A novel method to compare protein structures using local descriptors
© Daniluk and Lesyng; licensee BioMed Central Ltd. 2011
Received: 2 April 2011
Accepted: 17 August 2011
Published: 17 August 2011
Protein structure comparison is one of the most widely performed tasks in bioinformatics. However, currently used methods have problems with the so-called "difficult similarities", including considerable shifts and distortions of structure, sequential swaps and circular permutations. There is a demand for efficient and automated systems capable of overcoming these difficulties, which may lead to the discovery of previously unknown structural relationships.
We present a novel method for protein structure comparison based on the formalism of local descriptors of protein structure - DEscriptor Defined Alignment (DEDAL). Local similarities identified by pairs of similar descriptors are extended into global structural alignments. We demonstrate the method's capability by aligning structures in difficult benchmark sets: curated alignments in the SISYPHUS database, as well as SISY and RIPC sets, including non-sequential and non-rigid-body alignments. On the most difficult RIPC set of sequence alignment pairs the method achieves an accuracy of 77% (the second best method tested achieves 60% accuracy).
DEDAL is fast enough to be used in whole proteome applications, and by lowering the threshold of detectable structure similarity it may shed additional light on molecular evolution processes. It is well suited to improving automatic classification of structure domains, helping analyze protein fold space, or to improving protein classification schemes. DEDAL is available online at http://bioexploratorium.pl/EP/DEDAL.
The methods of protein structure alignment play a crucial role in computational and structural biology. However, despite extensive research, comparison of protein structures still remains an open subject. Even in the category of the most straightforward approaches which focus on finding the largest possible sets of superimposable amino-acids, treating structures as rigid entities and preserving the order of aligned residues, there is no definitive "best of all" method . Furthermore, there exists a growing set of known biologically significant similarities between protein structures with considerable spatial distortions, various segment swaps or circular permutations [2–5]. These "gold standard" alignments are prepared with substantial human intervention  and studies have shown that no automated techniques to date are capable of satisfactorily reproducing them .
The reason behind the aforementioned problems is the fact that proteins which in fact are fairly elastic objects are represented by fixed atomic coordinates in 3D space, usually obtained from crystallographic experiments and most methods focus on finding a superposition which would minimize the distance between the respective amino-acids. Such a paradigm greatly simplifies the difficult task of identifying equivalent residues and thus may be very appealing, but is incapable of distinguishing between regions which are strongly stabilized by actual protein interactions and those which are of looser composition. The major approaches to structure superposition, including comparing intramolecular inter-residue distances (SSAP , DALI , PAUL ), matching main-chain fragments (CE ), or Secondary Structure Elements (SSEs) (VAST , SARF , MATRAS , GANGSTA ), handle the limitations imposed by the rigid-body representation with varying degrees of success. Some methods use residue attached local frames of reference to identify partial superpositions which are then clustered (C α -match , 3D motifs , growing neighborhoods ). In principle, this approach allows for sequential rearrangements. The final alignment is inferred from the predominant superposition. Other methods use a one-dimensional representation of structure, where each residue is substituted with a characterization of its local features, and use dynamic programming to align such artificial sequences (e.g. SHEBA ). Still others employ alternative ways of describing protein structure, including Delaunay tessellation (TOPOFIT ), spherical polar Fourier representations (3D-BLAST ), and geometric hashing (C α -match ). To specifically address structural shifts and distortions, some methods search for "hinges" between superimposable rigid parts (FATCAT , FlexProt , ProtDeform , FlexSnap ). For an alternative classification, see a recent review .
Methods which attempt a decomposition of protein structures to smaller blocks are most likely to suffer from combinatorial complexity. While in principle they should be capable of finding alignments unconstrained by amino-acid sequence (i.e. with permutations or segment swaps), finding such an alignment is likely to be computationally prohibitive. Therefore, most approaches do not allow for sequential rearrangements. This is of less importance in the case of the methods using relatively large SSEs. However, one of the disadvantages of using SSEs is that the active sites are frequently small and contained in the coiled regions, and it is particularly important to align these correctly. Another method of curbing combinatorial complexity is to use the scoring function based on the rigid-body superposition, possibly allowing for "hinges" between superposable rigid parts. To date, we are aware of only one method capable of computing non-rigid alignments with sequential permutations (FlexSnap ). It should also be noted that methods tailored to the particular problem do not perform as well as mainstream approaches on the regular simple comparisons.
Finding an elegant way to address the aforementioned difficulties has been a motivation behind developing DEDAL. It is based on a formalism for representing and comparing local structure, the so called Local Descriptors of Protein Structure (LDPS) [27, 28]. In a much simpler implementation (called DAL) it has been used to identify regions of correctly predicted structure in models submitted to CASP [29, 30]. A single local descriptor contains information about the structure within a range of bonded and non-bonded interactions of a single amino-acid. Therefore, contrary to backbone segments or SSE, it can be treated as a complete self-contained structural entity. Alignments built from such blocks preserve contacts, which correspond to physical interactions between residues. Descriptors are large and specific enough to lessen the combinatorial burden and omit the sequential constraints. There is no thus need to use a global RMSD [31, 32] or other rigid-body measure to verify the feasibility of alignments.
