Prediction from amino acid sequence

Transmembrane a helices can be predicted from amino acid sequences... 

Earlier structure predictions with coronins resulted in models of five-bladed P propellers, because only five WD repeats were recognised. However, many p propeller structures contain WD repeats with litde or no obvious sequence homology to the canonical sequence. Additionally, while typical WD repeats possess four to eight copies and up to 16 copies have been reported, almost all structures ofWD repeat proteins in the PDB contain seven blades per propeller. Keeping this in mind, more detailed analyses of coronins have been carried out and additional WD-like repeats were identified based on secondary structure prediction from amino acid sequences, indicating the presence of seven copies for coronins I(IA) - and 3 (IC) and 14 for coronin 7. The fold of a seven-bladed propeller was then confirmed by the crystal structure of a coronin 1 (lA) fragment. ... 

In the absence of a high-resolution three-dimensional structure of a BFR, a tentative subunit conformation (Fig. 10) has been derived by secondary structure prediction from amino acid sequence data. A combined prediction based on eight different programs gave a helical dis-... 

Transmembrane Helices Can Be Accurately Predicted from Amino Acid Sequences... 

There is a considerable impetus to predict accurately protein structures from sequence information because of the protein sequence/structure deficit as a consequence of the genome and full-length cDNA sequencing projects. The molecular mechanical (MM) approach to modeling of protein structures has been discussed in section 9.2, and the protein secondary structure prediction from sequence by statistical methods has been treated in section 9.5. The prediction of protein structure using bioinformatic resources will be described in this subsection. The approaches to protein structure predictions from amino acid sequences (Tsigelny, 2002 Webster, 2000) include ... 

Different side chains have been found to have weak but definite preferences either for or against being in a helices. Thus Ala (A), Glu (E), Leu (L), and Met (M) are good a-helix formers, while Pro (P), Gly (G), Tyr (Y), and Ser (S) are very poor. Such preferences were central to all early attempts to predict secondary structure from amino acid sequence, but they are not strong enough to give accurate predictions. 

Since the outside of the barrel faces hydrophobic lipids of the membrane and the inside forms the solvent-exposed channel, one would expect the P strands to contain alternating hydrophobic and hydrophilic side chains. This requirement is not strict, however, because internal residues can be hydrophobic if they are in contact with hydrophobic residues from loop regions. The prediction of transmembrane p strands from amino acid sequences is therefore more difficult and less reliable than the prediction of transmembrane a helices. 

Predicting the Amino Acid Sequence Transcribed from DNA... 

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%. 

Ahmad, S., Gromiha, M.M., and Sarai, A. (2003) Real value prediction of solvent accessibility from amino acid sequence. Proteins 50(4), 629-635. 

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. 

Ganesh, C., N. Eswar, S. Srivastava, C. Ramakrishnan, and R. Varadarajan. 1999. Prediction of the maximal stability temperature of monomeric globular proteins solely from amino acid sequence. FEBS Lett 454 31-36. 

Hopp TP, Woods KR. (1981) Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci U S A78, 3824-8. 

As we begin to appreciate that there is much information about drug targets that genomics cannot provide, the focus has shifted from genomics to proteomics. The shift in focus parallels the appreciation of the complexity of proteins, which exceeds the complexity of DNA sequences. While a DNA sequence may allow one to predict the amino-acid sequence of a protein—in cases where an open reading frame sequence (with a start and stop codon) is apparent—it can neither assure expression nor provide information about protein function. 

Aalberse, R. C. and Stadler, B. M. 2006. In silico predictability of allergenicity From amino acid sequence via 3-D structure to allergenicity. Mol Nutr Food Res 50(7) 625-627. 

Neuberger G, Maurer-Stroh S, Eisenhaber B, Hartig A, Eisen-haber F. 2003. Prediction of peroxisomal targeting signal 1 containing proteins from amino acid sequence. J Mol Biol 328 581-592. 

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

Eisenhaber, F Persson, B. Argos, P. (1995). Protein structure prediction recognition of primary, secondary, and tertiary structural features from amino acid sequence. Crit Rev Biochem Mol Biol 30,1-94. 

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