nucleotide and nucleoside kinases. In these cases, although the representative aligned well with each cluster member, it did not align well with all members simultaneously, suggesting some slight deviations.
Figure 2 illustrates some examples from Type II interfaces in Table II. Each left panel presents the ribbon diagrams of two proteins which belong to two different SCOP families (Murzin et al., 1995) in the same interface cluster, clearly showing that the global structures are different. The interfaces are enlarged in the right panel. The ribbon diagrams display the functionally or structurally important residues of the individual proteins in blue. These residues were extracted either from a literature search or from sequence alignments of the protein within its functional family. Regardless of the functional families, if we carry out an alignment within each cluster, we observe that some residues are conserved (with a ratio of at least 50% with Blossum90 substitution matrix). The residue types of the conserved residues (hot spots) (Hu et al., 2000
; Ma et al., 2001
) are in red and the residue numbers are given in the left panel. These red residues might be important for the stability of the interfaces but not necessarily for the specific function as the individual members may have different functions.
|
The 1afrBD cluster
This cluster includes members of chromo domain-like (chromatin) proteins, aldolases and tryptophan synthase ß-subunit-like PLP-dependent enzymes. The overall structures of the members are displayed in Figure 3A. Here, we provide a comparison of the interfaces of 1dz1AB and 1f05AB.
|
There are 28 residues in these aligned interfaces (six on one side, 22 on the other). Two helices are on one side, a part of a long helix on the other. The consensus interface is displayed in yellow (Figure 3B) and the conserved residues in blue (Figure 2B). In 1dz1 all conserved residues coincide with nearby residues whereas in the 1f05 interface, the conserved residues are far from the interface. This may simply reflect the sizes of the proteins, since 1dz1 [PDB] is a much smaller protein than 1f05 [PDB] . The red residues in Figure 2B are the conserved residues (hot spots) in this cluster. There are a large number of hot spots in this cluster, mostly charged and polar residues. The inset in Figure 3B displays the aligned interface. These two proteins are believed to interact with a number of different proteins, so their common interface may be used as a target that binds nonspecifically to many proteins. These two proteins represent a similar interface between an enzyme and a non-enzymatic protein. Still, 1f05AB interface is a crystal interface, not a biological interface.
The 1aohAB cluster
Figure 4A displays four members of the cluster with functionally and structurally different protein families. These are either structural proteins (1aohAB and 1g1kAB) or fluorescent proteins (1b9cAB and 1g7kAB). 1aoh and 1g1k are in dimeric form and 1b9c and 1g7k are tetramers in the PDB entries.
|
The aligned interfaces are colored yellow (Figure 4B) and the conserved or functionally important residues are blue (Figure 2C). There are 48 residues in the two aligned sides of the interface (25 residues on one side and 23 on the other). The interface is made up of ß-sheets on both sides (each made up of three ß-strands). Some of the critical residues are in the interface. The inset in Figure 4B displays the details of the aligned interfaces. In these examples, both interfaces are crystal interfaces as opposed to the other members in the same cluster.
The 1e7kAB cluster
This interface cluster includes four members of protein complexes with varied functions, snake venom toxins, cysteine proteases and P-loop containing nucleotide triphosphate hydrolases. The ribbon diagrams are shown in Figure 5A. Below we detail the functions of 1kba and 1ef7.
|
Figure 5B displays the two structures with the common interface motif colored yellow. Forty-seven residues are structurally aligned in these two interfaces. These form two ß-sheets (with two ß-strands) on each side of the interface. There are 27 residues on one sheet and 20 on the other. The conserved residues are displayed in blue (Figure 2D, left panel). 1ef7AB is a crystal interface whereas 1kbaAB is a biological interface.
The 1qbzBC cluster
This cluster includes six members from the virus ectodomain and tropomyosin families. The overall structures are displayed in Figure 6A.
|
In this interface cluster, there are 98 residues aligned as a result of multiple structural alignment. The interface consists of two long helices, one with 57 residues and the other 41. The common interface is colored yellow (Figure 6B). Interestingly, unlike all interfaces in the examples above, the charged/polar residues do not dominate. Possibly, the helical motif's stability is maintained by the hydrophobic forces. Additionally, oppositely charged residues are 10 Å in proximity. In these examples, all interfaces are biological interfaces.
