Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

Abstract

Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug’s potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs’ sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.

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

“…25,[101][102][103][104][105][106][107][108][109][110][111] One of the most common degradation events is the chemical modification of Asn and Asp residues, which share a degradation pathway. 25 Many of the methods to predict such degradation are statisticalbased methods, and experimental data to derive such prediction models are either from in-house experiments 101,103,107,108,110 or from literature. 104,106,111 For example, to understand origins of Asn deamidation and Asp isomerization, Sydow et al 101 used mass spectrometry to experimentally characterize 37 antibodies that were subjected to forced degradation.…”

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning

“…It is highly likely that this model can be applied to other display platforms that use bio-panning as the selection process, such as yeast display library for fluorescence-activated cell sorting screening [54]. Recently, artificial intelligence has been applied to predict the physicochemical properties of antibody sequences [55][56][57][58][59] and/or optimize them [60][61][62].…”

Characterization and prediction of positional 4-hydroxyproline and sulfotyrosine, two post-translational modifications that can occur at substantial levels in CHO cells-expressed biotherapeutics

“…In recent years, several companies have reported the development of structure-based tools for the prediction of chemical PTMs like deamidation, isomerization, and oxidation hot spots in therapeutic antibodies. [22][23][24] Also, several opensource web-based predictors have been developed to suggest positions of enzyme-catalyzed PTMs based on consensus sequences/logos (e.g., www.expasy.org/proteomics, www.cbs. dtu.dk/databases/PTMpredictions, www.modpred.org/).…”

“…20 Recently, matrix-assisted laser desorption ionization (MALDI) was combined with in-source decay (ISD) fragmentation and ultrahigh resolution Fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS) for fast characterization of mAbs providing complementary sequence information compared to other MS-based techniques. 21 Other approaches have focused on the development of structure-based prediction tools for the identification of, for example, deamidation, isomerization, and oxidation hot spots in mAbs [22][23][24] or other in silico PTM prediction tools. [25][26][27] Herein, we describe the integration of innovative PTM analysis and in silico identification tool for the detection and verification of 4-hydroxyproline (4Hyp) and sulfotyrosine (sTyr) in antibody-based therapeutics.…”