In silico-based Aggregation Prediction

Protein aggregation has been increasingly recognized as a problem limiting the efficacy and shelf life of protein therapeutics and has become an indicator and cause of numerous disease states. 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 in silico-based aggregation prediction services to meet the diverse needs of our customers.

Prediction of Protein Aggregation

Aggregation is an irreversible form of protein self-association. Aggregates are made up of oligomers that can range in size from a few monomers to several hundreds of monomers. As aggregates continue to grow in size, they eventually lose their solubility and fall out of solution as precipitates. Currently, a number of computational methods that predicted the various steps involved in protein aggregation are developed. These methods can be grouped into three general classes: unfolding kinetics and native state thermal stability, colloidal stability, and sequence/structure-based aggregation liabilities.

Aggregation is an unfolding-limited process when the rate of aggregation is dependent on the rate of unfolding. In this case, aggregation obeys first-order reaction kinetics and aggregate formation occurs at the rate of monomer loss. In experiments, a number of proteins have been found to follow first-order aggregation kinetics. Computational tools that accurately predict unfolding rates have the potential to rank order highly similar molecules under identical experimental conditions.
Self-association interactions of protein can be quantified by the thermodynamic solution parameter known as the osmotic second virial coefficient (B22). Positive B22 values indicate protein-protein interactions are colloidally stable. When protein-protein interactions are favored over protein-solution interactions, B22 values are negative, indicating overall attraction between individual protein molecules. The B22 values can be obtained by a computational method to predict the aggregation of protein molecules in solution.
Irreversible aggregates are nucleated at specific sequence locations called aggregation-prone regions (APRs). APRs have unique amino acid residue compositions and sequence patterns. There are several sequences based computational tools to predict APRs in peptides and proteins, such as tools that use only sequence composition, tools that combine sequence composition with position-specific patterns, tools that utilize secondary structure prediction and conformational preferences, and tools that perform threading onto the cross-β structure.

Fig.1 The schematic diagram of the aggregation of therapeutic mAbs.

Services

Protein aggregates have the potential to elicit an immune response and reduce target binding affinity. Therefore, predicting the mechanisms of aggregation has become a central focus of the investigation. Innovations in analysis techniques, particularly of computational molecular modeling approaches have provided higher resolution information about the structure of aggregates as well as key insights into the mechanisms of aggregate formation. We can provide various aggregation prediction services, including unfolding kinetics and native state thermal stability, colloidal stability, and sequence/structure-based aggregation liabilities, 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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