Deleteriousness Prediction Service

Identifying mutations that affect protein function is of major interest to those studying the proteins and their implications in disease. Disease-causing mutations tend to occur in structurally and functionally important sites, and predicting mutations at these sites as deleterious or neutral may help identify disease-associated alleles. As a world-class provider of biotechnology, Creative Biolabs provides omnidirectional technologies to meet the diverse needs of our customers. With our professional experience and advanced protein engineering platform, we are confident in offering a variety of deleteriousness prediction services to meet the diverse needs of our customers.

Introduction to Deleteriousness Prediction

The discovery of single nucleotide polymorphisms (SNP) in the human genome has created an opportunity for high-throughput deleterious mutation prediction to discover and prioritize candidate human disease alleles from the pool of uncharacterized nonsynonymous SNPs (nsSNPs). The nsSNP occurring in a protein-coding region alters the encoded amino acid sequence, potentially affects protein structure and function, and further causes human inherited diseases. Typically, the deleterious nsSNPs prediction problem is formulated as a binary classification model using diverse genomic data as features to compare the deleterious nsSNP with neutral nsSNP. Users should provide information about protein ID or sequence, amino acid substitution, and/or multiple sequence alignment. After inputting all the required information, the classification tools can be implemented by extracting their own features and setting up the new classification model automatically. Finally, the deleterious score or the classification result may output by the tools.

Fig.1 An overview of deleteriousness prediction methods.

Services

The genomes of mammals contain thousands of deleterious mutations. It is important to be able to recognize them with high precision. Therefore, deleterious prediction becomes a more and more popular issue for research and guiding real experiments. We use sequence information, protein structure information and/or annotations from known databases or predictions results to collect and calculate classification features.

Sequence-based deleterious nsSNPs prediction methods usually take advantage of biochemical properties, physiochemical properties, sequence information, the evolutionary information of proteins, and the predicted 1D or 2D structure of proteins.
Structure-based prediction methods may search a protein structure database and get some structural features for further classification.
Annotation-based methods may take annotations from the SWISS-PROT database or use some published tools to get the preliminary scores for the query nsSNPs.

We can provide the above three deleteriousness prediction services to meet customers’ specific requirements.

Creative Biolabs has been involved in the field of protein engineering for many years and we are fully committed to working with you to facilitate the successful completion of the projects. We have accumulated a wealth of experience from the accomplished projects and are very proud of our high-quality platforms to meet diverse needs from our clients. If you are interested in our services, please contact us for more details.

All listed services and products are For Research Use Only. Do Not use in any diagnostic or therapeutic applications.

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