A data-mining approach for multiple structural alignment of proteins

Comparing the 3D structures of proteins is an important but computationally hard problem in bioinformatics. In this paper, we propose studying the problem when much less information or assumptions are available. We model the structural alignment of proteins as a combinatorial problem. In the problem, each protein is simply a set of points in the 3D space, without sequence order information, and the objective is to discover all large enough alignments for any subset of the input. We propose a data-mining approach for this problem. We first perform geometric hashing of the structures such that points with similar locations in the 3D space are hashed into the same bin in the hash table. The novelty is that we consider each bin as a coincidence group and mine for frequent patterns, which is a well-studied technique in data mining. We observe that these frequent patterns are already potentially large alignments. Then a simple heuristic is used to extend the alignments if possible. We implemented the algorithm and tested it using real protein structures. The results were compared with existing tools. They showed that the algorithm is capable of finding conserved substructures that do not preserve sequence order, especially those existing in protein interfaces. The algorithm can also identify conserved substructures of functionally similar structures within a mixture with dissimilar ones. The running time of the program was smaller or comparable to that of the existing tools.     

CLEMAPS: multiple alignment of protein structures based on conformational letters

CLEMAPS is a tool for multiple alignment of protein structures. It distinguishes itself from other existing algorithms for multiple structure alignment by the use of conformational letters, which are discretized states of 3D segmental structural states. A letter corresponds to a cluster of combinations of three angles formed by C(alpha) pseudobonds of four contiguous residues. A substitution matrix called CLESUM is available to measure the similarity between any two such letters. The input 3D structures are first converted to sequences of conformational letters. Each string of a fixed length is then taken as the center seed to search other sequences for neighbors of the seed, which are strings similar to the seed. A seed and its neighbors form a center-star, which corresponds to a fragment set of local structural similarity shared by many proteins. The detection of center-stars using CLESUM is extremely efficient. Local similarity is a necessary, but insufficient, condition for structural alignment. Once center-stars are found, the spatial consistency between any two stars are examined to find consistent star duads using atomic coordinates. Consistent duads are later joined to create a core for multiple alignment, which is further polished to produce the final alignment. The utility of CLEMAPS is tested on various protein structure ensembles.     

基于Progressive多序列比对方法的求解多序列比对的启发式算法

在生物信息学研究中,生物序列比对问题占有重要的地位。多序列比对问题是一个NPC问题,由于时间和空间的限制不能够求出精确解。文中简要介绍了Feng和Doolittle提出的多序列比对算法的基本思想,并改进了该算法使之具有更好的比对精度。实验结果表明,新算法对解决一般的progressive多序列比对方法中遇到的局部最优问题有较好的效果。     

Progressive combinatorial algorithm for multiple structural alignments: application to distantly related proteins

MALECON is a progressive combinatorial procedure for multiple alignments of protein structures. It searches a library of pairwise alignments for all three-protein alignments in which a specified number of residues is consistently aligned. These alignments are progressively expanded to include additional proteins and more spatially equivalent residues, subject to certain criteria. This action involves superimposing the aligned proteins by their hitherto equivalent residues and searching for additional Calpha atoms that lie close in space. The performance of MALECON is illustrated and compared with several extant multiple structure alignment methods by using as test the globin homologous superfamily, the OB and the Jellyrolls folds. MALECON gives better definitions of the common structural features in the structurally more diverse proteins of the OB and Jellyrolls folds, but it yields comparable results for the more similar globins. When no consistent multiple alignments can be derived for all members of a protein group, our procedure is still capable of automatically generating consistent alignments and common core definitions for subgroups of the members. This finding is illustrated for proteins of the OB fold and SH3 domains, believed to share common structural features, and should be very instrumental in homology modeling and investigations of protein evolution.     

A method for simultaneous alignment of multiple protein structures

Here, we present MultiProt, a fully automated highly efficient technique to detect multiple structural alignments of protein structures. MultiProt finds the common geometrical cores between input molecules. To date, most methods for multiple alignment start from the pairwise alignment solutions. This may lead to a small overall alignment. In contrast, our method derives multiple alignments from simultaneous superpositions of input molecules. Further, our method does not require that all input molecules participate in the alignment. Actually, it efficiently detects high scoring partial multiple alignments for all possible number of molecules in the input. To demonstrate the power of MultiProt, we provide a number of case studies. First, we demonstrate known multiple alignments of protein structures to illustrate the performance of MultiProt. Next, we present various biological applications. These include: (1) a partial alignment of hinge-bent domains; (2) identification of functional groups of G-proteins; (3) analysis of binding sites; and (4) protein-protein interface alignment. Some applications preserve the sequence order of the residues in the alignment, whereas others are order-independent. It is their residue sequence order-independence that allows application of MultiProt to derive multiple alignments of binding sites and of protein-protein interfaces, making MultiProt an extremely useful structural tool.     