Propensities of residues in Type I and in Type II interface clusters
The relative frequencies of different types of amino acids in the interfaces of proteinprotein complexes can be used to derive the residue propensities. The overall propensities of the 20 amino acids are calculated in the interfaces from the dataset containing all interface clusters. We compare the frequency patterns at the binding sites versus those in the overall structures. The propensity (Pi) of a residue (i = Ala, Val, Gly, ...) to occur at the interface is calculated as the fraction of the count of residue i in the interface as compared with its fraction in the whole chain:
![]() | (1) |
|
The change in accessible surface areas (ASAs) upon complex formation is used as a measure of the interface contact area. Figure 8 shows the accessible surface area distribution of the interfaces. Figure 8A displays the ASA for Type I interfaces and Figure 8B that for Type II. The ASAs range between 300 and 6000 Å2. Type I has a mean of 1967 ± 1079 Å2 and Type II a mean of 1450 ± 1211 Å2 (Table III). Type I interfaces have larger surface areas (358 interfaces were used in the calculations). Type II clusters have 94 interface members. Type I interfaces peak around 1500 Å2 whereas Type II interfaces peak around 1000 Å2. The numbers of residues in the parental chains to which the interfaces belong are also compared. The Type I and II interfaces have a mean of 370 ± 235 and 356 ± 226 residues, respectively (Table III). Type II interfaces have a similar number of residues, but they have smaller accessible surface areas, suggesting that these surfaces are not optimized through evolution as in the case of Type I interfaces, probably due to their different functions.
|
|
The planarities of the interfaces are also analyzed. These were obtained from the PPI Server. In the planarity calculations, the best fit plane through the three-dimensional coordinates of the atoms in the interface was obtained by using principal component analysis. The root mean square deviation (r.m.s.d.) of the atoms from the plane was next calculated and used as the measure of planarity. The larger the r.s.m.d. value, the less planar is the interface and, conversely, the smaller the r.s.m.d. value, the more planar is the interface. If we look at the two types separately (Table III), we observe that Type I interfaces have a planarity of 3.16 and Type II interfaces have a much smaller value of 1.86. Hence Type II interfaces are more planar than Type I interfaces, again suggesting a less favorable packing.
Hydrogen bonding across the interfaces
We analyzed the potential involvement of residues in hydrogen bonding across the interfaces. Two atoms from each side across the interface are said to form a hydrogen bond if the distance between their donors and receptors (McDonald and Thornton, 1994) is <3.5 Å. Table IV summarizes the results of the hydrogen bonds that originate from the interface residues as well as the type of H-bonding in the two types of interfaces. The first column gives the type of interface, i.e. I_SS indicates the Type I interface and H-bonds between side chainside chain (SS) groups, I_BB is for hydrogen bonds between backbonebackbone (BB) groups and I_SB is for hydrogen bonds formed between a side chain and a backbone (SB) group. The second column is the number of total hydrogen bonds formed across the interface. The third column is the distribution of H-bonds among different types of H-bond interaction, namely SS, BB and SB. The hydrogen bonds formed in Type I interfaces are equally distributed between the side chains and the backbones. On the other hand, Type II interfaces mainly utilize the side chains for H-bond formation. Hence Type II interfaces are not optimized by their structural complementarities.
|
The similarity between protein binding and protein folding has been discussed (Janin et al., 1988; Janin and Chothia, 1990
; Jones and Thornton, 1996
; Tsai and Nussinov, 1997
; Tsai et al., 1998
). Since the attractive and repulsive forces in folding the polypeptide chain are also responsible for proteinprotein associations, it is not surprising that protein cores and protein interfaces share similar secondary structure motifs (Tsai et al., 1997
).
