Nowadays, antibodies are widely applied in diagnostics and as therapeutic agents for a variety of disease. Affinity maturation is a process to operate the variable region of an antibody which undergoes somatic hypermutation, in order to introduce point mutations in the framework and CDR loops to select for a variant with improved binding affinity for its target. Achieving high-affinity binding is crucial for amplifying dissociation half-times, expanding detection limits, reducing drug dosages, as well as improving drug efficacy. Nevertheless, antibody affinity maturation in vivo usually fails to generate antibody drugs of the targeted potency. Thus, it’s necessary to make further affinity maturation in vitro via directed evolution or computational design. As an experienced leader in the antibody engineering field, Creative Biolabs has built a comprehensive in silico antibody design platform. We offer high-quality computer-aided affinity maturation service based on our advanced platform, and we will do our best to facilitate your project’s success.
In silico Affinity Maturation
Based on the better understanding of the nature of affinity maturation, and the capacity to selectively transform the specificity of antibody sequences, computer-aided affinity maturation is available with advanced antibody engineering technology. Computational design mainly relies on the accurate energetic evaluation and thorough conformational search. Typically, there are some challenges for protein-protein affinity design, for example, interfacial trapped water molecules, conformational change upon binding, polar and charged side chains, and the trade-off of protein-solvent with protein-protein interactions from the unbound to the bound state. Hence, a preeminent design strategy should make a considerable fraction of designs which are successful when tested experimentally, as well as produce substantial improvements across multiple systems. Although there are probably a number of mutations that confer increased binding affinity for a particular interaction, calculations need just determine a subset to be successful. Creative Biolabs uses thorough optimization techniques which exhaustively rank-order the best solutions in a discretized search space.
Fig 1. Modeling of the interaction between gastrin17 and scFvs. (Barderas, R., 2008)
Computer-aided Affinity Maturation Strategies
Several in silico strategies have been explored by scientists during the past years. In a former study, Clark et al. utilized computational design to mature an antibody and produce candidate sequences with higher predicted affinity. In this case, a triple mutant was generated with 10 times higher affinity by using a combination of side chain repacking and electrostatic optimization. Besides, a similar method has been used to improve the species cross-reactivity of an antibody, rather than improving affinity for a previously targeted antigen. By means of analyzing sequence differences between two serine protease orthologs, researchers developed new antibody designs by limiting the search space to positions that contact points of difference between orthologs. In this way, they enabled to target positions which would be most likely to build novel contacts across the binding interface to make interaction at a reasonable affinity. This strategy can create antibody mutants with improvement in affinity of over two orders of magnitude.
Based on our comprehensive PreciAb™ platform, designing and engineering novel antibodies with desired therapeutic properties is available. We customize the service according to the specific requirements from the customers. We also provide other epitope-specific antibody design services. Please contact us for more information and a detailed quote.
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