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Drug Discovery Today: Technologies
Vol. 5, No. 2–3 2008
Editors-in-Chief Kelvin Lam – Pfizer, Inc., USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Protein therapeutics
Computational design of protein therapeutics Inseong Hwang, Sheldon Park* Department of Chemical and Biological Engineering, University at Buffalo, SUNY, Buffalo, NY, 14260, USA
Computation is increasingly used to guide protein therapeutic designs. Some of the potential applications for computational, structure-based protein design include antibody affinity maturation, modulation of protein–protein interaction, stability improvement
Section Editors: Marco van de Weert and Eva Horn Moeller – Department of Pharmaceutics and Analytical Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Copenhagen, Denmark
and minimization of protein aggregation. The versatility of a computational approach is that different biophysical properties can be analyzed on a common framework. Developing a coherent strategy to address various protein engineering objectives will promote synergy and exploration. Advances in computational structural analysis will thus have a transformative impact on how protein therapeutics are engineered in the future. Introduction Protein molecules have several physicochemical characteristics that make them highly valuable as therapeutic molecules. For one, interactions involving proteins are usually specific, and protein drugs cause fewer unexpected side effects compared to small molecule drugs. Proteins are also an attractive platform for developing potential drugs, because they can be more easily ‘engineered.’ This refers both to the diversity of functions seen in natural proteins and to the ease with which structural and functional changes can be introduced in vitro by changing the underlying DNA sequence. Therefore, the technological infrastructure exists to facilitate the design and manufacturing of arbitrary proteins, which contrasts with *Corresponding author: S. Park (
[email protected]) 1740-6749/$ Published by Elsevier Ltd.
DOI: 10.1016/j.ddtec.2008.11.004
small molecule drugs that are much more difficult to synthesize and characterize. However, knowing how to make recombinant proteins in the laboratory does not imply that we can build a protein that is useful, interesting and has a desirable function. Successfully engineering protein therapeutics requires selecting appropriate molecular targets supported by sound science, deciding on the targeted structure of the therapeutic protein and finally choosing a sequence to fold to the structure. The sequence design step remains an active area of research, because the sequence–structure relationship is highly complex and defies easy definition. The efforts to map the target structure onto one or more sequences are at the heart of protein design. The field of protein design has matured significantly during the past 20 years, and has moved from heuristic expert design to the currently popular computational protein design [1]. The successful use of computation in protein design is demonstrated by several high profile examples in the literature, including the recent design of proteins with a novel topology [2] and catalytic activity [3,4]. Computation has also helped integrate protein design with protein engineering. For both protein engineering and design, the ultimate goal is to modulate the structure and function of a protein by introducing targeted mutations. To that end, the lessons learned from designing model proteins in the laboratory have accelerated e43
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Table 1. Main application areas of computation in therapeutic protein design. Targeted property
Interactions to be optimized
Computational goal
Challenges
Molecular recognition
Antibody–antigen binding Other protein–protein interactions
Affinity maturation Affinity maturation and specificity engineering
Flexible CDR, interactions involving polar residues Need for precise modeling of the biological mechanism
Pharmacokinetics Stability Aggregation
Protein–solvent interaction Homo-oligomerization
Improved core packing Aggregation propensity calculation
Immunogenicity
MHC II–antigen interaction
Identification of immunologic peptides
Reduced biological activity upon mutation Reduced biological activity and stability upon mutation; potential increase in immunogenicity Reduced biological activity upon mutation
the adoption of computation as a new tool for engineering protein therapeutics. This review focuses on the use of computation in engineering protein molecules with therapeutic potentials. The details of the individual research have previously been published, and therefore the emphasis will be on identifying the common themes among protein therapeutics and understanding the rationale for choosing specific design challenges. Potential uses of computation in protein therapeutic design are summarized in Table 1.
