Prediction of secondary structure

A common use of statistics in structural biology is as a tool for deriving predictive distributions of strucmral parameters based on sequence. The simplest of these are predictions of secondary structure and side-chain surface accessibility. Various algorithms that can learn from data and then make predictions have been used to predict secondary structure and surface accessibility, including ordinary statistics [79], infonnation theory [80], neural networks [81-86], and Bayesian methods [87-89]. A disadvantage of some neural network methods is that the parameters of the network sometimes have no physical meaning and are difficult to interpret. 

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. 

A central element in the prediction of secondary structure is the periodicity of sequence conservation, which has proven to be a good indicator in a number of membrane proteins (5). The periodicity is quantified by Fourier transform (FT) analysis. A... 

Many known drug receptors, and many prospective drug targets, exist as molecular arrays within membrane-bound macromolecules that cannot be readily crystallized neither can they be isolated or purified for the application of NMR methods. Moreover, even if an amino acid sequence were available, rule-based methods for the prediction of secondary structure, being derived as they are from a database of soluble proteins, cannot be applied with any confidence to the membrane-bound state. 

Figure 14.4 Prediction of secondary structure, with formate dehydrogenase as an example.

Jaeger JA, Turner DH, Zuker M. Improved predictions of secondary structures for RNA. Proc Natl Acad Sci USA 1989 86 7706-7710. 

Baumruk V. Pancoska P. Keiderling TA. Prediction of secondary structure using statistical analysis of electronic and vibrational circular dichroism and Fourier transform infrared spectra of proteins in H2O. J Mol Biol 1996 259 774-791. 

These findings can be of considerable impact for the conventional strategies of peptide synthesis. The segment condensation of medium-sized peptides with low solubility to larger peptide chains may result in the increased solubility of the reaction product due to conformational transitions. Whenever it is possible, amino acid residues with low tendencies for p-strueture formation should be inserted towards the middle of the peptide segments. In this respect, the prediction of secondary structures can be a useful tool in establishing the most efficient path for peptide synthesis. To this end, experimental elucidation of the conformational preferences of side-chain-protected trifunctional amino acids is of much relevance 250). 

Vivardli, F Giusti, G., Villani, M Campanini, R., Fariselli, P., Compiani, M. Casadio, R. (1995). LG ANN a parallel system combining a local genetic algorithm and neural networks for the prediction of secondary structure of proteins. ComputAppl Biosci 11,253-60. 

Chen, A., Kroon, P.A. and Poulter, C.D. (1994) Isoprenyl diphosphate synthases protein sequence comparisons, a phylogenetic tree and predictions of secondary structure. Protein Sci., 3, 600-7. 

Cid H, Bunster M, Arriagada E, Campos M. Prediction of secondary structure of proteins by means of hydrophobicity profiles. FEBS Lett. 1982 150 247-254. 

The relationship between NMR chemical shifts and the secondary structure of a protein has been well established (19,20,21). The C and carbonyl carbons experience an upfield shift in extended structures, such as a P-strand, and a downfield shift in helical structures. Both the Cp and the Ha proton chemical shifts exhibit the opposite correlation. These shifts have proven to be sufficiently consistent to permit the prediction of secondary structural elements for a number of proteins (1,19,20). Knowledge of the secondary structure of a protein can be useful in identifying spin-diffusion effects during the analysis of 4D N/ N-separated NOES Y data collected with long mixing times as described below. The secondary structure can also be used as a constraint in the calculation of protein global folds. 

Niermann, T., and Kirschmann, K. Improving the prediction of secondary structure of TIM-barreP structures. Protein Engineering 4, 359-370 (1991). 

Can one predict the secondary structure of proteins by using this knowledge of the conformational preferences of amino acid residues Predictions of secondary structure adopted by a stretch of six or fewer residues have proved to be from about 60% to 70% accurate. What stands in the way of more accurate prediction Note that the conformational preferences of amino acid residues are not tipped all the way to one structure (see Table 2.3). For example, glutamate, one of the strongest helix formers, prefers a helix to p strand by only a factor of two. The preference ratios of most other residues are smaller. 

King, R.D. 1996. Prediction of Secondary Structure. In Steinberg, M.J. (Hrsgb.), Protein Structure Prediction—A Practical Approach. Oxford Oxford University Press. 

Tertiary Structure - Attempts to predict tertiary structure of proteins have not been as successful as those for predicting secondary structure. Folding of sequences depends critically on specific side chain interactions, often far removed from one another in the amino acid sequence. Attempts to predict tertiary structure include efforts to recognize overall patterns in tertiary folding combined with the prediction of secondary structure. These efforts have led to the successful prediction of an ot//f-barrel structure for tryptophan synthase, which is in excellent agreement with the structure determined by x-ray diffraction. 

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