Multiple Structure Alignment Research Articles

Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment.

BackgroundThe quality of multiple protein structure alignments are usually computed and assessed based on geometric functions of the coordinates of the backbone atoms from the protein chains. These purely geometric methods do not utilize directly protein sequence similarity, and in fact, determining the proper way to incorporate sequence similarity measures into the construction and assessment of protein multiple structure alignments has proved surprisingly difficult.ResultsWe present Formatt, a multiple structure alignment based on the Matt purely geometric multiple structure alignment program, that also takes into account sequence similarity when constructing alignments. We show that Formatt outperforms Matt and other popular structure alignment programs on the popular HOMSTRAD benchmark. For the SABMark twilight zone benchmark set that captures more remote homology, Formatt and Matt outperform other programs; depending on choice of embedded sequence aligner, Formatt produces either better sequence and structural alignments with a smaller core size than Matt, or similarly sized alignments with better sequence similarity, for a small cost in average RMSD.ConclusionsConsidering sequence information as well as purely geometric information seems to improve quality of multiple structure alignments, though defining what constitutes the best alignment when sequence and structural measures would suggest different alignments remains a difficult open question.

Protein Kinase Cθ C2 Domain Is a Phosphotyrosine Binding Module That Plays a Key Role in Its Activation

Protein kinase Cθ (PKCθ) is a novel PKC that plays a key role in T lymphocyte activation. To understand how PKCθ is regulated in T cells, we investigated the properties of its N-terminal C2 domain that functions as an autoinhibitory domain. Our measurements show that a Tyr(P)-containing peptide derived from CDCP1 binds the C2 domain of PKCθ with high affinity and activates the enzyme activity of the intact protein. The Tyr(P) peptide also binds the C2 domain of PKCδ tightly, but no enzyme activation was observed with PKCδ. Mutations of PKCθ-C2 residues involved in Tyr(P) binding abrogated the enzyme activation and association of PKCθ with Tyr-phosphorylated full-length CDCP1 and severely inhibited the T cell receptor/CD28-mediated activation of a PKCθ-dependent reporter gene in T cells. Collectively, these studies establish the C2 domain of PKCθ as a Tyr(P)-binding domain and suggest that the domain may play a major role in PKCθ activation via its Tyr(P) binding.

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string. A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition. Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.

SALIGN: a web server for alignment of multiple protein sequences and structures

Accurate alignment of protein sequences and/or structures is crucial for many biological analyses, including functional annotation of proteins, classifying protein sequences into families, and comparative protein structure modeling. Described here is a web interface to SALIGN, the versatile protein multiple sequence/structure alignment module of MODELLER. The web server automatically determines the best alignment procedure based on the inputs, while allowing the user to override default parameter values. Multiple alignments are guided by a dendrogram computed from a matrix of all pairwise alignment scores. When aligning sequences to structures, SALIGN uses structural environment information to place gaps optimally. If two multiple sequence alignments of related proteins are input to the server, a profile-profile alignment is performed. All features of the server have been previously optimized for accuracy, especially in the contexts of comparative modeling and identification of interacting protein partners. The SALIGN web server is freely accessible to the academic community at http://salilab.org/salign. SALIGN is a module of the MODELLER software, also freely available to academic users (http://salilab.org/modeller). sali@salilab.org; madhusudhan@bii.a-star.edu.sg.

Multiple structure alignment with msTALI

BackgroundMultiple structure alignments have received increasing attention in recent years as an alternative to multiple sequence alignments. Although multiple structure alignment algorithms can potentially be applied to a number of problems, they have primarily been used for protein core identification. A method that is capable of solving a variety of problems using structure comparison is still absent. Here we introduce a program msTALI for aligning multiple protein structures. Our algorithm uses several informative features to guide its alignments: torsion angles, backbone Cα atom positions, secondary structure, residue type, surface accessibility, and properties of nearby atoms. The algorithm allows the user to weight the types of information used to generate the alignment, which expands its utility to a wide variety of problems.ResultsmsTALI exhibits competitive results on 824 families from the Homstrad and SABmark databases when compared to Matt and Mustang. We also demonstrate success at building a database of protein cores using 341 randomly selected CATH domains and highlight the contribution of msTALI compared to the CATH classifications. Finally, we present an example applying msTALI to the problem of detecting hinges in a protein undergoing rigid-body motion.ConclusionsmsTALI is an effective algorithm for multiple structure alignment. In addition to its performance on standard comparison databases, it utilizes clear, informative features, allowing further customization for domain-specific applications. The C++ source code for msTALI is available for Linux on the web at http://ifestos.cse.sc.edu/mstali.