Numerous studies of single-chain proteins have shown that evolution has selected a limited repertoire of favorable folds. Here we extend these observations to proteinprotein interfaces. We illustrate that there are recurring architectural motifs also at the interfaces, similarly fulfilling a range of functions. These recurring motifs can be used as structural templates in proteinprotein recognition (A.S.Aytuna, A.Gursoy and O.Keskin, unpublished results). In monomers, recurring structural motifs have frequently been referred to as either building blocks or biological functional units (Tsai and Nussinov, 1997). For example, the repeated strandhelixstrand motif forms a TIM barrel fold and the helixturnhelix motif has been recognized as a calcium binding site. Similarly, the Rossman fold has been shown to be associated with nucleotide binding. Whether specific interface motifs also relate to specific functions is still unclear. Hopefully, the interface clusters in Table II will prove useful in further addressing this question. We are currently annotating all domains involved in the proteinprotein interface dataset in an effort to address this question.
Conclusions
Recently, we have assembled a unique dataset of two-chain interfaces derived from the PDB. The interfaces are clustered based on their spatial structural similarities, regardless of the connectivity of their residues on the protein chains. The dataset includes 3799 clusters. From this dataset we have obtained 103 clusters which have at least five non-homologous members. These serve as a rich source of data for analysis of protein interfaces. Here, we have carried out a detailed examination of all members of the 103 clusters. We find that the interface clusters can be divided into three types: whereas most clusters consist of similar interfaces whose parent chains are also similar (Type I), this is not always the case. In some of our clusters the interfaces are similar; however the overall structures of the parent proteins from which the interfaces derive are different. These are labeled Type II interfaces. Type III consists of interface clusters where only one side of the interface is similar. Proteins belonging to this type have different functions. In all of the Type II and Type III cases that we have, the proteins belong to different (SCOP) families (Murzin et al., 1995), with different functions. One of the paradigms in protein science states that similarity between protein structures does not imply similarity in function. Our observations suggest a striking extension of this paradigm: similarity in interface architectures does not imply similarity in function. As in protein monomers, good favorable interface structural scaffolds have been re-used and adapted by evolution for diverse functions. The functions extend from enzymes/inhibitors to toxins and immunoglobulins. We did not observe homodimers among these proteins of similar interfaces, different global structures and functions. Homodimers are always classified as Type I. This is probably due to the smaller sizes of the monomers and the extensive interfaces in the two-state homodimers which cover large portions of the chains. As expected, we find that multi-functional interface clusters consisting of helices largely derive from proteins whose functions relate to muscle and to membranes.
We analyzed the Type I versus Type II clusters. As expected, we find that Type I is better packed, buries larger total and non-polar ASAs, is less planar and has better interface complementarity and more backbonebackbone hydrogen bonds.
The observation that globally different protein structures may associate in similar ways to yield similar motifs is extremely interesting. Clearly, there is a very large number of ways in which monomers can combinatorially assemble. Remarkably, among these there are preferred interface architectures and these are similar to those observed in monomers. This observation both underscores the view that the number of favorable motifs is limited in nature and highlights the analogy between binding and folding. It is further reminiscent of the combinatorial assembly of protein building blocks in folding.
Here, we observe that that there are many cases where evolutionarily related proteins have diverged from each other in function, yet maintained the interfaces they use to interact with other proteins. The question arises as to whether it is possible to infer from cases such as those in our dataset the time scales of evolutionary divergence. One possible way toward such a goal is through sequence analysis of classified structural alignment of the interfaces. Further, here we have looked at the functions of Type II proteins using qualitative descriptions. Current work assesses the functions of these proteins by using the Gene Ontology database (http://www.geneontology.org). Such an analysis should provide more quantitative means of comparing the functions of proteins in different clusters.
![]() |
Acknowledgments |
---|
![]() |
References |
---|
![]() ![]() ![]() ![]() ![]() |
---|
Barrett,A.J., Rawlings,N.D. and Woessner,J.F.,Jr (1998) In Barrett,A.J., Rawlings,N.D. and Woessner,J.F.,Jr (eds) Handbook of Proteolytic Enzymes. Academic Press, London.
Battistutta,R., Negro,A. and Zanotti,G. (2000) Proteins, 41, 429437.[CrossRef][ISI][Medline]
Berman,H.M., Westbrook,J., Feng,Z., Gilliland,G., Bhat,T.N., Weissig,H., Shindyalov,I.N. and Bourne,P.E. (2000) Nucleic Acids Res., 28, 235242.
