The Experts below are selected from a list of 44994 Experts worldwide ranked by ideXlab platform
Markus Porto - One of the best experts on this subject based on the ideXlab platform.
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sabertooth protein Structural Alignment based on a vectorial structure representation
BMC Bioinformatics, 2007Co-Authors: Florian Teichert, Ugo Bastolla, Markus PortoAbstract:Background: The task of computing highly accurate Structural Alignments of proteins in very short computation time is still challenging. This is partly due to the complexity of protein structures. Therefore, instead of manipulating coordinates directly, matrices of inter-atomic distances, sets of vectors between protein backbone atoms, and other reduced representations are used. These decrease the effort of comparing large sets of coordinates, but protein Structural Alignment still remains computationally expensive. Results: We represent the topology of a protein structure through a Structural profile that expresses the global effective connectivity of each residue. We have shown recently that this representation allows explicitly expressing the relationship between protein structure and protein sequence. Based on this very condensed vectorial representation, we develop a Structural Alignment framework that recognizes Structural similarities with accuracy comparable to established Alignment tools. Furthermore, our algorithm has favourable scaling of computation time with chain length. Since the algorithm is independent of the details of the Structural representation, our framework can be applied to sequence-to-sequence and sequence-to-structure comparison within the same setup, and it is therefore more general than other existing tools.
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sabertooth protein Structural Alignment based on a vectorial structure representation
BMC Bioinformatics, 2007Co-Authors: Florian Teichert, Ugo Bastolla, Markus PortoAbstract:The task of computing highly accurate Structural Alignments of proteins in very short computation time is still challenging. This is partly due to the complexity of protein structures. Therefore, instead of manipulating coordinates directly, matrices of inter-atomic distances, sets of vectors between protein backbone atoms, and other reduced representations are used. These decrease the effort of comparing large sets of coordinates, but protein Structural Alignment still remains computationally expensive. We represent the topology of a protein structure through a Structural profile that expresses the global effective connectivity of each residue. We have shown recently that this representation allows explicitly expressing the relationship between protein structure and protein sequence. Based on this very condensed vectorial representation, we develop a Structural Alignment framework that recognizes Structural similarities with accuracy comparable to established Alignment tools. Furthermore, our algorithm has favourable scaling of computation time with chain length. Since the algorithm is independent of the details of the Structural representation, our framework can be applied to sequence-to-sequence and sequence-to-structure comparison within the same setup, and it is therefore more general than other existing tools. We show that protein comparison based on a vectorial representation of protein structure performs comparably to established algorithms based on coordinates. The conceptually new approach presented in this publication might assist to unify the view on protein comparison by unifying structure and sequence descriptions in this context. The framework discussed here is implemented in the 'SABERTOOTH' Alignment server, freely accessible at http://www.fkp.tu-darmstadt.de/sabertooth/ .
Oliviero Carugo - One of the best experts on this subject based on the ideXlab platform.
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protein fold similarity estimated by a probabilistic approach based on cα cα distance comparison
Journal of Molecular Biology, 2002Co-Authors: Oliviero Carugo, Sandor PongorAbstract:The distribution of the C(alpha)-C(alpha) distances between residues separated by three to 30 amino acid residues is highly characteristic of protein folds and makes it possible to identify them from a straightforward comparison of the distance histograms. The comparison is carried out by contingency table analysis and yields a probability of identity (PRIDE score), with values between zero and 1. For closely related structures, PRIDE is highly correlated with the root-mean-square distance between C(alpha) atoms, but it provides a correct classification even for unrelated structures for which a Structural Alignment is not meaningful. For example, an analysis of the CATH database of fold structures showed that 98.8% of the folds fall into the correct CATH homologous superfamily category, based on the highest PRIDE score obtained. Structural Alignment and secondary-structure assignment are not necessary for the calculation of PRIDE, which is fast enough to allow the scanning of large databases.