Automated multiple structure alignment and detection of a common substructural motif

While a number of approaches have been geared toward multiple sequence alignments, to date there have been very few approaches to multiple structure alignment and detection of a recurring substructural motif. Among these, none performs both multiple structure comparison and motif detection simultaneously. Further, none considers all structures at the same time, rather than initiating from pairwise molecular comparisons. We present such a multiple structural alignment algorithm. Given an ensemble of protein structures, the algorithm automatically finds the largest common substructure (core) of C(alpha) atoms that appears in all the molecules in the ensemble. The detection of the core and the structural alignment are done simultaneously. Additional structural alignments also are obtained and are ranked by the sizes of the substructural motifs, which are present in the entire ensemble. The method is based on the geometric hashing paradigm. As in our previous structural comparison algorithms, it compares the structures in an amino acid sequence order-independent way, and hence the resulting alignment is unaffected by insertions, deletions and protein chain directionality. As such, it can be applied to protein surfaces, protein-protein interfaces and protein cores to find the optimally, and suboptimally spatially recurring substructural motifs. There is no predefinition of the motif. We describe the algorithm, demonstrating its efficiency. In particular, we present a range of results for several protein ensembles, with different folds and belonging to the same, or to different, families. Since the algorithm treats molecules as collections of points in three-dimensional space, it can also be applied to other molecules, such as RNA, or drugs.     

Multiple structural alignment by secondary structures: algorithm and applications

We present MASS (Multiple Alignment by Secondary Structures), a novel highly efficient method for structural alignment of multiple protein molecules and detection of common structural motifs. MASS is based on a two-level alignment, using both secondary structure and atomic representation. Utilizing secondary structure information aids in filtering out noisy solutions and achieves efficiency and robustness. Currently, only a few methods are available for addressing the multiple structural alignment task. In addition to using secondary structure information, the advantage of MASS as compared to these methods is that it is a combination of several important characteristics: (1) While most existing methods are based on series of pairwise comparisons, and thus might miss optimal global solutions, MASS is truly multiple, considering all the molecules simultaneously; (2) MASS is sequence order-independent and thus capable of detecting nontopological structural motifs; (3) MASS is able to detect not only structural motifs, shared by all input molecules, but also motifs shared only by subsets of the molecules. Here, we show the application of MASS to various protein ensembles. We demonstrate its ability to handle a large number (order of tens) of molecules, to detect nontopological motifs and to find biologically meaningful alignments within nonpredefined subsets of the input. In particular, we show how by using conserved structural motifs, one can guide protein-protein docking, which is a notoriously difficult problem. MASS is freely available at http://bioinfo3d.cs.tau.ac.il/MASS/.     

3DCoffee: combining protein sequences and structures within multiple sequence alignments

Most bioinformatics analyses require the assembly of a multiple sequence alignment. It has long been suspected that structural information can help to improve the quality of these alignments, yet the effect of combining sequences and structures has not been evaluated systematically. We developed 3DCoffee, a novel method for combining protein sequences and structures in order to generate high-quality multiple sequence alignments. 3DCoffee is based on TCoffee version 2.00, and uses a mixture of pairwise sequence alignments and pairwise structure comparison methods to generate multiple sequence alignments. We benchmarked 3DCoffee using a subset of HOMSTRAD, the collection of reference structural alignments. We found that combining TCoffee with the threading program Fugue makes it possible to improve the accuracy of our HOMSTRAD dataset by four percentage points when using one structure only per dataset. Using two structures yields an improvement of ten percentage points. The measures carried out on HOM39, a HOMSTRAD subset composed of distantly related sequences, show a linear correlation between multiple sequence alignment accuracy and the ratio of number of provided structure to total number of sequences. Our results suggest that in the case of distantly related sequences, a single structure may not be enough for computing an accurate multiple sequence alignment.     