Alignment of distantly related protein structures: algorithm, bound and implications to homology modeling

Building an accurate alignment of a large set of distantly related protein structures is still very challenging. This article presents a novel method 3DCOMB that can generate a multiple structure alignment (MSA) with not only as many conserved cores as possible, but also high-quality pairwise alignments. 3DCOMB is unique in that it makes use of both local and global structure environments, combined by a statistical learning method, to accurately identify highly similar fragment blocks (HSFBs) among all proteins to be aligned. By extending the alignments of these HSFBs, 3DCOMB can quickly generate an accurate MSA without using progressive alignment. 3DCOMB significantly excels others in aligning distantly related proteins. 3DCOMB can also generate correct alignments for functionally similar regions among proteins of very different structures while many other MSA tools fail. 3DCOMB is useful for many real-world applications. In particular, it enables us to find out that there is still large improvement room for multiple template homology modeling while several other MSA tools fail to do so. 3DCOMB is available at http://ttic.uchicago.edu/~jinbo/software.htm. jinboxu@gmail.com Supplementary data are available at Bioinformatics online.

Protein Threading Based on Multiple Protein Structure Alignment.

Protein threading, a method employed in protein three-dimensional (3D) structure prediction was only proposed in the early 1990's although predicting protein 3D structure from its given amino acid sequence has been around since 1970's. Here we describe a protein threading method/system that we have developed based on multiple protein structure alignment. In order to compute multiple structure alignments, we developed a similar structure search program on massive parallel computers and a program for constructing a multiple structure alignment from pairwise structure alignments, where the latter is based on the center star method for sequence alignment. A simple dynamic-programming based algorithm which uses a profile matrix obtained from the result of multiple structure alignment was also developed to compute a threading (i.e., an alignment between a target sequence and a known structure). Using this system, we participated in the threading category (category AL) of CASP3 (Third Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction). The results are discussed.

Approximation of RNA multiple structural alignment

In the context of non-coding RNA (ncRNA) multiple structural alignment, Davydov and Batzoglou (2006) introduced in [7] the problem of finding the largest nested linear graph that occurs in a set G of linear graphs, the so-called Max-NLS problem. This problem generalizes both the longest common subsequence problem and the maximum common homeomorphic subtree problem for rooted ordered trees. In the present paper, we give a fast algorithm for finding the largest nested linear subgraph of a linear graph and a polynomial-time algorithm for a fixed number ( k) of linear graphs. Also, we strongly strengthen the result of Davydov and Batzoglou (2006) [7] by proving that the problem is NP-complete even if G is composed of nested linear graphs of height at most 2, thereby precisely defining the borderline between tractable and intractable instances of the problem. Of particular importance, we improve the result of Davydov and Batzoglou (2006) [7] by showing that the Max-NLS problem is approximable within ratio O ( log m opt ) in O ( k n 2 ) running time, where m opt is the size of an optimal solution. We also present O ( 1 ) -approximation of Max-NLS problem running in O ( k n ) time for restricted linear graphs. In particular, for ncRNA derived linear graphs, a 1 4 -approximation is presented.

Smolign: A Spatial Motifs-Based Protein Multiple Structural Alignment Method

Availability of an effective tool for protein multiple structural alignment (MSTA) is essential for discovery and analysis of biologically significant structural motifs that can help solve functional annotation and drug design problems. Existing MSTA methods collect residue correspondences mostly through pairwise comparison of consecutive fragments, which can lead to suboptimal alignments, especially when the similarity among the proteins is low. We introduce a novel strategy based on: building a contact-window based motif library from the protein structural data, discovery and extension of common alignment seeds from this library, and optimal superimposition of multiple structures according to these alignment seeds by an enhanced partial order curve comparison method. The ability of our strategy to detect multiple correspondences simultaneously, to catch alignments globally, and to support flexible alignments, endorse a sensitive and robust automated algorithm that can expose similarities among protein structures even under low similarity conditions. Our method yields better alignment results compared to other popular MSTA methods, on several protein structure data sets that span various structural folds and represent different protein similarity levels. A web-based alignment tool, a downloadable executable, and detailed alignment results for the data sets used here are available at http://sacan.biomed. drexel.edu/Smolign and http://bio.cse.ohio-state.edu/Smolign.