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protein fold similarity estimated by a probabilistic approach based on cα cα distance comparison
Journal of Molecular Biology, 2002Co-Authors: Oliviero Carugo, Sandor PongorAbstract:The distribution of the C a -C a distances between residues separated by three to 30 amino acid residues is highly characteristic of protein folds and makes it possible to identify them from a straightforward comparison of the distance histograms. The comparison is carried out by contingency table analysis and yields a probability of identity (PRIDE score), with values between zero and 1. For closely related structures, PRIDE is highly correlated with the root-mean-square distance between C a atoms, but it provides a correct classification even for unrelated structures for which a Structural Alignment is not meaningful. For example, an analysis of the CATH database of fold structures showed that 98.8 % of the folds fall into the correct CATH homologous superfamily category, based on the highest PRIDE score obtained. Structural Alignment and secondarystructure assignment are not necessary for the calculation of PRIDE, which is fast enough to allow the scanning of large databases. # 2002 Academic Press
Sandor Pongor - One of the best experts on this subject based on the ideXlab platform.
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protein fold similarity estimated by a probabilistic approach based on cα cα distance comparison
Journal of Molecular Biology, 2002Co-Authors: Oliviero Carugo, Sandor PongorAbstract:The distribution of the C(alpha)-C(alpha) distances between residues separated by three to 30 amino acid residues is highly characteristic of protein folds and makes it possible to identify them from a straightforward comparison of the distance histograms. The comparison is carried out by contingency table analysis and yields a probability of identity (PRIDE score), with values between zero and 1. For closely related structures, PRIDE is highly correlated with the root-mean-square distance between C(alpha) atoms, but it provides a correct classification even for unrelated structures for which a Structural Alignment is not meaningful. For example, an analysis of the CATH database of fold structures showed that 98.8% of the folds fall into the correct CATH homologous superfamily category, based on the highest PRIDE score obtained. Structural Alignment and secondary-structure assignment are not necessary for the calculation of PRIDE, which is fast enough to allow the scanning of large databases.
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protein fold similarity estimated by a probabilistic approach based on cα cα distance comparison
Journal of Molecular Biology, 2002Co-Authors: Oliviero Carugo, Sandor PongorAbstract:The distribution of the C a -C a distances between residues separated by three to 30 amino acid residues is highly characteristic of protein folds and makes it possible to identify them from a straightforward comparison of the distance histograms. The comparison is carried out by contingency table analysis and yields a probability of identity (PRIDE score), with values between zero and 1. For closely related structures, PRIDE is highly correlated with the root-mean-square distance between C a atoms, but it provides a correct classification even for unrelated structures for which a Structural Alignment is not meaningful. For example, an analysis of the CATH database of fold structures showed that 98.8 % of the folds fall into the correct CATH homologous superfamily category, based on the highest PRIDE score obtained. Structural Alignment and secondarystructure assignment are not necessary for the calculation of PRIDE, which is fast enough to allow the scanning of large databases. # 2002 Academic Press
Florian Teichert - One of the best experts on this subject based on the ideXlab platform.
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sabertooth protein Structural Alignment based on a vectorial structure representation
BMC Bioinformatics, 2007Co-Authors: Florian Teichert, Ugo Bastolla, Markus PortoAbstract:Background: The task of computing highly accurate Structural Alignments of proteins in very short computation time is still challenging. This is partly due to the complexity of protein structures. Therefore, instead of manipulating coordinates directly, matrices of inter-atomic distances, sets of vectors between protein backbone atoms, and other reduced representations are used. These decrease the effort of comparing large sets of coordinates, but protein Structural Alignment still remains computationally expensive. Results: We represent the topology of a protein structure through a Structural profile that expresses the global effective connectivity of each residue. We have shown recently that this representation allows explicitly expressing the relationship between protein structure and protein sequence. Based on this very condensed vectorial representation, we develop a Structural Alignment framework that recognizes Structural similarities with accuracy comparable to established Alignment tools. Furthermore, our algorithm has favourable scaling of computation time with chain length. Since the algorithm is independent of the details of the Structural representation, our framework can be applied to sequence-to-sequence and sequence-to-structure comparison within the same setup, and it is therefore more general than other existing tools.