FoldMiner: structural motif discovery using an improved superposition algorithm

We report an unsupervised structural motif discovery algorithm, FoldMiner, which is able to detect global and local motifs in a database of proteins without the need for multiple structure or sequence alignments and without relying on prior classification of proteins into families. Motifs, which are discovered from pairwise superpositions of a query structure to a database of targets, are described probabilistically in terms of the conservation of each secondary structure element's position and are used to improve detection of distant structural relationships. During each iteration of the algorithm, the motif is defined from the current set of homologs and is used both to recruit additional homologous structures and to discard false positives. FoldMiner thus achieves high specificity and sensitivity by distinguishing between homologous and nonhomologous structures by the regions of the query to which they align. We find that when two proteins of the same fold are aligned, highly conserved secondary structure elements in one protein tend to align to highly conserved elements in the second protein, suggesting that FoldMiner consistently identifies the same motif in members of a fold. Structural alignments are performed by an improved superposition algorithm, LOCK 2, which detects distant structural relationships by placing increased emphasis on the alignment of secondary structure elements. LOCK 2 obeys several properties essential in automated analysis of protein structure: It is symmetric, its alignments of secondary structure elements are transitive, its alignments of residues display a high degree of transitivity, and its scoring system is empirically found to behave as a metric.     

Principal component method for assessing structural heterogeneity across multiple alignment media

The recent availability of residual dipolar coupling measurements in a variety of different alignment media raises the question to what extent biomolecular structure and dynamics are differentially affected by their presence. A computational method is presented that allows the sensitive assessment of such changes using dipolar couplings measured in six or more alignment media. The method is based on a principal component analysis of the covariance matrix of the dipolar couplings. It does not require a priori structural or dynamic information nor knowledge of the alignment tensors and their orientations. In the absence of experimental errors, the covariance matrix has at most five nonzero eigenvalues if the structure and dynamics of the biomolecule is the same in all media. In contrast, differential structural and dynamic changes lead to additional nonzero eigenvalues. Characteristic features of the eigenvalue distribution in the absence and presence of noise are discussed using dipolar coupling data calculated from conformational ensembles taken from a molecular dynamics trajectory of native ubiquitin.     

BAliBASE 3.0: latest developments of the multiple sequence alignment benchmark

Multiple sequence alignment is one of the cornerstones of modern molecular biology. It is used to identify conserved motifs, to determine protein domains, in 2D/3D structure prediction by homology and in evolutionary studies. Recently, high-throughput technologies such as genome sequencing and structural proteomics have lead to an explosion in the amount of sequence and structure information available. In response, several new multiple alignment methods have been developed that improve both the efficiency and the quality of protein alignments. Consequently, the benchmarks used to evaluate and compare these methods must also evolve. We present here the latest release of the most widely used multiple alignment benchmark, BAliBASE, which provides high quality, manually refined, reference alignments based on 3D structural superpositions. Version 3.0 of BAliBASE includes new, more challenging test cases, representing the real problems encountered when aligning large sets of complex sequences. Using a novel, semiautomatic update protocol, the number of protein families in the benchmark has been increased and representative test cases are now available that cover most of the protein fold space. The total number of proteins in BAliBASE has also been significantly increased from 1444 to 6255 sequences. In addition, full-length sequences are now provided for all test cases, which represent difficult cases for both global and local alignment programs. Finally, the BAliBASE Web site (http://www-bio3d-igbmc.u-strasbg.fr/balibase) has been completely redesigned to provide a more user-friendly, interactive interface for the visualization of the BAliBASE reference alignments and the associated annotations.     

A novel approach to structural alignment using realistic structural and environmental information

In the era of structural genomics, it is necessary to generate accurate structural alignments in order to build good templates for homology modeling. Although a great number of structural alignment algorithms have been developed, most of them ignore intermolecular interactions during the alignment procedure. Therefore, structures in different oligomeric states are barely distinguishable, and it is very challenging to find correct alignment in coil regions. Here we present a novel approach to structural alignment using a clique finding algorithm and environmental information (SAUCE). In this approach, we build the alignment based on not only structural coordinate information but also realistic environmental information extracted from biological unit files provided by the Protein Data Bank (PDB). At first, we eliminate all environmentally unfavorable pairings of residues. Then we identify alignments in core regions via a maximal clique finding algorithm. Two extreme value distribution (EVD) form statistics have been developed to evaluate core region alignments. With an optional extension step, global alignment can be derived based on environment-based dynamic programming linking. We show that our method is able to differentiate three-dimensional structures in different oligomeric states, and is able to find flexible alignments between multidomain structures without predetermined hinge regions. The overall performance is also evaluated on a large scale by comparisons to current structural classification databases as well as to other alignment methods.     