Bogan,A.A. and Thorn K.S. (1998) J. Mol. Biol., 280, 19.[CrossRef][ISI][Medline]
Boniecki,M., Rotkiewwicz,P., Skolnick,J. and Kolinski,A. (2003) J. Comput. Aided Mol. Des., 17, 725738.[CrossRef][ISI][Medline]
Brasher,S.V., Smith,B.O., Fogh,R.H., Nietlispach,D., Thiru,A., Nielsen,P.R., Broadhurst,R.W., Ball,L.J., Murzina,N.V. and Laue,E.D. (2000) EMBO J., 19, 15972000.
Brown,J.H., Kim,K.-H., Jun,G., Greenfield,N.J., Dominguez,R., Volkmann,N., Hitchcock-DeGregori,S.E. and Cohen,C. (2001) Proc. Natl Acad. Sci. USA, 98, 84968501.
Chakrabarti,P. and Janin,J. (2002) Proteins, 47, 334343.[CrossRef][ISI][Medline]
Chothia,C. (1992) Nature, 357, 543544.[CrossRef][ISI][Medline]
Cunningham,B.C. and Wells,J.A. (1991) Proc. Natl Acad. Sci. USA, 88, 34073411.
DeLano,W.L., Ultsch,M.H., de Vos,A.M. and Wells,J.A. (2000) Science, 287, 12791283.
Dewan,J.C., Grant,G.A. and Sacchettini,J.C. (1994) Biochemistry, 33, 1314713154.[CrossRef][ISI][Medline]
Finkelstein,A. and Ptitsyn,O.B. (1987) Prog. Biophys. Mol. Biol., 50, 171190.[CrossRef][ISI][Medline]
Finkelstein,A.V., Badretdinov,A.Y. and Ptitsyn,O.B. (1991) Proteins, 10, 287299.[ISI][Medline]
Gunar,G., Klemen
ic,I., Turk,B., Turk,V., Karaoglanovic-Carmona,A., Juliano,L. and Turk,D. (2000) Structure, 8, 305313.[ISI][Medline]
Harbury,P.B., Zhang,T., Kim,P.S. and Alber,T. (1993) Science, 262, 14011407.[ISI][Medline]
Henikoff,S. and Henikoff,J. (1992) Proc. Natl Acad. Sci. USA, 89, 1091510919.
Higgins,D., Thompson,J., Gibson,T, Thompson,J.D., Higgins,D.G. and Gibson,T.J. (1994) Nucleic Acids Res., 22, 46734680.[Abstract]
Hu,Z., Ma,B., Wolfson,H. and Nussinov,R. (2000) Proteins, 39, 331342.[CrossRef][ISI][Medline]
Janin,J. and Chothia,C.J. (1990) J. Biol. Chem., 265, 1602716030.
Janin,J., Miller,S. and Chothia,C. (1988) J. Mol. Biol., 204, 155164.[ISI][Medline]
Jones,S. and Thornton,J.M. (1995) Prog. Biophys. Mol. Biol., 63, 3165.[CrossRef][ISI][Medline]
Jones,S. and Thornton,J.M. (1996) Proc. Natl Acad. Sci. USA, 93, 1320.
Katchalski-Katzir,E., Shariv,I., Eisenstein,M., Friesem,A.A., Aflalo,C. and Vakser,I.A. (1992) Proc. Natl Acad. Sci. USA, 89, 21952199.
Keskin,O., Bahar,I., Badretdinov,A.Y., Ptitsyn,O.B. and Jernigan,R.L. (1998) Protein Sci., 7, 25782586.
Keskin,O., Jernigan,R.L. and Bahar,I. (2000) Biophys J., 78, 20932106.
Keskin,O., Tsai,C.J., Wolfson,H. and Nussinov,R. (2004) Protein Sci., 13, 10431055.
Keskin,O., Ma,B. and Nussinov,R. (2005) J. Mol. Biol., 345, 12811294.[CrossRef][ISI][Medline]
Kleanthous,C. (ed.) (2000) ProteinProtein Recognition, Frontiers in Molecular Biology. Oxford University Press, Oxford.
