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Computational design of antibody-affinity improvement beyond in vivo maturation.

ABSTRACT: Antibodies are used extensively in diagnostics and as therapeutic agents. Achieving high-affinity binding is important for expanding detection limits, extending dissociation half-times, decreasing drug dosages and increasing drug efficacy. However, antibody-affinity maturation in vivo often fails to produce antibody drugs of the targeted potency, making further affinity maturation in vitro by directed evolution or computational design necessary. Here we present an iterative computational design procedure that focuses on electrostatic binding contributions and single mutants. By combining multiple designed mutations, a tenfold affinity improvement to 52 pM was engineered into the anti-epidermal growth factor receptor drug cetuximab (Erbitux), and a 140-fold improvement in affinity to 30 pM was obtained for the anti-lysozyme model antibody D44.1. The generality of the methods was further demonstrated through identification of known affinity-enhancing mutations in the therapeutic antibody bevacizumab (Avastin) and the model anti-fluorescein antibody 4-4-20. These results demonstrate computational capabilities for enhancing and accelerating the development of protein reagents and therapeutics.

SUBMITTER: Lippow SM

PROVIDER: S-EPMC2803018 | biostudies-literature | 2007 Oct

REPOSITORIES: biostudies-literature

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Computational design of antibody-affinity improvement beyond in vivo maturation.

Lippow Shaun M SM Wittrup K Dane KD Tidor Bruce B

Nature biotechnology 20070923 10

Antibodies are used extensively in diagnostics and as therapeutic agents. Achieving high-affinity binding is important for expanding detection limits, extending dissociation half-times, decreasing drug dosages and increasing drug efficacy. However, antibody-affinity maturation in vivo often fails to produce antibody drugs of the targeted potency, making further affinity maturation in vitro by directed evolution or computational design necessary. Here we present an iterative computational design ...[more]

PMID: 17891135

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Project description:Antibodies isolated from human donors are increasingly being developed for anti-infective therapeutics. These antibodies undergo affinity maturation in vivo, minimizing the need for engineering of therapeutic leads for affinity. However, the affinities required for some therapeutic applications may be higher than the affinities of the leads obtained, requiring further affinity maturation in vitro. To improve the neutralization potency of natural human antibody MSL-109 targeting human cytomegalovirus (CMV), we affinity matured the antibody against the gH/gL glycoprotein complex. A phage display library where most of the six complementary-determining regions (CDRs) were allowed to vary in only one amino acid residue at a time was used to scan for mutations that improve binding affinity. A T55R mutation and multiple mutations in position 53 of the heavy chain were identified that, when present individually or in combination, resulted in higher apparent affinities to gH/gL and improved CMV neutralization potency of Fab fragments expressed in bacterial cells. Three of these mutations in position 53 introduced glycosylation sites in heavy chain CDR 2 (CDR H2) that impaired binding of antibodies expressed in mammalian cells. One high affinity (KD<10 pM) variant was identified that combined the D53N and T55R mutations while avoiding glycosylation of CDR H2. However, all the amino acid substitutions identified by phage display that improved binding affinity without introducing glycosylation sites required between two and four simultaneous nucleotide mutations to avoid glycosylation. These results indicate that the natural human antibody MSL-109 is close to a local affinity optimum. We show that affinity maturation by phage display can be used to identify and bypass barriers to in vivo affinity maturation of antibodies imposed by glycosylation and codon usage. These constraints may be relatively prevalent in human antibodies due to the codon usage and the amino acid sequence encoded by the natural human repertoire.

Project description:The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

Dataset Information

Computational design of antibody-affinity improvement beyond in vivo maturation.

Publications

Computational design of antibody-affinity improvement beyond in vivo maturation.

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