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Protein Sci. Aug 2008; 17(8): 1374–1382.
PMCID: PMC2492825

Novel protein folds and their nonsequential structural analogs

Abstract

Newly determined protein structures are classified to belong to a new fold, if the structures are sufficiently dissimilar from all other so far known protein structures. To analyze structural similarities of proteins, structure alignment tools are used. We demonstrate that the usage of nonsequential structure alignment tools, which neglect the polypeptide chain connectivity, can yield structure alignments with significant similarities between proteins of known three-dimensional structure and newly determined protein structures that possess a new fold. The recently introduced protein structure alignment tool, GANGSTA, is specialized to perform nonsequential alignments with proper assignment of the secondary structure types by focusing on helices and strands only. In the new version, GANGSTA+, the underlying algorithms were completely redesigned, yielding enhanced quality of structure alignments, offering alignment against a larger database of protein structures, and being more efficient. We applied DaliLite, TM-align, and GANGSTA+ on three protein crystal structures considered to be novel folds. Applying GANGSTA+ to these novel folds, we find proteins in the ASTRAL40 database, which possess significant structural similarities, albeit the alignments are nonsequential and in some cases involve secondary structure elements aligned in reverse orientation. A web server is available at http://agknapp.chemie.fu-berlin.de/gplus for pairwise alignment, visualization, and database comparison.

Keywords: nonsequential protein structure alignment, novel protein fold, protein fold space, protein structure/folding

The specific biochemical abilities of a protein result from its three-dimensional (3D) native structure. For enzymes, the structure optimizes the geometric arrangement of catalytically active amino acid side chains and cofactors, and simultaneously allows efficient access and removal of educts and products, respectively. For proteins, where one of the functions is to form specific complexes with other proteins, the shape of the contact surface and the residue pair interactions in the contact surface are also relevant (Shulman-Peleg et al. 2007). Consequently, proteins from different species that perform the same function often possess the same structures and the same key residues. However, there are exceptions where nature uses alternatively designed protein 3D structures with equivalent or different key residues and cofactors to perform the same function in different species.

Under physiological conditions, the native 3D structure of a protein is determined solely by the primary sequence (Anfinsen et al. 1961). On the other hand, the native 3D structure does not belong to a unique primary sequence. Mutational studies demonstrated that often, only a small fraction of amino acids is crucial to define and stabilize the 3D structures of proteins (Guo et al. 2004; Russ et al. 2006). Consequently, only structurally and functionally relevant residues of a protein are conserved among different species. This sequence conservation is used to assess the unknown function of proteins by sequence comparison, which may fail if the sequence homology is too low. In case the protein 3D structure is available, structure comparison can be more useful to assess the protein function, since the universe of protein structures is much smaller than the universe of protein sequences. The number of different protein folds is estimated to be about 1000 only (Wang 1998; Leonov et al. 2003). But there are also less optimistic views expecting a much larger number of distinct protein folds of 4000 or even 8000 (Govindarajan et al. 1999; Grant et al. 2004; Liu et al. 2004).

Sequence comparison as a tool to identify protein function is now well established and is sufficiently reliable if the sequence similarity is higher than 40%, but the results of sequence comparisons become increasingly uncertain with lower sequence identity (Whisstock and Lesk 2003; Lee et al. 2007). Since not only sequence but also structure similarity of proteins correlates with their function, structure comparison of proteins is most useful to characterize a protein of yet unknown function, if its 3D structure is available (Lee et al. 2007). This approach can be particularly successful, since at present the Protein Data Bank (PDB) (Berman et al. 2000) already contains a considerable fraction of the universe of protein folds to predict the structures of soluble proteins (Kolodny et al. 2005; Zhang and Skolnick 2005a).

