Structural alignment of proteins

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Structural alignment of proteins is a form of sequence alignment based on comparison of shape. These alignments attempt to establish equivalences between two or more polymer structures based on their shape and three-dimensional conformation. This process is usually applied to protein tertiary structures but can also be used for large RNA molecules. In contrast to simple structural superposition, where at least some equivalent residues of the two structures are known, structural alignment requires no a priori knowledge of equivalent positions. Structural alignment is a valuable tool for the comparison of proteins with low sequence similarity, where evolutionary relationships between proteins cannot be easily detected by standard sequence alignment techniques. Structural alignment can therefore be used to imply evolutionary relationships between proteins that share very little common sequence. However, caution should be used in using the results as evidence for shared evolutionary ancestry because of the possible confounding effects of convergent evolution by which multiple unrelated amino acid sequences converge on a common tertiary structure.

Structural alignments can compare two sequences or multiple sequences. Because these alignments rely on information about all the query sequences' three-dimensional conformations, the method can only be used on sequences where these structures are known. These are usually found by X-ray crystallography or NMR spectroscopy. It is possible to perform a structural alignment on structures produced by structure prediction methods. Indeed, evaluating such predictions often requires a structural alignment between the model and the true known structure to assess the model's quality. Structural alignments are especially useful in analyzing data from structural genomics and proteomics efforts, and they can be used as comparison points to evaluate alignments produced by purely sequence-based bioinformatics methods.[1]

The outputs of a structural alignment are a superposition of the atomic coordinate sets and a minimal root mean square distance (RMSD) between the structures. The RMSD of two aligned structures indicates their divergence from one another. Structural alignment can be complicated by the existence of multiple protein domains within one or more of the input structures, because changes in relative orientation of the domains between two structures to be aligned can artificially inflate the RMSD.

 

Data produced by structural alignment

The minimum information produced from a successful structural alignment is a set of superposed three-dimensional coordinates for each input structure. (Note that one input element may be fixed as a reference and therefore its superposed coordinates do not change.) The fitted structures can be used to calculate mutual RMSD values, as well as other more sophisticated measures of structural similarity such as the global distance test (GDT[2], the metric used in CASP). The structural alignment also implies a corresponding one-dimensional sequence alignment from which a sequence identity, or the percentage of residues that are identical between the input structures, can be calculated as a measure of how closely the two sequences are related.

 

Types of comparisons

Because protein structures are composed of amino acids whose side chains are linked by a common protein backbone, a number of different possible subsets of the atoms that make up a protein macromolecule can be used in producing a structural alignment and calculating the corresponding RMSD values. When aligning structures with very different sequences, the side chain atoms generally are not taken into account because their identities differ between many aligned residues. For this reason it is common for structural alignment methods to use by default only the backbone atoms included in the peptide bond. For simplicity and efficiency, often only the alpha carbon positions are considered, since the peptide bond has a minimally variant planar conformation. Only when the structures to be aligned are highly similar or even identical is it meaningful to align side-chain atom positions, in which case the RMSD reflects not only the conformation of the protein backbone but also the rotameric states of the side chains. Other comparison criteria that reduce noise and bolster positive matches include secondary structure assignment, native contact maps or residue interaction patterns, measures of side chain packing, and measures of hydrogen bond retention.[3]

 

Structural superposition

The most basic possible comparison between protein structures makes no attempt to align the input structures and requires a precalculated alignment as input to determine which of the residues in the sequence are intended to be considered in the RMSD calculation. Structural superposition is commonly used to compare multiple conformations of the same protein (in which case no alignment is necessary, since the sequences are the same) and to evaluate the quality of alignments produced using only sequence information between two or more sequences whose structures are known. This method traditionally uses a simple least-squares fitting algorithm, in which the optimal rotations and translations are found by minimizing the sum of the squared distances among all structures in the superposition. [4] More recently, maximum likelihood and Bayesian methods have greatly increased the accuracy of the estimated rotations, translations, and covariance matrices for the superposition [5] [6].

