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

GeneMedi’s LIBRA OPTMTM is a powerful optimization tool for antibody post-translational modifications (PTM) site de-risking. The LIBRA-OPTM combines sequence and structure-based in silico algorithm with antibody optimization for developing the therapeutic antibody based on post-translational modifications (PTM) site analysis, prediction and de-risking.

Knowledge base of antibody humanization

PTM Test
(following the demo format)        use the  Demo      Clear
Species Heavy chain
Light chain
* Click for antibody sequence format fast-autocheck
$ 0
  • Step1:Check antibody sequence (Mouse IgG only)
  • Step2:Fill in the Antibody name, email and submit the form
  • Step3:Click to pay. After we recevie the payment, a report will be sent to your email in three to five days.
Will be displayed on the first page of the report

1. What is antibody post-translational modifications (PTM)

Post-translational modifications (PTM) refer to the modification of amino acid side chain of some proteins after biosynthesis. It is a covalent processing events, which change the properties of a protein by proteolytic cleavage and adding a modifying group, such as acetyl, phosphoryl, glycosyl and methyl, to one or more amino acids.


In recent years, the medical application of therapeutical monoclonal antibodies (mAbs) has been rapidly growing due to its targetability, low-immunogenicity, and good tolerance. It has become an important therapeutic agent for numerous diseases including cancer, autoimmune, and infectious diseases. During the therapeutical monoclonal antibody development, different physical and chemical factors influence PTM of antibody,which affect the stability, immunogenicity, and binding with target antigens.

2. Antibody post-translational modifications (PTM) sites and the therapeutic antibody developability

Like all proteins, mAbs are susceptible to chemical degradation (e.g., oxidation) and enzymatic modifications (e.g., sulfation) during cell culture. Chemical and enzymatic modifications contribute to heterogeneity. For example, asparagine (Asn) deamidation could generate charge variants; tryptophan (Trp) oxidation could generate hydrophilic or hydrophobic variants. In addition, chemical modifications could affect the physical stability and biological activity of antibodies. For example, isomerization within the Fab region could reduce conformational stability, whereas deamidation within complementary-determining region (CDR) loops could reduce binding affinity.

3. How to de-risk the PTM site of your antibody? (The workflow)

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.

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