Correlating dynamic amino acid properties with success rate of crystallization of proteins from Bacteroides vulgatus

Predicting Crystallization Propensity of Proteins from Arabidopsis Thaliana

Background

Many studies have correlated characteristics of amino acids with crystallization propensity, as part of the effort to determine the factors that affect the propensity of protein crystallization. However, these characteristics are constant; that is, the encoded amino acid sequences have the same value for each type of amino acid. To overcome this inflexibility, three dynamic characteristics of amino acids and protein were introduced to analyze the crystallization propensity of proteins. Both logistic regression and neural network models were used to correlate each of two dynamic characteristics with the crystallization propensity of 301 proteins from Arabidopsis thaliana, and their results were compared with those obtained from each of 531 constant amino acid characteristics, which served as the benchmark.

Results

The neural network model was more powerful for predicting the crystallization propensity of proteins than the logistic regression model. Compared with the benchmark, the dynamic characteristics of amino acids provided good prediction results for the crystallization propensity, and the distribution probability gave the highest sensitivity. Using 90 % accuracy as a cutoff point, the predictable portion of A. thaliana portions was ranked, and the statistical analysis showed that the larger the predictable portion, the better the prediction.

Conclusions

These results demonstrate that dynamic characteristics have a certain relationship with the crystallization propensity, and they could be helpful for the prediction of protein crystallization, which may provide a theoretical concept for certain proteins before conducting experimental crystallization.

Electronic supplementary material

The online version of this article (doi:10.1186/s12575-015-0029-3) contains supplementary material, which is available to authorized users.

Association of combined features of amino acid and protein withcrystallization propensity of proteins fromCytophaga Hutchinsonii

Various features of amino acids have been so far associated with crystallization propensity of proteins. The majority of features generally represents a certain aspect of individual amino acid, for example, molecular weight of an amino acid. Meanwhile, a small portion of features, which represents a certain aspect of a whole protein, is also associated with crystallization propensity of proteins, such as protein length. Clearly, the features of individual amino acids distinguish themselves from the features of a whole protein. Therefore it would be more rationale to use the features, which combine both features of individual amino acids and a whole protein, to associate crystallization propensity of proteins, because the features of individual amino acids are not subject to amino acids‘ positions in a protein, for instance. In this study, each of three combined features of individual amino acids and a whole protein are associated with crystallization propensity of proteins fromCytophaga Hutchinsoniithrough logistic regression and neural network, and each of 535 features of individual amino acids is also associated with crystallization propensity of the proteins to serve as benchmark. The results show that the combined features have a good relationship with crystallization propensity of proteins fromCytophaga Hutchinsonii. This study provides the information that the combined features can be used for predicting crystallization propensity of protein.