Using more complex building blocks resulted in several problems which had to be solved. The first was assessing similarity between descriptors. In the case of single segments of the same length, it is easy to compute RMSD between respective amino-acids. When comparing descriptors, we first have to identify the mapping between their residues and only afterwards can we compute the RMSD between them. We consider the two descriptors similar and the resulting sequence alignment valid only when descriptors display sufficiently similar residue-residue contact patterns and the corresponding RMSD is sufficiently small. In our implementation we search for all alignments with a total RMSD not exceeding 2.5Å, and such that at least half of the segments in each of the descriptors are aligned.
To extend the alignment to whole structures we employ a three-stage process. First, we find all pairs of similar descriptors and their respective structural alignments. To discover significant similarities between local structures and avoid small accidental matches, we consider only alignments that consist of at least three segments, postponing the use of the two- and single segment descriptors to the final stage of the algorithm. Each such alignment can be considered as a building block for the alignment of whole structures. Obviously, not all blocks fit together, but those that do can be combined into larger alignments. In the second stage, we identify the largest sets (with respect to the number of residues) of non-conflicting descriptor pairs, i.e. the largest building block assemblages. From a mathematical point of view, this is a clique finding problem. In the final stage we use the remaining descriptor alignments, which were previously set aside, and add them to alignments from the second stage, but only if they overlap with the alignment being built. The resulting alignments have the following properties:
Each pair of aligned amino-acids belongs to at least one pair of aligned descriptors, which implies that their respective local neighborhoods are preserved,
There does not necessarily exist a superposition of aligned amino-acids, the alignment may have to be divided into several independently superposable parts,
Alignments may contain permutations of segments.
Our approach is of a non-rigid-body type but, contrary to other non-rigid-body methods, it does not attempt to find "hinges" which might make superposition possible. Rather, it ensures that alignment can be broken into separately superimposable regions, which are large enough to be structurally meaningful. In particular, separating stages two and three guarantees that each region will contain at least one three-segmented descriptor. The third property provides the ability to handle "difficult similarities". During the process of building an alignment, no restrictions are placed on the order of segments in the resulting mapping. Therefore, two similar proteins with different threading, but similar arrangements of secondary structure elements, can still be aligned. Using the terminology employed by CATH , it is possible to align two structures of the same architecture, even if their topologies are different.
The algorithm can be adjusted by modifying the internal scoring function. The most basic score is simply the number of aligned residues. It is also possible to limit the maximal offset between aligned residues. If the lengths of compared chains differ then offset is measured relative to the closest of shortest possible (i.e. ungapped) alignments or allowing gaps only in the shorter of the two protein chains. Sometimes, it is undesirable to find alignments with permutations. In such cases it is possible to take the largest sub-alignment which has no more than a given number of swaps. This, for example, permits searching explicitly for circularly permuted proteins.
As mentioned above, DEDAL is not restricted to finding rigid-body superpositions. This feature can be exploited in two ways. Firstly, it can be used to discover several disjoint, differently arranged similar substructures within one pair of proteins (e.g. domains or subdomains). Secondly, it can be used to address minute local differences which in a gradual continuous way may result in a global RMSD too large to handle for the traditional rigid-body methods.
The performance of structure superposition methods is commonly tested by (a) rigid-body RMSD and (b) the extent of the obtained superposition. While in many cases this is a valid approach, in many others alignments containing local alignment errors (induced by spatial proximity of residues rather than common architectural features or local similarity of the compared structures) are indistinguishable from correct ones. This in turn may result in misleading assessments of performance, especially in cases of low structure similarity, at which DEDAL is primarily aimed. Therefore, we resort to the manually curated structural alignments and a simple measure of how accurately they are reproduced by the automated approach. The numerical measure we use is the ratio of the number of residue pairs aligned in the same way in both the computed and curated alignments, and the size of the curated alignment. As a reference we use alignments compiled in (a) the SISYPHUS database , (b) the SISY set, a subset of the SISYPHUS database prepared by Mayr et al. , and (c) the RIPC set, containing selected challenging alignments, also prepared by Mayr et al. and based on the SCOP database . Using SISY and RIPC sets allows for a direct comparison with the Mayr et al. study.
SCOP - alignments comprising structures which can be related to domains in the SCOP database,
MD - alignments containing multi-domain structures,
MC - alignments containing multi-chain structures.
Structures in the PDB very often contain multiple chains filling a unit cell within a crystal. An undesired redundancy may be created if the entire contents of the unit cell are compared. Therefore, we have used the PDB "biological units" whenever possible.
The SISY set contains 69 non-redundant pairs selected from the SISYPHUS database by Mayr et al. . From each SISYPHUS multiple structure alignment they have selected the pair with the lowest sequence identity. Pairs with more than 40% identity or those including structures comprised of multiple chains were excluded.
