Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal structure of the antibody bound to the antigen for structural predictions. Here, we show an example of successful in silico affinity maturation of a hybridoma derived antibody, AB1, using just a homology model of the antibody fragment variable region and a protein-protein docking model of the AB1 antibody bound to the antigen, murine CCL20 (muCCL20). In silico affinity maturation, together with alanine scanning, has allowed us to fine-tune the protein-protein docking model to subsequently enable the identification of two single-point mutations that increase the affinity of AB1 for muCCL20. To our knowledge, this is one of the first examples of the use of homology modelling and protein docking for affinity maturation and represents an approach that can be widely deployed.
Antibodies are the most specific class of binding molecules known and their versatility has led to many successful therapeutics for the treatment of severe diseases. Structurally, antibodies are multi-domain proteins formed by beta-sheets that are held together by disulfide bridges. Two immunoglobulin domains, the variable light chain (VL) and the variable heavy chain (VH) domains, are joined together to create the variable fragment (Fv). Wu and Kabat’s original works [1] identified six hypervariable regions on the VH and VL domains and correctly predicted that such regions are responsible for the specific binding of the antigen. These loops, the complementarity-determining regions (CDRs), arise from a relatively conserved framework region (FR) and are typically in close spatial proximity to the antigen. The VL and VH domains together generate a binding site for the antigen that is in large part mediated by CDRs. Antibody discovery platforms use either a display-based library approach (phage, yeast, ribosome, mammalian, or other systems) or an immunisation and hybridoma screening strategy for antibody isolation. Once a panel of lead antibodies has been isolated, their binding affinity often requires optimisation if the antibody is to be a potential therapeutic. The display methods mentioned above can be used for in vitro affinity maturation because they allow for control of antigen concentration, presentation format, and deselections to eliminate unwanted specificities. These methods, along with other random mutagenesis methods, have proven very successful for affinity improvements [2–7]. However, the process of in vitro affinity maturation can be laborious and time consuming, taking many months, and more efficient methods to improve affinity would be beneficial. A number of strategies for in silico antibody affinity maturation have been reported, typically employing either a structure-based rationale [8–11] or a mini-library approach [12]. The success of these methods hinges primarily on two factors: first, the presence of a high-quality co-crystal structure, and second, an algorithm to calculate the energy change that occurs upon mutation. Application of free energy perturbation (FEP) [13–15] and potential-of-mean force (PMF) [16] methods to predict free energy changes in proteins has been reported in the literature [17–20]. However, they often require significant computational time and cost, which significantly limit their application for in silico antibody lead optimisation. More commonly, methods based on molecular mechanics (MM), coupled with an implicit solvent model such as generalized Born surface area (MM-GBSA), molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) [21,22] or the Lazaridis-Karplus solvation model (MM-LKSM), are employed to estimate free energies with significant savings in computing time and infrastructure [23–26], allowing thousands rather than dozens of mutations to be calculated. The disadvantage of using molecular mechanics calculations is that they do not account for global conformational changes that may arise upon mutation, resulting in ΔΔE values that are not truly representative of the real protein. This is reflected in the poor correlation between MM-GBSA/MM-PBSA/MM-LKSM free energy changes and experimental data, which is often less than 0. 5 (Pearson correlation) [27,28]. The possession of high-quality antibody structural information is widely considered to be an essential starting point for computational antibody affinity maturation. To our knowledge, there have been few published reports of in silico affinity maturation in which the co-crystal structure of the antibody-antigen complex is not available. In this scenario, affinity maturation is particularly challenging due to the many assumptions and errors that are propagated during computational calculations and simulations of large biological systems, especially if the wild-type antibody is already a strong binder with KD in the nanomolar range. Despite these challenges, we have decided to test such an approach, choosing to build a homology model of an Fv fragment and to use the nuclear magnetic resonance (NMR) structure of the antigen for protein-protein docking. We optimised the protein-protein docking with experimental guidance from a panel of alanine scanning mutations in the VH and VL domains and used the in silico complex to drive in silico affinity maturation. In this paper, we are the first to describe the application of such an approach to obtain affinity and activity gains for a high affinity antibody that, although relatively modest, demonstrate the potential of this methodology.