Improving antibody affinity Proteins in nature often interact with high specificity and affinity. It is not difficult to see that achieving the right balance of specificity and affinity would benefit from having a certain minimum molecular size. Large macromolecules, such as proteins, can make extensive use of both hydrophobic and hydrophilic contacts to optimize specificity and affinity. Hydrophobic interactions are important to increase affinity, because the burial of hydrophobic residues releases a large number of water molecules that entropically stabilizes the complex. By contrast, hydrophilic contacts are important for specificity, because hydrogen bonds and salt bridges are geometrically constrained interactions and require more careful positioning of the participating atoms. The van der Waals contacts are also important to achieve specificity by requiring shape complementarity. Improving the affinity of a protein drug is a strategy that is widely practiced when engineering therapeutic proteins, because high affinity makes a potential drug more efficacious and economical. Yet protein interactions in nature are optimized for specific biological functions rather than for affinity. As a result, computational analysis is playing an increasingly greater role in improving the affinity of interaction. The first example we examine is the computational affinity maturation of therapeutic antibodies. Affinity maturation for antibodies in vivo is a complex process involving diversity generation and clonal expansion. While antibodies with nanomolar affinity are easy to engineer by immunizing animals with antigenic molecules, subsequent improvement of affinity is often difficult and requires optimization in vitro. Computation is an effective tool in engineering affinity by e44
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rapidly searching through various mutations and evaluating the stability of the resulting complex. This strategy was used by Tidor and coworkers to improve the affinity of therapeutic and model antibodies in a predictable way [5]. They calculated the change in the free energy resulting from single mutations within the complementarity determining region (CDR) of the lysozyme antibody D1.3, and evaluated how well calculation predicts the improvement in binding. They observed that the electrostatic contribution to the free energy alone is a better predictor of the binding affinity than the total free energy. They similarly scanned the epidermal growth factor receptor drug cetuximab (Erbitux) for potential mutations to improve the electrostatic contributions to the free energy, and introduced single mutations at five positions. Although these single point mutants had marginally improved binding affinity compared to wild type, when three such single mutations were combined the binding affinity improved nearly tenfold to the final Kd 50 pM. In another study, Clark and coworkers used structure-based computational analysis to improve the affinity of an antibody fragment to the I-domain of the integrin VLA1 by an order of magnitude. Such antibodies may be useful as a therapeutic against inflammatory diseases caused by leukocyte recruitment [6]. While computational protein design has been applied to a variety of problems, including core packing and interface design [7], antibody–antigen interactions are particularly challenging because of the structural flexibility of the CDR residues. The structure used for calculation may differ from the actual mutant structure, and thus it is often difficult to predict the effects of a mutation with an accuracy expected for a well-defined structure. In practice, structural flexibility is addressed by using multiple backbone structures, from either NMR structures, simulations, or parameterization. Other challenges of using computation to improve antibody affinity include ordered solvent molecules at the interface and the importance of interactions involving polar residues (Fig. 1). Computational analysis of antibody–antigen interactions can be useful even when structural information is not available. Barderas et al. recently reported that the antibody– antigen complex may be iteratively modeled to guide the design of in vitro screening [8]. Starting with the low-affinity
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Drug Discovery Today: Technologies | Protein therapeutics
Figure 1. The role of computation in therapeutic protein design.
antibody TA4 against the hormone gastrin, the authors built a homology model of the antibody by selecting CDR template fragments from the Protein Data Bank (PDB) and optimized the structure through energy minimization. The gastrin epitope (LWEEEEE) was docked onto the antibody based on the hydrophobic and charge distribution of the side chains. The modeled complex structure helped identify the regions of suboptimal packing, which was explored experimentally to increase the affinity by 454-fold and improve the efficacy of the antibody against tumor cell lines (Fig. 2). Other systems besides antibodies have also been reported where improved affinity is targeted as a therapeutic strategy. These examples are discussed below.