The RIPC set comprises 40 pairs of SCOP domains also selected by Mayr et al. . These, albeit structurally related, are difficult to align due to repetitions, extensive insertions/deletions, circular permutations and/or considerable conformational variability. For 23 of these pairs, the authors provide reference alignments supported by evidence of sequence and function conservation.
Reconstruction of SISYPHUS alignments
We have executed the TS+CTS and CTS+CTS algorithms (see Methods) on all pairs of structures from the pruned SISYPHUS alignment set, computing for each algorithm at most the 5 largest alignments which differ significantly, and selecting the one that was most similar to the alignment curated in the SISYPHUS database. Computing more than one alignment is necessary because the SISYPHUS reference alignment is not always the largest or the best one, for example when the compared structures contain repeated motifs or internal symmetries which make alternative superpositions/alignments possible. This fact has been also noted by Mayr et al. .
Reconstruction of the SISY and RIPC alignments
Results of the Wilcoxon test for alignment accuracy in the SISY set
Results of the Wilcoxon test for alignment accuracy in the RIPC set
We have implemented the described algorithms in C on the Linux platform. The typical running time of a single comparison of a pair of structures using the TS and CTS algorithms ranges from seconds to a few minutes (on a 2.6 GHz AMD Opteron CPU), depending on the number of pairs of similar descriptors. In some cases, when structures are composed of several similar subdomains (e.g. propeller folds), the running time can reach several hours. We extracted 14 of the most computationally intensive cases and used them to test the REMC algorithm. We have experimentally determined the optimal number of replicas, the frequency of replica exchanges and the number of iterations required to reach globally the maximal score. The running time of the Monte-Carlo algorithm is mostly dependent on these three factors, therefore typically any pair of structures can be aligned in a few minutes. In the experiments described above, the REMC algorithm was used as a fallback option in the cases where combinatorial algorithms failed to finish in 120 seconds.
We have made DEDAL available online at http://bioexploratorium.pl/EP/DEDAL. The website also provides Linux binaries of the software. The server can be used to align structures identified by PDB or SCOP accession codes or supplied in uploaded files. Both TS+CTS and CTS+CTS algorithms are available, along with other modes potentially useful for the advanced user to cope with special cases, or to provide more insight into the behavior of DEDAL. It is also possible to define the parameters of the scoring function (k - maximal sequence offset, M - maximal number of swaps in the permutation, as explained in the Methods section). Results are presented in HTML format. Superpositions can be downloaded as PDB files or RasMol scripts, and also viewed through the Jmol applet. The alignments are available in FASTA format and as a list of corresponding residue ranges.
To illustrate the capacity of the descriptor based approach we present three cases of difficult structure alignments not handled effectively by methods limited by the rigid-body or sequence-dependence constraints.
DEDAL provides a direct approach to capturing similarity between proteins which is independent of rigid-body constraints. This is realized by systematically evaluating local structure context to identify similar regions of proteins while leaving aside regions which are different, where superposition is meaningless and should not be attempted. In addition, by focusing on local structure and carrying out a spatial rather than sequence attached analysis of matching substructures, it is not constrained by any particular order of structural features along the protein sequence. Because it identifies all local similarities between compared structures, it offers a rigorous and complete analysis. It is also very conservative in not extending the alignment beyond regions of pronounced structure similarity.
As structure comparison methods mature, the question as to whether compared structures are similar is being replaced by a need to determine the the exact nature of their similarity. The goal is to accurately indicate equivalent residues. Only manually curated alignments may be used to reliably assess this aspect of structure comparison. When tested on a relatively simple Conserved Domain Database , current automated techniques usually misalign residue pairs that are more than 3Å apart in the reference superposition, which amounts to 11 - 19% of the protein core residues . This is also true for pairs of proteins within the same superfamilies, where even modest spatial divergence may lead to alignment errors . On the more difficult test sets  (also used in this study), the quality of the alignments drops even further, to as low as 60% of the amino-acids correctly aligned over core and non-core residues. DEDAL represents a significant step forward in combating the above difficulties. While on the easier and medium difficulty test cases it is comparable to the best of other techniques, it outperforms them on the more demanding benchmarks. Thus, it effectively extends the ability to provide residue accuracy alignments to the most difficult cases, including discovering sequential permutations and spatial deformations. To our best knowledge, no other publicly accessible server offers this capability. The Linux binary of FlexSnap is publicly available but is less effective than DEDAL on both SISY and RIPC datasets. Local structure comparisons play an increasing role in the assignment of protein function [49, 50]. DEDAL offers an effective technique for this class of applications. Furthermore, as recently demonstrated by Kosloff and Kolodny , assignment of function may also be helped by focusing on structural dissimilarity among proteins that are related by homology. By identifying only the significant local similarities, DEDAL allows effective differentiation between similar and dissimilar regions of structure, which could help guide functional assignments within protein families.