Algorithms based on multidimensional rotations and modified quaternions have been developed to identify topological relationships between protein structures without the need for a predetermined alignment. Such algorithms have successfully identified canonical folds such as the four-helix bundle.[7] The SuperPose method is sufficiently extensible to correct for relative domain rotations and other structural pitfalls.[8]

 

Algorithmic complexity

Both the optimal "threading" of a protein sequence onto a known structure[9] and the production of an optimal multiple sequence alignment[10] have been shown to be NP-complete. However, this does not imply that the structural alignment problem is NP-complete. On the basis of the argument that a true optimal solution is not biologically meaningful due to the experimental error inherent in protein structure determination, an approximate polynomial-time algorithm for structural alignment that produces a family of "optimal" solutions within an approximation parameter for a given scoring function has been developed.[11] However, at O(n10 / ε6) for a globular protein of n residues, the algorithm is still too computationally expensive for practical use. As a consequence, practical algorithms that converge to the global solutions of the alignment, given a scoring function, do not exist. Most algorithms are, therefore, heuristic, but algorithms that guarantee the convergence to at least local maximizers of the scoring functions, and are practical, have been developed.[12]

 

Representation of structures

Protein structures have to be represented in some coordinate-independent space to make them comparable. This is typically achieved by constructing a sequence-to-sequence matrix or series of matrices that encompass comparative metrics: rather than absolute distances relative to a fixed coordinate space. An intuitive representation is the distance matrix, which is a two-dimensional matrix containing all pairwise distances between some subset of the atoms in each structure (such as the alpha carbons). The matrix increases in dimensionality as the number of structures to be simultaneously aligned increases. Reducing the protein to a coarse metric such as secondary structure elements (SSEs) or structural fragments can also produce sensible alignments, despite the loss of information from discarding distances, as noise is also discarded.[13] Choosing a representation to facilitate computation is critical to developing an efficient alignment mechanism.

 

Methods

Structural alignment techniques have been used in comparing individual structures or sets of structures as well as in the production of "all-to-all" comparison databases that measure the divergence between every pair of structures present in the Protein Data Bank (PDB). Such databases are used to classify proteins by their fold.

Illustration of the atom-to-atom vectors calculated in SSAP. From these vectors would be constructed a series of vector differences, e.g., between (FA) in Protein 1 and (SI) in Protein 2. The two sequences are plotted on the two dimensions of a matrix to form a difference matrix between the two proteins. Dynamic programming is applied to all possible difference matrices to construct a series of optimal local alignment paths that are then summed to form the summary matrix, on which a second round of dynamic programming is performed.
Illustration of the atom-to-atom vectors calculated in SSAP. From these vectors would be constructed a series of vector differences, e.g., between (FA) in Protein 1 and (SI) in Protein 2. The two sequences are plotted on the two dimensions of a matrix to form a difference matrix between the two proteins. Dynamic programming is applied to all possible difference matrices to construct a series of optimal local alignment paths that are then summed to form the summary matrix, on which a second round of dynamic programming is performed.

 

DALI

A common and popular structural alignment method is the DALI, or distance alignment matrix, method, which breaks the input structures into hexapeptide fragments and calculates a distance matrix by evaluating the contact patterns between successive fragments.[14] Secondary structure features that involve residues that are contiguous in sequence appear on the matrix's main diagonal; other diagonals in the matrix reflect spatial contacts between residues that are not near each other in the sequence. When these diagonals are parallel to the main diagonal, the features they represent are parallel; when they are perpendicular, their features are antiparallel. This representation is memory-intensive because the features in the square matrix are symmetrical (and thus redundant) about the main diagonal.

When two proteins' distance matrices share the same or similar features in approximately the same positions, they can be said to have similar folds with similar-length loops connecting their secondary structure elements. DALI's actual alignment process requires a similarity search after the two proteins' distance matrices are built; this is normally conducted via a series of overlapping submatrices of size 6x6. Submatrix matches are then reassembled into a final alignment via a standard score-maximization algorithm - the original version of DALI used a Monte Carlo simulation to maximize a structural similarity score that is a function of the distances between putative corresponding atoms. In particular, more distant atoms within corresponding features are exponentially downweighted to reduce the effects of noise introduced by loop mobility, helix torsions, and other minor structural variations.[13] Because DALI relies on an all-to-all distance matrix, it can account for the possibility that structurally aligned features might appear in different orders within the two sequences being compared.

The DALI method has also been used to construct a database known as FSSP (Fold classification based on Structure-Structure alignment of Proteins, or Families of Structurally Similar Proteins) in which all known protein structures are aligned with each other to determine their structural neighbors and fold classification. There is an EBI-maintained searchable database based on DALI as well as a downloadable program and web search based on a standalone version known as DaliLite.

 

SSAP

The SSAP (Sequential Structure Alignment Program) method uses double dynamic programming to produce a structural alignment based on atom-to-atom vectors in structure space. Instead of the alpha carbons typically used in structural alignment, SSAP constructs its vectors from the beta carbons for all residues except glycine, a method which thus takes into account the rotameric state of each residue as well as its location along the backbone. SSAP works by first constructing a series of inter-residue distance vectors between each residue and its nearest non-contiguous neighbors on each protein. A series of matrices are then constructed containing the vector differences between neighbors for each pair of residues for which vectors were constructed. Dynamic programming applied to each resulting matrix determines a series of optimal local alignments which are then summed into a "summary" matrix to which dynamic programming is applied again to determine the overall structural alignment.