Targeting protein–protein interactions Targeted disruption of crucial protein–protein interactions is the basis of several therapeutic strategies. For example, soluble intercellular adhesion molecule (ICAM)-1 with improved affinity for the lymphocytes antigen LFA-1 was proposed as a potential therapy for inflammatory diseases [9]. The rationale for the strategy is that engineered ICAM would prevent lymphocytes from adhering to ICAM-expressing cells and
Figure 2. Modeled TA4 (surface) with docked gastrin peptide (green). Modeling the complex structure was crucial to identify the antibody residues to target during in vitro affinity maturation. The coordinates were kindly provided by Dr. Ignacio Casal (Spanish National Cancer Research Center).
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would therefore help reduce inflammation. Similarly, autoimmune diseases caused by cytotoxic T lymphocytes (CTLs) may be treated using soluble recombinant T cell coreceptor CD8, because the activation of CTL responses requires simultaneous binding of TCR and CD8. Using wild-type CD8 structure, therefore, Cole et al. designed a soluble mutant with improved binding to HLA-1 [10].
T cell receptor A high affinity T cell receptor fragment (TCR) may be used to target cancer cells and virally infected cells displaying foreign peptides. Recently, Weng and coworkers reported the structure-based design of TCR with improved affinity for the peptide–major histocompatibility complex (pepMHC) [11]. To engineer TCR, they developed a novel prediction algorithm, ZAFFI, that uses an optimized energy-based scoring function and side chain packing to introduce mutations at the TCR interface. They demonstrated that ZAFFI can predict mutations in human A6 TCR in order to improve its binding to a low-affinity complex of a peptide from the human T cell lymphotrophic virus bound to HLA-A2 by nearly a 100-fold. Interestingly, their study found that shape complementarity and hydrophobic packing were the most important characteristics in high affinity mutants, as long as no significant electrostatic contributions are lost. This contrasts with the findings from Lippow et al. [5], which suggests that electrostatics alone is the most significant factor (see above).
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also interacts with the decoy receptors, DcR1 and DcR2, that do not lead to apoptosis. Because some tumor cells are DR5response, a designed TRAIL specific for DR5 may work as a tumor-selective therapy. Quax and coworkers thus applied an automatic design algorithm (FOLD-X) to create TRAIL variants with a strong preference for DR5 over other receptors. The designed variants show a large increase in biological activity against DR5-selective cell lines [13]. A similar structure-based design strategy was used subsequently to engineer DR4-specific variants to target different tumor types [14].
Antibody effector domain Computational design was also used to increase the effector activity of antibody. Therapeutic antibodies induce cellmediated cytotoxic effector functions, such as Ab-dependent cell-mediated cytotoxicity (ADCC), by interacting with various Fc receptor families. However, a subset of FcgR isoforms are inhibitory and reduce the efficacy of an antibody-based therapy. Therefore, a Fc variant that specifically targets activating Fcg receptors would be more effective as a therapeutic agent. A structure-based strategy was used to engineer Fc variants of anti-CD52 Ab alemtuzumab, anti-Her2 Ab trastuzumab and Ab rituximab against WIL2-S lymphoma, with preferential binding to activating FcgR [15]. The designed antibodies indeed had enhanced ADCC over wild type, and exhibited effector functions even when the antigen expression level was low.
Tumor necrosis factor
Increasing stability
An increase in the serum tumor necrosis factor (TNF) concentration is associated with inflammatory diseases, such as arthritis. To mitigate the action of TNF, Steed et al. computationally designed a dominant negative (DN) mutant that sequesters wild type in an inactive heterotrimeric complex [12]. As is typical with most structure-based designs, the authors first studied wild-type TNF complex bound to the receptor and, through modeling, identified the regions that are crucial for receptor activation. They then introduced mutations at the interface that would reduce binding to the TNF receptors while preserving their ability to form heterotrimers with native TNF. The DN-TNF inhibited the activity of native TNF in a concentration-dependent manner. Despite potential concerns of increased immunogenicity, this example showcases a novel therapeutic strategy that differs from the other treatments targeting the TNF pathway and has a greater potential to succeed because of the smaller size of the molecule.
