Protein structural classes

Frishman, D., and Mewes, H. W. (1997). Protein structural classes in five complete genomes. Nat. Struct. Biol. 4, 626—628. 

To further extend the analysis, accuracies were measured in terms of correlation coefficients and information independently based on protein structural classes in order to check if there are biases particuiar to specific chain folds. The results are shown in figures 1 and 2. 

Zhang, T-L., Ding, Y.-S. and Chou, K.-C. (2008) Prediction protein structural classes with pseudoamino acid composition approximate entropy and hydrophobidtypattern./. Theor. Biol., 250,186-193. 

Cao Y, Liu S, Zhang L, et al. Prediction of protein structural class with rough sets. BMC Bioinformatics 2006 7 20. 

Protein Structural Classes in a (20-1 )-D Amino Acid Composition Space. 

Understanding the Recognition of Protein Structural Classes by Amino Acid Composition. 

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... 

Pander J W and F M Richards 1987. Tertiary Templates for Proteins. Use of Packing Criteria in Enumeration of Allowed Sequences for Different Structural Classes. Journal of Molecular Bio 193 775-791. 

There are two main classes of loop modeling methods (1) the database search approaches, where a segment that fits on the anchor core regions is found in a database of all known protein structures [62,94], and (2) the conformational search approaches [95-97]. There are also methods that combine these two approaches [92,98,99]. 

Eortunately, a 3D model does not have to be absolutely perfect to be helpful in biology, as demonstrated by the applications listed above. However, the type of question that can be addressed with a particular model does depend on the model s accuracy. At the low end of the accuracy spectrum, there are models that are based on less than 25% sequence identity and have sometimes less than 50% of their atoms within 3.5 A of their correct positions. However, such models still have the correct fold, and even knowing only the fold of a protein is frequently sufficient to predict its approximate biochemical function. More specifically, only nine out of 80 fold families known in 1994 contained proteins (domains) that were not in the same functional class, although 32% of all protein structures belonged to one of the nine superfolds [229]. Models in this low range of accuracy combined with model evaluation can be used for confirming or rejecting a match between remotely related proteins [9,58]. 

JM Chandoma, M Karplus. Neural networks for secondary structure and structural class predictions. Protein Sci 4 275-285, 1995. 

JW Ponder, FM Richards. Tertiary templates for proteins Use of packing criteria m the enumeration of allowed sequences for different structural classes. J Mol Biol 193 775-792, 1987. 

The first six chapters of this book deal with the basic principles of protein structure as we understand them today, and examples of the different major classes of protein structures are presented. Chapter 7 contains a brief discussion on DNA structures with emphasis on recognition by proteins of specific nucleotide sequences. The remaining chapters illustrate how during evolution different structural solutions have been selected to fulfill particular functions. 

Protein structures can be divided into three main classes... 

For each fold one searches for the best alignment of the target sequence that would be compatible with the fold the core should comprise hydrophobic residues and polar residues should be on the outside, predicted helical and strand regions should be aligned to corresponding secondary structure elements in the fold, and so on. In order to match a sequence alignment to a fold, Eisenberg developed a rapid method called the 3D profile method. The environment of each residue position in the known 3D structure is characterized on the basis of three properties (1) the area of the side chain that is buried by other protein atoms, (2) the fraction of side chain area that is covered by polar atoms, and (3) the secondary stmcture, which is classified in three states helix, sheet, and coil. The residue positions are rather arbitrarily divided into six classes by properties 1 and 2, which in combination with property 3 yields 18 environmental classes. This classification of environments enables a protein structure to be coded by a sequence in an 18-letter alphabet, in which each letter represents the environmental class of a residue position. 

The second major class of protein structures contains structures based around parallel or mixed j8-sheets. Parallel /3-sheet arrays, as previously discussed, distribute hydrophobic side chains on both sides of the sheet. This means that neither side of parallel /3-sheets can be exposed to solvent. Parallel /3-sheets are thus typically found as core structures in proteins, with little access to solvent. 

Globular protein (Section 26.9) A protein that is coiled into a compact, nearly spherical shape. These proteins, which are generally water-soluble and mobile within the cell, are the structural class to which enzymes belong. 

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