SSAP originally produced only pairwise alignments but has since been extended to multiple alignments as well.[15] It has been applied in an all-to-all fashion to produce a hierarchical fold classification scheme known as CATH (Class, Architecture, Topology, Homology),[16] which has been used to construct the CATH Protein Structure Classification database.

 

Combinatorial extension

The combinatorial extension (CE) method is similar to DALI in that it too breaks each structure in the query set into a series of fragments that it then attempts to reassemble into a complete alignment. A series of pairwise combinations of fragments called aligned fragment pairs, or AFPs, are used to define a similarity matrix through which an optimal path is generated to identify the final alignment. Only AFPs that meet given criteria for local similarity are included in the matrix as a means of reducing the necessary search space and thereby increasing efficiency.[17] A number of similarity metrics are possible; the original definition of the CE method included only structural superpositions and inter-residue distances but has since been expanded to include local environmental properties such as secondary structure, solvent exposure, hydrogen-bonding patterns, and dihedral angles.[17]

An alignment path is calculated as the optimal path through the similarity matrix by linearly progressing through the sequences and extending the alignment with the next possible high-scoring AFP pair. The initial AFP pair that nucleates the alignment can occur at any point in the sequence matrix. Extensions then proceed with the next AFP that meets given distance criteria restricting the alignment to low gap sizes. The size of each AFP and the maximum gap size are required input parameters but are usually set to empirically determined values of 8 and 30 respectively.[17] Like DALI and SSAP, CE has been used to construct an all-to-all fold classification database from the known protein structures in the PDB.

 

Recent developments

Improvements in structural alignment methods constitute an active area of research, and new or modified methods are often proposed that are claimed to offer advantages over the older and more widely distributed techniques. A recent example, TM-align, uses a novel method for weighting its distance matrix, to which standard dynamic programming is then applied.[18][19] The weighting is proposed to accelerate the convergence of dynamic programming and correct for effects arising from alignment lengths. In a benchmarking study, TM-align has been reported to improve in both speed and accuracy over DALI and CE.[18]

 

RNA structural alignment

Structural alignment techniques have traditionally been applied exclusively to proteins, as the primary biological macromolecules that assume characteristic three-dimensional structures. However, large RNA molecules also form characteristic tertiary structures, which are mediated primarily by hydrogen bonds formed between base pairs as well as base stacking. Functionally similar noncoding RNA molecules can be especially difficult to extract from genomics data because structure is more strongly conserved than sequence in RNA as well as in proteins,[20] and the more limited alphabet of RNA decreases the information content of any given nucleotide at any given position.

A recent method for pairwise structural alignment of RNA sequences with low sequence identity has been published and implemented in the program FOLDALIGN.[21] However, this method is not truly analogous to protein structural alignment techniques because it computationally predicts the structures of the RNA input sequences rather than requiring experimentally determined structures as input. Although computational prediction of the protein folding process has not been particularly successful to date, RNA structures without pseudoknots can often be sensibly predicted using free energy-based scoring methods that account for base pairing and stacking[22] as well as by Bayesian statistical methods.[23] Recent advances have also allowed the prediction of pseudoknotted structures by the introduction of match weighting[24] or geometrical constraints, as in KINEFOLD.[25]

 

Software

Main article: Structural alignment software

Choosing a software tool for structural alignment can be a challenge due to the large variety of available packages that differ significantly in methodology and reliability. Due to its integration with other European Bioinformatics Institute web-based tools, the EBI DALI DaliLite webserver has an advantage in the production of single structural alignments for researchers interested in using the alignments to guide experimental work (rather than studying alignment methods themselves). Another useful EBI method is the Secondary Structure Matcher, which relies on the presence of at least two secondary structure elements. A more complete list of currently available and freely distributed structural alignment software can be found in structural alignment software.

