The Experts below are selected from a list of 52107 Experts worldwide ranked by ideXlab platform
Julian Lee - One of the best experts on this subject based on the ideXlab platform.
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Real Value Prediction of Solvent Accessibility by Using the k-Nearest Neighbor Method
Journal of the Korean Physical Society, 2009Co-Authors: Julian LeeAbstract:Prediction of a protein residue solvent accessibility gives useful guidance for the prediction of the full three-dimensional structure and function of a protein. Although solvent accessibility is a continuous quantity, it has been predicted mostly by assigning each residue into one of two or three classes by using various threshold values. Predictions of real values of solvent accessibility have been attempted only recently, but the k-nearest Neighbor Method has never been used. In this work, we apply a simple implementation of the k-nearest Neighbor Method to the real-value prediction of solvent accessibility, using PSI-BLAST profiles as feature vectors and obtain results comparable to those obtained by using other Methods.
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ICIC (3) - Fuzzy k -nearest Neighbor Method for protein secondary structure prediction and its parallel implementation
Computational Intelligence and Bioinformatics, 2006Co-Authors: Seung-yeon Kim, Jaehyun Sim, Julian LeeAbstract:Fuzzy k-nearest Neighbor Method is a generalization of nearest Neighbor Method, the simplest algorithm for pattern classification. One of the important areas for application of the pattern classification is the protein secondary structure prediction, an important topic in the field of bioinformatics. In this work, we develop a parallel algorithm for protein secondary structure prediction, based on the fuzzy k-nearest Neighbor Method, that uses evolutionary profile obtained from PSI-BLAST (Position Specific Iterative Basic Local Sequence Alignment Tool) as the feature vectors.
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Prediction of protein solvent accessibility using fuzzy k -nearest Neighbor Method
Bioinformatics, 2005Co-Authors: Jaehyun Sim, Seung-yeon Kim, Julian LeeAbstract:Motivation: The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest Neighbor Method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data. Results: We applied the fuzzy k-nearest Neighbor Method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our Method achieved 64.1% accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our Method also showed slightly better accuracies than other Methods by about 2--5% on the RS126 dataset and a benchmarking dataset with 229 proteins. Availability: Program and datasets are available at http://biocom1.ssu.ac.kr/FKNNacc/ Contact: [email protected]
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Prediction of the secondary structures of proteins by using PREDICT, a nearest Neighbor Method on pattern space
Journal of the Korean Physical Society, 2004Co-Authors: Keehyoung Joo, Seung-yeon Kim, Julian Lee, Jooyoung Lee, Ilsoo Kim, Sung Jong LeeAbstract:We introduce a novel Method for predicting the secondary structure of proteins, PREDICT (PRofile Enumeration DICTionary), in which the nearest-Neighbor Method is applied to a pattern space. For a given protein sequence, PSI-BLAST is used to generate a profile that defines patterns for amino acid residues and their local sequence environments. By applying the PSI-BLAST to protein sequences with known secondary structures, we construct pattern databases. The secondary structure of a query residue of a protein with unknown structure can be determined by comparing the query pattern with those in the pattern databases and selecting the patterns close to the query pattern. We have tested the PREDICT on the CB513 set (a set of 513 non-homologous proteins) in three different ways. The first test was based on a pattern database derived from 7777 proteins in the Protein Data Bank (PDB), including those homologous to proteins in the CB513 set and gave an average Q3 score of 78.8 % per chain. In the second test, in order to carry out a more stringent benchmark test on the CB513 set, we removed from the 7777 proteins all proteins homologous to the CB513 set, leaving 4330 proteins. Pattern databases were constructed based on these proteins, and the average Q3 score was 74.6 %. In the third test, we selected one query protein among the CB513 set and built pattern databases by using the remaining 512 proteins. This procedure was repeated for each of the 513 proteins, and the average Q3 score was 73.1 %. Finally, we participated in the CASP5 (group ID: 531) where we employed the first-layer database based on the 7777 proteins and the second-layer database based on the CB513 set. The PREDICT gave quite promising results with an average Q3 (Sov) score of 78.1 (77.4) % on 55 CASP5 targets.