FAST: a novel protein structure alignment algorithm

We present a novel algorithm named FAST for aligning protein three-dimensional structures. FAST uses a directionality-based scoring scheme to compare the intra-molecular residue-residue relationships in two structures. It employs an elimination heuristic to promote sparseness in the residue-pair graph and facilitate the detection of the global optimum. In order to test the overall accuracy of FAST, we determined its sensitivity and specificity with the SCOP classification (version 1.61) as the gold standard. FAST achieved higher sensitivities than several existing methods (DaliLite, CE, and K2) at all specificity levels. We also tested FAST against 1033 manually curated alignments in the HOMSTRAD database. The overall agreement was 96%. Close inspection of examples from broad structural classes indicated the high quality of FAST alignments. Moreover, FAST is an order of magnitude faster than other algorithms that attempt to establish residue-residue correspondence. Typical pairwise alignments take FAST less than a second with a Pentium III 1.2GHz CPU. FAST software and a web server are available at http://biowulf.bu.edu/FAST/.     

Performance evaluation of a new algorithm for the detection of remote homologs with sequence comparison

A detailed analysis of the performance of hybrid, a new sequence alignment algorithm developed by Yu and coworkers that combines Smith Waterman local dynamic programming with a local version of the maximum-likelihood approach, was made to access the applicability of this algorithm to the detection of distant homologs by sequence comparison. We analyzed the statistics of hybrid with a set of nonhomologous protein sequences from the SCOP database and found that the statistics of the scores from hybrid algorithm follows an Extreme Value Distribution with lambda approximately 1, as previously shown by Yu et al. for the case of artificially generated sequences. Local dynamic programming was compared to the hybrid algorithm by using two different test data sets of distant homologs from the PFAM and COGs protein sequence databases. The studies were made with several score functions in current use including OPTIMA, a new score function originally developed to detect remote homologs with the Smith Waterman algorithm. We found OPTIMA to be the best score function for both both dynamic programming and the hybrid algorithms. The ability of dynamic programming to discriminate between homologs and nonhomologs in the two sets of distantly related sequences is slightly better than that of hybrid algorithm. The advantage of producing accurate score statistics with only a few simulations may overcome the small differences in performance and make this new algorithm suitable for detection of homologs in conjunction with a wide range of score functions and gap penalties.     

Multiple protein structure alignment.

A method was developed to compare protein structures and to combine them into a multiple structure consensus. Previous methods of multiple structure comparison have only concatenated pairwise alignments or produced a consensus structure by averaging coordinate sets. The current method is a fusion of the fast structure comparison program SSAP and the multiple sequence alignment program MULTAL. As in MULTAL, structures are progressively combined, producing intermediate consensus structures that are compared directly to each other and all remaining single structures. This leads to a hierarchic "condensation," continually evaluated in the light of the emerging conserved core regions. Following the SSAP approach, all interatomic vectors were retained with well-conserved regions distinguished by coherent vector bundles (the structural equivalent of a conserved sequence position). Each bundle of vectors is summarized by a resultant, whereas vector coherence is captured in an error term, which is the only distinction between conserved and variable positions. Resultant vectors are used directly in the comparison, which is weighted by their error values, giving greater importance to the matching of conserved positions. The resultant vectors and their errors can also be used directly in molecular modeling. Applications of the method were assessed by the quality of the resulting sequence alignments, phylogenetic tree construction, and databank scanning with the consensus. Visual assessment of the structural superpositions and consensus structure for various well-characterized families confirmed that the consensus had identified a reasonable core.     

Multiple protein sequence alignment from tertiary structure comparison: assignment of global and residue confidence levels.

