Large Proteins Have a Great Tendency to Aggregate but a Low Propensity to Form Amyloid Fibrils

The assembly of soluble proteins into ordered fibrillar aggregates with cross-β structure is an essential event of many human diseases. The polypeptides undergoing aggregation are generally small in size. To explore if the small size is a primary determinant for the formation of amyloids under pathological conditions we have created two databases of proteins, forming amyloid-related and non-amyloid deposits in human diseases, respectively. The size distributions of the two protein populations are well separated, with the systems forming non-amyloid deposits appearing significantly larger. We have then investigated the propensity of the 486-residue hexokinase-B from Saccharomyces cerevisiae (YHKB) to form amyloid-like fibrils in vitro. This size is intermediate between the size distributions of amyloid and non-amyloid forming proteins. Aggregation was induced under conditions known to be most effective for amyloid formation by normally globular proteins: (i) low pH with salts, (ii) pH 5.5 with trifluoroethanol. In both situations YHKB aggregated very rapidly into species with significant β-sheet structure, as detected using circular dichroism and X-ray diffraction, but a weak Thioflavin T and Congo red binding. Moreover, atomic force microscopy indicated a morphology distinct from typical amyloid fibrils. Both types of aggregates were cytotoxic to human neuroblastoma cells, as indicated by the MTT assay. This analysis indicates that large proteins have a high tendency to form toxic aggregates, but low propensity to form regular amyloid in vivo and that such a behavior is intrinsically determined by the size of the protein, as suggested by the in vitro analysis of our sample protein

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

<p>Abstract</p> <p>Background</p> <p>All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.</p> <p>Results</p> <p>The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%.</p> <p>Conclusions</p> <p>This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.</p