Developing similarity matrices for antibody-protein binding interactions.

The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody-protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces

OptMAVEn – A New Framework for the <i>de novo</i> Design of Antibody Variable Region Models Targeting Specific Antigen Epitopes

<div><p>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 <i>de novo</i> 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 <i>in silico</i> the <i>in vivo</i> 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 <i>in silico</i> affinity maturation are consistent with what has been observed during <i>in vivo</i> affinity maturation. The results demonstrate that OptMAVEn can efficiently generate diverse computational antibody models with both optimized binding affinity to antigens and reduced immunogenicity.</p></div

Designed antibody model.

<p>(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).</p

Structures and binding modes of designed antibody models for epitopes of HA-all, HA-130, gp120-all, and gp120–365.

<p>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.</p

Forward designs using IPRO without specified positions for mutations.

a<p>The starting structures used for IPRO designs. GL in the parentheses indicate the structure is germline and AM is for affinity maturation.</p>b<p>The number of mutations identified by comparing GL and AM antibody sequences.</p>c<p>The numbers of designed mutations occurring both in GL and AM antibodies.</p>d<p>Interaction energy between an antigen and an antibody. All energies are in kcal/mol.</p>e<p>The IE difference calculated from AM - GL.</p

Summary of energies, mutations and HScores of the four best designed antibody models for epitopes of HA-all, HA-130, gp120-all, and gp120–365.

a<p>The epitope used for designs. See the definition in the method.</p>b<p>Before or after the design.</p>c<p>The entire complex energy. Unit in kcal/mol.</p>d<p>The interaction energy between the antibody and antigen. Unit in kcal/mol.</p>e<p>The number of mutations between the designed sequence and the initial sequence.</p>f<p>The humanness score. See the definition in the method.</p

Calculated HScores of human, mouse, rat and rabbit sequences.

a<p>The mean of HScore.</p>b<p>The standard deviation of HScore.</p

Examples of positioning successes and failures for peptide and protein binders. H and L chains are colored in cyan and green, respectively.

<p>Antigens are colored in yellow (native poses) and orange (best positioned poses).</p

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.

<p>(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. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105954#pone.0105954.s002" target="_blank">Figure S2</a> 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.</p