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Proc Natl Acad Sci U S A. Oct 13, 2009; 106(41): 17377–17382.
Published online Sep 24, 2009. doi:  10.1073/pnas.0907971106
PMCID: PMC2765090
Biophysics and Computational Biology

Structural relationships among proteins with different global topologies and their implications for function annotation strategies

Abstract

It has become increasingly apparent that geometric relationships often exist between regions of two proteins that have quite different global topologies or folds. In this article, we examine whether such relationships can be used to infer a functional connection between the two proteins in question. We find, by considering a number of examples involving metal and cation binding, sugar binding, and aromatic group binding, that geometrically similar protein fragments can share related functions, even if they have been classified as belonging to different folds and topologies. Thus, the use of classifications inevitably limits the number of functional inferences that can be obtained from the comparative analysis of protein structures. In contrast, the development of interactive computational tools that recognize the “continuous” nature of protein structure/function space, by increasing the number of potentially meaningful relationships that are considered, may offer a dramatic enhancement in the ability to extract information from protein structure databases. We introduce the MarkUs server, that embodies this strategy and that is designed for a user interested in developing and validating specific functional hypotheses.

Keywords: protein fold space, protein function annotation, protein structure alignment, protein structure similarity

The identification of structural and functional relationships between proteins based on similarities in their amino acid sequence is an essential component of modern biology. It has been recognized for some time that two proteins can be similar to one another in structure despite a lack of any detectable sequence similarity and that this information can be used to assign function. There has been considerable discussion over the past several years as to how structural similarities can most usefully be described (18). Widely used databases such as SCOP (9) and CATH (10) describe relationships between proteins using a hierarchy of classifications that reflect similarities in the spatial organization of secondary structure elements (SSEs). For example, proteins with the same overall SSE composition are described as belonging to the same “class,” and proteins with similar spatial arrangements of SSE's are described as belonging to the same “fold” or “topology.” Classification implies discreteness in the organization of structure space in that a protein that is assigned to one class or fold will not belong to another.

An alternative view suggests that protein structure space should be viewed as “continuous” rather than discrete (1, 2, 6, 8). Indeed, it has become apparent that structural relationships between protein domains exist at various scales; from small sets of SSE's (4), to larger fragments (1, 2), even when the proteins have been assigned to different folds and structural classes (3). Such structural and/or functional relationships between fragments of two different proteins have been extensively discussed (5, 7, 11, 12) and pose serious challenges to hierarchical classification schemes (13). For example, the fact that homologous proteins can belong to different folds raises the question of whether fold should be placed above or below homology in the hierarchy. A number of solutions have been suggested. These include modifications and additions to SCOP (12), new classification levels in CATH (14) and the identification of fragments that connect different folds (5). We have suggested an alternative approach to the problem that involves abandoning structure-based classification and instead relying on structural alignments alone to identify geometric relationships, which are then used as a basis for function annotation (6).

It is possible that, in some instances, geometric similarity simply reflects structural and energetic constraints associated with the packing of SSEs (see e.g. ref. 15). However, there has been increased recognition that there are evolutionary processes that allow proteins to change their global topologies while still maintaining a functional relationship (5, 11, 1618). A relevant example is provided by the family of phage Cro transcription factors; one member, P22 Cro, is an all-α protein whereas another member, λ Cro, is a mixed α/β protein. Although described as belonging to different folds (1921), there are local structural similarities between these proteins that reveal important functional information: Both proteins contain helix–turn–helix DNA binding motifs that superimpose to 1.6 Å rmsd.

Although the existence of structural fragments with a common function is implicit in recent discussions of the evolution of protein folds (16, 18, 21), they have not, to our knowledge, been used as part of a general strategy for function annotation. Here, we suggest how this goal can be realized. We first demonstrate that the presence of structurally similar fragments, even in the absence of global sequence or structural similarity, often implies the existence of a functional relationship between two proteins. We illustrate our approach by choosing a number of query proteins, identifying structural neighbors defined by containing at least three aligned SSEs with the query (see Materials and Methods) and then selecting a subset of such neighbors that share a common function. Our results suggest that there are a significant number of functional relationships between proteins that have been classified differently. Obscured by classification schemes, these relationships have important implications for the practical goal of extracting function from structure.

Results

Generic Cation-Binding Fragment.