Protein 3D structure comparison is still a challenging task and depends critically on the alignment algorithm, the similarity measure, and the fractions of the protein structures considered for the pairwise structure alignment (Kolodny et al. 2005). An actual but still incomplete listing of available methods for protein structure alignment can be found on the web page http://en.wikipedia.org/wiki/Structural_alignment_software, already containing more than 40 different programs, for example, DaliLite (Holm and Park 2000) and TM-align (Zhang and Skolnick 2005b).

To identify a potential new fold, the considered protein structure must be aligned to all representative protein structures of the PDB. Suitable databases of representative protein structures are the ASTRAL databases provided by SCOP (Chandonia et al. 2004). These databases contain subsets of the PDB with domain structures whose sequence similarities are below a threshold value of, say, 40% or 70% sequence identity. In the past, the structure alignment methods used to identify the same fold in a database were often restricted or biased considering protein structures possessing the same connectivity of secondary structure elements (SSEs) (i.e., α-helices and β-strands) as defined by the polypeptide chain. Only a few methods are available that allow for nonsequential protein structure alignments, for example, MASS (Dror et al. 2003), TOPOFIT (Ilyin et al. 2004), SCALI (Yuan and Bystroff 2005), and others (Szustakowski and Weng 2000, 2002; Shatsky et al. 2002; Shih and Hwang 2004; Chen et al. 2006). Recently, the program GANGSTA (Kolbeck et al. 2006) appeared, which ignores the loops connecting different SSEs, like, for example, MASS (Dror et al. 2003), PRISM (Yang and Honig 1999), and SARF (Alexandrov and Fischer 1996), and allows nonsequential protein structure alignment. In addition, it offers alignment of yet unknown protein structures against a database of more than 3000 domains of protein structures with <40% sequence identity. Since GANGSTA is relatively slow, we have redesigned it completely. GANGSTA+ is more than a factor of 10 faster, yields alignments of higher quality, and offers alignments of arbitrary protein structures against the complete ASTRAL40 (1.71) database containing about 7500 domains of protein structures with <40% sequence identity of the corresponding polypeptides (Chandonia et al. 2004).

Here, we like to demonstrate that protein folds, which are considered to be new, appear to be known folds if one considers only the topological arrangement of the SSEs and disregards the connectivity of the polypeptide chain defined by the loops connecting the SSEs. GANGSTA+ is also capable of performing structure alignments where the SSEs can be aligned in reverse orientation; that is, aligned SSE pairs are of the same type, but oriented such that the C-terminal end of one SSE is superimposed on the N-terminal end of the other SSE. This can be used to enhance the likeliness of finding similar structures for a given protein structure. We like to point out that this study can also be performed with several other protein structure alignment programs mentioned above. We use GANGSTA+ in this application since with our own method we have the procedures better under control. To contrast nonsequential with sequential alignment results, we applied TM-align (Zhang and Skolnick 2005b), which works exclusively sequentially.

Results

Large-scale database comparison

We applied TM-align (Zhang and Skolnick 2005b) and GANGSTA+ on all protein pairs from the ASTRAL40 (SCOP 1.71) database of protein structure alignment (DPSA) (see Materials and Methods) and evaluated the TM-score, Equation 3, of all protein pairs, obtained with TM-align and with GANGSTA+. Figure 1 shows the distribution of the highest TM-scores obtained by structure alignment of each protein of the DPSA with respect to the whole DPSA. The results illustrate that both methods yield comparable protein structure alignments. For high TM-scores, the score distribution generated with GANGSTA+ is slightly below that of TM-align, which is understandable, since GANGSTA+ optimizes the SAS but not the TM-score. Moreover, GANGSTA+ is a nonsequential protein structure alignment tool, solving a problem of higher complexity. Both methods operate in the time range of less than a second CPU time per protein pair on an AMD/OPTERON with 1600 MHz.

Figure 1.
TM-score, Equation 3, distribution of the highest-scoring protein structure alignments generated by (dashed line) TM-align (Zhang and Skolnick 2005b) and (solid line) GANGSTA+ for each DPSA entry with respect to whole DPSA.

