Protein structure prediction from

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 ... 

Ihe rule-based approach to protein structure prediction is obviously very reliant on th quality of the initial secondary structure prediction, which may not be particularly accurate The method tends to work best if it is known to which structural class the protein belongs this can sometimes be deduced from experimental techniques such as circular dichroism... 

The strongest verification for a 3D-protein model comes from the experimental 3D-structure. This is the objective of the Critical Assessment of Techniques for Protein Structure Prediction, CASP ( http //predic tioncenter.org), where the structural models are made in advance of the experimental structure of a particular protein. 

The protein structure prediction problem refers to the combinatorial problem to calculate the 3D structure of a protein from its sequence alone. It is one of the biggest challenges in structural bioinformatics. 

Nonglass pH electrodes, 14 24 Nonhalogenated resin systems, 20 115 Nonhalogenated solvents, 19 800 Nonhazardous waste, defined, 25 862 Nonheterocyclic compounds, pyridine ring syntheses from, 21 108—110 Nonhomologous extension modeling, in protein structure prediction, 20 837-839... 

Bonneau, R., et al.. Functional inferences from blind ab initio protein structure predictions. J Struct Biol, 2001, 134(2-3), 186-90. 

Figure 12.9. Protein structure prediction with PHD. The amino acid sequence of chicken lysozyme precursor (147 amino acids) is submitted to PredictProtein server for PHD structure predictions. The returned e-mail reports protein class based on secondary structures, predicted secondary structure composition (%H, %E, and %L), residue composition, data interpretation, and predicted data in two levels (brief and normal of which the normal is shown). Search for the database can be performed by making choice(s) from the list(s) of pop-up box(es).

Method for Protein Modeling and Design Applications to Docking and Structure Prediction from the Distorted Native Conformation. 

In lattice models, the location of each element on the lattice can be stored as a vector of coordinates [(X, F,), (X2, Y2), (X3, Y3),..., (Xn, F )], where (X Y,) are the coordinates of element i on a two-dimensional lattice (a three-dimensional lattice will require three coordinates for each element). Since lattices enforce a fixed geometry on the conformations they contain, conformations can be encoded more efficiently by direction vectors leading from one atom (or element) to the next. For example in a two-dimensional square lattice, where every point has four neighbors, a conformation can be encoded simply by a set of numbers (Lu L2, L3,..., L ), where L, g 1, 2,3,4 represents movement to the next point by going up, down, left, or right. Most applications of GAs to protein structure prediction utilize one of these representations. 

A wide variety of energy functions have been used as part of the various GA-based protein structure prediction protocols. These range from the hydrophobic potential in the simple HP lattice model [19] to energy models such as CHARMM, based on full fledged, detailed molecular mechanics [9]. Apparently, the ease by which various energy functions can be incorporated within the framework of GAs as fitness functions encouraged researchers to modify the energy function in very creative ways to include terms that are not used with the traditional methods for protein structure prediction. 

In Ref. [52] it was demonstrated that experimentally derived structural information such as the existence of S-S bonds, protein side-chain ligands to iron-sulfur cages, cross-links between side chains, and conserved hydrophobic and catalytic residues, can be used by GAs to improve the quality of protein structure prediction. The improvement was significant, usually nudging the prediction closer to the target by more than 2 A. However, even with this improvement, the overall prediction quality was still insufficient, usually off by more than 5 or 6 A from the target structure. This was probably due to the small number and the diverse nature of the experimental constraints. 

Cherkauer, K. J. Shavlik, J. W. (1993). Protein structure prediction selecting salient features from large candidate pools. Ismb 1,74-82. 

One approach to the protein structure prediction is to classify the folding patterns of globular proteins. This is based on the observation from examining known tertiary structures that the variety of protein folding patterns is significantly restricted. Therefore, it is likely that a protein may belong to one of the previously identified folding patterns. 

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