Folding class

Different protein folding classes can be identified by differences in their amino acid compositions (Nakashima et al., 1986) thus, we reasoned that, if disorder were encoded by the sequence, then regions of disorder would be analogous to a new folding class and hence should... 

Using a maize HDAC system, aroyl-pyrrolyl-hydroxamides (APHA) had been shown to exhibit 7-78-fold class Ha selectivity when appropriately modified. Continued fine-tuning of the APHA inhibitors, such as meta-fluorine-substituted 20, enabled the generation of an even more selective HDAC inhibitor, with HD1-A IC50 of 0.22 (iM, 176-fold selectivity and which inhibited human HDAC4 but not HDAC1 [53]. 

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. 

With the advent of the genome projects, it now becomes possible to explore how nature has achieved functional diversity from a limited set of structural types by examining structure-function relationships in protein superfamilies. Our ability to do this at a sophisticated level is currently limited by the relatively small number of fold classes that have been structurally characterized, but recent initiatives to determine many more three-dimensional structures can be expected to substantially address this deficit over the next few years (Sali, 1998). 

Although a full exposition of these additional superfamilies is beyond the scope of this discussion, a short description of two, the vicinal oxygen chelate fold (VOC) superfamily and the haloacid dehalogenase (HAD) superfamily, are included. Neither of these superfamilies belongs to the a/(3 barrel fold class and a discussion of the way in which each delivers function provides an important contrast to our discussion of the enolase superfamily. 

When the similar approach was applied to discriminate members of a given folding class from members of all other classes in the more comprehensive SCOP database, it was shown that specific amino acid properties work differently on different folding classes... 

Dubchak et al., 1997). Therefore, one may find an individual set of descriptors that works best on a particular folding class. 

Dubchak, I., Holbrook, S. R. Kim, S.-H. (1993a). Prediction of protein folding class from amino acid composition. Proteins 16,79-91. 

All the methods developed so far try to extract information, directly or indirectly (Lim, 1974), from the ever growing databases of X-ray crystallography resolved protein structures. Unfortunately, the rate at which new structures are added to the structure databases is far from optimal. Chothia (1992) estimated that all proteins, when their structures are known, would fall into about one thousand folding classes, more than half of them yet to be discovered. If so, this means that a great deal of information in the forthcoming structures is not available for the current methods, and therefore we still must rely on the future to see a coherent and realistic increase in the accuracy of secondary structure prediction methods. 

From the information values in figure 1 it is possible to observe that in general the prediction in all alpha and alpha+beta classes has a greater success than for all beta and alpha/beta classes. Also it shows a greater variability between predictive accuracies of the methods among each other as well as between the fold classes individually. 

The inherent variability of predictive success rate depending on the protein fold class brings important observations (1) When reporting accuracies the selection of the test set proteins should be balanced In order to include a representative number of each of the protein fold classes. (2) the prior knowledge of the protein fold class (Chou and Zhang, 1995) can be a valuable aid for the predictions and with that one can use the different algorithms in combination to predict a specific structural element of the chain. 

Figure 13.1. Structural classes of protein folds, showing how the folds can be classified into different structural classes. Top row the three basic fold classes a, containing only a helices a and p, containing a helices and p sheets and p, containing only p sheets. Middle row three different architectural subclasses of the a and p class triosephosphate isomerase (TIM) barrel, three-layer sandwich, and roll. Bottom row two different arrangements of the "three-layer sandwich . The spiral conformations are the a helices, and the broad arrows are the p sheets. (From Orengo, C. A., Michie, A. D., Jones, S. et al. [1997]. CATH - a hierarchic classification of protein domain structures [Figure 2]. Structure, 5, 1093-108. Copyright 1997, Elsevier Science. Reprinted with permission.)...

The relationship between structure and function is a true many-to-many relation. Recent studies have shown that particular functions can be mounted onto several different protein folds [85] and, conversely, several protein fold classes can perform a wide range of functions [259]. This limits our potential of deducing function from structure. But it is still possible to use aforementioned knowledge on the range of folds supporting a particular function and the range of functions implemented by particular folds in order to make functional prediction from structure. 

SK appears as three domains, termed a, p and y, of similar folding, separated by two coiled coils [33]. Domains a and yS each contain a major yS-sheet of five mixed yS-strands and an a-helix, a typical structure of the yS-grasp folding class. The y domain has only four yS-strands and contains a long coiled-coil segment instead of an a-helix. The a domain binds to plasmin mainly through interactions between the ySl and yS2 strands of SK a and a loop region of plasmin SK a also interacts with... 

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... 

The Option line specifies the folding class of the protein n uses no folding class for the prediction, a specifies a, b specifies (3, and a/b specifies a/fi. Only one sequence may be submitted per E-mail message. The results returned by the server are shown in modified form in Eigure 11.4. 

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