Nonsequential structure similarities of novel folds with the ASTRAL40 protein structure database

In 2007, Koo et al. published the crystal structure of the hypothetical protein TA0956 (2JMK) from Thermoplasma acidophilum, which was stated to possess a new fold (Koo et al. 2007). Database searches with TM-align (Zhang and Skolnick 2005b) and DaliLite (Holm and Park 2000) did not yield significant structural similarities to other proteins in the ASTRAL40 database (SCOP 1.71) (DPSA). In contrast to DaliLite and TM-align, GANGSTA+ was able to detect several nonsequential structure alignments with complete assignment of all seven SSEs of 2JMK by scanning the whole DPSA in ~72 min (i.e., about 0.6 s per protein pair) on an AMD/OPTERON with 1600 MHz CPU. The structure alignments of all three considered programs were evaluated by accounting the number of aligned residues and the RMSD. However, GANGSTA+ also considers proper assignment of SSE types (α-helix, β-strand) and completeness of assigned SSEs. Regarding RMSD and the number of aligned residues, the most similar structure to 2JMK is 1GO4 (Fig. 2C; Sironi et al. 2002). 1GO4 is a protein involved in cell cycle regulation. GANGSTA+ succeeded to align all seven SSEs of 2JMK to the structure of 1GO4 at RMSD = 1.8 Å with 61 residues in total (see Fig. 2A,B), while the sequence identity between the two proteins is only about 19%. However, in this structure alignment, five out of the seven SSEs from 2JMK have been aligned in reverse orientation albeit the SSE types, where properly assigned (Fig. 2B). Figure 2C displays the overall result of the applied database search correlating the number of aligned residues with the RMSD for all aligned protein pairs. In total, 1534 protein structure alignments of 2JMK were found, involving more than 40 aligned residues. From these alignments, 469 provide a complete type-consistent SSE assignment of 2JMK. But these alignments are all nonsequential in SSE connectivity, explaining that it is difficult to find these similar protein folds. Given the protein pair 2JMK and 1GO4, DaliLite (Holm and Park 2000) aligned 75 residues at RMSD = 11.0 Å (Z-Score = 1.7 < 2.0). But none of the SSEs was aligned in a type-consistent way. Also with TM-align (Zhang and Skolnick 2005b), the detected protein structure was less similar to the reference protein 2JMK. The best alignment result considered 67 residues at RMSD = 4.0 Å (TM-score = 0.24 < 0.50) (see also Table 1) with a single type-consistent SSE pair only (α-helix no. 3) (see Fig. 2A).

Table 1.
Database search of similar protein structures with GANGSTA+ and comparison of pairwise structure alignments made with DaliLite and TM-align
Figure 2.
(A) Protein structure alignment with GANGSTA+. New fold 2JMK (reference protein) (Koo et al. 2007) (dark colors, blue for loops and orange for SSEs) aligned with GANGSTA+ on 1GO4 (Sironi et al. 2002) (detected protein) (light colors, blue and orange) ...

In 2006, Sue et al. published the structure 2AJE, stating that this DNA-binding protein appears to be a new fold, where a particular argument was the additional C-terminal helix (SSE no. 4 in Fig. 3A) (Sue et al. 2006). In a structural database search of the DPSA using DaliLite and TM-align, no protein structures with significant similarities were found. However, application of GANGSTA+ with respect to the ASTRAL40 data set revealed significant nonsequential similarities to 1J7N, a domain of the anthrax lethal factor published in 2001 (Pannifer et al. 2001). GANGSTA+ aligned all four α-helices, including the additional C-terminal helix (see Fig. 3A) with a total of 53 aligned residues at a RMSD = 2.1 Å and a sequence identity of ~15%. The four α-helices of 2AJE have been aligned to 1J7N without the need of reverse SSE orientations (see Fig. 3B). The database scan took ~51 min (~0.4 s per protein pair). The overall results depicted in Figure 3C display only 31 protein structure alignments that involve more than 40 residues. Twenty-two of these structure alignments are complete, involving all four α-helices of 2AJE. But all these structure alignments are nonsequential in SSE connectivity, and therefore more difficult to find than sequential alignments. Given the protein pair 2AJE and 1J7N, DaliLite aligned 45 residues at RMSD = 3.9 Å (Z-Score = 0.9 < 2.0) with three type-consistent SSE pairs. Here, TM-align also aligns 45 residues with three type-consistent SSE pairs at RMSD = 3.4 Å (TM-score = 0.16 < 0.50) (see also Table 1).

