Secondary structural prediction, tertiary

Figure 11,4. ExPASy Proteomic tools. ExPASy server provides various tools for proteomic analysis which can be accessed from ExPASy Proteomic tools. These tools (locals or hyperlinks) include Protein identification and characterization, Translation from DNA sequences to protein sequences. Similarity searches, Pattern and profile searches, Post-translational modification prediction, Primary structure analysis, Secondary structure prediction, Tertiary structure inference, Transmembrane region detection, and Sequence alignment.

Secondary structural predictions about NPAs, and direct biophysical measurements, have demonstrated that the NPAs are rich in a-helix, with no p-structure either predicted from secondary structure prediction algorithms, or detected by circular dichroism (Kennedy et al, 1995b). In this they are the antithesis of the similarly sized cLBPs and lipocalins. The predictions are that each individual NPA unit protein will fold into four main regions of helix, and it has been speculated that the tertiary structure is as a four-bundle helix protein, similar to other invertebrate carrier proteins (Sheriff et al., 1987). 

Currently, there exists an enormous and growing deficit between the number of polypeptides whose amino acid sequence has been determined and the numbers of polypeptides whose three-dimensional structure has been resolved. Given the complexities of resolving three-dimensional structure experimentally, it is not surprising that scientists are continually attempting to develop methods by which they could predict higher order structure from amino acid sequence data. Although modestly successful secondary structure predictive approaches have been developed, no method by which tertiary structure may be predicted from primary data has thus far been developed. 

Fig. 23. Proposed active site arrangement of sn-glycerol-3-phosphate dehydrogenase (below), based on secondary structure predicted from the known primary structure, and on comparison with the known tertiary structure of glyceraldehyde-3-phosphate dehydrogenase (above). From the work of Rossmann and colleagues (94).

Table 10.1 summarizes neural network applications for protein structure prediction. Protein secondary structure prediction is often used as the first step toward understanding and predicting tertiary structure because secondary structure elements constitute the building blocks of the folding units. An estimated 90% or so of the residues in most proteins are involved in three classes of secondary structures, the a-helices, p-strands or reverse turns. Related to the secondary structure prediction are also the prediction of solvent accessibility, transmembrane helices, and secondary structure content (10.2). Neural networks have also been applied to protein tertiary structure prediction, such as the prediction of the backbones or side-chain packing, and to structural class prediction (10.3). 

It has been observed that the use of protein tertiary structural class improved the accuracy for a 2-state secondary structure prediction (Kneller et al., 1990). A modular network architecture was proposed using separate networks (i.e., a- or P-type network) for classification of different secondary structures (Sasagawa Tajima, 1993). Recently, Chandonia Karplus (1995) trained a pair of neural networks to predict the protein secondary structure and the structural class respectively. Using predicted class information, the secondary structure prediction network realized a small increase in accuracy. 

The earliest neural network attempt for protein tertiary structure prediction was done by Bohr et al. (1990). They predicted the binary distance constraints for the C-a atoms in protein backbone using a standard three-layer back-propagation network and BIN20 sequence encoding method for 61-amino acid windows. The output layer had 33 units, three for the 3-state secondary structure prediction, and the remaining to measure the distance constraints between the central amino acid and the 30 preceding residues. 

The basic information of protein tertiary structural class can help improve the accuracy of secondary structure prediction (Kneller et al., 1990). Chandonia and Karplus (1995) showed that information obtained from a secondary structure prediction algorithm can be used to improve the accuracy for structural class prediction. The input layer had 26 units coded for the amino acid composition of the protein (20 units), the sequence length (1 unit), and characteristics of the protein (5 units) predicted by a separate secondary structure neural network. The secondary structure characteristics include the predicted percent helix and sheet, the percentage of strong helix and sheet predictions, and the predicted number of alterations between helix and sheet. The output layer had four units, one for each of the tertiary super classes (all-a, all-p, a/p, and other). The inclusion of the single-sequence secondary structure predictions improved the class prediction for non-homologous proteins significantly by more than 11%, from a predictive accuracy of 62.3% to 73.9%. 

Yi TM, Lander ES. Protein secondary structure prediction using nearest neighbor methods. J. Mol. Biol. 1993 232 1117-1129. de Dios AC, Pearson JG, Oldfield E. Secondary and tertiary structural effects on protein NMR chemical shifts an ab initio approach. Science 1993 260 1491-1496. 

This section gives an overview of secondary structure and tertiary structure prediction methods for proteins. Methods for (secondary) structure prediction of other biomolecules, such as RNA are not discussed here. 

Method for Prediction of Tertiary Structure from Predicted Secondary Structure and Tertiary Restraints... 

Hu, H.-J., Pan, Y, Harrison, R., Tai, P.C. Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier. IEEE Trans. Nanobioscience 2004,3,265-71. 

Knowledge of secondary structure is necessary for prediction of tertiary structure... 

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