Secondary Structure and Folding Classes

In contrast, the j8-strand is a much more extended structure. Rather than hydrogen bonds forming within the secondary structural luiit itself, stabilization occms through bonding with one or more adjacent )8-strands. The overall structure formed through the interaction of these individual )8-strands is known as a fi-pleated sheet. These sheets can be parallel or antiparallel, depending on the orientation of the N-and C-terminal ends of each component )8-strand. A variant of the )8-sheet is the )8-turn in this structure the polypeptide chain makes a sharp, hairpin bend, producing an antiparallel j8-sheet in the process. 

In 1976, Levitt and Chothia proposed a classification system based on the order of secondary structural elements within a protein (Levitt and Chothia, 1976). Quite simply, an a-structure is made up primarily from a-helices, and a )8-structure is made up of primarily j8-strands. Myoglobin is the classic example of a protein composed entirely of a-helices, falling into the a class of structures (Takano, 1977). Plasto-cyanin is a good example of the fi class, where the hydrogen-bonding pattern between eight j8-strands form a compact, barrel-like structure (Guss and Freeman, 

The combination class, aff3, is made up of primarily )8-strands alternating with a-helices. Flavodoxin is a good example of an a/)8-protein its )8-strands form a central j8-sheet, which is surrounded by a-helices (Burnett et al., 1974). 

Predictive methods aimed at extracting secondary structural information from the linear primary sequence make extensive use of neural networks, traditionally used for analysis of patterns and trends. Basically, a neural network provides computational processes the ability to learn in an attempt to approximate human learning versus following instructions blindly in a sequential marmer. Every nemal network has an input layer and an output layer. In the case of secondary structure prediction, the input layer would be information from the sequence itself, and the output layer would be the probabilities of whether a particular residue could form a particular structure. Between the input and output layers would be one or more hidden layers where the actual learning would take place. This is accomplished by providing a training data set for the network. Here, an appropriate training set would be all sequences for which three-dimensional structures have been deduced. The network can process this information to look for what are possibly weak relationships between an amino acid sequence and the structures they can form in a particular context. A more complete discussion of neural networks as applied to secondary structure prediction can be found in Kneller et al. (1990). 

The impredict algorithm uses a two-layer, feed-forward neural network to assign the predicted type for each residue (Kneller et al., 1990). In making the predictions, the server uses a FASTA format file with the sequence in either one-letter or three-letter code, as well as the folding class of the protein (a, j8, or a//8). Residues are classified 

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Analysis (PSA) server of BMERC predicts secondary structures and folding classes from a query sequence. On the PSA home page at http //bmerc-www.bu.edu/psa/ index.html, select Submit a sequence analysis request to submit the query sequence and your e-mail address. The returned results include (a) probability distribution plots (conventional X/Y and contour plots) for strand, turn, and helix and (b) a list of structure probabilities for loop, helix, turn, and strand for every amino acid residues. 

The knowledge based structural prediction (Blundell et al, 1987) depends on analogies between a biomacromolecule of known sequence and other biomacromolecules of the same class with known 3D structure at all levels in the hierarchy of biomacromolecular organization. In the numerically based statistical methods, the structural rules and parameters (conformational propensities) for each residue are extracted by statistical analyses of the structural database and used to predict the structure along the sequence of the macromolecule. Examples of the statistical methods that are commonly applied to predict secondary structure and folding preference of proteins will be illustrated. 

Estrada, E. (2004a) A protein folding degree measure and its dependence on crystal packing, protein size, secondary structure, and domain structural class. /. Chem. Inf. Comput. Sci., 44, 1238-1250. 

Peptides consisting exclusively of fS- or y-amino acids (amino acids) have emerged as a promising new class of nonnatural oligomers (foldamers) that are able to fold into well-defined secondary structures [41--47]. So far, three different helical secondary structures and two turn motifs [177-181] as well as a parallel [177,179] and an antiparallel [179,182] sheet structure have been identified by two-dimensional NMR spectroscopy, circular dichroism (CD), and/or X-ray diffraction studies. In addition, cyclo- -tetrapeptides have been found to form nanotubes in the solid state [183] and have been used as transmembrane ion channels [184]. All these studies have demon-... 

The application of NMR spectroscopy in protein structure determination actually started with a small metalloprotein, metallothionine (MT) [42]. Metallothionines are a class of low molecular weight (typically 6-7 kDa) cysteine-rich proteins. The proteins lack a well-defined secondary structure and their folding is dictated mostly by a clustered network of cysteine residues and metal ions usually represented by Zn ... 

