Sequence and structure-based tool for therapeutic antibody post-translational modifications (PTM) sites analysis, prediction and de-risking

Experimental approaches for identifying PTM liabilities are time-consuming and require high quantities of the purified protein. The sample preparation and data analysis for peptide mapping is incredibly labor intensive. At earlier stages of drug development, the number of forced degradation conditions is limited by the low availability of the purified protein. Therefore, computational tools are becoming more common during developability assessments due to the low cost, lack of sample consumption, and high speed. In the past decade, computational tools have been used to predict PTM liable sites and engineer antibodies with better chemical stability.

we focused on the common PTM of mAbs, and summarize their causes, modification sites, the influences on the mAbs’ physicochemical properties, biological activities, and stabilities, as well as the main analytical strategies, aiming to provide some references for the in-depth quality analysis of therapeutical mAbs．

Computational approaches for PTM prediction can be divided into three categories: 1. sequence-based, 2. structure-based, and 3. physics-based. Sequence-based approaches either flag individual residues prone to chemical degradation (e.g., methionine oxidation) or liable motifs (e.g., NG, NS, and NT for deamidation). Structure-based approaches predict PTM liabilities by using structural features correlated with enzymatic and chemical modifications. Common structural features include, but are not limited to, secondary structure, water coordination number (WCN), solvent-accessible surface area (SASA) and machine learning algorithms.