Polypeptides structure-prediction methods

These predictive methods are very useful in many contexts for example, in the design of novel polypeptides for the identification of possible antigenic epitopes, in the analysis of common motifs in sequences that direct proteins into specific organelles (for instance, mitochondria), and to provide starting models for tertiary structure predictions. 

Currently, there exists an enormous and growing deficit between the number of polypeptides whose amino acid sequence has been determined and the numbers of polypeptides whose three-dimensional structure has been resolved. Given the complexities of resolving three-dimensional structure experimentally, it is not surprising that scientists are continually attempting to develop methods by which they could predict higher order structure from amino acid sequence data. Although modestly successful secondary structure predictive approaches have been developed, no method by which tertiary structure may be predicted from primary data has thus far been developed. 

Most such predictive methods are at best 50-70 per cent accurate. The relatively large inaccuracy stems from the fact that the folded (tertiary) structure imposes constraints upon the nature/extent of secondary structure within some regions of the polypeptide chain. Any generalized rules relating secondary structure to amino acid sequence data, by nature, will not take such issues into consideration. 

Some of the different theoretical approaches, prediction methods, and simulation of protein folding are briefly introduced here. The methods for evaluating, first the conformational preferences of a single peptide unit, and second the conformational preferences of a polypeptide chain, are discussed. Models that use short- and medium-range interactions are briefly analyzed. The results of these different predictive methods of secondary structures are compared. Actually the refinements of these predictive methods are under study in the main laboratories of this field of study. It is evidently of great... 

A particular goal of chemical theory is to predict protein structure from the amino acid sequence—to calculate how polypeptides fold into the compact geometries of proteins. One strategy is to develop methods (often based on bioinformatics) for predicting structures approximately and then refining the structures... 

Ideally, one should be able to predict a three-dimensional structure from the amino acid sequence. There has been considerable activity along these lines. Current methods can predict the structural class of a protein or domain. Although about two-thirds of all residues in a polypeptide chain can be assigned to the correct secondary structure, the three-dimensional structure cannot be predicted. 

So far, we have investigated higher-order structure of polypeptides by solid-state high-resolution NMR not only using experimental but also theoretical methods[2-4]. The chem cal shifts can be characterized by variations in the electronic states of the local conformation as defined by the dihedral angles(4>,W). Ando et al. have calculated contour map for the Cp carbons of an alanine dipeptide by using the FPT INDO method within the semi-empirical MO framework. The calculated map reasonably predicts the experimental version. This shows that the chemical shift behavior of the L-alanine residue Cp-carbonyl carbons in the... 

In addition to X-ray diffraction and NMR, which are direct techniques, methods based on the calculation of predicted three-dimensional structures of molecules in the range of 3 to 50 amino acids based on energy considerations are under rapid development. These approaches use what are commonly called molecular dynamics and energy minimization equations to specify the most probable conformation of polypeptides and small proteins. Often, when combined with information from other sources, such as X-ray crystallography or NMR studies, they have been demonstrated to be quite useful. However, when standing alone, their power and the accuracy of their predictive capability remains to be seen. 

Combinatorial Optimization Methods for Predicting the Backbone Structure in Polypeptides. ... 

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