At SilicoGenesis we enable our partners to develop better biologic drug candidates, faster. Our in silico design process delivers only the best candidates for expression and testing.
Given sequence data relating to target proteins, antibodies or nanobodies, we predict the three-dimensional structures based on the sequence data.
Using your sequence or structure data, we fully characterise proteins to the required project specifications. This includes surface characteristics, epitope prediction, paratope prediction, and protein-protein interaction site prediction.
We perform structure-based virtual screening or design de novo protein scaffolds in order to produce binders for your target protein, using your sequence or structure data. This process can involve a predefined target region or a predicted PPI site or epitope.
Given your sequence or structure data, we optimise the affinity or cross-reactivity of a drug-target complex according to the project requirements.
Given your sequence or structure data, we optimise the affinity or cross-reactivity of a drug-target complex according to the project requirements.
We assess the key characteristics of your candidates relating to developability and sequence liabilities in order to mitigate downstream issues during the drug development and testing process.
We recently collaborated with a large European pharmaceutical company on an affinity maturation project for a difficult cytokine target. Given the sequence of the antibody candidate, we predicted its structure and PPI interaction with the target antigen. Although it was a difficult target, we successfully modelled the three-dimensional structure of the complex and used this highly accurate model to perform in silico saturation mutagenesis using our AI/ML technology. Our processes were able to correctly identify all of the top affinity-enhancing mutations in the CDR regions confirmed by in vitro saturation mutagenesis using ELISA and SPR.
Tech used:
As part of our ongoing collaboration with a large European pharmaceutical company, we performed rapid in silico affinity maturation of a lead candidate in less than a month. Using the crystal structure of the lead candidate in complex with the antigen, we performed saturation mutagenesis using our AI/ML technology to identify mutations likely to improve binding affinity. The top single point mutations were then modelled in combinations in order to deliver a shortlist of candidates for in vitro expression and verification.
Tech used:
In collaboration with an academic partner based in Europe, we performed antigen engineering on a type-1 transmembrane protein tyrosine phosphatase. Given the sequence of two candidate antibodies, we predicted their structures and their interactions with the target antigen. We used highly accurate three-dimensional models of the antigen in complex with each of the antibodies to perform mutagenesis of the antigen. These mutations were introduced in order to prevent the candidate antibodies from binding to the antigen, as part of a strategy for a novel oncological treatment.
Tech used:
Recently, SilicoGenesis began a collaboration with the Laboratory for Thrombosis Research (KU Leuven) and PharmAbs (KU Leuven Antibody Centre). The project involves in silico antibody engineering and optimisation as well as in vitro and in vivo testing and validation. The goal of the project is to determine if the engineered lead molecule has improved developability characteristics and preserves binding affinity and species cross-reactivity.
Tech used:
Let us help you fasttrack your drug discovery and optimisation pipelines. Contact us for a brief chat and find out how we can collaborate. E-mail communication is preferred.
BioIncubator, Gaston Geenslaan 1, 3001 Leuven, Belgium
info@silicogenesis.com
+27 83 585 6050
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Leuven, Belgium and Johannesburg, South Africa.