Figure 3.
(A) Protein structure alignment with GANGSTA+. New fold 2AJE (Sue et al. 2006) (reference protein) (dark colors, blue for loops and orange for SSEs) aligned with GANGSTA+ on 1J7N (Pannifer et al. 2001) (detected protein) (light colors, blue and orange) ...

A third example is 2ES9, deposited in the PDB in the year 2005 (Benach et al. 2005). It was claimed to possess a new fold, according to the SCOP classification library (Murzin et al. 1995). We searched with DaliLite for structures similar to 2ES9, which yielded a sequential structure alignment of significant similarity (Z-Score = 5.2 > 2.0) to the structure of 1SZA published in 2004 (Meinhart and Cramer 2004). DaliLite aligns 67 residues at RMSD = 2.5 Å for this protein pair. However, the generated alignment is insufficient to describe the fold of 2ES9 in sufficient detail, since only the four-helix bundle, which is a common motive (Mehl et al. 2003; Eckenhoffa et al. 2005), was aligned, while the fifth lateral α-helix was skipped.

The structure alignment with GANGSTA+ for 2ES9 with respect to the ASTRAL40 data set took 40 min (~0.3 s per protein pair) and revealed a nonsequential alignment with 1SXJ, involving all five SSEs of 2ES9 (see Fig. 4A,B). Figure 4C depicts the overall result from database search. In this case, GANGSTA+ found 485 protein structure alignments, involving more than 40 residues. From these structure alignments, 140 involve all five SSEs of 2ES9. But also here, all structure alignments found with GANGSTA+ are nonsequential in SSE connectivity. In the case of 1SXJ, 69 residues were aligned at RMSD = 1.8 Å with a sequence identity of only ~12%. Four out of the five SSEs of 2ES9 were aligned in reverse orientation on the equivalent SSEs in 1SXJ (see Fig. 4B). The structure of 1SXJ was published in 2004 (Bowman et al. 2004). Both proteins 1SXJ found with GANGSTA+ and 1SZA found with DaliLite are involved in DNA or RNA polymerization, respectively. Hence, the connection between 2ES9 and 1SZA found with DaliLite is relevant.

Figure 4.
(A) Protein structure alignment with GANGSTA+. New fold 2ES9 (Benach et al. 2005) (reference protein) (dark colors, blue for loops and orange for SSEs) aligned with GANGSTA+ on 1SXJ (Bowman et al. 2004) (detected protein) (light colors, blue and orange) ...

Given the protein pair 2ES9 and 1SXJ, DaliLite aligned 57 residues at RMSD = 2.5 Å with a Z-Score of 3.2 > 2.0. It succeeded in aligning the four-helix bundle, but again skipped the lateral α-helix (see Fig. 5A,B). Although TM-align aligned all five SSEs of 2ES9 and 1SXJ considering 65 residues, the detected structural similarity was low yielding a RMSD of 4.0 Å with a TM-score of 0.41 < 0.50 (see Table 1). Given the protein pair 2ES9 and 1SZA, GANGSTA+ revealed comparable similarities as detected by DaliLite. It aligned 49 residues sequentially at 2.1 Å RMSD, and similar to DaliLite it could not find an α-helix in 1SZA that corresponds to the lateral helix in 2ES9. The results of this example illustrate that the detection of a structure motive that resembles a four-helix bundle with respect to the enormous number of four-helix bundle related motives requires a sensitive and robust nonsequential structure alignment approach.

