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.
Highlights
Antibody-based therapy characterized by its high specificity to targeted antigens has been adopted for treatments of cancer, autoimmune disease and inflammatory
AGGRESCAN calculates the prediction score for aggregation ‘‘hot spot’’ using the aggregation-propensity values for each of the 20 amino acids derived from previously experimental data
To the best knowledge of authors, there is only one study focusing on the prediction of antibody amyloidogenesis [41]
Summary
Antibody-based therapy characterized by its high specificity to targeted antigens has been adopted for treatments of cancer, autoimmune disease and inflammatory. Monoclonal antibodies are usually derived from murine and suffer from short half-life and undesirable immunogenicity that largely reduce the therapeutic effects [1,2]. Humanized antibodies are developed to overcome the above-mentioned problems by grafting murine variable domains, the specificity-determining residues, and complementarity-determining residues [3,4,5,6]. The humanization process might decrease thermal stability of antibodies that could affect their affinities to targets and lead to amyloid fibril formation [3,7,8]. The experiments for humanizing antibodies are expensive and time-consuming. It is desirable to develop an accurate method for predicting antibody amyloidogenesis
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