BiMFG: BIOINFORMATICS TOOLS FOR MARINE AND FRESHWATER SPECIES

Biomolecule sequences and structures of land, air and water species are determined rapidly and the data entries are unevenly distributed for different organisms. It frequently leads to the BLAST results of homologous search containing undesirable entries from organisms living in different environments. To reduce irrelevant searching results, a separate database for comparative genomics is urgently required. A comprehensive bioinformatics tool set and an integrated database, named Bioinformatics tools for Marine and Freshwater Genomics (BiMFG), are constructed for comparative analyses among model species and underwater species. Novel matching techniques based on conserved motifs and/or secondary structure elements are designed for efficiently and effectively retrieving and aligning remote sequences through cross-species comparisons. It is especially helpful when sequences under analysis possess low similarities and unresolved structural information. In addition, the system provides core techniques of multiple sequence alignment, multiple second structure profile alignment and iteratively refined multiple structural alignments for biodiversity analysis and verification in marine and freshwater biology. The BiMFG web server is freely available for use at http://bimfg.cs.ntou.edu.tw/.

ModBase, a database of annotated comparative protein structure models, and associated resources

ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence–structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains 10 355 444 reliable models for domains in 2 421 920 unique protein sequences. ModBase allows users to update comparative models on demand, and request modeling of additional sequences through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are available through the ModBase interface as well as the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the SALIGN server for multiple sequence and structure alignment (http://salilab.org/salign), the ModEval server for predicting the accuracy of protein structure models (http://salilab.org/modeval), the PCSS server for predicting which peptides bind to a given protein (http://salilab.org/pcss) and the FoXS server for calculating and fitting Small Angle X-ray Scattering profiles (http://salilab.org/foxs).

Correlation Analysis for Protein Evolutionary Family Based on Amino Acid Position Mutations and Application in PDZ Domain

BackgroundIt has been widely recognized that the mutations at specific directions are caused by the functional constraints in protein family and the directional mutations at certain positions control the evolutionary direction of the protein family. The mutations at different positions, even distantly separated, are mutually coupled and form an evolutionary network. Finding the controlling mutative positions and the mutative network among residues are firstly important for protein rational design and enzyme engineering.MethodologyA computational approach, namely amino acid position conservation-mutation correlation analysis (CMCA), is developed to predict mutually mutative positions and find the evolutionary network in protein family. The amino acid position mutative function, which is the foundational equation of CMCA measuring the mutation of a residue at a position, is derived from the MSA (multiple structure alignment) database of protein evolutionary family. Then the position conservation correlation matrix and position mutation correlation matrix is constructed from the amino acid position mutative equation. Unlike traditional SCA (statistical coupling analysis) approach, which is based on the statistical analysis of position conservations, the CMCA focuses on the correlation analysis of position mutations.ConclusionsAs an example the CMCA approach is used to study the PDZ domain of protein family, and the results well illustrate the distantly allosteric mechanism in PDZ protein family, and find the functional mutative network among residues. We expect that the CMCA approach may find applications in protein engineering study, and suggest new strategy to improve bioactivities and physicochemical properties of enzymes.

Dali server: conservation mapping in 3D

Our web site (http://ekhidna.biocenter.helsinki.fi/dali_server) runs the Dali program for protein structure comparison. The web site consists of three parts: (i) the Dali server compares newly solved structures against structures in the Protein Data Bank (PDB), (ii) the Dali database allows browsing precomputed structural neighbourhoods and (iii) the pairwise comparison generates suboptimal alignments for a pair of structures. Each part has its own query form and a common format for the results page. The inputs are either PDB identifiers or novel structures uploaded by the user. The results pages are hyperlinked to aid interactive analysis. The web interface is simple and easy to use. The key purpose of interactive analysis is to check whether conserved residues line up in multiple structural alignments and how conserved residues and ligands cluster together in multiple structure superimpositions. In favourable cases, protein structure comparison can lead to evolutionary discoveries not detected by sequence analysis.