Figure 5.
(A) Protein structure alignment with DaliLite. New fold 2ES9 (Benach et al. 2005) (reference protein) (dark colors, blue for loops and orange for SSEs) aligned with GANGSTA+ on 1SXJ (Bowman et al. 2004) (detected protein) (light colors, blue and orange) ...

GANGSTA+ allows the detection of structural similarities between proteins also, if the assignment of SSE pairs is restricted to SSEs having the same orientation. With these constraints, we performed additional database searches for the two protein structures, 2JMK and 2ES9, with the DPSA, since without such constraints the best structure alignments were obtained with reverse orientations of some of the aligned SSEs. Initially, we restricted the alignments to avoid only inversion of α-helix orientations, but allowed inversion of β-strand orientations. This partial restriction is of particular interest, since inversion of a helix axis goes along with inversion of the helix dipole moment, while inversion of a strand has fewer consequences for function and energetics of a protein. Under these conditions, GANGSTA+ found for 2JMK nonsequential structure similarities to the DPSA entry 1Q6Z (Bera et al., in press), aligning 60 residues at RMSD = 2.0 Å involving inversion of two β-strand orientations. Since it is mandatory for applications with GANGSTA+ to rank structure alignments highest, which align all SSEs of the reference protein, all seven SSEs of 2JMK were aligned SSE-type consistent with 1Q6Z. Avoiding also inversion of β-strand orientations, GANGSTA+ detected nonsequential similarities between 2JMK and 1VJU (SGPP), aligning only 45 residues at RMSD = 2.4 Å. Note that for this more conservative alignment result, the number of aligned residue pairs was below the total number of residues of 58 belonging to the SSEs of the reference protein 2JMK. Here, still, all seven SSEs of 2JMK were aligned in the same orientation as the equivalent SSEs in 1VJU (see Supplemental Figs. S1A–C).

Finally, we performed a search with GANGSTA+ to detect similarities for 2ES9 with the DPSA with SSE pairs that possess the same orientation. This yielded the nonsequential structure alignment of 2ES9 with 1H6K (Mazza et al. 2001) with complete SSE assignment, aligning 48 residues at RMSD = 2.2 Å (see Supplemental Figs. S2A–C). 1H6K was published by Mazza et al. in 2001 and is a nuclear protein containing an RNA-binding motif. Thus, likewise, 1SXJ and 1SZA, the function of 1H6K also relates to nucleic acids (Berman et al. 2000). These results illustrate that GANGSTA+ is capable of generating high-quality nonsequential structure alignments, considering SSE pairs with the same or reverse orientations, depending on the needs of the application.

Discussion

The protein structure alignment tool GANGSTA+ solves alignment problems in a three-stage hierarchical approach starting with an alignment on the secondary structure level, where only α-helices and β-strands are considered. In the second stage, the residue pair assignment is performed on the basis of the results from the first stage. In a subsequent last stage, a refinement of the residue pair assignment is performed to complete the SSE assignment from the first stage, and to find possible reassignments of SSEs, and to extend the residue pair assignment beyond the SSE boundaries. The latter procedure leads often to a larger number of aligned residue pairs as the number of residues in the aligned SSEs of the smaller protein. This is clearly visible in the diagrams correlating RMSD with the number of aligned residues for the proteins whose structures were found to be similar to the reference protein structure (Figs. 2C, ,3C,3C, and and4C).4C). In all three cases of proteins considered to be new folds, (2JMK) (Koo et al. 2007), (2AJE) (Sue et al. 2006), and (2ES9) (Benach et al. 2005), GANGSTA+ found structure alignments where the number of aligned residue pairs was larger than the total number of residues in all SSEs considered for the alignment. See the alignment results in the correlation diagrams (Figs. 2C, ,3C,3C, and and4C),4C), where the total number of residues in all considered SSEs are marked by the red vertical line.