Because of the relatively large granularity of structure description and inclusion of the 3D structural context, DEDAL has the capacity for structure comparisons involving large sets of structures. Therefore it is well suited to improve automatic classification of structure domains, help analyze protein fold space, or to study molecular evolution processes. These areas reflect our future research interests. The presented methodology is being generalized to the structural multi-alignment problem.
Local Descriptors of Protein Structure
Descriptors have already been applied in several studies [27, 28, 52–55]. Here we use an improved version of the local descriptor methodology described in . Every descriptor is built around its central amino-acid. In the first step, we identify residues close to the central amino-acid. For each pair of residues we compute distances between C α atoms (d α ) and geometrical centers of side-chains R C (d C ) (For glycine R C = C α , and for alanine R C = C β .). If either d α ≤ 6.5Å, or d C ≤ 8Å and d α - d C ≥ 0.75Å (second condition favors residues whose side-chains point towards each other), we consider two residues to be in contact. In the second step, we build elements around selected residues by taking four sequential neighbours, two on each side. Finally, overlapping elements are merged into segments.
Thresholds used for contact determination are based on the range of intra-molecular interactions. However, in this study we came to the conclusion that contacts with distances close to their respective cutoffs require special treatment. Otherwise, when comparing two descriptors, an element which barely fits within a threshold in one descriptor might have a counterpart just outside of it in the second one. In such a case, two otherwise similar descriptors might be considered different. Therefore, we use a rough set approach . We use a tightened set of thresholds for determining contacts (5.5Å and 7Å instead of 6.5Å and 8Å, respectively). If a contact satisfies lower thresholds, a corresponding element is considered certain. Otherwise, if it satisfies regular thresholds, it is considered optional.
Descriptors were designed to explore the structural neighborhood of their central amino-acid. Some descriptors, especially those built around surface residues, comprise only one or two segments. Frequently in this study, we refer to about three- or more segmented descriptors, which are expected to reflect the characteristics of a particular protein fold (e.g. three adjacent strands of a β-sheet). In the case of the hairpin-like motifs, segments are divided at the hairpin to mirror the secondary structure more accurately. This scheme of counting segments is required to properly define three-segmented descriptors as crucial to a given conformation and alignment, and was applied for the first time in this study.
where , and are coordinates of the C α atom of the ith residue.
central residues and their elements must be aligned exactly,
contacts between central residue and other residues must be preserved,
RMSD of aligned elements must not exceed 1.5Å,
for each pair of aligned elements, RMSD of substructures consisting of these elements and respective central elements must not exceed 2.5Å (i.e. elements should have the same position relative to the central element),
at least half of the segments must be aligned,
RMSD of aligned residues must not exceed 2.5Å.
We search through all alignments satisfying the conditions above. Firstly, we find all pairs of elements satisfying conditions 3 and 4. In the second stage, we construct all possible assemblies of those pairs and check for condition 6. If it is not met, these sets are reduced by removing the least fitting pairs of elements, until either condition 6 is met, or condition 5 is no longer satisfied. It should be noted that this process is totally sequence independent (i.e. the order of aligned segments in their respective proteins can differ). Because elements are the smallest indivisible blocks, it is possible that one segment will be aligned to two smaller ones which are a few residues apart. When computing condition 5, unaligned contacts which are optional in both descriptors are disregarded. It should also be noted that the approach of Bhattacharya et al.  uses a somewhat similar concept of local neighborhoods (k nearest residue neighbors) to carry out the structural alignment. They attempt to find a maximal common subgraph between their k-structures (in our case this task is accomplished through a contact guided systematic search). They report results for comparisons of 6 residues per neighborhood and note difficulties for comparing neighborhoods larger than 15 residues. Finally, they do not explore informational potential offered by the neighborhood approach to generate non-rigid body superpositions.
Graph representation and clique finding
In comparing two protein structures our first step is to find all similarities between their descriptors. All descriptors generated from the first structure are compared with all descriptors from the other, and alignments satisfying conditions described in the previous section are recorded. They are divided into two sets. The first set S3 contains alignments which have at least 3 segments. The second set S1 contains all the remaining alignments. The rationale behind this division is that alignments from S3 are likely large enough to encompass a significant similarity by themselves. Alignments in S1 are small and should be used only to extend structural alignments built with blocks from S3.
Each pair of aligned descriptors can be viewed as a partial alignment between structures. Such partial alignments can be combined to form a larger alignment if they are consistent in the overlapping parts or do not overlap at all. The solution computed by DEDAL is the largest (highest scoring) alignment that can be constructed from alignments of the individual descriptors. One should note that a set of partial alignments can be combined if and only if all its members are consistent with each other.
Finding the best alignment between structures is an extension of the clique finding problem in graphs. Let us assume that alignments between descriptors are nodes of an undirected graph G, and that there is an edge between two nodes if the corresponding alignments are consistent. In such case a clique in graph G can be interpreted as a valid alignment between the structures (Clique in a graph is a subset of nodes such that every two nodes in the subset are connected by an edge.). As long as the function used to score the alignments doesn't decrease with the clique growth, maximal alignments can be found by looking for the maximal cliques.
