Protein secondary structures structure prediction from

Figure 10.1 Neural network for protein secondary structure prediction. (Adopted from Qian Sejnowski, 1988).

Chou P Y and G D Fasman 1978. Prediction of the Secondary Structure of Proteins from Tlieir Amino Acid Sequence. Advances in Enzymology 47 45-148. 

PY Chou, CD Easman. Prediction of the secondary structure of proteins from their ammo acid sequence. Adv Enzymol Relat Areas Mol Biol 47 45-148, 1978. 

The structure of IFN-x was also examined by CD [10]. Analysis of the IFN-x spectra predicts that the secondary structural elements derived from CD spectra indicate approximately 70% a-helix. The remainder of the molecule is either predicted to be random or a combination of (3 sheet and turn. Since it is known that algorithms that predict secondary structures from CD spectra are most accurate at identifying a helices, we are confident that IFN-x is mainly a helical. The CD spectra for the synthetic peptides of IFN-x were also obtained. The peptides IFN-x(l-37), IFN-x(62-92), IFN-x(l 19-150), and IFN-x(139-172) all show the presence of a helix, while IFN-x(34-64) and IFN-x(90-122) are mainly random. The presence of an a helix in the peptides supports the CD analysis of the intact protein and also roughly indicates the location of helical segments. 

PREDICTION OF PROTEIN SECONDARY STRUCTURES FROM SEQUENCES... 

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.

Source Data from Liljas A, Rossmann MG. X-ray studies of protein interactions. Annu Rev Biochem 43 475-505, 1974 Argos P, Schwarz JS, Schwarz J. An assessment of protein secondary structure prediction methods based on amino acid sequence. Biochim Biophys Acta 439 261-273, 1976. 

Another frequently used global information that covers protein context is the residue frequencies. The composition is often calibrated with that from the database as in Gamier et al. (1996), where only observed frequencies of amino acids and amino acid pairs are used for protein secondary structure prediction. 

Gamier, J Gibrat, J. F. Robson, B. (1996). GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 266,540-53. 

Neural network method is often quoted as a data-driven method. The weights are adjusted on the basis of data. In other words, neural networks learn from training examples and can generalize beyond the training data. Therefore, neural networks are often applied to domains where one has little or incomplete understanding of the problem to be solved, but where training data is readily available. Protein secondary structure prediction is one such example. Numerous rules and statistics have been accumulated for protein secondary structure prediction over the last two decades. Nevertheless, these... 

Manavalan P., Johnson W.C. Jr. (1987) Variable Selection Method Improves the Prediction of Protein Secondary Structure from Circular Dichroism Spectra, Anal. Biochem. 167, 76-85. 

While there is no structural information on coronin constructs that only contain the N-terminal WD repeat (propeller) domain, severe a tion has been reported for coronin 1 (lA) truncation mutants lacking the C-terminal extension and the coiled coil domain. The crystal structure of coronin 1 (1A) implies an eminendy important role of the C-terminal extension domain for the stability of the protein, especially for the residues direedy interactii with the p propeller. Appleton et aL have thus put forward the hypothesis that the region 1-392 of coronin 1 (lA), which comprises the WD repeat domain and the C-terminal extension, represents a folding unit, albeit this awaits experimental validation. Notably, a construct of coronin 3 (1C) extending from the last blade of the p propeller to the coiled coil domain (300-474), possesses secondary structure as predicted and thus appears to be folded. It still remains a matter of speculation, whether blade 7 adopts its native conformation within this construct because strand D is missing. 

Frishman, D. and Argos, R, Incorporation of non-local interactions in protein secondary structure prediction from the amino acid sequence, Prot. Eng., 9, 133, 1996. 

Geourjon, C. and G. Deleage, SOPMA significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci, 1995. 11(6) p. 681-4. 

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. 

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... 

There are four structural arenas covered by secondary structure prediction methodologies from the amino acid sequence alone (1) typical protein secondary structures as helices, j0-strands, turns and coil (c.f., 60-62) (li) transmembrane helices (iii) signal and target sequences and (Iv) antigenic sites. The prediction area is usually in a constant state of review. It is intended here to give only a summary of the techniques and their usefulness and to discuss some of the more recent developments. Recent reviews will be given. 

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