Once proteins are divided into domains the domains are then classified hierarchically. At the top of the classification we usually find the class of a protein domain, which is generally determined from its overall composition in secondary structure elements. Three main classes of protein domains exist mainly a domains, mainly (3 domains, and mixed a p domains (the domains in the a — p class are sometimes subdivided into domains with alternating a/p secondary structures and domains with mixed a + p secondary structures). In each class, domains are clustered into folds according to their topology. A fold is determined from the number, arrangement, and connectivity of the domain s secondary structure elements. The folds are subdivided into superfamilies. A superfamily contains protein domains with similar functions, which suggests a common ancestry, often in the absence of detectable sequence similarity. Sequence information defines families, i.e., subclasses of superfamilies that regroup domains whose sequences are similar. 

In each SCOP class, proteins are clustered into groups based on their structure similarity. Each cluster is referred to by SCOP as a fold. Proteins share a common fold if they have the same major secondary structures in the same arrangement and with the same topological connections. Proteins with the same fold may differ at the level of their peripheral elements, which can include secondary structures and turn regions. Note that these peripheral elements can represent up to 50% of the structure. Proteins catalogued together in the same fold may have no common evolutionary origin. 

Proteins and polypeptides have two major classes of chromophores, the amide groups of the peptide backbone, which absorb light in the far UV (below 250 nm) and the aromatic amino acid side chains and disulfide bonds, which absorb light in both the near (320-250 nm) and far-UV. Far-UV ORD and CD are useful for studying protein structure and folding because many conformations that are common in proteins, including a-helixes, j5-pleated sheets, poly-L-proline Il-like helices and turns, have characteristic spectra. Figure 1 illustrates representative CD spectra of model polypeptides with different secondary structures. In addition, the chromophores of the aromatic amino acids of proteins are often in... 

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. 

In this respect, the CUE domain is not a isolated case. There are a number of other domain families, each of them only defined in the bioinformatical sense, that have significant matches within established UBA or CUE domain regions. Based on this similarity and on secondary-structure predictions, it can be expected that all of those domain types assume the typical UBA-like three-helix bundle fold. However, it is not clear if all of those domains also bind to ubiquitin, or if they have evolved to different binding properties. Many of the UBA-like domain classes are unpublished. Nevertheless, they should be briefly discussed here, as they are a logical extension of the UBA/CUE paradigm. 

A representative sampling of non-heme iron proteins is presented in Fig. 3. Evident from this atlas is the diversity of structural folds exhibited by non-heme iron proteins it may be safely concluded that there is no unique structural motif associated with non-heme iron proteins in general, or even for specific types of non-heme iron centers. Protein folds may be generally classified into several categories (i.e., all a, parallel a/)3, or antiparallel /8) on the basis of the types and interactions of secondary structures (a helix and sheet) present (Richardson, 1981). Non-heme iron proteins are found in all three classes (all a myohemerythrin, ribonucleotide reductase, and photosynthetic reaction center parallel a/)8 iron superoxide dismutase, lactoferrin, and aconitase antiparallel )3 protocatechuate dioxygenase, rubredoxins, and ferredoxins). This structural diversity is another reflection of the wide variety of functional roles exhibited by non-heme iron centers. 

The overall three-dimensional structure of a protein is called the tertiary structure. The tertiary structure represents the spatial packing of secondary structures (Ofran and Rost, 2005). As for secondary structures, there are several different classes of tertiary structures. More advanced classification schemes take into account common topologies, motifs, or folds (Wishart, 2005). Common tertiary folds include the a/p-barrel, the four-helix bundle, and the Greek key (we will discuss protein folding further in Chapter 14). Any change to any part of the structure of a protein will have an impact on its biological activity (Thomas, 2003). 

There are different classes of protein sequence databases. Primary and secondary databases are used to address different aspects of sequence analysis. Composite databases amalgamate a variety of different primary sources to facilitate sequence searching efficiently. The primary structure (amino acid sequence) of a protein is stored in primary databases as linear alphabets that represent the constituent residues. The secondary structure of a protein corresponding to region of local regularity (e.g., a-helices, /1-strands, and turns), which in sequence alignments are often apparent as conserved motifs, is stored in secondary databases as patterns. The tertiary structure of a protein derived from the packing of its secondary structural elements which may form folds and domains is stored in structure databases as sets of atomic coordinates. Some of the most important protein sequence databases are PIR (Protein Information Resource), SWISS-PROT (at EBI and ExPASy), MIPS (Munich Information Center for Protein Sequences), JIPID (Japanese International Protein Sequence Database), and TrEMBL (at EBI). ... 

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