Accurate solution - TS and CTS algorithms
We use a branch-and-bound algorithm, which attempts to build all possible cliques, while preserving a required number of the highest scoring alignments. The algorithm traverses a decision tree, where each node corresponds to a decision whether to add a respective descriptor pair to the clique or not (nodes at the kth level of the tree correspond to the decision of including the kth graph node in the subset).
Obviously if a node cannot be a part of a clique in a given branch it is always rejected. In order to make this computation feasible we introduced two optimizations (cuts). A tree branch is abandoned if it is headed by a clique, which can be unambiguously expanded with a previously rejected node. In such a case all maximal cliques in that branch should contain that node, but such cliques belong to another branch of a decision tree. This ensures than only maximal cliques are obtained and each is constructed exactly once. Another optimization is based on the assumption that only the largest alignments (in terms of the number of aligned residues) should be considered. Therefore, if the lower bound of the size of a significant solution is already known (i.e. a sufficient number of alignments has already been found), it can be used to abandon certain tree branches as long as the estimate of the maximal alignment size is lower. Such estimate can be computed as a sum of a size of the alignment being built, and a number of residues outside this alignment covered by descriptor pairs, which are yet to be considered. Some of them are contradictory, and cannot be combined in one alignment, but still such upper bound is frequently low enough to abandon significant portions of a decision tree. We call this method a Tree-Search algorithm (TS).
We have also developed a modified version of the TS algorithm which extends the clique only if the subalignment which is being added has common residues with the alignment being extended. This mode can be used to make sure that the computed alignment comprises only one structurally continuous fragment. It is also used to extend alignments found by the TS algorithm in the set S3 with elements from S1. We call this algorithm a Constrained Tree-Search algorithm (CTS). In the second phase of the computation, either algorithm can be used to assemble elements from S3; CTS is always used during the third step. Abbreviations TS+CTS and CTS+CTS denote these two variants, respectively.
randomly pick a graph node d which doesn't belong to C n ,
take a set C n +1containing d and elements from C n which are connected to d (one sees it is a clique),
if there are graph nodes which belong to every maximal clique containing C n +1, add them to C n +1.
The parameters of the REMC method (i.e. number of steps, number of replicas, their temperatures, and exchange frequency) have been chosen to reproduce accurate results in the shortest time. Our computational experiments have shown that in all tested cases REMC converges to the accurate solution.
Finding a useful alignment between two protein structures usually involves a compromise between the size of the alignment and its quality. Although DEDAL is designed to handle sequence permutations, segment swaps, etc., there are situations when it is desirable to construct alignments which preserve topology. Therefore, we introduce two control parameters: the maximal number of allowed sequence swaps (M), and maximal accepted sequence offset (k). If M is smaller than the actual number of swaps in the alignment, we compute only the largest sub-alignment containing at most M swaps. Sequence offset is used to obtain sequence dependent comparisons. It is assumed that there exists a direct 1:1 correspondence between the sequences of the proteins, and only residues aligned with offset not greater than k will be counted. This mode is especially useful for comparing models of the same protein in structure prediction applications [29, 30]. Regarding the quality of the alignment, RMSD and other measures which evaluate distances between respective residues in a certain superposition are most useful if the alignment is constructed using a rigid-body strategy. In our case, every aligned residue pair belongs to at least one pair of similar descriptors satisfying the conditions given above. Thus the local alignment quality is already assured by similarity of respective descriptors. To evaluate the global quality we assess the spatial arrangement of the local components. We enumerate all pairs of the aligned residues which are in contact in at least one of the aligned structures. Then for each such contact we compute the RMSD of the respective five residue pieces (elements) of the backbone. These distances are averaged for each residue over all its contacts and for the whole alignment over all aligned residues. The result can be viewed as an average "tension" exerted on the two structures, when superimposed as elastic objects. This value raised to the power of 2 is subtracted from the number of aligned residues.
Authors would like to thank Krzysztof Fidelis at the Genome Center, University of California, Davis for sharing ideas and help in improving the manuscript, and Andriy Kryshtafovych at the Genome Center, University of California, Davis and Torgeir Hvidsten at the Umeå Plant Science Center, Umeå University for valuable suggestions, as well as Aleksander Dębiński and Bartosz Wilczyński from the University of Warsaw for help in setting up the DEDAL server. Research support was provided by the Polish Ministry of Science and Higher Education [N N301 243736], the Biocentrum-Ochota project [POIG.02.03.00-00-003/09 - ERDF, the Operational Programme Innovative Economy 2007-2013], and by the University of Warsaw [BST/BF]. Computations were carried out at the CoE BioExploratorium Computing Centre of the University of Warsaw.
