Prediction of Aggregation Prone Regions of Therapeutic Proteins

Abstract

Therapeutic proteins such as antibodies are playing an increasingly prominent role in the treatment of numerous diseases including cancer and rheumatoid arthritis. However, these proteins tend to degrade due to aggregation during manufacture and storage. Aggregation decreases protein activity and raises concerns about an immunological response. We have recently developed a method based on full antibody atomistic simulations to predict antibody aggregation prone regions [Proc. Natl. Acac. Sci. 2009, 106, 11937]. This method is based on "spatial-aggregation-propensity (SAP)", a measure of the dynamic exposure of hydrophobic patches. In the present paper, we expand on this method to analyze the aggregation prone regions over a wide parameter range. We also explore the effect of different hydrophilic mutations on these predicted aggregation prone regions to engineer antibodies with enhanced stability. The mutation to lysine is more effective than serine but less effective than glutamic acid in enhancing antibody stability. Furthermore, we show that multiple simultaneous mutations on different SAP peaks can have a cumulative effect on enhancing protein stability. We also investigate the accuracy of various cheaper alternatives for SAP evaluation because the full antibody atomistic simulations are highly computationally expensive. These cheaper alternatives include antibody fragment (Fab, Fc) simulations, implicit solvent models, or direct computations from a static structure (i.e., a structure from X-ray or homology modeling). The SAP evaluation from the static structure is 200,000 times faster but less accurate compared to the SAP from explicit atom simulations. Nevertheless, the SAP from a static structure still predicts most of the major aggregation prone regions, making it a potential approach for use in high-throughput applications. Thus, the SAP technology described here could be employed either in high-throughput developability screening of therapeutic protein candidates or to improve their stability at later stages of manufacturing.

The effect of charge mutations on the stability and aggregation of a human single chain Fv fragment

“…The more challenging problem is identifying partially-folded regions on a protein that expose hot spots buried in nucleation or aggregate growth steps. A recently developed approach, the spatial aggregation predictor (SAP) identifies aggregation prone segments as hydrophobic regions with high dynamic exposure [17,18]. Whether or not SAP accurately predicts aggregation prone regions is not known since only aggregation rate data has been predicted, rather than the actual location of known aggregation hot spots.…”

Characterization of Aggregation Propensity of a Human Fc-Fusion Protein Therapeutic by Hydrogen/Deuterium Exchange Mass Spectrometry

“…The hydrophobicity of FcP1 was further analyzed by SAP calculations. SAP analysis uses information on both the solvent-accessible area and the hydrophobicity of protein amino acid residues via molecular simulations to identify protein aggregation hot spots [33,34]. HDX and SAP analyses both pinpointed a potential aggregation interface in the C H 2 region of Fc, which further reveals the importance of the C H 2 region in the aggregation of IgG-type molecules as observed previously [28][29][30].…”

“…SAP identifies the location and size of these aggregation-prone regions. Chennamsetty et al [33,34,47] demonstrated that SAP calculations can be used to determine critical regions of aggregation in therapeutic antibodies. The aggregation-prone motifs for the Fc region of antibodies have been identified with use of SAP and other methods [47].…”

Rational design of therapeutic mAbs against aggregation through protein engineering and incorporation of glycosylation motifs applied to bevacizumab

“…One engineering strategy to disrupt these APR is to mutate high SAP value residues to hydrophilic and charged residues, such as lysine, while avoiding mutations in protein-binding regions. 14,[31][32][33] Both the Fab and Fc domains of bevacizumab could drive its aggregation; however, since mutations stabilizing the Fc domain of IgG1 molecules have already been discussed in previous publications, 14 in this work, we focus on mutations stabilizing the Fab domain of bevacizumab. In fact, recent work has also suggested a role of the Fab domain of bevacizumab in its aggregation via the interaction of a Fab arm with the K445 residue of the CH3 domain of a second bevacizumab.…”