Synthetic antibodies: there is no limit to perfection
Research on synthetic antibodies brings the era of pre-symptomatic diagnosis closerNanonews Network based on materials from Arizona State University:
Research into synthetic antibodies offers hope for new diagnostics
Antibodies are the "watchdogs" of human health, continuously circulating through the body and noting with amazing accuracy even the most insignificant changes associated with infectious and other diseases. They are also biochemical memory banks that faithfully record information about pathogens they encounter and effectively store this data for future use.
Stephen Albert Johnston, Neal Woodbury and their colleagues from the Biodesign Institute at Arizona State University are studying the mechanisms of antibody functioning, in particular, the ability of these "sentinels" to bind – with high affinity and specificity – to their protein targets. A deeper understanding of the vast world of antibodies could lead to a new generation of fast and inexpensive diagnostic tools and accelerate the development of new vaccines and medicines.
The specificity of antibodies is their ability to react only with a certain protein–antigen. Affinity (Latin affinis – related) is the main characteristic of the specificity of antibodies: the binding strength of the active centers of the antibody molecule with the determinant (reactive) groups of the antigen – VM.
Using as a basis a script written by nature itself, scientists are working on the creation of synthetic antibodies, or synthel, using a method developed at the Center for Innovations in Medicine (Center for Innovations in Medicine) Johnston. Having studied a wide panorama of antibody activity detected from blood samples, they use this information to develop a pre-symptomatic diagnosis of diseases. Such "immune signatures," as Johnston called them, provide scientists with a dynamic picture of human health.
In two new articles, the researchers demonstrated a simple method of enhancing the affinity of synthetic antibodies, which are chains of 20 links-amino acids connected randomly. Using random peptide sequences deposited on microchip plates, they also obtained information about the active regions, or epitopes, of natural antibodies. These articles have recently been published in the journals PloS ONE and Molecular and Cellular Proteomics.
Although antibodies have long been used in biomedical research, conventional methods of obtaining them are time-consuming and expensive. As a rule, such antibodies are produced by the animal's body, which responds to the introduction of a certain protein by producing protein-specific antibodies, which can then be extracted.
In earlier work, Johnston's group showed that artificial high-affinity antibodies can be obtained synthetically using a simple method. This method turns the traditional approach upside down. Instead of starting with a specific protein and trying to create an antibody corresponding to it, scientists first create a synthetic antibody, and then determine the protein with which it interacts effectively, using screening of potential partner proteins.
The first step in this process is to obtain random combinations of 20 amino acids. Then approximately 10,000 of these random peptides are applied to the slide of the microchip. On this set of random sequences, the protein to which the antibody is selected is tested, and peptides with high affinity are detected based on the screening result. Two such peptides can be combined and a synthelo can be obtained, the affinity of which is the sum of the affinities of each of the peptides. Thus, two weakly binding peptides combine their forces to form a structure with high affinity, useful for studying the proteome – the vast world of proteins necessary for the course of almost all biological processes.
In an article published in PlosOne (Thermodynamic Additivity of Sequence Variations: An Algorithm for Creating High Affinity Peptides Without Large Libraries or Structural Information), lead author Matthew Greving and his colleagues describe a strategy for further enhancing the affinity of such peptides.
"The problem is that the microchip contains about 10,000 peptides, but this is less than one quadrillionth of the possible sequence variants. That is, we are sampling from a very small part of them," Greving explains. Consequently, the probability of creating a sequence of 20 amino acids showing optimal affinity remains quite low.
To enhance affinity, the main sequence is first selected. (In this study, one of these sequences was the TNF-1 peptide consisting of 17 amino acids – a key regulator of immune system cells). The main sequence is used as a template for creating additional peptide sequences in which a single amino acid at each position of the peptide chain is successively replaced by another amino acid.
Using this method, scientists have created 96 variants of peptides. These improved variants are tested for affinity to the desired protein, after which a map is compiled showing the degree of its severity. The most successful variants can be assembled into a new peptide with high affinity, the binding force of which is equal to the sum of the binding forces of its components.
With this simple algorithm, you can quickly optimize peptides from random sequences, improving their affinity from 100 to 1000 times. The method can also be used to increase the specificity of peptides, allowing the bioconstruction to attach to a specific protein and excluding its binding to undesirable targets.
In a paper published in Molecular and Cellular Proteomics (Exploring antibody recognition of sequence space through random-sequence peptide), scientists answer the question whether a similar microchip with random peptides can help the process of creating epitope maps reflecting active binding regions of antibodies. Epitope mapping is one of the methods of determining whether a given antibody is applicable to a particular application, and faster and more efficient mapping technology is very important for biomedicine.
Antibodies with known epitopes were tested on a microchip with random peptide sequences. To find out if random peptides can help identify antibody epitopes, the scientists used bioinformatics techniques to identify peptides with a high degree of affinity.
They applied two methods. In the first, high-affinity random peptides were compared with epitopes of antibodies binding to them, and their similarity was analyzed statistically. Another method identified peptides for signature motifs consisting of at least 7 amino acids (or for a combination of two shorter motifs). The lead author Rebecca Halperin and her colleagues managed to show that statistically significant information on epitopes can really be gleaned from such bioinformatic tests.
Both works bring closer the development of methods for high-performance and inexpensive study of natural antibodies. Their further improvement should make it possible to diagnose the exact cause of the disease, based only on the analysis of the immune response.
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01.02.2011