Stability is important for potential therapeutic proteins because stable proteins remain active longer after administration and require less frequent usage at a lower dosage. Additionally, a stable recombinant protein often expresses at a higher level and retains structure and function better during the manufacturing process, which lowers its production and distribution costs. Studies using model proteins have demonstrated that computation can be effective in designing mutations to stabilize a protein by quantitatively evaluating the contributions from various interactions [16]. Recent examples of stability optimized therapeutic proteins include various hormones found to be defective in human diseases as well as engineered therapeutic antibodies.
TRAIL The specificity of interaction may also be targeted for therapeutic effects. The TNF-related apoptosis inducing ligand (TRAIL) induces apoptosis by interacting with the death receptors DR4 and DR5 to induce apoptosis. But the molecule e46
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Human fibroblast growth hormone Improving stability can be achieved using either sequence or structural information. In a sequence-driven design, the consensus sequence among related proteins is first established, and mutations are introduced to increase the sequence similarity between the mutant and the consensus sequence. As a demonstration of this approach, introducing point mutations in human fibroblast growth hormone-1 based on a sequence comparison with other FGF family members improves the thermal stability of the molecule by 278C
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[17]. Importantly, the mutant had a three- to four-fold longer half life than wild type and exhibited stronger mitogenic activity in a culture study, thus proving the validity of a therapeutic strategy based on stability optimization. That these mutants all had similar affinities toward the FGF receptor suggests that structural stability and binding affinity are two distinct metrics of the therapeutic efficacy of a protein drug.
Human growth hormone At Xencor, the proprietary Protein Design Algorithm (PDA) was used to stabilize both human growth hormone (hGH) and human granulocyte colony stimulating factor by mutating core residues [18,19]. The use of computation was crucial in the design because of the enormous number of the potential sequences that had to be screened. Engineering hGH, for example, required introducing 6–10 mutations based on screening residues at 45 different positions, which would have been difficult to discover experimentally.
Drug Discovery Today: Technologies | Protein therapeutics
computation to predict aggregation propensity based on intermolecular b-sheet formation [22]. Their algorithm applies statistical mechanics to compute the partitioning of a sequence into different potential conformations, including random coils, b-turns, a-helices and b-aggregates. The algorithm uses the parameters developed for secondary structure prediction but also introduces new concepts such as strand burial to better predict aggregation properties. In an impressive demonstration of its accuracy, the algorithm successfully predicted the aggregation of 179 peptides from 21 different proteins as well as 71 additional disease-related peptides. Considering that there are over 20 known aggregation diseases that afflict the human population, for example Alzheimer’s disease and Parkinson’s disease, these algorithms and other similar prediction methods may play important roles in the future development of novel therapeutics to inhibit neurodegenerative aggregation.
Reducing immunogenicity Minimizing aggregation Therapeutic proteins must be both thermally stable and resistant to aggregation. Aggregation of therapeutic proteins and peptides can cause serious problems during production, storage and administration, resulting in decreased activity and unwanted immunogenic reactions [20]. Because many proteins tend to aggregate at high concentrations, understanding how to minimize the aggregation potential of a protein would constitute a significant progress toward developing protein drugs. Computation can be an effective tool to address the challenge by identifying the mutations to reduce aggregation while maintaining the physiological function of the protein molecule. To that end, several computational models have been developed to predict protein aggregation propensity based on the physicochemical properties of polypeptides. Although human calcitonin (hCT) is highly efficacious against a range of human symptoms, e.g., osteoporosis in menopausal women, the extreme propensity of the protein to aggregate