MUSTANG-MR Structural Sieving Server: Applications in Protein Structural Analysis and Crystallography

BackgroundA central tenet of structural biology is that related proteins of common function share structural similarity. This has key practical consequences for the derivation and analysis of protein structures, and is exploited by the process of “molecular sieving” whereby a common core is progressively distilled from a comparison of two or more protein structures. This paper reports a novel web server for “sieving” of protein structures, based on the multiple structural alignment program MUSTANG.Methodology/Principal Findings“Sieved” models are generated from MUSTANG-generated multiple alignment and superpositions by iteratively filtering out noisy residue-residue correspondences, until the resultant correspondences in the models are optimally “superposable” under a threshold of RMSD. This residue-level sieving is also accompanied by iterative elimination of the poorly fitting structures from the input ensemble. Therefore, by varying the thresholds of RMSD and the cardinality of the ensemble, multiple sieved models are generated for a given multiple alignment and superposition from MUSTANG. To aid the identification of structurally conserved regions of functional importance in an ensemble of protein structures, Lesk-Hubbard graphs are generated, plotting the number of residue correspondences in a superposition as a function of its corresponding RMSD. The conserved “core” (or typically active site) shows a linear trend, which becomes exponential as divergent parts of the structure are included into the superposition.ConclusionsThe application addresses two fundamental problems in structural biology: First, the identification of common substructures among structurally related proteins—an important problem in characterization and prediction of function; second, generation of sieved models with demonstrated uses in protein crystallographic structure determination using the technique of Molecular Replacement.

MULTIPLE STRUCTURE ALIGNMENT BASED ON GEOMETRICAL CORRELATION OF SECONDARY STRUCTURE ELEMENTS

Protein structure alignment facilitates the analysis of protein functionality. Through superimposed structures and the comparison of variant components, common or specific features of proteins can be identified. Several known protein families exhibit analogous tertiary structures but divergent primary sequences. These proteins in the same structural class are unable to be aligned by sequence-based methods. The main objective of the present study was to develop an efficient and effective algorithm for multiple structure alignment based on geometrical correlation of secondary structures, which are conserved in evolutionary heritage. The method utilizes mutual correlation analysis of secondary structure elements (SSEs) and selects representative segments as the key anchors for structural alignment. The system exploits a fast vector transformation technique to represent SSEs in vector format, and the mutual geometrical relationship among vectors is projected onto an angle-distance map. Through a scoring function and filtering mechanisms, the best candidates of vectors are selected, and an effective constrained multiple structural alignment module is performed. The correctness of the algorithm was verified by the multiple structure alignment of proteins in the SCOP database. Several protein sets with low sequence identities were aligned, and the results were compared with those obtained by three well-known structural alignment approaches. The results show that the proposed method is able to perform multiple structural alignments effectively and to obtain satisfactory results, especially for proteins possessing low sequence identity.

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.

Identification of a developmentally and hormonally regulated Delta-Class glutathione S-transferase in rice moth Corcyra cephalonica

Glutathione S-transferases (GSTs) are a large family of multifunctional enzymes, known for their role in cellular detoxification. Here we report a cytosolic GST with optimal activity at alkaline pH (8.3) from the visceral fat body of late-last instar (LLI) larvae of a lepidopteran insect rice moth Corcyra cephalonica. All previously known GSTs are active between pH 6.0 to 6.5. Purification and characterization revealed the Corcyra cephalonica GST (CcGST) as a 23-kDa protein. HPLC and 2D analysis showed a single isoform of the protein in the LLI visceral fat body. Degenerate primer based method identified a 701-nucleotide cDNA and the longest open reading frame contained 216 amino acids. Multiple sequence and structural alignment showed close similarity with delta-class GSTs. CcGST is present mainly in the fat body with highest activity at the late-last instar larval stage. Juvenile hormone (JH) negatively inhibits the CcGST activity both ex vivo and in vivo. We speculate that high expression and activity of CcGST in the fat body of the late-last instar larvae, when endogenous JH titer is low may have role in the insect post-embryonic development unrelated to their previously known function.

