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Publication Abstract from PubMed

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 naive 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.

Robotic QM/MM-driven maturation of antibody combining sites.,Smirnov IV, Golovin AV, Chatziefthimiou SD, Stepanova AV, Peng Y, Zolotareva OI, Belogurov AA Jr, Kurkova IN, Ponomarenko NA, Wilmanns M, Blackburn GM, Gabibov AG, Lerner RA Sci Adv. 2016 Oct 19;2(10):e1501695. eCollection 2016 Oct. PMID:27774510^[1]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.