Network Protein Sequence Analysis

Figure 12.6. Secondary structure prediction of duck lysozyme at NPS . The predicted secondary structures of duck lysozyme at Network Protein Sequence Analysis (NPS ) with GOR IV method are depicted in different representations.

Figure 12,7. Secondary structure consensus prediction at NPS . Network Protein Sequence Analysis (NPS ) offers numerous methods for secondary structure prediction of proteins. The secondary structure consensus prediction (Sec.Cons.) of duck lysozyme is derived from simultaneous execution of predictions with more than one methods.

The statistical methods for predicting secondary structures of proteins from amino acid sequences are widely practiced among investigators in biochemistry and can be accessed at Network Protein Sequence Analysis (NPS ) via http //npsa-pbil.ibcp.fr... 

The method of Rost and Sander (Rost and Sander, 1993), which combines neural networks with multiple sequence alignments known as PHD, is available from the PredictProtein (Rost, 1996) server of Columbia University (http //cubic.bioc. columbia.edu/predictprotein/). This Web site offers the comprehensive protein sequence analysis and structure prediction (Figure 12.8). For the secondary structure prediction, choose Submit a protein sequence for prediction to open the submission form. Enter e-mail address, paste the sequence, choose options, and then click the... 

Table 11.1 Neural network applications for protein sequence analysis.

Neural network applications for protein sequence analysis are summarized in Table 11.1. Like the DNA coding region recognition problem, signal peptide prediction (11.2) involves both search for content and search for signal tasks. An effective means for protein sequence analysis is reverse database searching to detect functional motifs or sites (11.3) and identify protein families (11.4). Most of the functional motifs are also... 

How have neural networks been used in genome informatics applications In Part II, we have summarized them based on the types of applications for DNA sequence analysis, protein structure prediction and protein sequence analysis. Indeed, the development of neural network applications over the years has resulted in many successful and widely used systems. Current state-of-the-art systems include those for gene recognition, secondary structure prediction, protein classification, signal peptide recognition, and peptide design, to name just a few. 

Detailed information on DNA sequence analysis software (151), commercial software on IBM personal computers (152) and online services and networks (153) can be found in a most relevant text entitled Nucleic Acid and Protein Sequence Analysis A Practical Approach (1987, IRL Press), and edited by M.J. Bishop and C.J. Rawlings. The chapter on networks is essential reading for inter-... 

An interesting example of using Kohonen maps for the analysis of protein sequences is given in a journal article by Hanke (Hanke Reich, 1996). In this application, a trained Kohonen map network was used to identify protein families, aligned sequences or segments of similar secondary structure, in a highly visual manner. 

Hanke and Reich used Kohonen nets as a visualization tool for the analysis of protein sequence similarity (ISO). The proeedure eonverts sequenee (domains, aligned sequences, and segments of seeondary strueture) into a ehar-acteristie signal matrix. This eonversion depends on the property or replaee-ment seore vector selected by the user. The trained Kohonen network is functionally equivalent to an unsupervised nonlinear eluster analyzer. Protein families, or aligned sequences, or segments of similar seeondary strueture aggregate as clusters and their proximity may be inspeeted. 

Second, Reinhardt and Hubbard (1998) performed a prediction using neural networks. From some statistical consideration, they selected three locations for prokaryotes (cytoplasmic, extracellular, and periplasmic) and four locations for eukaryotes, excluding plants (cytoplasmic, extracellular, mitochondrial, and nuclear). They did not include the membrane proteins because they can be distinguished rather reliably using existing methods. One potential problem of their analysis is that they only excluded sequence pairs with more than 90% identity. Nevertheless, the distinctions between pairs of groups were rather clear. The high accuracy between nuclear and cytoplasmic proteins was especially impressive. 

---