Alexander Tropsha - One of the best experts on this subject based on the ideXlab platform.
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quantitative structure activity relationship analysis of pyridinone hiv 1 reverse transcriptase inhibitors using the k nearest Neighbor Method and qsar based database mining
Journal of Computer-aided Molecular Design, 2005Co-Authors: Jose L Medinafranco, Alexander Golbraikh, Scott Oloff, Rafael Castillo, Alexander TropshaAbstract:We have developed quantitative structure–activity relationship (QSAR) models for 44 non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs) of the pyridinone derivative type. The k nearest Neighbor (kNN) variable selection approach was used. This Method utilizes multiple descriptors such as molecular connectivity indices, which are derived from two-dimensional molecular topology. The modeling process entailed extensive validation including the randomization of the target property (Y-randomization) test and the division of the dataset into multiple training and test sets to establish the external predictive power of the training set models. QSAR models with high internal and external accuracy were generated, with leave-one-out cross-validated R2 (q2) values ranging between 0.5 and 0.8 for the training sets and R2 values exceeding 0.6 for the test sets. The best models with the highest internal and external predictive power were used to search the National Cancer Institute database. Derivatives of the pyrazolo[3,4-d]pyrimidine and phenothiazine type were identified as promising novel NNRTIs leads. Several candidates were docked into the binding pocket of nevirapine with the AutoDock (version 3.0) software. Docking results suggested that these types of compounds could be binding in the NNRTI binding site in a similar mode to a known non-nucleoside inhibitor nevirapine.
Jose L Medinafranco - One of the best experts on this subject based on the ideXlab platform.
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quantitative structure activity relationship analysis of pyridinone hiv 1 reverse transcriptase inhibitors using the k nearest Neighbor Method and qsar based database mining
Journal of Computer-aided Molecular Design, 2005Co-Authors: Jose L Medinafranco, Alexander Golbraikh, Scott Oloff, Rafael Castillo, Alexander TropshaAbstract:We have developed quantitative structure–activity relationship (QSAR) models for 44 non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs) of the pyridinone derivative type. The k nearest Neighbor (kNN) variable selection approach was used. This Method utilizes multiple descriptors such as molecular connectivity indices, which are derived from two-dimensional molecular topology. The modeling process entailed extensive validation including the randomization of the target property (Y-randomization) test and the division of the dataset into multiple training and test sets to establish the external predictive power of the training set models. QSAR models with high internal and external accuracy were generated, with leave-one-out cross-validated R2 (q2) values ranging between 0.5 and 0.8 for the training sets and R2 values exceeding 0.6 for the test sets. The best models with the highest internal and external predictive power were used to search the National Cancer Institute database. Derivatives of the pyrazolo[3,4-d]pyrimidine and phenothiazine type were identified as promising novel NNRTIs leads. Several candidates were docked into the binding pocket of nevirapine with the AutoDock (version 3.0) software. Docking results suggested that these types of compounds could be binding in the NNRTI binding site in a similar mode to a known non-nucleoside inhibitor nevirapine.
Seung-yeon Kim - One of the best experts on this subject based on the ideXlab platform.
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ICIC (3) - Fuzzy k -nearest Neighbor Method for protein secondary structure prediction and its parallel implementation
Computational Intelligence and Bioinformatics, 2006Co-Authors: Seung-yeon Kim, Jaehyun Sim, Julian LeeAbstract:Fuzzy k-nearest Neighbor Method is a generalization of nearest Neighbor Method, the simplest algorithm for pattern classification. One of the important areas for application of the pattern classification is the protein secondary structure prediction, an important topic in the field of bioinformatics. In this work, we develop a parallel algorithm for protein secondary structure prediction, based on the fuzzy k-nearest Neighbor Method, that uses evolutionary profile obtained from PSI-BLAST (Position Specific Iterative Basic Local Sequence Alignment Tool) as the feature vectors.