An algorithm is presented for the accurate and rapid generation of multiple protein sequence alignments from tertiary structure comparisons. A preliminary multiple sequence alignment is performed using sequence information, which then determines an initial superposition of the structures. A structure comparison algorithm is applied to all pairs of proteins in the superimposed set and a similarity tree calculated. Multiple sequence alignments are then generated by following the tree from the branches to the root. At each branchpoint of the tree, a structure-based sequence alignment and coordinate transformations are output, with the multiple alignment of all structures output at the root. The algorithm encoded in STAMP (STructural Alignment of Multiple Proteins) is shown to give alignments in good agreement with published structural accounts within the dehydrogenase fold domains, globins, and serine proteinases. In order to reduce the need for visual verification, two similarity indices are introduced to determine the quality of each generated structural alignment. Sc quantifies the global structural similarity between pairs or groups of proteins, whereas Pij' provides a normalized measure of the confidence in the alignment of each residue. STAMP alignments have the quality of each alignment characterized by Sc and Pij' values and thus provide a reproducible resource for studies of residue conservation within structural motifs.     

多序列渐进式比对算法研究与比较

多序列比对是一种重要的生物信息学工具,在生物的进化分析以及蛋白质的结构预测方面有着重要的应用。以ClustalW为代表的渐进式多序列比对算法在这个领域取得了很大的成功,成为应用最为广泛的多序列比对程序。但其固有的缺陷阻碍了比对精度的进一步提高,近年来出现了许多渐进式比对算法的改进算法,并取得良好的效果。本文选取了其中比较有代表性的几种算法对其基本比对思想予以描述,并且利用多序列比对程序平台BAliBASE和仿真程序ROSE对它们的精度和速度分别进行了比较和评价。     

A rapid protein structure alignment algorithm based on a text modeling technique

Structural alignment of proteins is widely used in various fields of structural biology. In order to further improve the quality of alignment, we describe an algorithm for structural alignment based on text modelling techniques. The technique firstly superimposes secondary structure elements of two proteins and then, models the 3D-structure of the protein in a sequence of alphabets. These sequences are utilized by a step-by-step sequence alignment procedure to align two protein structures. A benchmark test was organized on a set of 200 non-homologous proteins to evaluate the program and compare it to state of the art programs, e.g. CE, SAL, TM-align and 3D-BLAST. On average, the results of all-against-all structure comparison by the program have a competitive accuracy with CE and TM-align where the algorithm has a high running speed like 3D-BLAST.     

Structure alignment via Delaunay tetrahedralization

A novel protein structure alignment technique has been developed reducing much of the secondary and tertiary structure to a sequential representation greatly accelerating many structural computations, including alignment. Constructed from incidence relations in the Delaunay tetrahedralization, alignments of the sequential representation describe structural similarities that cannot be expressed with rigid-body superposition and complement existing techniques minimizing root-mean-squared distance through superposition. Restricting to the largest substructure superimposable by a single rigid-body transformation determines an alignment suitable for root-mean-squared distance comparisons and visualization. Restricted alignments of a test set of histones and histone-like proteins determined superpositions nearly identical to those produced by the established structure alignment routines of DaliLite and ProSup. Alignment of three, increasingly complex proteins: ferredoxin, cytidine deaminase, and carbamoyl phosphate synthetase, to themselves, demonstrated previously identified regions of self-similarity. All-against-all similarity index comparisons performed on a test set of 45 class I and class II aminoacyl-tRNA synthetases closely reproduced the results of established distance matrix methods while requiring 1/16 the time. Principal component analysis of pairwise tetrahedral decomposition similarity of 2300 molecular dynamics snapshots of tryptophanyl-tRNA synthetase revealed discrete microstates within the trajectory consistent with experimental results. The method produces results with sufficient efficiency for large-scale multiple structure alignment and is well suited to genomic and evolutionary investigations where no geometric model of similarity is known a priori.     

Fast structure alignment for protein databank searching.

A fast method is described for searching and analyzing the protein structure databank. It uses secondary structure followed by residue matching to compare protein structures and is developed from a previous structural alignment method based on dynamic programming. Linear representations of secondary structures are derived and their features compared to identify equivalent elements in two proteins. The secondary structure alignment then constrains the residue alignment, which compares only residues within aligned secondary structures and with similar buried areas and torsional angles. The initial secondary structure alignment improves accuracy and provides a means of filtering out unrelated proteins before the slower residue alignment stage. It is possible to search or sort the protein structure databank very quickly using just secondary structure comparisons. A search through 720 structures with a probe protein of 10 secondary structures required 1.7 CPU hours on a Sun 4/280. Alternatively, combined secondary structure and residue alignments, with a cutoff on the secondary structure score to remove pairs of unrelated proteins from further analysis, took 10.1 CPU hours. The method was applied in searches on different classes of proteins and to cluster a subset of the databank into structurally related groups. Relationships were consistent with known families of protein structure.