 

See also

  • Multiple sequence alignment
  • Sequence alignment software
  • Sequence alignment
  • Structural Classification of Proteins

 

References

  1. ^ Zhang Y, Skolnick J. (2005). The protein structure prediction problem could be solved using the current PDB library. Proc Natl Acad Sci USA 102(4):1029-34.
  2. ^ Zemla A. (2003). LGA - A Method for Finding 3-D Similarities in Protein Structures. Nucleic Acids Research, 31(13):3370-3374.
  3. ^ Godzik A. (1996). The structural alignment of proteins: is there a unique answer? Protein Sci 5:1325-8.
  4. ^ Martin ACR. http://www.bioinf.org.uk/software/profit/doc/ The software package ProFit (implements McLachlan, AD (1982). Rapid Comparison of Protein Structures. Acta Cryst A38, 871-873.)
  5. ^ Theobald DL, Wuttke DS (2006). Empirical Bayes hierarchical models for regularizing maximum likelihood estimation in the matrix Gaussian Procrustes problem. Proceedings of the National Academy of Sciences 103(49):18521-18527. Open Access
  6. ^ Theobald DL, Wuttke DS (2006). THESEUS: Maximum likelihood superpositioning and analysis of macromolecular structures. Bioinformatics 22(17):2171-2172. Open Access http://www.theseus3d.org/
  7. ^ Diederichs K. (1995). Structural superposition of proteins with unknown alignment and detection of topological similarity using a six-dimensional search algorithm. Proteins 23(2):187-95.
  8. ^ Maiti R, Van Domselaar GH, Zhang H, Wishart DS. (2004). SuperPose: a simple server for sophisticated structural superposition. Nucleic Acids Res 32(Web Server issue):W590-4.
  9. ^ Lathrop RH. (1994). The protein threading problem with sequence amino acid interaction preferences is NP-complete. Protein Eng 7(9):1059-68.
  10. ^ Wang L, Jiang T. (1994) On the complexity of multiple sequence alignment. J Comput Biol 1:337-348.
  11. ^ Kolodny R, Linial N. (2004). Approximate protein structural alignment in polynomial time PNAS 101(33): 12201-12206.
  12. ^ Martinez L, Andreani, R, Martinez, JM. (2007). Convergent algorithms for protein structural alignment BMC Bioinformatics 8:306.
  13. ^ a b Mount DM. (2004). Bioinformatics: Sequence and Genome Analysis 2nd ed. Cold Spring Harbor Laboratory Press: Cold Spring Harbor, NY.
  14. ^ Holm L, Sander C (1996). Mapping the protein universe. Science 273: 595-603.
  15. ^ Taylor WR, Flores TP, Orengo CA. (1994). Multiple protein structure alignment. Protein Sci 3(10):1858-70.
  16. ^ Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM. (1997) CATH: A hierarchical classification of protein domain structures. Structure 5(8): 1093-1108.
  17. ^ a b c Shindyalov IN, Bourne PE (1998) Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng 11(9) 739-747.
  18. ^ a b Zhang Y, Skolnick J. (2005) TM-align: A protein structure alignment algorithm based on TM-score. Nucleic Acids Research 33: 2302-2309.
  19. ^ Zhang Y, Skolnick J. (2004) Scoring function for automated assessment of protein structure template quality. Proteins 57: 702-710.
  20. ^ Torarinsson E, Sawera M, Havgaard JH, Fredholm M, Gorodkin J. (2006). Torarinsson E, Sawera M, Havgaard JH, Fredholm M, Gorodkin J. Genome Res 16(7):885-9.
  21. ^ Havgaard JH, Lyngso RB, Stormo GD, Gorodkin J. (2005). Pairwise local structural alignment of RNA sequences with sequence similarity less than 40%. Bioinformatics 21(9):1815-24.
  22. ^ Mathews DH, Turner DH. (2006). Prediction of RNA secondary structure by free energy minimization. Curr Opin Struct Biol 16(3):270-8.
  23. ^ Ding Y. (2006). Statistical and Bayesian approaches to RNA secondary structure prediction. RNA 12(3):323-31.
  24. ^ Liu H, Xu D, Shao J, Wang Y. (2006). An RNA folding algorithm including pseudoknots based on dynamic weighted matching. Comput Biol Chem 30(1):72-6.
  25. ^ Xayaphoummine A, Bucher T, Isambert H. (2005). Kinefold web server for RNA/DNA folding path and structure prediction including pseudoknots and knots. Nucleic Acids Res 33(Web Server issue):W605-10.

 

Further reading

  • Bourne PE, Shindyalov IN. (2003): Structure Comparison and Alignment. In: Bourne, P.E., Weissig, H. (Eds): Structural Bioinformatics. Hoboken NJ: Wiley-Liss. ISBN 0-471-20200-2
  • Yuan X, Bystroff C.(2004) "Non-sequential Structure-based Alignments Reveal Topology-independent Core Packing Arrangements in Proteins", Bioinformatics. Nov 5, 2004
  • Jung J, Lee B. (2000). Protein structure alignment using environmental profiles. Protein Eng 13:535-543.
  • Ye Y, Godzik A. (2005). Multiple flexible structure alignment using partial order graphs Bioinformatics 21(10): 2362-2369 Abstract
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