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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of molecular evolution 36 (1993), S. 586-595 
    ISSN: 1432-1432
    Keywords: Evolution strategy ; Feature extraction ; Filter induction ; Neural network ; Prediction ; Signal peptidase
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Summary Four different artificial neural network architectures have been tested for their suitability to extract and predict sequence features. For optimization of the network weights an evolutionary computing method has been applied. The networks have feedforward architecture and provide adaptive neural filter systems for pattern recognition in primary structures and sequence classification. The recognition and prediction of signal peptidase cleavage sites ofE. coli periplasmic protein precursors serves as an example for filter development. The primary structures are represented by seven physicochemical residue properties. This amino acid description provides the feature space for network optimization. The properties hydrophobicity, hydrophilicity, side-chain volume, and polarity allowed an accurate classification of the data. A three-layer network architecture reached a learning success of 100%; the highest prediction accuracy in an independent test set of sequences was 97%. This network architecture appears to be most suited for the analysis ofE. coli signal peptidase cleavage sites. Further suggestions about the design and future applications of artificial neural networks for protein sequence analysis are made.
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 73 (1995), S. 245-254 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract The applicability of artificial neural filter systems as fitness functions for sequence-oriented peptide design was evaluated. Two example applications were selected: classification of dipeptides according to their hydrophobicity and classification of proteolytic cleavage-sites of protein precursor sequences according to their mean hydrophobicities and mean side-chain volumes. The cleavage-sites covered 12 residues. In the dipeptide experiments the objective was to separate a selected set of molecules from all other possible dipeptide sequences. Perceptrons, feedforward networks with one hidden layer, and a hybrid network were applied. The filters were trained by a (1,λ) evolution strategy. Two types of network units employing either a sigmoidal or a unimodal transfer function were used in the feedforward filters, and their influence on classification was investigated. The two-layer hybrid network employed gaussian activation functions. To analyze classification of the different filter systems, their output was plotted in the two-dimensional sequence space. The diagrams were interpreted as fitness landscapes qualifying the markedness of a characteristic peptide feature which can be used as a guide through sequence space for rational peptide design. It is demonstrated that the applicability of neural filter systems as a heuristic method for sequence optimization depends on both the appropriate network architecture and selection of representative sequence data. The networks with unimodal activation functions and the hybrid networks both led to a number of local optima. However, the hybrid networks produced the best prediction results. In contrast, the filters with sigmoidal activation produced good reclassification results leading to fitness landscapes lacking unreasonable local optima. Similar results were obtained for classification of both dipeptides and cleavage-site sequences.
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 73 (1995), S. 245-254 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract. The applicability of artificial neural filter systems as fitness functions for sequence-oriented peptide design was evaluated. Two example applications were selected: classification of dipeptides according to their hydrophobicity and classification of proteolytic cleavagesites of protein precursor sequences according to their mean hydrophobicities and mean side-chain volumes. The cleavage-sites covered 12 residues. In the dipeptide experiments the objective was to separate a selected set of molecules from all other possible dipeptide sequences. Perceptrons, feedforward networks with one hidden layer, and a hybrid network were applied. The filters were trained by a (1, λ) evolution strategy. Two types of network units employing either a sigmoidal or a unimodal transfer function were used in the feedforward filters, and their influence on classification was investigated. The two-layer hybrid network employed gaussian activation functions. To analyze classification of the different filter systems, their output was plotted in the two-dimensional sequence space. The diagrams were interpreted as fitness landscapes qualifying the markedness of a characteristic peptide feature which can be used as a guide through sequence space for rational peptide design. It is demonstrated that the applicability of neural filter systems as a heuristic method for sequence optimization depends on both the appropriate network architecture and selection of representative sequence data. The networks with unimodal activation functions and the hybrid networks both led to a number of local optima. However, the hybrid networks produced the best prediction results. In contrast, the filters with sigmoidal activation produced good reclassification results leading to fitness landscapes lacking unreasonable local optima. Similar results were obtained for classification of both dipeptides and cleavage-site sequences.
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 74 (1996), S. 203-207 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract.  Optimization by a simple evolution strategy based on a mutation and selection scheme without recombination was tested for its efficiency in multimodal search space. A modified Rastrigin function served as an objective function providing fitness landscapes with many local optima. It turned out that the evolutionary algorithm including adaptive stepsize control is well-suited for optimization. The process is able to efficiently surmount local energy barriers and converge to the global optimum. The relation between the optimization time available and the optimal number of offspring was investigated and a simple rule proposed. Several numbers of offspring are nearly equally suited in a smooth search space, whereas in rough fitness landscapes an optimum is observed. In either case both very large and very small numbers of offspring turned out to be unfavourable for optimization.