- Kolodny R, Koehl P, Levitt M: Comprehensive evaluation of protein structure alignment methods: scoring by geometric measures. J Mol Biol 2005, 346(4):1173–88. 10.1016/j.jmb.2004.12.032PubMed CentralView ArticlePubMedGoogle Scholar
- Lindqvist Y, Schneider G: Circular permutations of natural protein sequences: structural evidence. Curr Opin Struct Biol 1997, 7(3):422–7. 10.1016/S0959-440X(97)80061-9View ArticlePubMedGoogle Scholar
- Grishin NV: Fold change in evolution of protein structures. J Struct Biol 2001, 134(2–3):167–85. 10.1006/jsbi.2001.4335View ArticlePubMedGoogle Scholar
- Shih ES, Hwang MJ: Alternative alignments from comparison of protein structures. Proteins 2004, 56(3):519–27. 10.1002/prot.20124View ArticlePubMedGoogle Scholar
- Abyzov A, Ilyin VA: A comprehensive analysis of non-sequential alignments between all protein structures. BMC Struct Biol 2007, 7: 78. 10.1186/1472-6807-7-78PubMed CentralView ArticlePubMedGoogle Scholar
- Andreeva A, Prlic A, Hubbard TJ, Murzin AG: SISYPHUS-structural alignments for proteins with non-trivial relationships. Nucleic Acids Res 2007, 35(Database issue):D253–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Mayr G, Domingues FS, Lackner P: Comparative analysis of protein structure alignments. BMC Struct Biol 2007, 7: 50. 10.1186/1472-6807-7-50PubMed CentralView ArticlePubMedGoogle Scholar
- Orengo CA, Taylor WR: SSAP: sequential structure alignment program for protein structure comparison. Methods Enzymol 1996, 266: 617–35.View ArticlePubMedGoogle Scholar
- Holm L, Sander C: Protein structure comparison by alignment of distance matrices. J Mol Biol 1993, 233: 123–38. 10.1006/jmbi.1993.1489View ArticlePubMedGoogle Scholar
- Wohlers I, Domingues FS, Klau GW: Towards optimal alignment of protein structure distance matrices. Bioinformatics 2010, 26(18):2273–80. 10.1093/bioinformatics/btq420View ArticlePubMedGoogle Scholar
- Shindyalov IN, Bourne PE: Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng 1998, 11(9):739–47. 10.1093/protein/11.9.739View ArticlePubMedGoogle Scholar
- Madej T, Gibrat JF, Bryant SH: Threading a database of protein cores. Proteins 1995, 23(3):356–69. 10.1002/prot.340230309View ArticlePubMedGoogle Scholar
- Alexandrov N: SARFing the PDB. Protein Engineering 1996, 9(9):727. 10.1093/protein/9.9.727View ArticlePubMedGoogle Scholar
- Kawabata T, Nishikawa K: Protein structure comparison using the markov transition model of evolution. Proteins 2000, 41: 108–22. 10.1002/1097-0134(20001001)41:1<108::AID-PROT130>3.0.CO;2-SView ArticlePubMedGoogle Scholar
- Guerler A, Knapp EW: Novel protein folds and their nonsequential structural analogs. Protein Sci 2008, 17(8):1374–82. 10.1110/ps.035469.108PubMed CentralView ArticlePubMedGoogle Scholar
- Bachar O, Fischer D, Nussinov R, Wolfson H: A computer vision based technique for 3-D sequence-independent structural comparison of proteins. Protein Eng 1993, 6(3):279–88. 10.1093/protein/6.3.279View ArticlePubMedGoogle Scholar
- Pennec X, Ayache N: A geometric algorithm to find small but highly similar 3D substructures in proteins. Bioinformatics 1998, 14(6):516–22. 10.1093/bioinformatics/14.6.516View ArticlePubMedGoogle Scholar
- Bhattacharya S, Bhattacharyya C, Chandra NR: Comparison of protein structures by growing neighborhood alignments. BMC Bioinformatics 2007, 8: 77. 10.1186/1471-2105-8-77PubMed CentralView ArticlePubMedGoogle Scholar
- Jung J, Lee B: Protein structure alignment using environmental profiles. Protein Eng 2000, 13(8):535–43. 10.1093/protein/13.8.535View ArticlePubMedGoogle Scholar
- Ilyin VA, Abyzov A, Leslin CM: Structural alignment of proteins by a novel TOPOFIT method, as a superimposition of common volumes at a topomax point. Protein Sci 2004, 13(7):1865–74. 10.1110/ps.04672604PubMed CentralView ArticlePubMedGoogle Scholar
- Mavridis L, Ritchie DW: 3d-blast: 3d protein structure alignment, comparison, and classification using spherical polar fourier correlations. Pac Symp Biocomput 2010, 281–92.Google Scholar
- Ye Y, Godzik A: Flexible structure alignment by chaining aligned fragment pairs allowing twists. Bioinformatics 2003, 19(Suppl 2):ii246–55. 10.1093/bioinformatics/btg1086View ArticlePubMedGoogle Scholar