Sporulation initiation phosphotransferase (Spo0F) (Fig. 1A), which binds a magnesium ion, is classified as a flavodoxin-like protein in SCOP1.71 and a 3-layered alpha/beta sandwich in CATH3.1.0. There are 87 proteins with this fold in SCOP1.71 and 1,336 proteins with this topology in CATH3.1.0, based on a non-redundant dataset at the 60% sequence identity level in both cases. In contrast, using our structural alignment program, Ska (1, 22), we find that Spo0F has 1866 neighbors belonging to 97 different SCOP folds, 5 different CATH architectures and 37 different CATH topologies (representative alignments to each fold or topology are provided in the SI Appendix). Many of these structural neighbors have metal binding sites that occupy the same position in space as the magnesium site in Spo0F, even if the proteins belong to very different folds.

Fig. 1.
Alignment of Spo0F with structurally similar proteins. (A) Backbone of Spo0F (PDB entry 1F51, chain E). (B) Backbone of AlaD (1EB3). (C) Backbone of the iron ABC transporter (1Y4T, chain A). The colored regions indicate the structurally similar subset ...

Fig. 1 illustrates the relationship between Spo0F and two of its structural neighbors: 5-aminolaevulinic acid dehydratase (AlaD) from S. cerevisiae, which is a zinc binding protein (Fig. 1B), and the iron ABC transporter from C. jejuni (Fig. 1C). Seven of the 10 SSE's in Spo0F have equivalent SSE's in AlaD, superposing with an rmsd of 3.2 Å and 6 SSE's in Spo0F have equivalent SSE's in the iron ABC transporter superposing to 4.0 Å rmsd (Fig. 1D). Moreover, the three metals occupy approximately the same position in space within the aligned fragment. A structure-based sequence alignment reveals that some of the liganding residues also align (Fig. 1E) even though the metals are different and hence the identities of their ligands are different. In all, we identify 160 proteins with metal chelating residues that align to those in Spo0F, representing 23 different SCOP folds, 3 different CATH architectures and 11 different CATH topologies.

The structural fragment shown in Fig. 1 can clearly be used to house binding sites for very different metals, but its role appears to be even more general. Fig. 2A shows a structural superposition of Spo0F, a UDP-glucosyl transferase, spermidine synthase, and acetylcholinesterase. In each of these structures the residue occupying structurally equivalent position to the liganding residue D1254 of Spo0F (Fig. 2 B and C) interacts with a positively charged amino group either belonging to another amino acid in the protein or to a bound ligand. These include a histidine from UDP-glucosyl transferase, a spermine and acetylcholine. In each case, the positively charged amino group is highlighted in Fig. 2B in sphere representation. The residues structurally equivalent to D1254 are shown in the alignment in Fig. 2C. These include: D121 of 2ACW, which forms an ion pair with His-22 [this pair is thought to act as a base in the catalytic reaction (23)]; D196 of spermine synthase, which interacts with atom N10 of spermine; and E199 in acetylcholinesterase, which is known to play a role in stabilizing the positively charged acetylcholine substrate (24). As shown in Fig. 2D the structural fragments identified here have six SSE's in common with each of the cation binding fragments described in Fig. 1, suggesting the existence of a conserved motif that stabilizes positive charges.

Fig. 2.
A generic cation binding fragment. (A) Multiple structure alignment with Ska of Spo0F (red) acetylcholinesterase (blue, PDB entry 2ACE), spermidine synthase (yellow, 3B7P), and a UDP-glycosyl transferase from M. truncatula (green, 2ACW). Only the structurally ...

Conserved Sugar-Binding Fragment.

Ligand binding sites also appear to be conserved across folds. Fig. 3 shows the structures of three carbohydrate binding proteins: the sialic acid-binding VP8 domain of the capsid protein from the CRW-8 strain of rotavirus (“jelly roll” fold), mannose-binding garlic lectin (“β-prism II”), and protein RSC2107, a methyl-fucose binding protein from Ralstonia solanacearum (“β-propeller”). Despite their different classifications, all three proteins share a conserved substructure (see Fig. 3D) consisting of a three-stranded and a four-stranded β-sheet packed together. The Cα rmsd for the superposition of any two of these fragments is <4.5 Å and, as is evident from Fig. 3 A–C, each is associated with carbohydrate binding. The binding sites appear at different locations on the surfaces of the fragment, either packing against the faces of one of the two β-sheets (sites 1 and 3) or in between the two sheets (site 2). The capsid protein binds sialic acids at sites 1 and 2; garlic lectin binds mannose at sites 1 and 3 (as well as a third site not contained in the conserved substructure); and RSC2107 binds fucose at sites 2 and 3.

