Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

Abstract

BackgroundAll 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.ResultsThe 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%.ConclusionsThis 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.

Prediction and Analysis of Antibody Amyloidogenesis from Sequences

“…These general methods trained using only amyloidogenic proteins aim to predict which regions of a sequence are potentially amyloidogenic. Therefore, it is not easy to distinguish between amyloidogenic and non-amyloidogenic antibodies with highly similar sequences [41].…”

“…Recently, a computational method was proposed to predict amyloidogenesis in antibodies by using Naïve Bayes classifiers and decision tree methods with reasonably high prediction accuracies [41]. However, there are two shortcomings in utilizing this method [41].…”

“…However, there are two shortcomings in utilizing this method [41]. (1) The necessary alignment procedure needs experts to manually adjust the alignment.…”

Dana-Farber repository for machine learning in immunology

“…Machine learning (ML) in bioinformatics plays an important role, principally in developing accurate in silico methods for bioinformatics (Baldi and Brunak, 2001; Zhang and Rajapakse, 2009). The applications of ML have proven valuable in immunology; examples include the analysis of antigens (Lafuente and Reche, 2009), the analysis of allergenicity (Muh et al ., 2009), the study of antibodies and their properties (David et al ., 2010), the design of vaccine protocols (Palladini et al ., 2010), and the classification of immunological profiles (Herz and Yanover, 2007). These developments help improve practical applications such as discovery, design, and optimization of vaccines.…”

Antibody Engineering

“…In terms of improvements of the properties in antibodies, such as immunogenicity and solubility, sequence-based methods have shown promise in antibody engineering (110,111).…”