Correct protein structures

ERRAT contains a database of acceptable nonbonded atom-atom distances. It classifies the atom-atom distances in a proposed structure and then does a statistical evaluation of those atom-atom distance interactions. The parameters used to construct the database were derived from known protein structures of varying fold classifications. The known structures used for the database were required to have a maximum resolution of 2.5 A, an R-factor less than 25%, be monomeric or homo-oligomeric, be native structures, and contain peptide bond dihedral angles 15° from ideal values based on the secondary structure. A total of 96 solved X-ray protein structures from the PDB were used as the dataset of correct protein structures. The six types of atom-atom distances were restricted to be atoms from different amino acid residues or to be atoms that interact with each other with a through-space distance no greater than 3.5 The average and standard deviation of each atom-atom distance interaction fraction (the fraction of specific atom-atom pairwise distances in a protein structure), as illustrated in Eq. [4] for CC distances... 

Basic data obtained from NMR studies consist of distance and torsion angle restraints. Once resonances have been assigned, nuclear Overhauser effect (NOE) contacts are selected and their intensities are used to calculate interproton distances. Information on torsion angles are based on the measurement of coupling constants and analysis of proton chemical shifts. Together, this information is used to formulate a nonlinear optimization problem, the solution of which should provide the correct protein structure. Typically, a hybrid energy function of the following form is employed ... 

In the 1994 Asilomar meeting, none of the 3D ab initio methods was able to predict the correct protein structure. Since that time, new methods have been proposed which indicate possible directions for the future. Several groups have obtained promising results using distance geometry methods, Simplified force fields in combination with dynamic optimization strategies have yielded promising, but... 

Ithough knowledge-based potentials are most popular, it is also possible to use other types potential function. Some of these are more firmly rooted in the fundamental physics of iteratomic interactions whereas others do not necessarily have any physical interpretation all but are able to discriminate the correct fold from decoy structures. These decoy ructures are generated so as to satisfy the basic principles of protein structure such as a ose-packed, hydrophobic core [Park and Levitt 1996]. The fold library is also clearly nportant in threading. For practical purposes the library should obviously not be too irge, but it should be as representative of the different protein folds as possible. To erive a fold database one would typically first use a relatively fast sequence comparison lethod in conjunction with cluster analysis to identify families of homologues, which are ssumed to have the same fold. A sequence identity threshold of about 30% is commonly... 

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]. 

In general, the R factor is between 0.15 and 0.20 for a well-determined protein structure. The residual difference rarely is due to large errors in the model of the protein molecule, but rather it is an inevitable consequence of errors and imperfections in the data. These derive from various sources, including slight variations in conformation of the protein molecules and inaccurate corrections both for the presence of solvent and for differences in the orientation of the microcrystals from which the crystal is built. This means that the final model represents an average of molecules that are slightly different both in conformation and orientation, and not surprisingly the model never corresponds precisely to the actual crystal. 

Although there are but 30,000 different genes in the hiunan body, it has been shown that as many as 200,000 proteins are produced for use in the individual human ceUs. Thus, it is clear that the data given in Table 2-6 may be regarded as preliminary. Time will show whether the observations made in Table 2-6 regarding protein structure are correct. 

Protein stability is just the difference in free energy between the correctly folded structure of a protein and the unfolded, denatured form. In the denatured form, the protein is unfolded, side chains and the peptide backbone are exposed to water, and the protein is conformationally mobile (moving around between a lot of different, random structures). The more stable the protein, the larger the free energy difference between the unfolded form and the native structure. 

Disulfide bridges are, of course, true covalent bonds (between the sulfurs of two cysteine side chains) and are thus considered part of the primary structure of a protein by most definitions. Experimentally they also belong there, since they can be determined as part of, or an extension of, an amino acid sequence determination. However, proteins normally can fold up correctly without or before disulfide formation, and those SS links appear to influence the structure more in the manner of secondary-structural elements, by providing local specificity and stabilization. Therefore, it seems appropriate to consider them here along with the other basic elements making up three-dimensional protein structure. 

Aside from the direct techniques of X-ray or electron diffraction, the major possible routes to knowledge of three-dimensional protein structure are prediction from the amino acid sequence and analysis of spectroscopic measurements such as circular dichroism, laser Raman spectroscopy, and nuclear magnetic resonance. With the large data base now available of known three-dimensional protein structures, all of these approaches are making considerable progress, and it seems possible that within a few years some combination of noncrystallo-graphic techniques may be capable of correctly determining new protein structures. Because the problem is inherently quite difficult, it will undoubtedly be essential to make the best possible use of all hints available from the known structures. 

---