The Experts below are selected from a list of 1455 Experts worldwide ranked by ideXlab platform
Pierre M Durand - One of the best experts on this subject based on the ideXlab platform.
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robust sequence alignment using evolutionary rates coupled with an amino acid substitution matrix
BMC Bioinformatics, 2015Co-Authors: Andrew Ndhlovu, Scott Hazelhurst, Pierre M DurandAbstract:Background Selective pressures at the DNA level shape genes into profiles consisting of patterns of rapidly evolving sites and sites withstanding change. These profiles remain detectable even when protein sequences become extensively diverged. A common task in molecular biology is to infer functional, structural or evolutionary relationships by querying a database using an algorithm. However, problems arise when sequence similarity is low. This study presents an algorithm that uses the evolutionary rate at codon sites, the dN/dS (ω) parameter, coupled to a substitution matrix as an alignment metric for detecting distantly related proteins. The algorithm, called BLOSUM-FIRE couples a newer and improved version of the original FIRE (Functional Inference using Rates of Evolution) algorithm with an amino acid substitution matrix in a dynamic scoring function. The enigmatic hepatitis B virus X protein was used as a test case for BLOSUM-FIRE and its associated database EvoDB.
Andreas Bender - One of the best experts on this subject based on the ideXlab platform.
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benchmarking of protein descriptor sets in proteochemometric modeling part 1 comparative study of 13 amino acid descriptor sets
Journal of Cheminformatics, 2013Co-Authors: Gerard J. P. Van Westen, Remco F. Swier, Jörg K. Wegner, Adriaan P. Ijzerman, Herman W. T. Van Vlijmen, Andreas BenderAbstract:Background While a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA).
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benchmarking of protein descriptor sets in proteochemometric modeling part 1 comparative study of 13 amino acid descriptor sets
Journal of Cheminformatics, 2013Co-Authors: Gerard J. P. Van Westen, Remco F. Swier, Jörg K. Wegner, Adriaan P. Ijzerman, Herman W. T. Van Vlijmen, Andreas BenderAbstract:While a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA). In describing amino acid similarities, MSWHIM, T-scales and ST-scales show related behavior, as do the VHSE, FASGAI, and ProtFP (PCA3) descriptor sets. Conversely, the ProtFP (PCA5), ProtFP (PCA8), Z-Scales (Binned), and BLOSUM descriptor sets show behavior that is distinct from one another as well as both of the clusters above. Generally, the use of more principal components (>3 per amino acid, per descriptor) leads to a significant differences in the way amino acids are described, despite that the later principal components capture less variation per component of the original input data. In this work a comparison is provided of how similar (and differently) currently available amino acids descriptor sets behave when converting structure to property space. The results obtained enable molecular modelers to select suitable amino acid descriptor sets for structure-activity analyses, e.g. those showing complementary behavior.
Gerard J. P. Van Westen - One of the best experts on this subject based on the ideXlab platform.
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benchmarking of protein descriptor sets in proteochemometric modeling part 1 comparative study of 13 amino acid descriptor sets
Journal of Cheminformatics, 2013Co-Authors: Gerard J. P. Van Westen, Remco F. Swier, Jörg K. Wegner, Adriaan P. Ijzerman, Herman W. T. Van Vlijmen, Andreas BenderAbstract:Background While a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA).
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benchmarking of protein descriptor sets in proteochemometric modeling part 1 comparative study of 13 amino acid descriptor sets
Journal of Cheminformatics, 2013Co-Authors: Gerard J. P. Van Westen, Remco F. Swier, Jörg K. Wegner, Adriaan P. Ijzerman, Herman W. T. Van Vlijmen, Andreas BenderAbstract:While a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA). In describing amino acid similarities, MSWHIM, T-scales and ST-scales show related behavior, as do the VHSE, FASGAI, and ProtFP (PCA3) descriptor sets. Conversely, the ProtFP (PCA5), ProtFP (PCA8), Z-Scales (Binned), and BLOSUM descriptor sets show behavior that is distinct from one another as well as both of the clusters above. Generally, the use of more principal components (>3 per amino acid, per descriptor) leads to a significant differences in the way amino acids are described, despite that the later principal components capture less variation per component of the original input data. In this work a comparison is provided of how similar (and differently) currently available amino acids descriptor sets behave when converting structure to property space. The results obtained enable molecular modelers to select suitable amino acid descriptor sets for structure-activity analyses, e.g. those showing complementary behavior.