Molecular mechanics techniques allow thousands of mutations to be made in a protein system but suffer from a lack of conformational sampling. To understand why AB1-C1 and AB1-C16 mutations improve affinity, we employed FEP combined with the enhanced sampling method replica exchange with solute tempering (REST), FEP+ [35], to calculate free-energy changes in an enhanced conformational sampling framework. Both single mutations AB1-C1 and AB1-C16 showed improved affinities for the muCCL20 antigen, of –1. 00 ± 0. 26 kcal mol-1 (H: S28R) and –0. 16 ± 0. 06 kcal mol-1 (L: G35Y), respectively. This result echoed the greater affinity improvement of the AB1-C1 mutation over the AB1-C16 mutation that was observed in the binding ELISA. Inspection of both representative mutant structures indicated significant rearrangement (Fig 8A and 8B) in which “backbone” RMSDs of 2. 6 Å and 2. 4 Å were observed between the minimised input structure and the H: S28R and L: G35Y mutants, respectively. Structural analysis of H: S28R showed that the introduction of arginine in the H: 28 position facilitates the formation of a π-cation interaction between H: R28 and A: Y4 of the antigen (Fig 8F). As with the docked structure, (Fig 8C) enhanced sampling of the native structure (Fig 8E) indicates that there are no interactions present between L: S28 and muCCL20; hence the observed free energy change can be attributed to the introduction of a new intermolecular interaction. Interestingly, the mutation of L: G35 to tyrosine also introduces a new π-cation interaction between L: Y35 and R66 of the muCCL20. The presence of the salt bridge between the adjacent L: D34 residue and A: R25 (Fig 8H), which is not present in the docked conformation, can be explained through the representative conformation, i. e. the centroid of the most populated cluster, of the native state. The presence of the L: D34/A: R25 interaction in the native state, Fig 8I, indicates that the change in free energy cannot be assigned to the formation of a new salt bridge since it is present in both reference (native) and final states. Therefore, the modest binding free energy change can be accounted for by the π-cation interaction. The weaker change in ΔΔGbinding observed for L: G35Y compared to H: S28R is likely due to poorer interaction geometry, 5. 7 Å/51. 5°, compared to 4. 9 Å/28. 5° for H: R28/A: Y4. Although both of these mutations were selected on the basis of MM ΔΔEbinding calculations (H: S28R, –9. 07 AU; L: G35Y, –1. 46 AU), the elucidation of these interactions is not possible by mapping the mutations onto the docked conformations (Fig 8C and 8G) or via molecular mechanics methods (Fig 8D and 8H) and are likely to be accessible only with enhanced sampling techniques.
In silico affinity maturation is a challenging yet promising area of protein engineering and antibody therapeutics. In this study, we chose a more unprecedented scenario, where the co-crystal structure of the antibody-antigen complex is not available with the wildtype antibody’s starting KD in the low nanomolar range, to test out the potential of de-novo in silico-based affinity maturation. In this case study, careful consideration was given to every step in the process especially protein docking and the challenges it presents. We chose to confirm the docking poses using in silico and experimental alanine scanning mutagenesis to build confidence in the predictions. It is interesting to note that in the case of alanine scanning predictions, 14/23 mutations showed a reduction in binding, yet 9 mutations still retained binding when tested experimentally. This allowed us to hone in on the correct antibody-antigen interface to design affinity-improving mutations. Given the combination of potential errors from homology modelling, docking, and mutagenesis, it is remarkable that from a panel of only 19 variants, selected from three varying mutation protocols and energy functions, we were able to identify two affinity-improving mutations. The adoption of three protocols, Discovery Studios, Schrödinger (MM-GBSA) and Rosetta (MM-LKSM), for affinity maturation proved useful in this study, since neither of the two hits that were identified were picked up by more than one method (Table 3). AB1-C1 (H: S28R) was discovered from the Schrödinger protocol and AB1-C16 (L: G35Y) came from Discovery Studio. Unlike the Assisted Design of Antibody and Protein Therapeutics (ADAPT) approach [11], which uses multiple protocols to find an inter-method consensus, thus strengthening the confidence in a particular mutation, we find that multiple methods provided greater breadth of mutational space and avoided a particular residue or site bias, albeit with a relatively limited data set. Despite the aforementioned challenges, the MM approach in this work was especially useful in the filtering of docked poses and the identification of key residues. Indeed, the novel utilisation of alanine scanning with experimental validation and the demonstrable necessity for redocking are significant findings of this work. In silico alanine scanning, however, may be expected to be more reliable than in silico affinity maturation, since engineering the loss of specific, high-energy interactions such as salt bridges is easier than introducing new gain-of-function mutations that may require significant structural rearrangement. The approach with in silico affinity maturation outlined here has the potential advantage of generating significant savings in both time and resources for identifying potential hits. Based on our experience, a typical empirical affinity optimization campaign could take at least 6 months, whereas computational strategies coupled with focused experimental evaluations could generate significant time savings. In addition, without requiring the generation of antigen-antibody co-crystal structures, our approach saved around 3–18 months from a typical crystal structure-based in silico affinity maturation workflow. In addition, in silico design has the potential to streamline specificity and developability design parameters into the affinity-matured molecules. Whilst our work highlights the synergy from combining