Laskowski,R.A. (1995) J. Mol. Graph., 13, 323330.[CrossRef][ISI][Medline]
LoConte,L., Chothia,C. and Janin,J. (1999) J. Mol. Biol., 285, 21772198.[CrossRef][ISI][Medline]
Ma,B., Wolfson,H.J. and Nussinov,R. (2001) Curr. Opin. Struct. Biol., 11, 364369.[CrossRef][ISI][Medline]
Ma,B., Shatsky,M., Wolfson,H.J. and Nussinov,R. (2002) Protein Sci., 11, 184197.
McDonald,I.K. and Thornton,J.M. (1994) J. Mol Biol., 238, 777793.[CrossRef][ISI][Medline]
Moult,J. and Melamud,E. (2000) Curr. Opin. Struct. Biol., 10, 384389.[CrossRef][ISI][Medline]
Murzin,A.G., Brenner,S.E., Hubbard,T. and Chothia,C. (1995) J. Mol. Biol., 247, 536540.[CrossRef][ISI][Medline]
Nagano,N., Orengo,C.A. and Thornton,J.M. (2002) J. Mol. Biol., 321, 741765.[CrossRef][ISI][Medline]
Nooren,I.M.A. and Thornton,J.M. (2003) J. Mol. Biol., 325, 9911018.[CrossRef][ISI][Medline]
Nussinov,R. and Wolfson,H.J. (1991) Proc. Natl Acad. Sci. USA, 88, 1049510499.
Orengo,C.A., Todd,A.E. and Thornton,J.M. (1999) Curr. Opin. Struct. Biol., 9, 374382.[CrossRef][ISI][Medline]
Shatsky,M., Nussinov,R. and Wolfson,H.J. (2004) Proteins, 56, 143156.[CrossRef][Medline]
Singh,P.B., Miller,J.R., Pearce,J., Kothary,R., Burton,R.D., Paro,R., James,T.C. and Gaunt,S.J. (1991) Nucleic Acids Res., 19, 789794.[Abstract]
Tavares,G.A., Béguin,P. and Alzari,P.M. (1997) J. Mol. Biol., 273, 701713.[CrossRef][ISI][Medline]
Thorell,S., Gergely,P.,Jr, Banki,K., Perl,A. and Schneider,G. (2000) FEBS Lett., 475, 205208.[CrossRef][ISI][Medline]
Thornton,J.M., Todd,A.E., Milburn,D., Borkakoti,N. and Orengo,C.A. (2000) Nat. Struct. Biol., 7, 991994.[CrossRef][Medline]
Todd,A.E., Orengo,C.A. and Thornton,J.M. (2002) Structure, 10, 14351451.[CrossRef][ISI][Medline]
Tsai,C.J. and Nussinov,R. (1997) Protein Sci., 6, 14261437.
Tsai,C.J., Lin,S.L., Wolfson,H.J. and Nussinov,R. (1996) J. Mol. Biol., 260, 604620.[CrossRef][ISI][Medline]
Tsai,C.J., Xu,D. and Nussinov,R. (1998) Fold. Des., 3, R71R80.[ISI][Medline]
Valdar,W.S.J. and Thornton,J.M. (2001) Proteins, 42, 108124.[CrossRef][ISI][Medline]
Wallis,R., Leung,K.Y., Osborne,M.J., James,R., Moore,G.R. and Kleanthous,C. (1998) Biochemistry, 37, 476485.[CrossRef][ISI][Medline]
Weissenhorn,W., Carfi,A., Lee,K.-H., Skehel,J.J. and Wiley,D.C. (1998) Mol. Cell, 2, 605616.[ISI][Medline]
Wells,J.A. and de Vos,A.M. (1996) Annu. Rev. Biochem., 65, 609634.[CrossRef][ISI][Medline]
Wu,C.H. et al. (2002) Nucleic Acids Res., 30, 3537.
Zhang,Y., Kolinski,A. and Skolnick,J. (2003) Biophys. J., 85, 11451164.
Received August 10, 2004; revised November 12, 2004; accepted December 3, 2004.
Edited by Dek Woolfson