Three-stage prediction of protein β-sheets by neural networks, alignments and graph algorithms
Motivation: Protein β-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein β-sheets, however, remains challenging because protein β-sheets require formation of hydrogen bonds between linearly distant residues. Previo...
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| Vydáno v: | Bioinformatics Ročník 21; číslo suppl-1; s. i75 - i84 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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England
Oxford University Press
01.06.2005
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| ISSN: | 1367-4803, 1460-2059 |
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| Abstract | Motivation: Protein β-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein β-sheets, however, remains challenging because protein β-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting β-sheet topological features, such as β-strand alignments, in general have not exploited the global covariation and constraints characteristic of β-sheet architectures. Results: We propose a modular approach to the problem of predicting/assembling protein β-sheets in a chain by integrating both local and global constraints in three steps. The first step uses recursive neural networks to predict pairing probabilities for all pairs of interstrand β-residues from profile, secondary structure and solvent accessibility information. The second step applies dynamic programming techniques to these probabilities to derive binding pseudoenergies and optimal alignments between all pairs of β-strands. Finally, the third step uses graph matching algorithms to predict the β-sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global β-strand pairing constraints. The approach is evaluated using cross-validation methods on a large non-homologous dataset and yields significant improvements over previous methods. Availability: http://www.igb.uci.edu/servers/psss.html Contact: pfbaldi@ics.uci.edu |
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| AbstractList | Protein beta-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein beta-sheets, however, remains challenging because protein beta-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting beta-sheet topological features, such as beta-strand alignments, in general have not exploited the global covariation and constraints characteristic of beta-sheet architectures.MOTIVATIONProtein beta-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein beta-sheets, however, remains challenging because protein beta-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting beta-sheet topological features, such as beta-strand alignments, in general have not exploited the global covariation and constraints characteristic of beta-sheet architectures.We propose a modular approach to the problem of predicting/assembling protein beta-sheets in a chain by integrating both local and global constraints in three steps. The first step uses recursive neural networks to predict pairing probabilities for all pairs of interstrand beta-residues from profile, secondary structure and solvent accessibility information. The second step applies dynamic programming techniques to these probabilities to derive binding pseudoenergies and optimal alignments between all pairs of beta-strands. Finally, the third step uses graph matching algorithms to predict the beta-sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global beta-strand pairing constraints. The approach is evaluated using cross-validation methods on a large non-homologous dataset and yields significant improvements over previous methods.RESULTSWe propose a modular approach to the problem of predicting/assembling protein beta-sheets in a chain by integrating both local and global constraints in three steps. The first step uses recursive neural networks to predict pairing probabilities for all pairs of interstrand beta-residues from profile, secondary structure and solvent accessibility information. The second step applies dynamic programming techniques to these probabilities to derive binding pseudoenergies and optimal alignments between all pairs of beta-strands. Finally, the third step uses graph matching algorithms to predict the beta-sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global beta-strand pairing constraints. The approach is evaluated using cross-validation methods on a large non-homologous dataset and yields significant improvements over previous methods.http://www.igb.uci.edu/servers/psss.html.AVAILABILITYhttp://www.igb.uci.edu/servers/psss.html. MOTIVATION: Protein beta -sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein beta -sheets, however, remains challenging because protein beta -sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting beta -sheet topological features, such as beta -strand alignments, in general have not exploited the global covariation and constraints characteristic of beta -sheet architectures. RESULTS: We propose a modular approach to the problem of predicting/assembling protein beta -sheets in a chain by integrating both local and global constraints in three steps. The first step uses recursive neural networks to predict pairing probabilities for all pairs of interstrand beta -residues from profile, secondary structure and solvent accessibility information. The second step applies dynamic programming techniques to these probabilities to derive binding pseudoenergies and optimal alignments between all pairs of beta -strands. Finally, the third step uses graph matching algorithms to predict the beta -sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global beta -strand pairing constraints. The approach is evaluated using cross-validation methods on a large non-homologous dataset and yields significant improvements over previous methods. AVAILABILITY: http://www.igb.uci.edu/servers/psss.html Protein beta-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein beta-sheets, however, remains challenging because protein beta-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting beta-sheet topological features, such as beta-strand alignments, in general have not exploited the global covariation and constraints characteristic of beta-sheet architectures. We propose a modular approach to the problem of predicting/assembling protein beta-sheets in a chain by integrating both local and global constraints in three steps. The first step uses recursive neural networks to predict pairing probabilities for all pairs of interstrand beta-residues from profile, secondary structure and solvent accessibility information. The second step applies dynamic programming techniques to these probabilities to derive binding pseudoenergies and optimal alignments between all pairs of beta-strands. Finally, the third step uses graph matching algorithms to predict the beta-sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global beta-strand pairing constraints. The approach is evaluated using cross-validation methods on a large non-homologous dataset and yields significant improvements over previous methods. http://www.igb.uci.edu/servers/psss.html. Motivation: Protein β-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein β-sheets, however, remains challenging because protein β-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting β-sheet topological features, such as β-strand alignments, in general have not exploited the global covariation and constraints characteristic of β-sheet architectures. Results: We propose a modular approach to the problem of predicting/assembling protein β-sheets in a chain by integrating both local and global constraints in three steps. The first step uses recursive neural networks to predict pairing probabilities for all pairs of interstrand β-residues from profile, secondary structure and solvent accessibility information. The second step applies dynamic programming techniques to these probabilities to derive binding pseudoenergies and optimal alignments between all pairs of β-strands. Finally, the third step uses graph matching algorithms to predict the β-sheet architecture of the protein by optimizing the global pseudoenergy while enforcing strong global β-strand pairing constraints. The approach is evaluated using cross-validation methods on a large non-homologous dataset and yields significant improvements over previous methods. Availability: http://www.igb.uci.edu/servers/psss.html Contact: pfbaldi@ics.uci.edu |
| Author | Baldi, Pierre Cheng, Jianlin |
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| Snippet | Motivation: Protein β-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein... Protein beta-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein... MOTIVATION: Protein beta -sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of... |
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| SubjectTerms | Algorithms Computational Biology - methods Humans Hydrogen Bonding Models, Chemical Models, Molecular Nerve Net Protein Conformation Protein Structure, Secondary Proteins - chemistry ROC Curve X-Ray Diffraction |
| Title | Three-stage prediction of protein β-sheets by neural networks, alignments and graph algorithms |
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