Proteins secondary structure, accuracy

Rost, B. Sander, C. (1993). Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232,584-99. 

In protein secondary structure prediction, where a three-category (a, P, and coil or loop) prediction is made, the accuracy can be measured by a 3 x 3 accuracy table, as in Rost and Sander (1993). 

Table 8.1 A 3x3 accuracy matrix for evaluating protein secondary structure prediction.

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. 

Figure 3. Multidimensional scaling analysis of the dissimilarities between accuracies of different protein secondary structure prediction methods. The method codes can be found in Table I.

Paiau, J., Argos, P., Puigdomenech, P. (1982) Protein Secondary Structure - Studies on the Limits of Prediction Accuracy, Int. J. Pept. Protein Res. 19 394-401. 

Rost, B. 2003. Rising accuracy of protein secondary structure prediction. In Protein structure determination, analysis, and modeling for drug discovery, ed. D. Chasman, 207-49. New York Dekker. 

Rost B and C Sander 1993 Prediction of Protein Secondary Structure at Better than 70% Accuracy Journal of Molecular Biology 232 584-599. 

Montgomerie, S., Sundararaj, S., Gallin, W.J., Wishart, D.S. Improving the accuracy of protein secondary structure prediction using structural aUgnment. BMC Bioinformatics 2006, 7,301. 

Frishman, D. and Argos, P. (1997) Seventy-five percent accuracy in protein secondary structure prediction. Proteins 27, 329-335. 

Mehta, P. K., Heringa, J., and Argos, P. (1995). A simple and fast approach to prediction of protein secondary structure from multiply aligned sequences with accuracy above 70%. Protein Sci. 4, 2517-2525. 

Seventy-Five Percent Accuracy in Protein Secondary Structure Predicition. 

With regard to theoretical methods, several approaches based on statistical, hydro-phobic and pattern recognition methods have been proposed (Sawyer and Holt, 1993). Cumulative or joint prediction methods, with supplementary information from spectroscopic methods and the use of templates and sequence information from related proteins, were shown to improve the confidence of prediction, as assessed by comparison to X-ray crystallographic structures. Despite the great interest and advances in research in these areas, the accuracy of these secondary structure predictions (i.e. theoretical methods) still remains at only about 60%. Even when the structure of structurally related or homologous proteins is known, the accuracy of prediction is only 70.9% (Mehta et aL, 1995). Furthermore, these methods cannot easily be applied to monitor changes in protein secondary structure induced by processing. 

Gamier J, D Osguthorpe and B Robson 1978. Analysis of the Accuracy and ImpUcatiotrs of Simple Mel for Predicting the Secondary Structure of Globular Proteins. Journal of Mokadar Biology 120 97-i... 

Several programs are now available that use multiple alignment of homologous proteins for prediction of secondary structure. One such program, called PHD, which was developed by Chris Sander and coworkers, EMBL, Heidelberg, has reached a mean accuracy of prediction of 72% for new structures. 

Methods for the prediction of the secondary structure of a set of homologous proteins can reach an accuracy of about 75%, most of the errors occur at the ends of a helices or p strands. The central regions of these secondary structure elements are often correctly predicted but the methods do not always correctly distinguish between a helices and p strands. 

Gamier, J., Osguthorpe, D.J., Robson, B. Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. 

This branch of bioinformatics is concerned with computational approaches to predict and analyse the spatial structure of proteins and nucleic acids. Whereas in many cases the primary sequence uniquely specifies the 3D structure, the specific rules are not well understood, and the protein folding problem remains largely unsolved. Some aspects of protein structure can already be predicted from amino acid content. Secondary structure can be deduced from the primary sequence with statistics or neural networks. When using a multiple sequence alignment, secondary structure can be predicted with an accuracy above 70%. 

A number of tools have been around for several years that aim to predict likely areas of secondary structure from a primary protein sequence or based on an RNA sequence [50]. These models have varying degrees of accuracy of prediction, with some of the best reaching up to 70% accuracy [57]. 

Both methods are also limited in accuracy of secondary structure determinations because spectral peaks must be deconvolved estimates are made of the overlapping contributions of different structural regions. These estimates may introduce error based on the reference spectra used and because deconvolution methods equate crystallographic secondary structure with the secondary structure of the protein in solution (Pelton and McLean, 2000). As amyloid fibrils are neither crystalline nor soluble, there may be even greater error in estimates of secondary structure. To compound the problem, estimates of /f-sheet content are less reliable than those of a-helix, because of the flexibility and variable twist of / -structure (Pelton and McLean, 2000). In addition, / -sheet and turn bands overlap in FTIR spectroscopy (Jackson and Mantsch, 1995 Pelton and McLean, 2000). Side chains also contribute to spectral peaks in both methods, and they can skew estimates of secondary structure if not properly accounted for. In FTIR spectra, up to 10-15% of the amide I band may arise from side chain contributions (Jackson and Mantsch, 1995). 

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