Multiple structure alignment and consensus identification for proteins

BackgroundAn algorithm is presented to compute a multiple structure alignment for a set of proteins and to generate a consensus (pseudo) protein which captures common substructures present in the given proteins. The algorithm represents each protein as a sequence of triples of coordinates of the alpha-carbon atoms along the backbone. It then computes iteratively a sequence of transformation matrices (i.e., translations and rotations) to align the proteins in space and generate the consensus. The algorithm is a heuristic in that it computes an approximation to the optimal alignment that minimizes the sum of the pairwise distances between the consensus and the transformed proteins.ResultsExperimental results show that the algorithm converges quite rapidly and generates consensus structures that are visually similar to the input proteins. A comparison with other coordinate-based alignment algorithms (MAMMOTH and MATT) shows that the proposed algorithm is competitive in terms of speed and the sizes of the conserved regions discovered in an extensive benchmark dataset derived from the HOMSTRAD and SABmark databases.The algorithm has been implemented in C++ and can be downloaded from the project's web page. Alternatively, the algorithm can be used via a web server which makes it possible to align protein structures by uploading files from local disk or by downloading protein data from the RCSB Protein Data Bank.ConclusionsAn algorithm is presented to compute a multiple structure alignment for a set of proteins, together with their consensus structure. Experimental results show its effectiveness in terms of the quality of the alignment and computational cost.

2°Struc - the Protein Secondary Structure Analysis Server

Protein secondary structure can be defined by the pattern of hydrogen bonding between backbone amide and carboxyl groups, whereby the protein is constrained to adopt repetitive dihedral angle conformations. “Define Secondary Structure of Proteins” (DSSP)(Kabsch and Sander, 1983) is the de facto standard for annotation using rules similar to those described by Pauling and Corey, (1951) to assign eight secondary structure states. However, other methods have been developed to address problems including poor edge residue definition, low-resolution structure elucidation and Cα only structures. These methods define secondary structure in different ways resulting in a wide variation in assignment at the amino acid and segment levels. To enable investigation of this variation we present 2°Struc; a web server that analyses protein secondary structure content derived from a number of available methods. The output is in five sections. Protein structure summary details the ‘whole protein’ percentage structure content and provides a numerical comparison of each method relative to DSSP using several commonly applied metrics including percentage similarity and Matthews correlation coefficient. Structure summary by chain displays percentage content and provides an option to compare each structure assignment method using the Jmol molecular viewer. Multiple structure alignment uses a three-state representation colored to display secondary structure assignments relative to PDB and UniProt sequence records for each method. A majority vote consensus is also provided. Original multiple structure alignment provides a second colored alignment displaying unmodified structure assignments. Sequence structure alignments shows comparative unmodified and modified three-state output relative to UniProt and ATOM record sequences, with an option to download a PDF file containing information about the method run, percentage structure content, chain length and diagram of the three-state structure assignment, also provided. (Supported by a grant from the BBSRC Bioinformatics and Biological Resources Fund, UK).

3DM: Systematic analysis of heterogeneous superfamily data to discover protein functionalities

Ten years of experience with molecular class-specific information systems (MCSIS) such as with the hand-curated G protein-coupled receptor database (GPCRDB) or the semiautomatically generated nuclear receptor database has made clear that a wide variety of questions can be answered when protein-related data from many different origins can be flexibly combined. MCSISes revolve around a multiple sequence alignment (MSA) that includes "all" available sequences from the entire superfamily, and it has been shown at many occasions that the quality of these alignments is the most crucial aspect of the MCSIS approach. We describe here a system called 3DM that can automatically build an entire MCSIS. 3DM bases the MSA on a multiple structure alignment, which implies that the availability of a large number of superfamily members with a known three-dimensional structure is a requirement for 3DM to succeed well. Thirteen MCSISes were constructed and placed on the Internet for examination. These systems have been instrumental in a large series of research projects related to enzyme activity or the understanding and engineering of specificity, protein stability engineering, DNA-diagnostics, drug design, and so forth.