Folding rule

Folding of polypeptides is subject to an array of physical and chemical constraints. A sampling of the prominent folding rules that have emerged provides an opportunity to introduce some simple motifs. 

Question What generalizations or folding rules can be drawn from the known data base of experimentally determined protein structures ... 

Todd and Neidle defined a PQS sequence as having the form G3.5N1.7G3.5 N1.7G3.5N1.7G3.5. This is similar to the Folding Rule of Huppert and Balasubramanian (implemented in the program quadparser), which was G3+Ni.7G3+Ni.7G3+Ni 7G3+. Thcsc definitions differ essentially only for continuous runs of guanine in excess of 5 bases, which are counted in the latter case but not the former. 

Modern DNA technology is now being used to study the folding rules. The amino acid composition of a protein may be altered at a specific point or points by site-directed mutagenesis and the effect of the alteration on the tertiary structure of the protein determined. 

Process plant design has come a long way from the early 1930s when process designers used the rule-of-thumb that a process faciUty could not be scaled-up more than 10-fold (2). American Oil s Ultracracking unit (Texas City, Texas) for example, was designed from data from a small pilot plant with a scale-up factor of 80,000 (3). 

These results indicate that is it possible to change the fold of a protein by changing a restricted set of residues. They also confirm the validity of the rules for stability of helical folds that have been obtained by analysis of experimentally determined protein structures. One obvious impliction of this work is that it might be possible, by just changing a few residues in Janus, to design a mutant that flip-flops between a helical and p sheet structures. Such a polypeptide would be a very interesting model system for prions and other amyloid proteins. 

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%. 

Natural mutation of amino acids in the core of a protein can stabilize the same fold with different complementary amino acid types, but they can also cause a different fold of that particular portion. If the sequence identity is lower than 30% it is much more difficult to identify a homologous structure. Other strategies like secondary structure predictions combined with knowledge-based rules about reciprocal exchange of residues are necessary. If there is a reliable assumption for common fold then it is possible to identify intra- and intermolecular interacting residues by search for correlated complementary mutations of residues by correlated mutation analysis, CMA (see e.g., http //www.fmp-berlin.de/SSFA). 

In order to model the restrictions imposed by chain connectivity additional rules are required. Two different sets of rules are used, both of which lead to similar results. The first set derives from the inability of a chain to extend once it has been folded over. The Monte Carlo simulation does not explicitly include folds, but any stem which is completely surrounded by other stems is assumed to have folded and additional units are unable to add in the z-direction. In Fig. 4.3 we denote by all those positions where a new unit may add, all other surface sites are blocked. 

Fig. 4.3.a, b. The geometry of the crystal used in the 3D Monte Carlo simulation, b Illustration of one set of rules which mimic the connectivity of the chains. Any stem which is completely surrounded by other stems is assumed to have folded and therefore cannot lengthen denotes sites where new units may not be added... 

The particular models used to demonstrate the theory obviously have many drawbacks as true representations of polymer crystals. These could include the lack of a fold energy, no distinction between new molecules and those already attached, neglect of chain ends, a somewhat arbitrary choice of pinning rules etc. However, they all serve their purpose in that they show that an energetic free energy barrier is not necessary to obtain the experimental curves. A truly representative growth picture can probably only be achieved via molecular dynamics. 

The raw materials are usually mutually contaminated to about 5 %. Thus, DSC can be ruled out. The compounds are made by aldol condensations and cross Canizzaro reactions between acetaldehyde and formaldehyde. An elegant method of distinguishing between the two raw materials takes advantage of the 10 fold solubility difference between the two in methanol and water. 

Example 1.7 predicted that power per unit volume would have to increase by a factor of 100 in order to maintain the same mixing time for a 1000-fold scaleup in volume. This can properly be called absurd. A more reasonable scaleup rule is to maintain constant power per unit volume so that a 1000-fold increase in reactor volume requires a 1000-fold increase in power. Use the logic of Example 1.7 to determine the increase in mixing time for a 1000-fold scaleup at constant power per unit volume. 

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