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The interaction between the immune system and cancer growth and development has long been established, and multiple ways are used to evade immune system detection. The use of vaccines to alter the immune environment may provide significant clinical response improvements. But most of the vaccines currently being tested are immunosuppressive. The use of in silico-based epitope prediction methods to produce reasonably designed vaccines can improve the effectiveness of vaccines. Alfa Cytology provides multiple algorithms to predict the binding affinity of MHC molecules/peptide complexes.
Although computer prediction of antigen epitopes still requires experimental data support to ultimately determine the truly effective antigen, the extensive and rapid prediction in the early stage has significantly reduced the amount of experimental repetition.
High Efficiency
In Silico prediction services analyze large datasets and screen a large number of potential immunogenic targets in a relatively short period. Compared to traditional experimental methods, this saves time and resources.
Priority Ranking
In Silico prediction services use computational algorithms to filter candidate targets based on various criteria and prioritize them, thereby helping to narrow down the search range for potential immunogenic targets.
Diverse Methods
In Silico prediction services can integrate multiple types of omics data, thereby extracting meaningful information and identifying potential targets that may not be recognizable by analyzing a single dataset.
Alfa Cytology provides in Silico prediction services for tumor antigens. We provide a variety of technical services to meet the epitope prediction needs for different antigen types. The results of this service provide valuable information to customers for making important decisions in strategic planning.
It is worth mentioning that we provide prediction services for MHC binding sites, which play a crucial role in presenting antigens to T cells. MHC binding prediction algorithms assess the binding affinity between antigens and specific MHC alleles. These methods analyze peptide sequences, MHC binding motifs, and the structural characteristics of MHC-peptide complexes to predict potential antigen-MHC binding.
| Service Options | Description |
| Sequence-based Antigens Prediction Services | Computer prediction models based on peptide binding fragment gene sequences are referred to as sequence-based algorithms but can be further classified into subclasses based on the methods of the algorithms or the test data used. These subclasses include base alignment/localization for specific structural domains, quantitative matrix-based methods, and machine learning-based algorithms. |
| Structure-based Antigens Prediction Services | Structural prediction tools use a combination of sequence information, combined data, homologous structural information (i.e. crystallographic data of known pMHC complexes), and computational methods to predict pMHC complexes. |
| Multiple Algorithms Identify Epitopes Services | These services utilize advanced algorithms and databases to predict potential epitopes within tumor antigens. We provide predictions of peptide sequences likely to bind to MHC molecules and trigger an immune response. |
| Machine Learning and Data Mining Services | Machine learning techniques analyze large-scale genomic and proteomic data to identify potential immunogenic targets. These algorithms can identify patterns, correlations, and predictive models based on features extracted from the data, helping to prioritize candidate immunogens. |
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As our understanding of cancer immunology deepens, in Silico prediction services will play a crucial role in guiding the development of effective and personalized cancer immunotherapies. Alfa Cytology is committed to providing customers with cancer antigens prediction and identification services, if you are interested in our service program, feel free to contact us.