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Prediction of protein solvent accessibility using fuzzy k -nearest Neighbor Method
Bioinformatics, 2005Co-Authors: Jaehyun Sim, Seung-yeon Kim, Julian LeeAbstract:Motivation: The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest Neighbor Method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data. Results: We applied the fuzzy k-nearest Neighbor Method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our Method achieved 64.1% accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our Method also showed slightly better accuracies than other Methods by about 2--5% on the RS126 dataset and a benchmarking dataset with 229 proteins. Availability: Program and datasets are available at http://biocom1.ssu.ac.kr/FKNNacc/ Contact: [email protected]
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Prediction of the secondary structures of proteins by using PREDICT, a nearest Neighbor Method on pattern space
Journal of the Korean Physical Society, 2004Co-Authors: Keehyoung Joo, Seung-yeon Kim, Julian Lee, Jooyoung Lee, Ilsoo Kim, Sung Jong LeeAbstract:We introduce a novel Method for predicting the secondary structure of proteins, PREDICT (PRofile Enumeration DICTionary), in which the nearest-Neighbor Method is applied to a pattern space. For a given protein sequence, PSI-BLAST is used to generate a profile that defines patterns for amino acid residues and their local sequence environments. By applying the PSI-BLAST to protein sequences with known secondary structures, we construct pattern databases. The secondary structure of a query residue of a protein with unknown structure can be determined by comparing the query pattern with those in the pattern databases and selecting the patterns close to the query pattern. We have tested the PREDICT on the CB513 set (a set of 513 non-homologous proteins) in three different ways. The first test was based on a pattern database derived from 7777 proteins in the Protein Data Bank (PDB), including those homologous to proteins in the CB513 set and gave an average Q3 score of 78.8 % per chain. In the second test, in order to carry out a more stringent benchmark test on the CB513 set, we removed from the 7777 proteins all proteins homologous to the CB513 set, leaving 4330 proteins. Pattern databases were constructed based on these proteins, and the average Q3 score was 74.6 %. In the third test, we selected one query protein among the CB513 set and built pattern databases by using the remaining 512 proteins. This procedure was repeated for each of the 513 proteins, and the average Q3 score was 73.1 %. Finally, we participated in the CASP5 (group ID: 531) where we employed the first-layer database based on the 7777 proteins and the second-layer database based on the CB513 set. The PREDICT gave quite promising results with an average Q3 (Sov) score of 78.1 (77.4) % on 55 CASP5 targets.
Rafael Castillo - One of the best experts on this subject based on the ideXlab platform.
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quantitative structure activity relationship analysis of pyridinone hiv 1 reverse transcriptase inhibitors using the k nearest Neighbor Method and qsar based database mining
Journal of Computer-aided Molecular Design, 2005Co-Authors: Jose L Medinafranco, Alexander Golbraikh, Scott Oloff, Rafael Castillo, Alexander TropshaAbstract:We have developed quantitative structure–activity relationship (QSAR) models for 44 non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs) of the pyridinone derivative type. The k nearest Neighbor (kNN) variable selection approach was used. This Method utilizes multiple descriptors such as molecular connectivity indices, which are derived from two-dimensional molecular topology. The modeling process entailed extensive validation including the randomization of the target property (Y-randomization) test and the division of the dataset into multiple training and test sets to establish the external predictive power of the training set models. QSAR models with high internal and external accuracy were generated, with leave-one-out cross-validated R2 (q2) values ranging between 0.5 and 0.8 for the training sets and R2 values exceeding 0.6 for the test sets. The best models with the highest internal and external predictive power were used to search the National Cancer Institute database. Derivatives of the pyrazolo[3,4-d]pyrimidine and phenothiazine type were identified as promising novel NNRTIs leads. Several candidates were docked into the binding pocket of nevirapine with the AutoDock (version 3.0) software. Docking results suggested that these types of compounds could be binding in the NNRTI binding site in a similar mode to a known non-nucleoside inhibitor nevirapine.
