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  • 1
    Electronic Resource
    Electronic Resource
    Neural networks to study invariant features of protein folding (1999)
    Springer
    Theoretical chemistry accounts 101 (1999), S. 21-26 
    Details
    ISSN: 1432-2234
    Keywords: Key words: Initiation sites of protein folding ; Neural networks ; Self-stabilising helices ; Homologous proteins ; Positional invariance of initiation sites
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract. Protein secondary structures result both from short-range and long-range interactions. Here neural networks are used to implement a procedure to detect regions of the protein backbone where local interactions have an overwhelming effect in determining the formation of stretches in α-helical conformation. Within the framework of a modular view of protein folding we have argued that these structures correspond to the initiation sites of folding. The hypothesis to be tested in this paper is that sequence identity beside ensuring similarity of the three-dimensional conformation also entails similar folding mechanisms. In particular, we compare the location and sequence variability of the initiation sites extracted from a set of proteins homologous to horse heart cytochrome c. We present evidence that the initiation sites conserve their position in the aligned sequences and exhibit a more reduced variability in the residue composition than the rest of the protein.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s002140050400
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    The prediction of protein secondary structure with a Cascade Correlation Learning Architecture of neural networks (1997)
    Springer
    Neural computing & applications 6 (1997), S. 57-62 
    Details
    ISSN: 1433-3058
    Keywords: Cascade correlation learning algorithm ; Neural networks ; Pattern recognition ; Predictive methods ; Protein secondary structure prediction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A Cascade Correlation Learning Architecture (CCLA) of neural networks is tested on the task of predicting the secondary structure of proteins. The results are compared with those obtained with Neural Networks (NN) trained with the back-propagation algorithm (BPNN) and generated with genetic algorithms. CCLA proceeds towards the global minimum of the error function more efficiently than BPNN. However, only a slight improvement in the average efficiency value is noticeable (61.82% as compared with 61.61% obtained with BPNN). The values of the three correlation coefficients for the discriminated secondary structures are also rather similar (Ct8,C α ,C β and Ccoil are 0.36, 0.29 and 0.36 with CCLA, and 0.36, 0.31 and 0.35 with BPNN). This indicates that the efficiency of the prediction does not depend upon the training algorithm, and confirms our previous observation that when single sequences are used as input code to the network system, different NN architectures can perform similarly.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01670152
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    Paper (German National Licenses)
    Fulltext
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