Robotic QM/MM-driven maturation of antibody combining sites.

Abstract:

: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.

journal_name

Sci Adv

journal_title

Science advances

authors

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

doi

10.1126/sciadv.1501695

subject

Has Abstract

pub_date

2016-10-19 00:00:00

pages

e1501695

issue

10

issn

2375-2548

pii

1501695

journal_volume

2

pub_type

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