Evolution-based Prediction of Effects of Mutations

Creative Biolabs is a professional protein engineering expert with extensive experience in structure optimization, protein engineering, and safety evaluation. Equipped with a team of experts and state-of-art technologies, we can offer high-quality services for predicting the effect of mutations in various proteins. We are capable of providing our clients with a variety of customized services on mutation assessment and will work with you to develop the most appropriate strategy for your projects.

Introduction to Mutations in Protein Engineering

Protein engineering plays a critical role in regulating a wide variety of biological processes in mammals. Meanwhile, pilot studies have demonstrated that mutations should be the main cause of genome diversity that may lead to loss-of-function of various protein-associated pathways. Moreover, many studies have indicated that genetic mutations can impact the affinity and specificity of proteins, as well as the formation of protein interaction networks. For example, recent researchers have revealed a mutation of Gibbs on protein-protein binding assays. The results suggest that this mutation has triggered the structural changes of proteins, leading to unstable protein binding at specific sites. In addition, the mutation in protein engineering has also stimulated considerable efforts because it has proposed for many promising applications, especially for industrial and therapeutic purposes. As a result, scientists have developed a full range of biophysical techniques to measure the molecular structure and protein interactions in preclinical trials. Among them, deep sequencing and computational methods have been considered as the most powerful strategies for analyzing the effect of a large number of mutations on proteins.

Fig.1 Evolution-based Prediction of Effects of Mutations.

Service

For decades, mutations in proteins have been regarded as genetic factors of a number of diseases in humans. Many scientists have endeavored to reengineer nature proteins for a large scale of applications. Creative Biolabs offers a series of mutation prediction services to enhance the success of directed evolution for protein engineering. In general, the variants can be identified by comparing the protein sequences with other protein families. It has been widely used for excluding mutations that are predicted to break the protein’s structure.

Up to now, we provide a panel of assays for predicting the effects of mutations, ranging from protein folding and stability assay, protein function assay, protein-protein interactions assay, to protein expression assay. Furthermore, we also offer a wide collection of services to assess the mutations in different complicated contexts of diseases, such as cancers. For instance, single nucleotide polymorphisms (SNPs) have been commonly used for extracting protein sequence annotations and structural attributes in predicting the effect of mutations in several cancer tissues. Additionally, a range of methods, such as deep mutational scanning and computational predicting, have been established for analyzing mutations in specific proteins. As noted, we have successfully predicted the protein-protein binding at specific sites on a large-scale study based on interface structure profiles. The data indicate that this assay is a powerful tool for testing protein binding affinity changes upon mutations.

Fig.2 Approaches to protein engineering illustrated in the X-ray structure of diisopropylfluorophosphatase. (Wilding, 2019)

Creative Biolabs offers high-quality service covering the entire protein engineering process to best suit your technical, program and budget requirements which can greatly assist your research, pre-clinical development and clinical trials in animals. We are always assisting our clients with the most satisfactory data for your projects. For more detailed information, please feel free to contact us or send us an inquiry.

Reference

Wilding, M.; et al. Protein engineering: the potential of remote mutations. Biochem Soc Trans. 2019, 47(2): 701-711.

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