A study of the structural correlates of affinity maturation: Antibody affinity as a function of chemical interactions, structural plasticity and stability

[Antibody proteomics]

Antibodies represent an essential component of humoral immunity; therefore their study is important for molecular biology and medicine. The unique property of antibodies to specifically recognize and bind a certain molecular target (an antigen) determines their widespread application in treatment and diagnostics of diseases, as well as in laboratory and biotechnological practices. High specificity and affinity of antibodies is determined by the presence of primary structure variable regions, which are not encoded in the human genome and are unique for each antibody-producing B cell clone. Hence, there is little or no information about amino acid sequences of the variable regions in the databases. This differs identification of antibody primary structure from most of the proteomic studies because it requires either B cell genome sequencing or de novo amino acid sequencing of the antibody. The present review demonstrates some examples of proteomic and proteogenomic approaches and the methodological arsenal that proteomics can offer for studying antibodies, in particular, for identification of primary structure, evaluation of posttranslational modifications and application of bioinformatics tools for their decoding.

Central Nervous System Delivery of Antibodies and Their Single-Domain Antibodies and Variable Fragment Derivatives with Focus on Intranasal Nose to Brain Administration

IgG antibodies are some of the most important biopharmaceutical molecules with a high market volume. In spite of the fact that clinical therapies with antibodies are broadly utilized in oncology, immunology and hematology, their delivery strategies and biodistribution need improvement, their limitations being due to their size and poor ability to penetrate into tissues. In view of their small size, there is a rising interest in derivatives, such as single-domain antibodies and single-chain variable fragments, for clinical diagnostic but also therapeutic applications. Smaller antibody formats combine several benefits for clinical applications and can be manufactured at reduced production costs compared with full-length IgGs. Moreover, such formats have a relevant potential for targeted drug delivery that directs drug cargo to a specific tissue or across the blood–brain barrier. In this review, we give an overview of the challenges for antibody drug delivery in general and focus on intranasal delivery to the central nervous system with antibody formats of different sizes.

A Chemometric Approach Toward Predicting the Relative Aggregation Propensity: Aβ(1-42)

A number of algorithms have been developed to predict the aggregation propensity of peptides and proteins, but virtually none have the ability to provide sequence-specific information on what physicochemical properties are most important in altering aggregation propensity. In this study, a chemometric approach using reduced amino acid properties is used to examine the aggregation behavior of a highly amyloidogenic peptide, Aβ(1-42). Specific residues are identified as being critical to the aggregation process. At each of these positions, the important physicochemical properties are identified that would either accelerate or inhibit fibril formation.

Tailored amino acid diversity for the evolution of antibody affinity

Antibodies are a unique class of proteins with the ability to adapt their binding sites for high affinity and high specificity to a multitude of antigens. Many analyses have been performed on antibody sequences and structures to elucidate which amino acids have a predominant role in antibody interactions with antigens. These studies have generally not distinguished between amino acids selected for broad antigen specificity in the primary immune response and those selected for high affinity in the secondary immune response. By studying a large data set of affinity matured antibodies derived from in vitro directed evolution experiments, we were able to specifically highlight a subset of amino acids associated with affinity improvements. In a comparison of affinity maturations using either tailored or full amino acid diversification, the tailored approach was found to be at least as effective at improving affinity while requiring fewer mutagenesis libraries than the traditional method. The resulting sequence data also highlight the potential for further reducing amino acid diversity for high affinity binding interactions.

Engineering protein therapeutics: predictive performances of a structure-based virtual affinity maturation protocol

The implementation of a structure based virtual affinity maturation protocol and evaluation of its predictivity are presented. The in silico protocol is based on conformational sampling of the interface residues (using the Dead End Elimination/A* algorithm), followed by the estimation of the change of free energy of binding due to a point mutation, applying MM/PBSA calculations. Several implementations of the protocol have been evaluated for 173 mutations in 7 different protein complexes for which experimental data were available: the use of the Boltzamnn averaged predictor based on the free energy of binding (ΔΔG(*)) combined with the one based on its polar component only (ΔΔE(pol*)) led to the proposal of a subset of mutations out of which 45% would have successfully enhanced the binding. When focusing on those mutations that are less likely to be introduced by natural in vivo maturation methods (99 mutations with at least two base changes in the codon), the success rate is increased to 63%. In another evaluation, focusing on 56 alanine scanning mutations, the in silico protocol was able to detect 89% of the hot-spots.

