AI-based GPCR Biologics Drug Discovery Services
The ability of a machine to mimic the cognitive processes involved in learning and problem-solving in the human brain is known as artificial intelligence (AI). AI systems based on technology can mimic human intelligence using a variety of advanced tools and networks. In order to save time and increase profitability, AI-based technologies are being deployed more and more at various phases of the drug discovery process. AI-based GPCR biologics drug development services from Creative Biolabs offer the notable acceleration that AI technology has made possible in this important area.
Workflow of AI-based GPCR Antibody Drug Discovery
- Epitope mapping
Predicting the portions of each protein involved in their interaction, or epitope and paratope prediction, is the first application of AI in the context of antibody development. Initial attempts at solving this issue were limited to linear epitope prediction; but, as more sophisticated algorithms, including docking and machine-learning taught scoring functions, were gradually introduced, relevant accuracy levels, like those of epitope3D, RosettaDock, or MAbTope, were achieved. All that is needed for high-throughput epitope mapping using MAbTope is the antibody sequences and a coarse-grained formalism based on docking. In more than 80% of instances, it enables the identification of the correct epitope area. This technique has been effectively used in numerous instances, such as when a 3D homology model needs to be constructed and the target's crystal structure is unknown.
- Screening clones
The initial set of hits is primarily picked on the recombinant target utilizing traditional biology techniques based on hybridomas or display technologies, either in bacteriophages or yeasts, whether working from immunological animals or previously established antibody banks. The primary condition for success is high affinity.
The first clone selection process has been substantially enhanced by single B cell technologies. Using single-cell technology, the animal's B-cells, which each produce a distinct antibody in their membrane, can be directly picked based on their affinity for the target, as opposed to creating a bank from the immune repertoire. Natural-paired sequences can then be produced by sequencing each of the antibodies that the B-cells were kept coded for separately.
- Affinity evaluation and optimization
Since affinity evaluation is frequently the first step in antibody characterization, experimental methodologies allow for a relatively high throughput when compared to other in vitro experiments. While more accurate but low throughput evaluation is carried out in SPR to provide the ground-truth KD, rough but large-scale evaluation is frequently carried out in ELISA. Nevertheless, the quantity of clones that may be assessed is constrained by the need for these technologies to produce both antigens and antibodies. Therefore, affinity prediction based on antigen and antibody sequences and structures would enable the assessment of much bigger ensembles. Furthermore, a lot of these techniques rely on the precise structural assembly of the antibody and target, which is typically not known and especially not for very big antibody collections.
- Off-targets prediction
Off-target binding is one aspect that is frequently disregarded when searching for antibodies. There are certain expensive and time-consuming experimental techniques for assessing cross-reactivity, such as protein arrays and tissue cross-reactivity. A computer technique has been devised that makes it possible to accurately forecast off-target binding. This technique encodes the CDRs of the antibodies using both the sequence and the expected 2D structure of the antibodies. Based on how similar the item sets are, these encodings can then be compared using a particular score.
Fig.1 AI-based approaches for Ab sequence design.1
AI-based GPCR antibody discovery reduces the number of biological tests required and increases the likelihood of success, significantly speeding up the process and lowering expenses. Please contact us for more information about our AI-based GPCR biologics drug discovery services.
Reference
- Kim, et al.; "Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions." Bioengineering 11.2 (2024): 185.