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sabertooth protein Structural Alignment based on a vectorial structure representation
BMC Bioinformatics, 2007Co-Authors: Florian Teichert, Ugo Bastolla, Markus PortoAbstract:The task of computing highly accurate Structural Alignments of proteins in very short computation time is still challenging. This is partly due to the complexity of protein structures. Therefore, instead of manipulating coordinates directly, matrices of inter-atomic distances, sets of vectors between protein backbone atoms, and other reduced representations are used. These decrease the effort of comparing large sets of coordinates, but protein Structural Alignment still remains computationally expensive. We represent the topology of a protein structure through a Structural profile that expresses the global effective connectivity of each residue. We have shown recently that this representation allows explicitly expressing the relationship between protein structure and protein sequence. Based on this very condensed vectorial representation, we develop a Structural Alignment framework that recognizes Structural similarities with accuracy comparable to established Alignment tools. Furthermore, our algorithm has favourable scaling of computation time with chain length. Since the algorithm is independent of the details of the Structural representation, our framework can be applied to sequence-to-sequence and sequence-to-structure comparison within the same setup, and it is therefore more general than other existing tools. We show that protein comparison based on a vectorial representation of protein structure performs comparably to established algorithms based on coordinates. The conceptually new approach presented in this publication might assist to unify the view on protein comparison by unifying structure and sequence descriptions in this context. The framework discussed here is implemented in the 'SABERTOOTH' Alignment server, freely accessible at http://www.fkp.tu-darmstadt.de/sabertooth/ .
Haim J Wolfson - One of the best experts on this subject based on the ideXlab platform.
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multiple Structural Alignment by secondary structures algorithm and applications
Protein Science, 2009Co-Authors: Oranit Dror, Hadar Benyamini, Ruth Nussinov, Haim J WolfsonAbstract: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/.
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mass multiple Structural Alignment by secondary structures
Bioinformatics, 2003Co-Authors: Oranit Dror, Hadar Benyamini, Ruth Nussinov, Haim J WolfsonAbstract:We present a novel method for multiple Alignment of protein structures and detection of Structural motifs. To date, only a few methods are available for addressing this task. Most of them are based on a series of pairwise comparisons. In contrast, MASS (Multiple Alignment by Secondary Structures) considers all the given structures at the same time. Exploiting the secondary structure representation aids in filtering out noisy results and in making the method highly efficient and robust. MASS disregards the sequence order of the secondary structure elements. Thus, it can find non-sequential and even non-topological Structural motifs. An important novel feature of MASS is subset Alignment detection: It does not require that all the input molecules be aligned. Rather, MASS is capable of detecting Structural motifs shared only by a subset of the molecules. Given its high efficiency and capability of detecting subset Alignments, MASS is suitable for a broad range of challenging applications: It can handle large-scale protein ensembles (on the order of tens) that may be heterogeneous, noisy, topologically unrelated and contain structures of low resolution.
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multiprot a multiple protein Structural Alignment algorithm
Workshop on Algorithms in Bioinformatics, 2002Co-Authors: Maxim Shatsky, Ruth Nussinov, Haim J WolfsonAbstract:We present a fully automated highly efficient technique which detects the multiple Structural Alignments of protein structures. Our method, MultiProt, finds the common geometrical cores between the input molecules. To date, only few methods were developed to tackle the Structural multiple Alignment problem. Most of them require that all the input molecules be aligned, while our method does not require that all the input molecules participate in the Alignment. Actually, it efficiently detects high scoring partial multiple Alignments for all possible number of molecules from the input. To demonstrate the power of the presented method we provide a number of experimental results performed by the implemented program. Along with the known multiple Alignments of protein structures, we present new multiple Structural Alignment results of protein families from the All beta proteins class in the SCOP classification.