Fig. 3.
A carbohydrate binding fragment. (A–C) The structures of three carbohydrate binding proteins: the VP8 domain of the capsid protein from the CRW-8 strain of porcine rotavirus (PDB entry 2I2S, magenta and gray ribbon representation) (A), garlic ...

The results summarized in Fig. 3 suggest that there is a considerable amount of information available in existing databases that could be exploited to infer the location of binding sites. We have developed an approach to identify the location of binding sites based on those observed in structural neighbors. The underlying idea is that the same geometric transformation that aligns one protein, A, that contains a ligand, with another, B, whose ligand binding sites are unknown, will place the ligand of A in the coordinate system of B, suggesting a possible binding site on B (6, 25, 26). Fig. 3E provides an example of this procedure applied to the structural neighbors of the VP8 domain. The ligands from the structural neighbors are colored according to the fold classification of the proteins to which they bind. Three clusters of ligands are identified, corresponding to sites 1, 2 and 3. The VP8 domain only binds sialic acids (shown in blue) in sites 1 and 2 but the location of each of these sites can clearly be identified both from proteins in the same fold (ligands in magenta) and different folds (ligands colors in green and red). It is clear from the figure that proteins in the β-prism and β–propeller folds can be used to predict the location of carbohydrate binding sites in proteins in the jelly roll fold, and vice versa.

In addition to the structural relationships presented here, there is evidence for a more general functional relationship between “jelly-roll,” “β-propeller,” and “β-prism” proteins, that goes beyond just carbohydrate binding. The jelly-roll proteins facilitate viral entry into bacterial cells whereas the β-propeller proteins facilitate bacterial entry into eukaryotic cells. Although the specific function of the β-prism proteins has not been identified, it is known that they are involved in apoptosis (27). An intriguing possibility, suggested by the common sugar binding function of the three groups of proteins, is that β-prism proteins play a role in autophagy, a mechanism of apoptosis that involves fusion of the phagosome with the lysozome. Autophagy is mediated by a similar mechanism to that used in sugar-mediated viral and bacterial entry; binding to sugar modified proteins on a target membrane.

Identifying Potential Ligands in Proteins of Unknown Function.

To further illustrate how structural relationships between protein fragments can be used to infer function, we have predicted the binding function of a structural genomics target that has not yet been annotated. The structure of TM1055 from Thermatoga maritima was determined by the Northeast Structural Genomics Consortium (NESG) and there is currently no publication that describes this protein's structure or function. TM1055 has a deep cavity on its surface (Fig. 4A) that is recognized by our SCREEN program (28) as the most likely location on the protein surface to bind a ligand. Following the procedure described in Materials and Methods, we identified structural neighbors of TM1055 and rotated any ligands into the coordinate frame of TM1055, retaining those ligands that were close to the cavity. In all, we found 1793 structural neighbors belonging to 70 different SCOP folds, 3 different CATH architectures, 10 different CATH topologies and 48 different CATH homologous superfamilies. These proteins collectively bind nearly 500 distinct ligands as judged by their having unique identifiers in the Protein Data Bank (PDB) file.

Fig. 4.
Identifying a potential ligand that binds to a protein of unknown function. (A) Molecular surface of protein TM1055 highlighting a cleft identified by the program SCREEN (28) as the most likely ligand binding site on the protein surface, colored by solvent ...

Clustering these ligands based on a standard measure of small molecule similarity (see Materials and Methods) showed that the largest cluster (259 ligands) contained ligands that were enriched in rings and double bonds. Fig. 4B shows the structure of TM1055 with four of the ligands from this cluster, each of which is associated with a protein in a different SCOP fold and different CATH homologous superfamily. The ligands have been placed within the structure of TM1055 using the same procedure described for the sugar binding proteins. Although no ligand fits into the cleft of TM1055 in its entirety, each contains an aromatic moiety that occupies a position that could be fully accommodated by the cleft with minimal conformational change (Fig. 4C) suggesting that TM1055 binds a ligand with an aromatic moiety. Fig. 4D shows the fragment that is common to TM1055 and the four structural neighbors (Fig. 4B). Despite the difference in overall topologies these proteins share a fragment that, in all cases, produces a cleft that can accommodate aromatic moieties. Strong support for the prediction that TM1055 binds an aromatic group is provided by the recently determined structure of the molybdenum cofactor (MOCO) binding protein (MBP) from C. rheinhardtii (PDB entry 2IZ6), which was solved without a bound ligand. This protein is quite similar to TM1055 and the two proteins share 26% sequence identity. MBP binds MOCO (which contains an aromatic moiety) in a cleft that is structurally equivalent to the prominent cleft on the surface of TM1055; and a residue known to be critical for MOCO binding (M50) is conserved in TM1055 (M48).