computational and experimental approaches, we also acknowledge the need for significant development in this field. Conventional experimental random mutagenesis strategies (such as parsimonious mutagenesis or display library approaches), sometimes with the help of co-crystal structures, are highly effective for antibody affinity improvement and can often reach beyond 10- to 100-fold [3,5, 36]. It has been much more challenging to reach that goal with a focused in silico design approaches based on crystal structures [9,37–40], with even fewer successes in the absence of crystal structures of the antibody-antigen complex. A recently published case study demonstrated an improvement of the binding affinity of a single-domain intrabody to α-synuclein from micromolar affinity to 66 nM [41]. This work and ours have shown encouraging signs for advances in the de novo antibody affinity maturation space, but also show the challenges of pushing toward sub-nanomolar affinity. In addition, there is an opportunity to improve the positive hit rate from 2 out of 19 mutants, which is not much higher than what is observed in a random mutagenesis approach. We have shown that methods such as FEP+ can provide some progress; however, this method has not yet been optimised for biologics applications and can only predict impacts of single point mutations with some accuracy. It would also be prohibitively computationally costly to employ as a saturation mutagenesis approach, taking into account protein dynamics, side chain flexibility and physics-based free energy calculations. Utilising multiple MM-GBSA/MM-LKSM methods may also present an opportunity for advancement in this respect, acting as a pseudo-sampling approach in which the energy function of each method samples the conformation in a unique way and may reveal effects that are neglected by other protocols. Here we present an interesting case study of in silico affinity optimisation in a scenario where co-crystal structure is not available. We demonstrate a novel strategy to achieve moderate affinity improvement using the integrated approach of in silico prediction and experimental guidance. Despite the limitations of the state of the technology that our work highlights, such as the need for more accurate energy prediction methods for large protein complexes, we hope to offer a path toward more advancement in the field of computational protein design and engineering.
The AB1 Fv model was generated on February 15,2017 with SAbPred [29], using AbodyBuilder [42] to search for a framework template structure, in this instance, 1MH5 for both VH and VL domains, from the curated antibody structure database. SabDab [43] and CDR loops were modelled with FREAD [44]. The nuclear magnetic resonance solution structure of muCCL20 was obtained from the protein data bank (PDB ID, 1HA6) [45]. The structural ensemble consisted of 20 low-energy structures, the first of which was representative of the average structure of the ensemble and was used for all subsequent in silico experiments. The antibody Fv and muCCL20 structures were imported into the Discovery Studio 2016 software package [32], and both structures were prepared with the clean protein tool and subsequently typed with the CHARMM-Polar-H force field [46] prior to docking. Rigid-body docking was performed with ZDOCK [47], using an angular step size of 15° and a distance cut-off of 10 Å, and non-IMGT CDR residues of the antibody were blocked. Subsequent refinement was performed with RDock [47]. After alanine scanning mutagenesis and binding studies were performed, the antibody and antigen were redocked. The same protocol was employed, but with the additional parameters that all six AB1 residues that were identified as important for binding must be within 10 Å of muCCL20. In silico alanine scanning was initially performed on each of the five top candidate poses. Mutagenesis was performed in the Discovery Studio 2016 software package, and each mutant structure was minimised prior to ΔΔE calculations. Mutagenesis was also performed on the redocked conformations, again using the same Discovery Studio protocol employed for alanine scanning, in which 10 single-point mutations were performed on each of the 1925 output poses.
The role of computational techniques in therapeutic protein development is multifaceted and includes structure prediction (homology modelling), interface identification (docking), and mutational energy change calculation. Success has been reported in the areas of protein structure prediction and interface prediction (see competition results such as Critical Assessment of Structure Prediction [CASP] and Critical Assessment of Predicted Interactions [CAPRI]), but perhaps one of the greatest challenges is the translation of in silico derived binding energy changes upon mutation into affinity matured antibody variants. In these applications, it is important to choose the correct structural models, or approximations, that make sense across all aspects of in silico protein design. The challenges are compounded when no antibody-antigen co-crystal structure is available and there is a high degree of uncertainty around the protein-protein interface. Although the field is arguably far from its goal of precisely correlating computational predictions with experimental data, we show that even in the absence of a co-crystal structure, it is possible to identify modest affinity-improving mutations by using in silico mutagenesis in combination with homology modelling, protein docking, and simple experimental checkpoints.
enzyme-linked immunoassays
crystal structure
condensed matter physics
mutation
protein structure prediction
protein structure
molecular biology techniques
alanine scanning
crystallography
immunologic techniques
research and analysis methods
amino acid analysis
solid state physics
proteins
immunoassays
molecular biology
molecular biology assays and analysis techniques
physics
mutagenesis
biochemistry
point mutation
gene identification and analysis
genetics
mutation detection
biology and life sciences
physical sciences
macromolecular structure analysis
Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design