Prediction and Analysis of Antibody Amyloidogenesis from Sequences

Abstract

Antibody amyloidogenesis is the aggregation of soluble proteins into amyloid fibrils that is one of major causes of the failures of humanized antibodies. The prediction and prevention of antibody amyloidogenesis are helpful for restoring and enhancing therapeutic effects. Due to a large number of possible germlines, the existing method is not practical to predict sequences of novel germlines, which establishes individual models for each known germline. This study proposes a first automatic and across-germline prediction method (named AbAmyloid) capable of predicting antibody amyloidogenesis from sequences. Since the amyloidogenesis is determined by a whole sequence of an antibody rather than germline-dependent properties such as mutated residues, this study assess three types of germline-independent sequence features (amino acid composition, dipeptide composition and physicochemical properties). AbAmyloid using a Random Forests classifier with dipeptide composition performs well on a data set of 12 germlines. The within- and across-germline prediction accuracies are 83.10% and 83.33% using Jackknife tests, respectively, and the novel-germline prediction accuracy using a leave-one-germline-out test is 72.22%. A thorough analysis of sequence features is conducted to identify informative properties for further providing insights to antibody amyloidogenesis. Some identified informative physicochemical properties are amphiphilicity, hydrophobicity, reverse turn, helical structure, isoelectric point, net charge, mutability, coil, turn, linker, nuclear protein, etc. Additionally, the numbers of ubiquitylation sites in amyloidogenic and non-amyloidogenic antibodies are found to be significantly different. It reveals that antibodies less likely to be ubiquitylated tend to be amyloidogenic. The method AbAmyloid capable of automatically predicting antibody amyloidogenesis of novel germlines is implemented as a publicly available web server at http://iclab.life.nctu.edu.tw/abamyloid.

Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design

“…60 In this context, David et al 61 have developed an algorithm for predicting amyloidogenesis of light chains of antibodies based on a Bayesian classifier and a decision tree. Furthermore, using the same data set of the antibody light chains, Liaw et al 62 have developed an algorithm called AbAmyloid based on a Random Forests classifier with information of dipeptide composition.…”

Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens

“…RandomForest31 is another popular decision tree-based ensemble algorithm that has been shown to be useful in many applications32333435. The RandomForest classifier aims to reduce variance to avoid overfitting problems and improve prediction performances of decision trees by training a set of fully grown decision trees utilizing bootstrap samples from the training dataset and randomly selected features363738.…”

An ensemble classifier based prediction of G-protein-coupled receptor classes in low homology

“…It is instructive to point out the reasons for the better performance with the current method. Although the hydrophobicity has been proved to be an important physicochemical property to represent the gene [37] or protein [19], it is still insufficient to represent the information compared with what released from PSSF. The PSSF consisted with 12 dimensions is powerful for supplying sufficient protein secondary structure information to construct PseAA, which is different from the redundancy of DiAA and TriAA.…”