Using Relationships Between Fragments to Extract Functional Information.

The results presented in the previous sections indicate that structural alignments between proteins that have been classified differently can be used to identify structurally similar fragments that in many cases share a common function. Although the use of only three query proteins may suggest that our results may represent special cases, we emphasize that each choice was essentially random. Spo0F was chosen as an example of a broadly represented set of α/β proteins. VP8 was chosen so as to determine if a conclusion based on its crystal structure, that it contains two sialic binding sites, could have been independently deduced. Finally, TM1055 is a structural genomics target with no known function and it was of interest as a test of how functional hypotheses could be derived from remote structural homologs. Despite a limited number of examples, our results consistently indicate that there are a large number of structurally and potentially functionally related fragments common to proteins classified differently, which can be used to extract functional information from structure. A difficulty with such an approach, however, is the large number of false positives that will inevitably be associated with a less stringent definition of what constitutes “significant” structural similarity. For example, although many different folds contain a metal binding site that is structurally equivalent to the one in Spo0F, the absolute number of proteins containing such a site is small compared with the total number of structural neighbors identified. How can this difficulty be overcome?

In general, starting with a large set of structural neighbors of a query protein identified independent of classification, a combination of other computational tools can then be used to filter and analyze this set. For example, in the Spo0F analysis, aligned metal liganding residues can be identified from a list of structural neighbors using UniProt sequence “features” (functional annotations that are associated with specific residues in a sequence). Such features can also be used to define a set of functions that occur at a particular location in a structure. In another type of application, patterns in the location of ligand binding sites can be found by restricting the original set of structural neighbors to proteins with a particular GO annotation. For example, all of the proteins shown in Fig. 3 correspond to structural neighbors of VP8 that have the GO annotation, “sugar binding.” The insights that were obtained for these examples suggest the value of a flexible strategy for function annotation that can, under user control, be adapted to the needs of a particular problem.

We have developed a function annotation server, MarkUs (29), that is designed to facilitate a dynamic, interactive strategy that, as suggested by the analyses described above, has the potential to discover functional relationships. MarkUs allows a researcher interested in exploring a particular hypothesis, or developing a new one, to ask specific functional questions (e.g., “What are the possible functions of a region of a protein believed to be functionally important?” or “Where are the likely binding sites on a protein believed to bind carbohydrates?”). Such questions can be addressed through comparative analysis and filtering based on conservation patterns, biophysical properties and existing functional annotation databases. We illustrate this process in Fig. 5, which provides a detailed description of the MarkUs functionality in the context of the analysis of the sugar-binding properties of the viral VP8 domain discussed in Fig. 3.

Fig. 5.
Representative web page of the MarkUs protein function annotation server highlighting a subset of MarkUs functionalities used in the analysis of ligand binding sites of the structural neighbors of the VP8 domain discussed in Fig. 3. The “annotation ...

Discussion

Are the structural and functional similarities described above the result of convergent or divergent evolution? The existence of common structural fragments in proteins with very different global topologies is consistent with recently discussed hypotheses (21, 30, 31) that a relatively small number of ancestral structural modules, which were associated with particular functions (e.g., metal binding, sugar binding, aromatic binding) may each have diverged into a large set of structurally related fragments with diverse but related sets of functionalities. This picture of the evolution of protein structure and function suggests that homologous proteins undergo structural changes that result in their potentially being classified differently even though they all retain a structurally conserved functional fragment (see also refs. 7, 1618, and 21). Such fragments would then be represented in the current repertoire of proteins either as isolated domains with a single functionality or as components of more complex domains with multiple functions.

However, we do not exclude the possibility that some of the relationships we have identified are the result of convergent evolution. For example, in the case of the Spo0F, the cation binding sites are located in “crevices” that are formed naturally when loops connecting β-strands appear on opposite sides of a β-sheet (32). Thus, such sites could in principle have evolved independently. More generally, Russell et al. (25) identified “supersites” that are structurally equivalent ligand binding sites shared by a group of proteins, which in some cases were classified as belonging to a different fold. Although this was used as an argument that supersites arose from convergent evolution, the process of evolutionary drift (18) suggests that this need not be the case.

