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, 9 (8), e105954
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OptMAVEn--a New Framework for the De Novo Design of Antibody Variable Region Models Targeting Specific Antigen Epitopes

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OptMAVEn--a New Framework for the De Novo Design of Antibody Variable Region Models Targeting Specific Antigen Epitopes

Tong Li et al. PLoS One.

Abstract

Antibody-based therapeutics provides novel and efficacious treatments for a number of diseases. Traditional experimental approaches for designing therapeutic antibodies rely on raising antibodies against a target antigen in an immunized animal or directed evolution of antibodies with low affinity for the desired antigen. However, these methods remain time consuming, cannot target a specific epitope and do not lead to broad design principles informing other studies. Computational design methods can overcome some of these limitations by using biophysics models to rationally select antibody parts that maximize affinity for a target antigen epitope. This has been addressed to some extend by OptCDR for the design of complementary determining regions. Here, we extend this earlier contribution by addressing the de novo design of a model of the entire antibody variable region against a given antigen epitope while safeguarding for immunogenicity (Optimal Method for Antibody Variable region Engineering, OptMAVEn). OptMAVEn simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing the critical structural features responsible for affinity maturation of antibodies. In addition, a humanization procedure was developed and incorporated into OptMAVEn to minimize the potential immunogenicity of the designed antibody models. As case studies, OptMAVEn was applied to design models of neutralizing antibodies targeting influenza hemagglutinin and HIV gp120. For both HA and gp120, novel computational antibody models with numerous interactions with their target epitopes were generated. The observed rates of mutations and types of amino acid changes during in silico affinity maturation are consistent with what has been observed during in vivo affinity maturation. The results demonstrate that OptMAVEn can efficiently generate diverse computational antibody models with both optimized binding affinity to antigens and reduced immunogenicity.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. OptMAVEn workflow. Step 1: Antigen positioning.
Step 2: Assembly of antibody models targeting the antigen epitope. Step 3: Affinity maturation and humanization of antibody models by redesigning.
Figure 2
Figure 2. Illustrations of antibody-binding site and the algorithm of antigen positioning. H and L chains are colored in cyan and green, respectively; epitope is colored in magenta.
(A) Database of 750 antibody-antigen structures. H and L chains are colored in cyan and green. Antigens are in different colors. (B) All the complex structures superimposed onto a reference antibody structure whose coordinate center of CDRs attachment points was placed on the origin. (C) A rectangular box that covers all the mean epitope coordinates. Figure S2 shows the distributions for the mean coordinates of all the epitopes along the X, Y, and Z axes. (D) The virtual antibody-binding site. (E) An antigen initial conformation. Epitope is colored in magenta. (F) The rotated antigen conformation having the most negative epitope coordinates. (G) A positioned antigen conformation with epitope's geometry center at one grid point. (H) A rotated antigen conformation around Z axis.
Figure 3
Figure 3. Examples of positioning successes and failures for peptide and protein binders. H and L chains are colored in cyan and green, respectively.
Antigens are colored in yellow (native poses) and orange (best positioned poses).
Figure 4
Figure 4. Designed antibody model.
(A)–(D) Model structures for epitopes of HA-all, HA-130, gp120-all and gp120–365 before (in yellow) and after (in orange) refinements. (E)–(H) MILP reselected best scored MAPs after refinements. H and L chains are colored in cyan and green, respectively. V, CDR3 and J represent corresponding MAPs (See definition in Method).
Figure 5
Figure 5. Alignments between designed and initial antibody model sequences for epitopes of HA-all, HA-130, gp120-all and gp120–365.
FRs, CDRs regions and the lengths of sequences are indicated on top of each alignment. Yellow shading shows introduced amino acid mutations.
Figure 6
Figure 6. Counts of the type of amino acid mutations before (blue) and after (red) computational affinity maturation from the four best designed antibody models for epitopes of HA-all, HA-130, gp120-all, and gp120–365.
Figure 7
Figure 7. Structures and binding modes of designed antibody models for epitopes of HA-all, HA-130, gp120-all, and gp120–365.
H and L chains are colored in cyan and green, respectively. Antigens are colored in orange. Hydrogen bonds are highlighted in dashed line and colored in magenta. (A)–(D) Overall complex structures. (E) and (F) Antibody models that recognize 130-loop in the receptor-binding site of HA1. (G) Interaction of receptor analog LSTc in the receptor-binding site of HA1. (H) Interaction of CD4 and CD4-binding loop of gp120. (I) and (J) Antibody models that recognize of CD4-binding loop of gp120.

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Grant support

This work was supported by the National Science Foundation CBET-1133040 (http://www.nsf.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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