Design of therapeutic proteins with enhanced stabilityProc Natl Acad Sci USA20091061193711942 doi:?10

Design of therapeutic proteins with enhanced stabilityProc Natl Acad Sci USA20091061193711942 doi:?10.1073/pnas.0904191106. Footnotes Previously published online: www.landesbioscience.com/journals/mabs/article/9773. technological value of understanding and controlling protein stability is substantial. Protein stability is crucial for function, and many disease-related cellular processes are associated with protein destabilization. For example, a single amino acid substitution in hemoglobin leads to protein aggregation, and to sickle cell anemia.1 Proteins have also become increasingly important as therapeutics2 and of those, monoclonal antibodies currently represent the fastest TRC051384 growing class of therapeutics. 3 The recent significant increase in the number of protein-based pharmaceuticals has created a new challenge. Many therapeutic proteins are manufactured and stored as liquid solutions at very high concentrations of the product. As the percent of aggregation increases, the efficacy of the product decreases, and undesired side effects TRC051384 such as immunological response upon administration may occur.4,5 Thus, assuring stability of protein pharmaceuticals for the shelf-life of the product is imperative. There are two main approaches to stabilize, and hence extend the shelf life and overall efficacy, of protein drugs. One is to optimize the drug formulation, for example by adding stabilizing excipients.6C8 A second approach is to alter the protein sequence itself, for example by substituting non-polar or polar amino acids with charged amino acids on the protein TRC051384 surface.9,10 Although both approaches have been successfully used, many methods for stabilization require time- and resource-consuming trial-and-error experiments. At the same time, detailed predictive algorithms of aggregation are not available for large proteins such as antibodies. Existing computational methods analyze small proteins or search for TRC051384 specific structural motifs such as hydrophobicity or -sheet propensity. 11C14 These studies lack a detailed account of dynamically exposed and spatially close patches that can contribute to aggregation. Thus, we developed a microscopic tool to find patches responsible for aggregation. We find that many properties that are not taken into account in existing methods, such as protein dynamic fluctuations and spatial clustering of amino acids Rabbit polyclonal to HPSE distant in the primary protein sequence, are important to obtain an accurate tool. Such a screening tool will be of great value for the developability assessment and stabilization of candidate protein drugs from the discovery phase. Our recent article, Design of Therapeutic Proteins with Enhanced Stability, describes a new, rational and simulation-based technology for the identification of aggregation hot-spots in proteins.15,16 We call this technology Spatial Aggregation Propensity (SAP). Each amino acid of the protein sequence is assigned a SAP value based on the amino acid hydrophobicity, the extent of surface exposure, the sum of hydrophobic contributions of other amino acids within a pre-assigned radius, and the sum of contributions from the dynamics of the computational simulations: Here, math xmlns:mml=”http://www.w3.org/1998/Math/MathML” display=”block” id=”M1″ overflow=”scroll” mrow mtable columnalign=”left” mtr columnalign=”left” mtd columnalign=”left” mrow msub mrow mrow mo ( /mo mtable columnalign=”left” mtr mtd mi S /mi mi p /mi mi a /mi mi t /mi mi i /mi mi a /mi mi l /mi mo ? /mo mi a /mi mi g /mi mi g /mi mi r /mi mi e /mi mi g /mi mi a /mi mi t /mi mi i /mi mi o /mi mi n /mi mo ? /mo /mtd /mtr mtr mtd mi p /mi mi r /mi mi o /mi mi p /mi mi e /mi mi n /mi mi s /mi mi i /mi mi t /mi mi y /mi mtext ? /mtext mo stretchy=”false” ( /mo mi S /mi mi A /mi mi P /mi mo stretchy=”false” ) /mo /mtd /mtr /mtable mo ) /mo /mrow /mrow mrow mi a /mi mi t /mi mi o /mi mi m /mi mi i /mi mtext ?i /mtext /mrow /msub mo = /mo mstyle displaystyle=”true” munder mo /mo mtable columnalign=”left” mtr mtd mrow mtext Simulation /mtext /mrow /mtd /mtr mtr mtd mrow mtext Average /mtext /mrow /mtd /mtr /mtable /munder mrow mrow mo /mo mrow mstyle displaystyle=”true” munder mo /mo mtable columnalign=”left” mtr mtd mrow mtext Residues?with?at?least /mtext /mrow /mtd /mtr mtr mtd mrow mtext one?side?chain?atom /mtext /mrow /mtd /mtr mtr mtd mrow mtext within?R?from?atom?i /mtext /mrow /mtd /mtr /mtable /munder mrow /mrow /mstyle /mrow /mrow /mrow /mstyle /mrow /mtd /mtr mtr columnalign=”left” mtd columnalign=”left” mrow mrow mrow mrow mo ( /mo mrow mfrac mtable columnalign=”left” mtr mtd mtext SAA?of?side?chain?atoms /mtext /mtd /mtr mtr mtd mtext within?radius?R /mtext /mtd /mtr TRC051384 /mtable mtable columnalign=”left” mtr mtd mtext SAA?of?side?chain?atoms /mtext /mtd /mtr mtr mtd mtext of?fully?exposed?residue /mtext /mtd /mtr /mtable /mfrac mo /mo mtext Residue?Hydrophobicity /mtext /mrow mo ) /mo /mrow /mrow mo /mo /mrow /mrow /mtd /mtr /mtable /mrow /math SAA is the solvent accessible area of side chain atoms contained within radius R from the central atom. SAA is computed at each simulation snapshot. SAA of side chain of fully exposed residue (say for amino acid X) is obtained by calculating the SAA of side chains of the middle residue in the fully extended conformation of tripeptide Ala-X-Ala. Residue Hydrophobicity is obtained from the hydrophobicity scale of Black and Mould. 17 The scale is normalized such that Glycine has a hydrophobicity of zero. Protein regions within radius R with high SAP values (0.0 SAP 0.5) usually correspond to hydrophobic amino acids of high exposure that spatially form a hydrophobic patch. Regions with low SAP values (?0.5 SAP 0.0) usually correspond to hydrophilic amino acids surrounded by other polar residues. Although a certain SAP value accounts for a spatial region of radius R, this value is assigned to the central residue for convenience. Then, a SAP map for the protein is generated by color coding the amino acids in the protein structure based on their SAP values (Fig. 1). In our color-coding scheme, red represents high SAP, blue is low SAP, and the color intensity is commensurate with SAP. Thus, the SAP map gives a clear view of the protein aggregation-prone regions, colored in red (Fig. 1). Open in a separate window Figure 1 The Spatial-aggregation-propensity (SAP).