Herman W. T. Van Vlijmen - One of the best experts on this subject based on the ideXlab platform.
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benchmarking of protein descriptor sets in proteochemometric modeling part 1 comparative study of 13 amino acid descriptor sets
Journal of Cheminformatics, 2013Co-Authors: Gerard J. P. Van Westen, Remco F. Swier, Jörg K. Wegner, Adriaan P. Ijzerman, Herman W. T. Van Vlijmen, Andreas BenderAbstract:Background While a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA).
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benchmarking of protein descriptor sets in proteochemometric modeling part 1 comparative study of 13 amino acid descriptor sets
Journal of Cheminformatics, 2013Co-Authors: Gerard J. P. Van Westen, Remco F. Swier, Jörg K. Wegner, Adriaan P. Ijzerman, Herman W. T. Van Vlijmen, Andreas BenderAbstract:While a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA). In describing amino acid similarities, MSWHIM, T-scales and ST-scales show related behavior, as do the VHSE, FASGAI, and ProtFP (PCA3) descriptor sets. Conversely, the ProtFP (PCA5), ProtFP (PCA8), Z-Scales (Binned), and BLOSUM descriptor sets show behavior that is distinct from one another as well as both of the clusters above. Generally, the use of more principal components (>3 per amino acid, per descriptor) leads to a significant differences in the way amino acids are described, despite that the later principal components capture less variation per component of the original input data. In this work a comparison is provided of how similar (and differently) currently available amino acids descriptor sets behave when converting structure to property space. The results obtained enable molecular modelers to select suitable amino acid descriptor sets for structure-activity analyses, e.g. those showing complementary behavior.
Adriaan P. Ijzerman - One of the best experts on this subject based on the ideXlab platform.
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benchmarking of protein descriptor sets in proteochemometric modeling part 1 comparative study of 13 amino acid descriptor sets
Journal of Cheminformatics, 2013Co-Authors: Gerard J. P. Van Westen, Remco F. Swier, Jörg K. Wegner, Adriaan P. Ijzerman, Herman W. T. Van Vlijmen, Andreas BenderAbstract:Background While a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA).
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benchmarking of protein descriptor sets in proteochemometric modeling part 1 comparative study of 13 amino acid descriptor sets
Journal of Cheminformatics, 2013Co-Authors: Gerard J. P. Van Westen, Remco F. Swier, Jörg K. Wegner, Adriaan P. Ijzerman, Herman W. T. Van Vlijmen, Andreas BenderAbstract:While a large body of work exists on comparing and benchmarking of descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 different protein descriptor sets have been compared with respect to their behavior in perceiving similarities between amino acids. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI and BLOSUM, and a novel protein descriptor set termed ProtFP (4 variants). We investigate to which extent descriptor sets show collinear as well as orthogonal behavior via principal component analysis (PCA). In describing amino acid similarities, MSWHIM, T-scales and ST-scales show related behavior, as do the VHSE, FASGAI, and ProtFP (PCA3) descriptor sets. Conversely, the ProtFP (PCA5), ProtFP (PCA8), Z-Scales (Binned), and BLOSUM descriptor sets show behavior that is distinct from one another as well as both of the clusters above. Generally, the use of more principal components (>3 per amino acid, per descriptor) leads to a significant differences in the way amino acids are described, despite that the later principal components capture less variation per component of the original input data. In this work a comparison is provided of how similar (and differently) currently available amino acids descriptor sets behave when converting structure to property space. The results obtained enable molecular modelers to select suitable amino acid descriptor sets for structure-activity analyses, e.g. those showing complementary behavior.