Robotic QM/MM-driven maturation of antibody combining sites

• Published in: Science advances 2016-10, Vol.2 (10), p.e1501695
• Main Authors: Smirnov, Ivan V, Golovin, Andrey V, Chatziefthimiou, Spyros D, Stepanova, Anastasiya V, Peng, Yingjie, Zolotareva, Olga I, Belogurov, Jr, Alexey A, Kurkova, Inna N, Ponomarenko, Natalie A, Wilmanns, Matthias, Blackburn, G Michael, Gabibov, Alexander G, Lerner, Richard A
• Format: Article
• Language: English
• Subjects: Immunology; SciAdv r-articles
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.1501695

In vitro selection of antibodies from large repertoires of immunoglobulin (Ig) combining sites using combinatorial libraries is a powerful tool, with great potential for generating in vivo scavengers for toxins. However, addition of a maturation function is necessary to enable these selected antibodies to more closely mimic the full mammalian immune response. We approached this goal using quantum mechanics/molecular mechanics (QM/MM) calculations to achieve maturation in silico. We preselected A17, an Ig template, from a naïve library for its ability to disarm a toxic pesticide related to organophosphorus nerve agents. Virtual screening of 167,538 robotically generated mutants identified an optimum single point mutation, which experimentally boosted wild-type Ig scavenger performance by 170-fold. We validated the QM/MM predictions via kinetic analysis and crystal structures of mutant apo-A17 and covalently modified Ig, thereby identifying the displacement of one water molecule by an arginine as delivering this catalysis.