SCOWLP classification: Structural comparison and analysis of protein binding regions

1 Extraction of interfaces and contacting domains

An accurate definition of the interacting residues is crucial to have a proper clustering of a family PBR. We extracted all protein interfaces from the SCOWLP database, in which the interactions are defined at atomic level and based on their physiochemical properties [2, 15]. Protein domains interacting with peptidic ligands and residues interacting through a water molecule (wet spots) are also taken into account. We consider "interface" all domain-domain interactions; that means those belonging to the same protein and also to different proteins. SCOWLP contains 79,803 interfaces contained in 2,561 SCOP families. We grouped the domains participating in each interface by SCOP families, obtaining for each family a list of contacting domains with the residues forming part of the binding region.

2 Pair-wise structural alignments (PSAs)

A reliable alignment is indispensable to calculate the similarities among binding regions. For this purpose we used MAMMOTH, which has shown proven accuracy to structurally align protein families [16]. We performed all-against-all PSAs of the contacting domains for each family to be able to measure the similarity among binding regions. SCOWLP contains about 160,000 contacting domains uneven distributed by families. This represents 276 million PSAs performed in a cluster of five Pentium IV 2.6 GHz. The alignments were performed taking the Cα atoms into account and using a gap penalty function for opening and extension [17]. The root-mean-squared deviation (RMSD) was not considered for measuring the similarity between two interfaces, as the superimposed members of the same family share a common structure.

3 Similarity Index (Si)

The residues described in SCOWLP to be forming and interface were mapped onto the domain-pair structural alignment. We calculated a similarity index (Si) based on the number of interacting residues that overlap and the length of both interacting regions by (Fig. 1):

where a and b represent the two domain structures aligned. The number of interacting residues that match in the PSA is represented by IR_{overlap(a, b)}. This value is divided by the average number of the interacting residues in both domains excluding the interacting residues located in gap regions in the structural alignment (IR _gaps ).

4 Clustering binding regions

To re-compute the distances we used the complete-linkage method [9], which considers the distance between two clusters to be equal to the minimum similarity of the two members.

5 Binding region definition by Si cut-offs

The result of the clustering can be represented in an intuitive tree or dendrogram, which shows how the individual contacting domains are successively merged at greater distances into larger and fewer clusters. The final PBRs depend on the Si cut-off that is set up. We can observe in Fig. 2A that the total number of binding regions for all the SCOP families grows exponentially as the Si cut-off increases. Based on our observations of a representative group of families we set up an empirical maximum similarity cut-off value of 0.4. We pre-calculated the results for Si cut-offs at 0, 0.1, 0.2, 0.3 and 0.4 to offer a range of values that allow flexibility in the final analysis of PBRs. The SCOWLP web application offers the possibility to display the classification at any of these cut-off values.

Our classification clustered 160,000 contacting domains from 2,561 families in 9,334 binding regions. About 65% of the families contain more than one binding region (Fig. 2B). These values are obtained for similarity zero and may vary depending on the similarity cut-off applied.

6 Interface definitions

In order to differentiate binding regions having single-interfaces from multi-interfaces (Fig. 1B), we identified in each binding region the partner for each contacting domain. Each binding region was divided in sub-clusters when there were different domain families interacting in the same binding region. This resulted in a total of 10,300 interfaces. The classification shows a 78% of the binding regions having a single-interface and the rest having mainly 2 or 3 interfaces per region (Fig. 2C). These numbers have to be carefully interpreted by taking into account the limitation of the structural information contained in the PDB (i.e. 1,715 binding regions contain a unique member in the PDB and therefore only one known interface per binding region).

Implementation

We used MySQL and Java programming language to generate the classification of PBRs. Calculations were performed on a cluster of five Pentium IV 2.6 GHz. The PBRs classification has been included into the SCOWLP database. SCOWLP will be updated with every SCOP release.

Extraction of similarities

The classification of PBRs requires a proper definition of the similarity between binding regions. For this purpose it is essential to have (a) a reliable source of interface definitions, (b) high quality alignments, and (c) a adequate similarity function:

c) The similarity index penalizes gap regions

The Si reflects the overlap of interacting residues between two binding regions in domains belonging to the same protein family. It is important to consider the number of interacting residues per domain, which allows us to obtain the percentage of interacting residues that is overlapping over the total (see Methods). This helps to distinguish whether a binding region is identical, different or included into another one.