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 74 (1996), S. 203-207 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract Optimization by a simple evolution strategy based on a mutation and selection scheme without recombination was tested for its efficiency in multimodal search space. A modified Rastrigin function served as an objective function providing fitness landscapes with many local optima. It turned out that the evolutionary algorithm including adaptive stepsize control is wellsuited for optimization. The process is able to efficiently surmount local energy barriers and converge to the global optimum. The relation between the optimization time available and the optimal number of offspring was investigated and a simple rule proposed. Several numbers of offspring are nearly equally suited in a smooth search space, whereas in rough fitness landscapes an optimum is observed. In either case both very large and very small numbers of offspring turned out to be unfavourable for optimization.
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Journal of computer aided molecular design 14 (2000), S. 487-494 
    ISSN: 1573-4951
    Keywords: de novo design ; diversity ; docking ; genetic algorithm ; peptide mimetic ; pharmacophore ; similarity ; thrombin ; virtual screening
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract An evolutionary algorithm was developed for fragment-based de novo design of molecules (TOPAS, TOPology-Assigning System). This stochastic method aims at generating a novel molecular structure mimicking a template structure. A set of ∼25,000 fragment structures serves as the building block supply, which were obtained by a straightforward fragmentation procedure applied to 36,000 known drugs. Eleven reaction schemes were implemented for both fragmentation and building block assembly. This combination of drug-derived building blocks and a restricted set of reaction schemes proved to be a key for the automatic development of novel, synthetically tractable structures. In a cyclic optimization process, molecular architectures were generated from a parent structure by virtual synthesis, and the best structure of a generation was selected as the parent for the subsequent TOPAS cycle. Similarity measures were used to define `fitness', based on 2D-structural similarity or topological pharmacophore distance between the template molecule and the variants. The concept of varying library `diversity' during a design process was consequently implemented by using adaptive variant distributions. The efficiency of the design algorithm was demonstrated for the de novo construction of potential thrombin inhibitors mimicking peptide and non-peptide template structures.
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  • 7
    Electronic Resource
    Electronic Resource
    New York : Wiley-Blackwell
    Biopolymers 38 (1996), S. 13-29 
    ISSN: 0006-3525
    Keywords: Chemistry ; Polymer and Materials Science
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: An artificial neural network has been developed for the recognition and prediction of transmembrane regions in the amino acid sequences of human integral membrane proteins. It provides an additional prediction method besides the common hydrophobicity analysis by statistical means. Membrane/nonmembrane transition regions are predicted with 92% accuracy in both training and independent test data. The method used for the development of the neural filter is the algorithm of structure evolution. It subjects both the architecture and parameters of the system to a systematical optimization process and carries out local search in the respective structure and parameter spaces. The training technique of incomplete induction as part of the structure evolution provides for a comparatively general solution of the problem that is described by input-output relations only. Seven physicochemical side-chain properties were used to encode the amino acid sequences. It was found that geometric parameters like side-chain volume, bulkiness, or surface area are of minor importance. The properties polarity, refractivity, and hydrophobicity, however, turned out to support feature extraction. It is concluded that membrane transition regions in proteins are encoded in sequences as a characteristic feature based on the respective side-chain properties. The method of structure evolution is described in detail for this particular application and suggestions for further development of amino acid sequence filters are made. © 1996 John Wiley & Sons, Inc.
    Additional Material: 9 Ill.
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  • 8
    Electronic Resource
    Electronic Resource
    Weinheim : Wiley-Blackwell
    Chemie in unserer Zeit 30 (1996), S. 172-181 
    ISSN: 0009-2851
    Keywords: Chemistry ; Chemistry
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Additional Material: 8 Ill.
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    Weinheim : Wiley-Blackwell
    Zeitschrift für die chemische Industrie 105 (1993), S. 1192-1194 
    ISSN: 0044-8249
    Keywords: Chemistry ; General Chemistry
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Additional Material: 1 Ill.
    Type of Medium: Electronic Resource
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  • 10
    ISSN: 0044-8249
    Keywords: Chemistry ; General Chemistry
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
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