Open AccessClassification model of amino acid sequences prone to aggregation of therapeutic proteins
Author(s) -
Monika Marczak,
Krystyna Okoniewska,
Tomasz Grabowski
Publication year - 2016
Publication title -
in silico pharmacology
Language(s) - English
Resource type - Journals
ISSN - 2193-9616
DOI - 10.1186/s40203-016-0019-4
Subject(s) - amino acid , computational biology , antibody , sequence (biology) , drug , chemistry , biology , biochemistry , immunology , pharmacology
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.