Ligands and proteins possess internal degrees of freedom and can adopt various conformational states. Furthermore, many family members often contain sequence inclusions/deletions in loops or C-/N-termi, or even additional secondary structure elements, which are often involved in protein interactions. For these reasons, we calculate the Si without considering the interacting residues belonging to gap regions in the PSA. This is graphically illustrated in Figure 3A. Two proteins belonging to the same family differ in an insertion of 55 residues, which creates a gap region in the PSA. This additional region is involved in binding and, therefore, increases the number of interacting residues for the protein containing it.

In general, dismissing interacting residues belonging to gap regions in PSAs produces a condensation effect on the clusters at high level of similarity. Additionally, it can also cause reorganization of cluster members at lower levels of similarity. Ignoring gaps for Si calculation and applying flexible similarity cut-offs might help in the final clustering and consequent analysis. This is illustrated in Figure 3B, where two contacting domains of the same family presenting two different overlapping binding regions (peptide-binding and crystal packing) are clustered differently depending on considering or excluding gap regions and by applying flexible Si cut-offs. As an example, applying a 0.2 cut-off when excluding gaps clusters all peptide-binding interfaces separated from the crystal packing interface. This clustering may facilitate further analysis of these different binding regions and their properties.

Aggregation using the complete-linkage method

Some protein families bind through multiple binding regions with different ranges of residue overlapping. This produces extensions of the binding region definitions and association of two clearly defined regions by a third into a bigger single one. To cope with these usual situations, instead of using the average-linkage used by other authors [10, 14], we have rather applied the complete-linkage [9] due to two main properties:

Property 1: Complete-linkage is sensitive to zero similarity

This method defines at similarity zero all binding regions that do not share interacting residues. Besides, it also assumes that in the same binding region all the members must have some range of similarity among them; otherwise they are split in two separate clusters. This is illustrated in Figure 4A (left panel), where a binding region of domain X might appear as a single one due to the overlapping of several interfaces (A to G). The handling of the three "connector interfaces" (C, D, G) will be responsible of the definition of the final clusters at similarity zero. The clustering is decided based on the higher similarity; C is more similar to B than to D and, on the other hand, G is more similar to F than to D. Therefore, the connectors G and C will be part of the cluster EF and AB respectively, whereas D will belong to a separate cluster. At no similarity, complete-linkage differentiates three binding regions, whereas single-linkage offers only one cluster containing all interfaces. In single-linkage the members having no direct similarity (D, F) are included in the same cluster if there is a "connector interface" (G) having some similarity with both. This enables progressive extensions of a binding region depending on the Si cut-off applied. The average-linkage method would have intermediate properties.

Threshold values define the final PBRs

The clustering process can be represented by a dendrogram, which shows how the individual objects are successively merged at greater distances into larger and fewer clusters. The branches are proportional in length to the estimated similarity of each binding region with the others. The final clusters depend on the similarity cut-off that is set up.

Binding regions of a family can often present overlapping residues, which makes their definition to be sometimes unclear and arbitrary. Some times there is no unique criteria to adopt in order to define clear PBRs and, in these cases, an appropriate classification may depend on user-based considerations. Illustrative examples are: i) being able to distinguish multi-interfaces versus multi-regions (Fig. 1B) in a protein family, ii) distinction of domain-domain versus domain-peptide interfaces, and iii) being able to separate and analyze "non-biological" interfaces.

This panorama encouraged us to proceed with the application of several cut-offs within an empirical range of similarities by taking advantage of the clustering properties of the complete-linkage method. The minimum Si cut-off value was fixed to zero to give a general view of the binding regions used by a family (property 1). The maximum value was fixed to 0.4 based on our observations (see Si cutoff and Definition section). We also pre-calculated the results for 0.1, 0.2, 0.3 Si cut-offs to allow flexibility in the analysis of PBRs. Figure 4B shows all the structurally known binding regions of the PTB domain and the clusters for different Si cut-offs for complete- and average-linkage. It can be appreciated that the slope is not so drastic in complete – as it is in the average-linkage method. Although offering a similar grouping of elements, the complete-linkage method produces dilatation of the differences among the elements (property 2) and assists in the application of different cut-offs for separation of clusters. As an example, a cut in a specific point (highlighted in yellow bars) gives a wider similarity range for complete – than for average-linkage. The introduced flexibility for choosing cut-offs offers, for example, the possibility to differentiate sub-clusters (i.e. 2NMB:AB and 1XR0:BA in Figure 4B) and decide to include or exclude them in a specific binding region for comparative analysis.