Generation of recombinant antibodies and means for increasing their affinity

Highly specific interaction with foreign molecules is a unique feature of antibodies. Since 1975, when Keller and Milstein proposed the method of hybridoma technology and prepared mouse monoclonal antibodies, many antibodies specific to various antigens have been obtained. Recent development of methods for preparation of recombinant DNA libraries and in silico bioinformatics approaches for protein structure analysis makes possible antibody preparation using gene engineering approaches. The development of gene engineering methods allowed creating recombinant antibodies and improving characteristics of existing antibodies; this significantly extends the applicability of antibodies. By modifying biochemical and immunochemical properties of antibodies by changing their amino acid sequences it is possible to create antibodies with properties optimal for certain tasks. For example, application of recombinant technologies resulted in antibody preparation of high affinity significantly exceeding the initial affinity of natural antibodies. In this review we summarize information about the structure, modes of preparation, and application of recombinant antibodies and their fragments and also consider the main approaches used to increase antibody affinity.

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

Background

All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.

Results

The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%.

Conclusions

This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.

An efficient visualization tool for the analysis of protein mutation matrices

Background

It is useful to develop a tool that would effectively describe protein mutation matrices specifically geared towards the identification of mutations that produce either wanted or unwanted effects, such as an increase or decrease in affinity, or a predisposition towards misfolding. Here, we describe a tool where such mutations are efficiently identified, categorized and visualized. To categorize the mutations, amino acids in a mutation matrix are arrang according to one of three sets of physicochemical characteristics, namely hydrophilicity, size and polarizability, and charge and polarity. The magnitude and frequences of mutations for an alignment are subsequently described using color information and scaling factors.

Results

To illustrate the capabilities of our approach, the technique is used to visualize and to compare mutation patterns in evolving sequences with diametrically opposite characteristics. Results show the emergence of distinct patterns not immediately discernible from the raw matrices.

Conclusion

Our technique enables effective categorization and visualization of mutations by using specifically-arranged mutation matrices. This tool has a number of possible applications in protein engineering, notably in simplifying the identification of mutations and/or mutation trends that are associated with specific engineered protein characteristics and behavior.

Thyroid peroxidase as an autoantigen

Thyroid peroxidase (TPO) evokes high-affinity, IgG-class autoantibodies [TPO autoantibodies (TPOAbs)] and TPO-specific T cells that are markers of thyroid infiltration or implicated in thyroid destruction, respectively. A diverse repertoire of human monoclonal TPOAbs, unparalleled in other autoimmune diseases, provides invaluable probes for investigating antibody epitopes. Human TPOAbs recognize an immunodominant region comprising overlapping A and B domains on conformationally intact TPO. Amino acids recognized by TPOAbs are located in the regions with homology to myeloperoxidase (MPO) and the complement control protein (CCP) but not in the epidermal growth factor (EGF)-like region. T cells recognize epitopes in the MPO-like region but not in the CCP- or EGF-like regions in humans. Monoclonal human TPOAbs modulate processing of TPO protein to provide peptides for some T cells. A human T cell clone expressed transgenically in mice induces lymphocytic infiltration and hypothyroidism. This T cell's epitope is only generated by thyrocyte processing of endogenous TPO. Further, intact TPO expressed in vivo is also required for induction of TPOAbs in mice that resemble human autoantibodies. Overall, some TPO-specific T cells and the majority of autoantibodies in humans develop in response to TPO presented by thyroid cells, rather than to TPO released by damaged thyrocytes.