- Shatsky M, Nussinov R, Wolfson HJ: FlexProt: alignment of flexible protein structures without a predefinition of hinge regions. J Comput Biol 2004, 11: 83–106. 10.1089/106652704773416902View ArticlePubMedGoogle Scholar
- Rocha J, Segura J, Wilson RC, Dasgupta S: Flexible structural protein alignment by a sequence of local transformations. Bioinformatics 2009, 25(13):1625–31. 10.1093/bioinformatics/btp296PubMed CentralView ArticlePubMedGoogle Scholar
- Salem S, Zaki M, Bystroff C: FlexSnap: Flexible Non-sequential Protein Structure Alignment. Algorithms for Molecular Biology 2010, 5: 12. 10.1186/1748-7188-5-12PubMed CentralView ArticlePubMedGoogle Scholar
- Hasegawa H, Holm L: Advances and pitfalls of protein structural alignment. Curr Opin Struct Biol 2009, 19(3):341–8. 10.1016/j.sbi.2009.04.003View ArticlePubMedGoogle Scholar
- Hvidsten TR, Kryshtafovych A, Komorowski J, Fidelis K: A novel approach to fold recognition using sequence-derived properties from sets of structurally similar local fragments of proteins. Bioinformatics 2003, 19(Suppl 2):ii81–91. 10.1093/bioinformatics/btg1064View ArticlePubMedGoogle Scholar
- Hvidsten TR, Kryshtafovych A, Fidelis K: Local descriptors of protein structure: a systematic analysis of the sequence-structure relationship in proteins using short- and long-range interactions. Proteins 2009, 75(4):870–84. 10.1002/prot.22296View ArticlePubMedGoogle Scholar
- Kryshtafovych A, Milostan M, Szajkowski L, Daniluk P, Fidelis K: CASP6 data processing and automatic evaluation at the protein structure prediction center. Proteins 2005, 61(Suppl 7):19–23.View ArticlePubMedGoogle Scholar
- Kryshtafovych A, Prlic A, Dmytriv Z, Daniluk P, Milostan M, Eyrich V, Hubbard T, Fidelis K: New tools and expanded data analysis capabilities at the Protein Structure Prediction Center. Proteins 2007, 69(Suppl 8):19–26.PubMed CentralView ArticlePubMedGoogle Scholar
- Kabsch W: A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A 1976, 32(5):922–923. 10.1107/S0567739476001873View ArticleGoogle Scholar
- Kabsch W: A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A 1978, 34(5):827–828. 10.1107/S0567739478001680View ArticleGoogle Scholar
- Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM: CATH-a hierarchic classification of protein domain structures. Structure 1997, 5(8):1093–108. 10.1016/S0969-2126(97)00260-8View ArticlePubMedGoogle Scholar
- Murzin AG, Brenner SE, Hubbard T, Chothia C: SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995, 247(4):536–40.PubMedGoogle Scholar
- Zemla A: LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Res 2003, 31(13):3370–4. 10.1093/nar/gkg571PubMed CentralView ArticlePubMedGoogle Scholar
- Holm L, Park J: DaliLite workbench for protein structure comparison. Bioinformatics 2000, 16(6):566–7. 10.1093/bioinformatics/16.6.566View ArticlePubMedGoogle Scholar
- Ponting CP, Russell RB: Swaposins: circular permutations within genes encoding saposin homologues. Trends Biochem Sci 1995, 20(5):179–80. 10.1016/S0968-0004(00)89003-9View ArticlePubMedGoogle Scholar
- Liepinsh E, Andersson M, Ruysschaert JM, Otting G: Saposin fold revealed by the NMR structure of NK-lysin. Nat Struct Biol 1997, 4(10):793–5. 10.1038/nsb1097-793View ArticlePubMedGoogle Scholar
- Kervinen J, Tobin GJ, Costa J, Waugh DS, Wlodawer A, Zdanov A: Crystal structure of plant aspartic proteinase prophytepsin: inactivation and vacuolar targeting. EMBO J 1999, 18(14):3947–55. 10.1093/emboj/18.14.3947PubMed CentralView ArticlePubMedGoogle Scholar
- Niemann HH, Knetsch ML, Scherer A, Manstein DJ, Kull FJ: Crystal structure of a dynamin GTPase domain in both nucleotide-free and GDP-bound forms. EMBO J 2001, 20(21):5813–21. 10.1093/emboj/20.21.5813PubMed CentralView ArticlePubMedGoogle Scholar
- Shin DH, Lou Y, Jancarik J, Yokota H, Kim R, Kim SH: Crystal structure of YjeQ from Thermotoga maritima contains a circularly permuted GTPase domain. Proc Natl Acad Sci USA 2004, 101(36):13198–203. 10.1073/pnas.0405202101PubMed CentralView ArticlePubMedGoogle Scholar