Binding regions vs. interfaces clustering

In this section we compare SCOWLP with a different method, PRISM [12], to give insights to users into the utilization of our approach and its biological applications compared to other strategies to classify protein interactions. Whereas SCOWLP compares and classifies interfaces based on defined binding regions in the fold of each counterpart (at family level), PRISM compares full interfaces (both partners) in a sequence position independent manner. By using a geometric hashing algorithm it groups interfaces by similarities of the space distribution of interacting residues independently of the fold. Although being two different approaches, both methods can provide a similar number and composition of clusters for a specific protein family; however, differences may also exist in other cases. The following examples are intended to illustrate it (0.2 similarity cut-off used). (1) If a protein family interacts with two different proteins using the same binding region (Single region-multi-interface – Fig. 1B), SCOWLP would always include both interfaces in the same cluster, whereas PRISM would do it only in case it considers similar the distribution of the interfacial residues. This is exemplified in Figure 4B. SCOWLP includes 1j0w:AB in the same binding region cluster as 1m7e:BA and 1p3r:BA, whereas PRISM classifies 1j0w:AB unaccompanied in an only-one-member interface cluster. The same applies to the case of classification of protein-peptide interfaces, where conformational differences of the short peptidic sequences may cause a different PRISM-architecture and, therefore, a separate classification. SCOWLP groups several peptides binding to the same binding region of the PTB domain in one single cluster of 16 members (Figure 4B, cluster 1aqc:AC to 1shc:AB); however, PRISM groups these interfaces in two different clusters of six and seven members. For this specific example, the difference in overall numbers of interfaces is due to the fact that some of the protein-peptide interfaces obtained with SCOWLP are missing in the PRISM clustering (1uef:BD, 1m7e:CF and 1oqn:BD). (2) In the case of structural symmetry (i.e. symmetrical protein assemblies and crystal packing), PRISM would include all interfaces in a cluster, whereas SCOWLP would have separated clusters for each binding region. (3) PRISM takes protein chains as a domain unit and therefore does not consider intra-interacting domains, which are considered in SCOWLP.

Web application

We implemented the hierarchical classification of PBRs into the SCOWLP web application. Based on a selected SCOP family, SCOWLP retrieves its binding regions and a summary of the interacting information. The results are generated based on a user-selected similarity cut-off. The analysis of the binding regions can be performed in three different ways (Fig. 5): [a] visualizing the spatial location of each binding region on a representative family structure by using Jmol plug-in [18], [b] keyword search for PDB ids and chains to identify specific complexes, or [c] visualizing the structure-based aligned representative sequences for a binding region with highlighted interacting residues. Once the binding region of interest is localized, a tree-based structure shows three additional classification levels (Fig. 5d): binding region (BR), interface (IF) and contacting domain (DC). All domains in a family that contain interacting information are structurally aligned and their sequences are displayed. Upon selection, the interacting residues can be coloured based on their physico-chemical properties (hydrophobic, hydrophilic or both), and also by the water contribution to the interfacial interactions (dry, wet or dual interaction). A label with the interacting correspondences will appear on each interacting residue when pointed with the mouse. The physico-chemical properties allow the user to distinguish conserved vs. variable interactions.

In Figure 5, the PTB domain is used as an example of the utility of the SCOWLP database for analysis of PBRs. In this example, the clustering is selected for similarity cut-off value 0.4 (corresponding dendrogram shown in Figure 4B). A structure-based alignment of the PBRs is obtained, and all interacting residue patterns are highlighted (panel c). A specific binding region is expanded to display all interfaces; in this case corresponding to PTB binding to phospho-tyrosine peptidic ligands. This binding region gets automatically displayed in the 3D viewer for graphical inspection (panel a). This interface is expanded to obtain a structure-based alignment of all PTB domains that use this binding region for recognition. The secondary structure of the domain is displayed at the top of the alignment to help with interpretation of interacting information. The interacting residues are highlighted with different colouring; in this case based on the water contribution to their interfacial interactions (panel d). This information allows comparative analysis of the interfaces, including conservation vs. variation of the interactions. In this example we easily are able to analyze (at structure and sequence level) all the interfaces of the PTB domain with different phospho-tyrosine peptides and their interaction patterns. In the example, the three main recognition regions described for the X11 PTB and a peptide motif from the Alzeimer's amyloid precursor protein (APP; PDB entry 1AQC) are displayed and structurally aligned with the recognition regions of other peptides known to bind PTBs in this region. Also, specific differences in the interaction pattern can be further analyzed individually by clicking on each PDB entry code. Analysis of the conservation/variability of the interactions describing an interface may be of great utility for understanding energetic and evolutionary aspects of protein interactions and for helping in rational engineering and design.