The four key features of GANGSTA+ are: (1) It can perform sequential, but alternatively, also nonsequential structure alignments, disregarding the polypeptide connectivity in the latter case; (2) it assigns SSE pairs only if they are of the same type (α-helix or β-strand); (3) it is capable of aligning SSEs having the same or reverse mutual orientations; and (4) it maximizes the number of assignable SSEs. GANGSTA+ manages to find nonsequential structure alignments, since it ignores the loops connecting the SSEs in the first SSE alignment stage. Considering the loops in the initial stage of structure alignment would favor sequential SSE alignments. However, after the SSE assignment is terminated in the final (third) stage of structure alignment, the residue pair assignment also involves residues belonging to the loop regime of the protein structures. While GANGSTA+ assigns SSEs only type consistently, that is, helix on helix and strand on strand, it has the option to align SSEs in the same or opposite orientations.

GANGSTA+ provides nonsequential protein structure alignments in the same time range as the fastest commonly used sequential structure alignment methods with less than a second per protein pair, on average, on an AMD/OPTERON with 1600 MHz. Furthermore, a comparison with TM-align and the TM-score illustrates that GANGSTA+ is able to solve sequential protein structure alignments according to the TM-score with comparable quality.

We demonstrated that GANGSTA+ is able to detect nonsequential similarities for protein chains stated to possess new folds considering three examples. Although the question whether a new protein structure contains a new fold or not remains difficult to judge, the results illustrate that the application of nonsequential structure alignment tools can yield additional insight to understand protein structures and fold characteristics, presenting new starting points for protein function analysis and protein structure comparison. GANGSTA+ is not bound to a sequential connectivity of SSEs in the polypeptide chains of proteins. Thus, it can detect structure similarities of different proteins that have common ancestors, but whose SSE connectivity was reshuffled by genetic operations during evolution (Cooper et al. 1997). Although DaliLite and TM-align proved to be very accurate structural alignment methods on representative data sets (Hou et al. 2002; Day et al. 2003; Pandit et al. 2006; Barthel et al. 2007), they are restricted or biased toward sequential structure alignments. This can lead to failures in detection of structural relations between protein chains that possess nonsequential similarity.

In future investigations, we aim to unravel functional relationships of proteins with yet unknown functions using GANGSTA+ to detect nonsequential structure similarity. Furthermore, we aim to improve protein structure prediction approaches on the basis of nonsequential structural relations. In this context, multiple structure alignments with GANGSTA+ that can be used to define new sequence similarity measures for sequence alignment methods could be a promising direction (Schwartz and Dayhoff 1978; Pearson and Lipman 1988; Henikoff and Henikoff 1992; Altschul et al. 1997; Pearson and Sierk 2005).

Further investigations will focus on structurally similar proteins with SSEs pairs aligned in reverse orientation. In contrast to inversion of β-strand orientation, the inversion of an α-helix axis goes along with the inversion of the large helix dipole (Chakrabarti 1994). The helix dipole can have a strong influence on protein stability, its intrinsic function (Chou et al. 1988; Fairman et al. 1989; Aqvist et al. 1991; Ben-Tal and Honig 1996; Sengupta 2005), and ability of complex formation with other proteins (Miura et al. 1999). β-Strands in proteins can be organized in alternative orientations (parallel or antiparallel), which indicates functional robustness of proteins toward inverse orientations of β-strands. Therefore, we would expect to observe significantly more β-strand inversions than α-helix inversions. GANGSTA+ enables us to analyze the functional relevance of the α-helices dipole orientation, and its impact on SSE arrangements in common structural motifs for large databases. We are able to discriminate between single α-helix inversions or arbitrary many inversions of each SSE type, for example, to count parallel and antiparallel β-strands within a certain protein family. These possibilities underline the wide range of GANGSTA+ applicability to analyze the protein fold spaces and its properties.

