Multiple sequence alignment prediction from

Thompson and Goldstein [89] improve on the calculations of Stolorz et al. by including the secondary structure of the entire window rather than just a central position and then sum over all secondary strucmre segment types with a particular secondary structure at the central position to achieve a prediction for this position. They also use information from multiple sequence alignments of proteins to improve secondary structure prediction. They use Bayes rule to fonnulate expressions for the probability of secondary structures, given a multiple alignment. Their work describes what is essentially a sophisticated prior distribution for 6 i(X), where X is a matrix of residue counts in a multiple alignment in a window about a central position. The PDB data are used to form this prior, which is used as the predictive distribution. No posterior is calculated with posterior = prior X likelihood. 

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%. 

The method of Rost and Sander (Rost and Sander, 1993), which combines neural networks with multiple sequence alignments known as PHD, is available from the PredictProtein (Rost, 1996) server of Columbia University (http //cubic.bioc. columbia.edu/predictprotein/). This Web site offers the comprehensive protein sequence analysis and structure prediction (Figure 12.8). For the secondary structure prediction, choose Submit a protein sequence for prediction to open the submission form. Enter e-mail address, paste the sequence, choose options, and then click the... 

Five pA3-crystallin sequences from mouse, human, rat, bovine and chicken lenses, PB2-ciystallin sequences from mouse and human lenses, and PB2-ciystallin sequences from bovine, rat and chicken lenses were used (Sergeev Hejtmandk, unpublished data). All these sequences and the bovine sequence of yB-crystallin were mailed to the PHD server for the multiple sequence alignment and secondary structure prediction ( S). Files Igcs and Iblb from the January 1995 Release of the Brookhaven Protein Data Bank were used for yB-and PB2-crystallins, respectively (9). File Iblb contained four molecules A, B, C and D grouped as two dimers, AB and CD. Assignment of the secondary structure for PDB files was carried out according to the locally adopted version oftheDSSP(lO). 

Ortiz, A. R., A. Kolinski, and J. Skolnick, Tertiary structure prediction of the KIX domain of CBP using Monte Carlo simulations driven by restraints derived from multiple sequence alignments. Proteins, 1998. 30(3) p. 287-94. 

This may be trivial if sequence identity between the target sequence and a known 3D stmcture is high (say > 30%), as then simple pair-wise sequence comparison methods (FASTA, SSEARCH ) will easily identify the relation-ship. Where sequence identity is lower and a superfamily or even fold relationship must be identified, recognition of the stmctural similarity between two sequences may be very difficult. Sequence-only methods, such as PSI-BLAST, hidden Markov models and intermediate sequence search, use information from multiple sequence alignments to represent the characteristics shared by related sequences (sequence profiles), and use this to search for stmctural homologues. These profiles can then be augmented by secondary stmcture prediction. ... 

Secondary Structure Prediction and the Prediction of Buried Residues From Multiple Sequence Alignment... 

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