- Anand B, Verma SK, Prakash B: Structural stabilization of GTP-binding domains in circularly permuted GTPases: implications for RNA binding. Nucleic Acids Res 2006, 34(8):2196–205. 10.1093/nar/gkl178PubMed CentralView ArticlePubMedGoogle Scholar
- Bewley CA, Gustafson KR, Boyd MR, Covell DG, Bax A, Clore GM, Gronenborn AM: Solution structure of cyanovirin-N, a potent HIV-inactivating protein. Nat Struct Biol 1998, 5(7):571–8. 10.1038/828View ArticlePubMedGoogle Scholar
- Yang F, Bewley CA, Louis JM, Gustafson KR, Boyd MR, Gronenborn AM, Clore GM, Wlodawer A: Crystal structure of cyanovirin-N, a potent HIV-inactivating protein, shows unexpected domain swapping. J Mol Biol 1999, 288(3):403–12. 10.1006/jmbi.1999.2693View ArticlePubMedGoogle Scholar
- Barrientos LG, Louis JM, Botos I, Mori T, Han Z, O'Keefe BR, Boyd MR, Wlodawer A, Gronenborn AM: The domain-swapped dimer of cyanovirin-N is in a metastable folded state: reconciliation of X-ray and NMR structures. Structure 2002, 10(5):673–86. 10.1016/S0969-2126(02)00758-XView ArticlePubMedGoogle Scholar
- Marchler-Bauer A, Anderson JB, Cherukuri PF, DeWeese-Scott C, Geer LY, Gwadz M, He S, Hurwitz DI, Jackson JD, Ke Z, Lanczycki CJ, Liebert CA, Liu C, Lu F, Marchler GH, Mullokandov M, Shoemaker BA, Simonyan V, Song JS, Thiessen PA, Yamashita RA, Yin JJ, Zhang D, Bryant SH: CDD: a Conserved Domain Database for protein classification. Nucleic Acids Res 2005, 33(Database issue):D192–6.PubMed CentralView ArticlePubMedGoogle Scholar
- Kim C, Lee B: Accuracy of structure-based sequence alignment of automatic methods. BMC Bioinformatics 2007, 8: 355. 10.1186/1471-2105-8-355PubMed CentralView ArticlePubMedGoogle Scholar
- Pirovano W, Feenstra KA, Heringa J: The meaning of alignment: lessons from structural diversity. BMC Bioinformatics 2008, 9: 556. 10.1186/1471-2105-9-556PubMed CentralView ArticlePubMedGoogle Scholar
- Liu ZP, Wu LY, Wang Y, Zhang XS, Chen L: Bridging protein local structures and protein functions. Amino Acids 2008, 35(3):627–50. 10.1007/s00726-008-0088-8View ArticlePubMedGoogle Scholar
- Redfern OC, Dessailly B, Orengo CA: Exploring the structure and function paradigm. Curr Opin Struct Biol 2008, 18(3):394–402. 10.1016/j.sbi.2008.05.007PubMed CentralView ArticlePubMedGoogle Scholar
- Kosloff M, Kolodny R: Sequence-similar, structure-dissimilar protein pairs in the PDB. Proteins 2008, 71(2):891–902. 10.1002/prot.21770PubMed CentralView ArticlePubMedGoogle Scholar
- Björkholm P, Daniluk P, Kryshtafovych A, Fidelis K, Andersson R, Hvidsten TR: Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts. Bioinformatics 2009, 25(10):1264–70. 10.1093/bioinformatics/btp149PubMed CentralView ArticlePubMedGoogle Scholar
- Drabikowski M, Nowakowski S, Tiuryn J: Library of local descriptors models the core of proteins accurately. Proteins 2007, 69(3):499–510. 10.1002/prot.21393View ArticlePubMedGoogle Scholar
- Strömbergsson H, Kryshtafovych A, Prusis P, Fidelis K, Wikberg JE, Komorowski J, Hvidsten TR: Generalized modeling of enzyme-ligand interactions using proteochemometrics and local protein substructures. Proteins 2006, 65(3):568–79. 10.1002/prot.21163View ArticlePubMedGoogle Scholar
- Strömbergsson H, Daniluk P, Kryshtafovych A, Fidelis K, Wikberg JE, Kleywegt GJ, Hvidsten TR: Interaction Model Based on Local Protein Substructures Generalizes to the Entire Structural Enzyme-Ligand Space. J Chem Inf Model 2008, 48(11):2278–88. 10.1021/ci800200eView ArticlePubMedGoogle Scholar
- Pawlak Z: Rough sets: theoretical aspects of reasoning about data Theory and decision library. Series D, System theory, knowledge engineering, and problem solving. Volume 9. Dordrecht; Boston: Kluwer Academic Publishers; 1991.View ArticleGoogle Scholar
- Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E: Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 1953, 21(6):1087. 10.1063/1.1699114View ArticleGoogle Scholar
- Swendsen RH, Wang JS: Replica Monte Carlo simulation of spin glasses. Phys Rev Lett 1986, 57(21):2607–2609. 10.1103/PhysRevLett.57.2607View ArticlePubMedGoogle Scholar
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