Materials and Methods

Structure alignment

Here we provide a short overview of the protein structure alignment method used in the present study. A more detailed technical description is given in the ESM. GANGSTA+ as well as GANGSTA (Kolbeck et al. 2006) have in common the ability to align protein structures hierarchically starting with an alignment of secondary structure elements (SSE; first stage). Only α-helices and β-strands are considered as SSEs. Nonsequential structure alignment is facilitated, since loops and coils connecting the SSEs are ignored. GANGSTA used a genetic algorithm to explore similarities between two protein structures based on contact maps of SSE, while GANGSTA+ uses a combinatorial approach, which is more efficient and reliable. For the highest ranked SSE assignments, preliminary alignments on the residue level are performed (second stage), using rigid body energy minimization with attractive soft interactions between Cα atoms belonging to different proteins. This minimizes the spatial distances of the assigned Cα atom pairs. In stage three, the preliminary structural overlay is used to assign the Cα atoms of both proteins to points on the same rectangular grid. Cα atom pairs assigned to the same grid points are used for a more accurate and complete SSE assignment. Finally, the assignment on the residue level (second stage) is repeated, also aligning residues belonging to loops and coils.

Scoring results of structure alignments

Protein structure alignments are evaluated with the structure alignment score (SAS) (Kolodny et al. 2005):

equation image

that weights the RMSD of Cα atoms relative to the number of aligned residues Naligned. In GANGSTA, we used the more complex GANGSTA score:

equation image

where Ngap is the number of unassigned SSEs of the reference protein; qst res, varying between 0 and 1, is the residue contact map overlap; and ΔSSE measures the similarity of the SSE pair distance map between the two aligned protein structures as defined in Kolbeck et al. (2006). To prevent division by zero, ε = 10−5 is used. Gs is particularly useful to detect more distant similarities between protein structures. In the present application, we focus on protein structure pairs possessing a high degree of similarity, where the more simple similarity measure of SAS, Equation 1, is sufficient. No different alignments were found using Gs instead. Both scores; SAS and Gs, are available in GANGSTA+.

The TM-score (Zhang and Skolnick 2005b), TMS, is also used for a large-scale database comparison of sequential protein structure alignments from GANGSTA+ and TM-align (see section on large-scale database comparison). It is defined by

equation image

where LRef is the total number of residues of the generally smaller reference protein to which other protein structures from a database are aligned, Naligned is the number of aligned residues, di is the Cα–Cα distance between the ith pair of aligned residues, and d0 is a distance parameter normalizing the distances to make the average TM-score independent of the protein size for random structure pairs (see details in Zhang and Skolnick 2005b).

Database for structure alignments

The database of proteins used for structure alignment (DPSA) by GANGSTA+ involves all protein domains from ASTRAL40 (SCOP 1.71) (Murzin et al. 1995) with at least three SSEs (7347 protein chains in total), yielding about 27 106 possible pairs for protein structure alignment. Application of GANGSTA+ to the whole DPSA yielded about 8.4 106 pairs of successfully aligned protein structures with an SAS <10, where >50% of the SSEs from the smaller protein are involved in the structure alignment.

Acknowledgments

The authors thank Björn Kolbeck, Tobias Schmidt-Goenner, and Gernot Kieseritzky for useful discussions. This project was funded by the International Research Training Group (IRTG) on “Genomics and Systems Biology of Molecular Networks” (GRK1360, Deutsche Forschungsgemeinschaft [DFG]).

Footnotes

Supplemental material: see www.proteinscience.org

Reprint requests to: Ernst-Walter Knapp, Department of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36A, 14195 Berlin, Germany; e-mail: ed.nilreb-uf.eimehc@ppank; fax: 49-30-838-56921.

Article published online ahead of print. Article and publication date are at http://www.proteinscience.org/cgi/doi/10.1110/ps.035469.108.

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