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Updated: Mar 18, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
Classification model of amino acid sequences prone to aggregation of therapeutic proteins
Monika Marczak1, Krystyna Okoniewska2, Tomasz Grabowski3
1Dofarm, ul. Aksamitna 15, 05-870, Błonie, Poland.
Background:
Total body clearance of biological drugs is for the most part dependent on the receptor mechanisms (receptor mediated clearance) and the concentration of antibodies aimed at administered drug - anti-drug-antibodies (ADA). One of the significant factors that induces the increase of ADA level after drug administration could be the aggregates present in the finished product or formed in the organism. Numerous attempts have been made to identify the sequence fragments that could be responsible for forming the aggregates - aggregate prone regions (APR).
Purpose:
The aim of this study was to find physiochemical parameters specific to APR that would differentiate APR from other sequences present in therapeutic proteins.
Methods:
Two groups of amino acid sequences were used in the study. The first one was represented by the sequences separated from the therapeutic proteins (n = 84) able to form APR. A control set (CS) consisted of peptides that were chosen based on 22 tregitope sequences.
Results:
Classification model and four classes (A, B, C, D) of sequences were finally presented. For model validation Cooper statistics was presented.
Conclusions:
The study proposes a classification model of APR. This consists in a distinction of APR from sequences that do not form aggregates based on the differences in the value of physicochemical parameters. Significant share of electrostatic parameters in relation to classification model was indicated.
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