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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.

SUBMITTER: Smirnov IV

PROVIDER: S-EPMC5072179 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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

Smirnov Ivan V IV Golovin Andrey V AV Chatziefthimiou Spyros D SD Stepanova Anastasiya V AV Peng Yingjie Y Zolotareva Olga I OI Belogurov Alexey A AA Kurkova Inna N IN Ponomarenko Natalie A NA Wilmanns Matthias M Blackburn G Michael GM Gabibov Alexander G AG Lerner Richard A RA

Science advances 20161019 10

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 A ...[more]

PMID: 27774510

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Project description:The protein mediated hydrolysis of nucleoside triphosphates such as ATP or GTP is one of the most important and challenging biochemical reactions in nature. The chemical environment (water structure, catalytic metal, and amino acid residues) adjacent to the hydrolysis site contains hundreds of atoms, usually greatly limiting the amount of the free energy sampling that one can achieve from computationally demanding electronic structure calculations such as QM/MM simulations. Therefore, the combination of QM/MM molecular dynamics with the recently developed transition-tempered metadynamics (TTMetaD), an enhanced sampling method that can provide a high-quality free energy estimate at an early stage in a simulation, is an ideal approach to address the biomolecular nucleoside triphosphate hydrolysis problem. In this work the ATP hydrolysis process in monomeric and filamentous actin is studied as an example application of the combined methodology. The performance of TTMetaD in these demanding QM/MM simulations is compared with that of the more conventional well-tempered metadynamics (WTMetaD). Our results show that TTMetaD exhibits much better exploration of the hydrolysis reaction free energy surface in two key collective variables (CVs) during the early stages of the QM/MM simulation than does WTMetaD. The TTMetaD simulations also reveal that a key third degree of freedom, the O-H bond-breaking and proton transfer from the lytic water, must be biased for TTMetaD to converge fully. To perturb the NTP hydrolysis dynamics to the least extent and to properly focus the MetaD free energy sampling, we also adopt here the recently developed metabasin metadynamics (MBMetaD) to construct a self-limiting bias potential that only applies to the lytic water after its nucleophilic attack of the phosphate of ATP. With these new, state-of-the-art enhanced sampling metadynamics techniques, we present an effective and accurate computational strategy for combining QM/MM molecular dynamics simulation with free energy sampling methodology, including a means to analyze the convergence of the calculations through robust numerical criteria.

Dataset Information

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

Publications

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

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