Independent of evolutionary origins, the fact that structurally similar fragments often share a common function even if they are incorporated in proteins with different global topologies indicates that valuable information will be lost if annotation is based on discrete classifications. Thus, searching for structural relationships independent of classification should significantly increase the number of functional inferences that can be derived. Of course, one could consider defining a new category of fold-independent structural modules with a common function but we believe that any structure-based classification scheme will necessarily be restrictive. Rather, our analysis suggests the need for a new generation of computational and data management tools that allow a user to explore sequence, structural and functional databases in an interactive fashion and to develop and validate hypotheses without the limitations imposed by predefined decisions about which relationships are meaningful. We have outlined a general strategy, embodied in the MarkUs server, which is specifically designed with this goal in mind.

We have focused here on the use of structural similarity between local regions of the protein backbone. The many tools that have been developed recently to identify functional sites based, for example, on specific configurations of small sets of amino acids (8, 3335) or similarities in local surface patches (3638) suggest that even more remote structural similarities could be usefully exploited. A potentially powerful strategy would be one that combines searching broadly for possible structural relationships with filtering based on specific local features (e.g., ligand binding residues, active site and cavity shape comparisons). However, this kind of residue-specific information is not generally used as component of function annotation strategies. Although UniProt sequence features do provide residue-specific information, they lack the much richer and versatile descriptions of function provided by databases such as GO. An explicit association of such “controlled vocabularies” with particular structure/sequence features would overcome some of the inherent shortcomings of annotation transfer based solely on overall sequence/structural similarity (39) and allow the primarily manual analyses we describe here to be carried in a more automated way.

Finally, it is important to compare the approach proposed here with that embodied in the function annotation servers that have been developed in recent years [e.g., ProFunc (40) and ProKnow (41)]. The design of these servers reflects, to some extent, the fact that structural genomics initiatives have generated a large number of protein structures for which there is little or no functional information. Function annotation servers apply a variety of advanced sequence and structure analysis tools to these proteins and provide a user with web pages that contain functional inferences. In contrast, the strategy we are proposing is intended primarily for a researcher with expertise in a particular protein or family of proteins who wishes to develop or validate specific functional hypotheses. This type of goal can best be addressed with interactive computational tools that do not rely on predefined classifications and which allow a researcher to make decisions about which relationships are likely to be meaningful in the context of a particular problem. We believe that this perspective will also enhance the value of structural genomics initiatives, by maximizing the number of relationships between proteins that can be discovered and by facilitating a more unrestricted navigation in sequence–structure–function space.

Materials and Methods

All proteins were structurally aligned with the program Ska (22), a version of the structure alignment program PrISM (1), which was modified to allow alignments to be considered significant even if only a fragment of one protein is aligned to the other. We require a minimum of three secondary structure elements to define a fragment. A 60% non-redundant database of proteins from the PDB was searched for structural homologs of each query protein. Proteins with a protein structural distance (PSD) (1) <0.8 were kept for functional analysis. Alignments and transformations between Spo0F and representative structures classified as belonging to different folds/topologies are provided in the SI Appendix. Ligands from structural neighbors were placed into the coordinate system of each query protein by transforming the coordinates of the ligand using the transformation that structurally superimposes the query protein and structural neighbor. To identify proteins with similar metal binding sites to Spo0F, structural neighbors were examined to identify which ones had at least one metal chelating residue (taken to be Asp, Glu, His and Cys within 4 Å of Ca, Mg, Zn, K, Na, Mn, Cu, or Fe ion) that aligned to one of the metal chelating residues of Spo0F. The ligands in Fig. 2 were identified by manually examining a subset of the structural neighbors of Spo0F. Ligands shown in Fig. 3 were derived from structural neighbors of the VP8 domain, which had the GO annotation “sugar binding.” Ligands shown in Fig. 4 were identified by considering only those structural neighbors that had ligands with atoms that fell within 4 Å of any solvent accessible atom of the residues lining the cavity identified by SCREEN (28) after transformation into the coordinate system of TM1055. Clustering of the ligands for Fig. 4 was carried out using the Jarvis-Patrick algorithm with ligand similarity determined using a Tanimoto distance (see SI Appendix for details).

Supplementary Material

Supporting Information:

Acknowledgments.

We thank Fabian Dey for carrying out the clustering of ligands identified in the structural neighbors of TM1055. This work was supported by National Institutes of Health Grants GM030518, GM074958, and CA121852.

Footnotes

The authors declare no conflict of interest.

This article contains supporting information online at www.pnas.org/cgi/content/full/0907971106/DCSupplemental.

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