prevents its therapeutic use. Fowler et al. analyzed the effects of mutations on the aggregation properties of hCT using an algorithm previously developed to analyze the aggregation kinetics of short polypeptides [21]. Their strategy relies on identifying the residues that play an active role in oligomerization based on physicochemical analysis and introduce mutations that reduce net hydrophobicity, disrupt hydrophobic patches and stabilize a-helix formation to minimize potential aggregation. It is also imperative that the introduced mutations do not compromise the therapeutic efficacy of the protein. They demonstrated that the hCT mutants predicted to have reduced aggregation propensity were indeed more stable against aggregation – although their activity was somewhat reduced compared to wild type. An alternative physical model of protein aggregation, TANGO, was developed by Serrano and coworkers that uses
Administration of therapeutic proteins may potentially result in immune reaction that becomes apparent during clinical trials [23]. Neutralizing antibodies can compromise the therapeutic effects and raise both safety and efficacy concerns. Because immunogenicity may occur through several independent mechanisms, various strategies are used to reduce unwanted side effects, including antibody epitope removal, minimized antigen processing and solubility improvement by PEGylation. Because foreign antigens are processed and displayed by the antigen-presenting cells as part of the pepMHC II, recognizing the peptides that interact with the MHC II molecules can help minimize the immunogenicity of an engineered protein. The residues that interact with the MHC II molecules, known as agretopes, are available in databases that were originally developed for a vaccine design (reviewed in [24]). In a convergent evolution of protein engineering and vaccine development, a common bioinformatic strategy can be used to recognize the peptides that are likely to be displayed by the MHC II molecules. For example, IMGT (http://www.imgt.org) provides comprehensive immunoinformatics databases for the immunoglobulins, T cell receptors and the MHC molecules of human and other vertebrates [25]. In a more ambitious approach, the Immune Epitope Database and Analysis Resource (IEDB, http://www.immuneepitope.org) tries to facilitate the integration of the biochemical data from various sources by gathering information for the T cells and antibodies from human, nonhuman primates and laboratory animals in one place [26]. Removal of MHC II agretopes for the immunogenicity reduction has an advantage over antibody epitope removal because the molecular origins of binding and specificity are better defined and more static [23]. The MHC–agretope complexes can be modeled by threading peptides through fixed MHC molecules. This is a valid approximation because the www.drugdiscoverytoday.com
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Table 2. Algorithms mentioned in this review. Algorithms
Reported use
Refs
ZAFFI
Affinity improvement
[11]
PDA
Specificity engineering, thermal stabilization, antibody effector function
[12,15,18,19]
FOLD-X
Specificity engineering
[13]
TANGO
Prediction of protein aggregation propensity
[22]
conformation of the MHC molecule does not change significantly with the bound peptides [27]. However, unlike the MHC I molecules, the MHC II molecules tend to be more flexible and adopt different conformations when binding different peptides. Double threading method was reported recently to address this difficulty, in which both the MHC residues and the bound peptide are threaded to predict the complex structure and calculate the binding energy [28]. There are several unique but related methods that are currently used in the binding energy calculation [29–31]. Once the potential immunogenic regions are identified they can be removed by mutation and experimentally confirmed.
10
11
12 13
14
15
Conclusion Once an area of mere academic interest, computational protein design has moved to the forefront of drug development in the recent years. Broadly, two factors motivate the new paradigm of computational design of protein therapeutics. First, computation is efficient and can search through more sequences than a human mind or an experiment can. Second, the quantitative nature of computational analysis promotes synergy between disparate disciplines and enables facile extension of cumulative knowledge. We may expect to see further integration of computational toolkits (see Table 2 for a list of the algorithms discussed in this article) into therapeutic protein designs as structure–function relationships in protein become better understood.
16 17
18 19
20 21
22
23
References 1 Park, S. et al. (2004) Advances in computational protein design. Curr. Opin. Struct. Biol. 14, 487–494 2 Kuhlman, B. et al. (2003) Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368 3 Rothlisberger, D. et al. (2008) Kemp elimination catalysts by computational enzyme design. Nature 453, 190–195 4 Jiang, L. et al. (2008) De novo computational design of retro-aldol enzymes. Science 319, 1387–1391 5 Lippow, S.M. et al. (2007) Computational design of antibody-affinity improvement beyond in vivo maturation. Nat. Biotechnol. 25, 1171–1176 6 Clark, L.A. et al. (2006) Affinity enhancement of an in vivo matured therapeutic antibody using structure-based computational design. Protein Sci. 15, 949–960 7 Dantas, G. et al. (2003) A large scale test of computational protein design: folding and stability of nine completely redesigned globular proteins. J. Mol. Biol. 332, 449–460 8 Barderas, R. et al. (2008) Affinity maturation of antibodies assisted by in silico modeling. Proc. Natl. Acad. Sci. U. S. A. 105, 9029–9034 9 Song, G. et al. (2006) Rational design of intercellular adhesion molecule-1 (ICAM-1) variants for antagonizing integrin lymphocyte function-
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24 25
26 27 28 29
30
31
associated antigen-1-dependent adhesion. J. Biol. Chem. 281, 5042– 5049 Cole, D.K. et al. (2007) Computational design and crystal structure of an enhanced affinity mutant human CD8 alphaalpha coreceptor. Proteins 67, 65–74 Haidar, J.N. et al. (2008) Structure-based design of a T-cell receptor leads to nearly 100-fold improvement in binding affinity for pepMHC. Proteins 10.1002/prot.22203 Steed, P.M. et al. (2003) Inactivation of TNF signaling by rationally designed dominant-negative TNF variants. Science 301, 1895–1898 van der Sloot, A.M. et al. (2006) Designed tumor necrosis factor-related apoptosis-inducing ligand variants initiating apoptosis exclusively via the DR5 receptor. Proc. Natl. Acad. Sci. U. S. A. 103, 8634–8639 Tur, V. et al. (2008) DR4-selective tumor necrosis factor-related apoptosisinducing ligand (TRAIL) variants obtained by structure-based design. J. Biol. Chem. 283, 20560–20568 Lazar, G.A. et al. (2006) Engineered antibody Fc variants with enhanced effector function. Proc. Natl. Acad. Sci. U. S. A. 103, 4005–4010 Malakauskas, S.M. and Mayo, S.L. (1998) Design, structure, and stability of a hyperthermophilic protein variant. Nat. Struct. Biol. 5, 470–475 Zakrzewska, M. et al. (2005) Highly stable mutants of human fibroblast growth factor-1 exhibit prolonged biological action. J. Mol. Biol. 352, 860– 875 Filikov, A.V. et al. (2002) Computational stabilization of human growth hormone. Protein Sci. 11, 1452–1461 Luo, P. et al. (2002) Development of a cytokine analog with enhanced stability using computational ultrahigh throughput screening. Protein Sci. 11, 1218–1226 Schellekens, H. (2002) Bioequivalence and the immunogenicity of biopharmaceuticals. Nat. Rev. Drug Discov. 1, 457–462 Fowler, S.B. et al. (2005) Rational design of aggregation-resistant bioactive peptides: reengineering human calcitonin. Proc. Natl. Acad. Sci. U. S. A. 102, 10105–10110 Fernandez-Escamilla, A.M. et al. (2004) Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat. Biotechnol. 22, 1302–1306 Chirino, A.J. et al. (2004) Minimizing the immunogenicity of protein therapeutics. Drug Discov. Today 9, 82–90 Vivona, S. et al. (2008) Computer-aided biotechnology: from immunoinformatics to reverse vaccinology. Trends Biotechnol. 26, 190–200 Lefranc, M.P. (2008) IMGT((R)), the International ImMunoGeneTics Information System((R)) for Immunoinformatics: methods for querying IMGT((R)) Databases, Tools, and Web Resources in the context of immunoinformatics. Mol. Biotechnol. 40, 101–111 Peters, B. and Sette, A. (2007) Integrating epitope data into the emerging web of biomedical knowledge resources. Nat. Rev. Immunol. 7, 485–490 Jojic, N. et al. (2006) Learning MHC I – peptide binding. Bioinformatics 22, e227–235 Zaitlen, N. et al. (2008) Shift-invariant adaptive double threading: learning MHC II-peptide binding. J. Comput. Biol. 15, 927–942 Nielsen, M. et al. (2007) Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinform. 8, 238 Cui, J. et al. (2007) Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties. Mol. Immunol. 44, 866–877 Ranganathan, S. and Tong, J.C. (2007) A practical guide to structure-based prediction of MHC-binding peptides. Methods Mol. Biol. 409, 301–308
