Kinetic and Affinity Predictions of a Protein-Protein Interaction Using Multivariate Experimental Design

Abstract

We measured the influence of 14 mutations and 5 environmental variables (buffer perturbation) on the association and dissociation rate of a camel single domain antibody (cAb-Lys3) interacting with hen egg white lysozyme using a surface plasmon resonance-based biosensor. Based on this data set, we constructed quantitative predictive models for both kinetic (k a and k d ) constants as for the affinity constant (K d ). Mutations, after parameterization by quantitative descriptors, and buffers were selected using multivariate experimental design. These models were able to predict the corresponding parameters of four new variants of cAb-Lys3. Moreover, the models provide insights to the important chemical aspects of the interacting residues, which are difficult to deduce from the crystal structure. Our approach provides useful physicochemical information of protein-protein interactions in general. The information obtained from this kind of analysis complements and goes beyond that of conventional methods like alanine scanning and substitution by closely related amino acids. The mathematical modeling may contribute to a rational approach in the optimization of bio-molecules of biotechnological interest.Protein-protein interactions attract a lot of attention in this era of proteomics and antibody technology. Naive and synthetic antibody libraries in combination with phage and ribosome display are now routinely used to obtain antibody molecules with desired properties and specificities (1-6). However, the affinity and/or stability of the original binder retrieved from the library often requires improvements. Molecular evolution techniques (1-8) and rational design using sophisticated computer programs (9 -16) are two approaches to achieve this goal. Drawbacks to the former strategy are its dependence on the generation of large libraries, the difficulty in discrimination between binders with small differences in affinity, and the bias in favor of binders with improved expression levels (1,3). Additionally, codon bias and the generation of nonfunctional or deleterious mutants are major drawbacks of this strategy (1, 3). The rational design strategy largely depends on the availability of the structure of the protein-protein complex, and, although predictive algorithms have been improved significantly over the years, predictions of interaction energies, especially those related to amino acid modifications, remain highly problematic (9 -16).It is generally accepted that experimental data on the energetic contribution of the modified amino acids are required to develop algorithms relating binding energetics to particular amino acid modifications. Many analyses have used site-directed mutagenesis, especially alanine scanning (17-21) and substitution by closely related amino acids (21-23), to determine the energetic contributions of the amino acid side chains and functional groups. These strategies have shown to be very attractive because of their simplicity. However, they often lead to misinterpretations and inconclusive resul...

Replacing affinity with binding kinetics in QSAR studies resolves otherwise confounded effects

“…The results from the De Genst [18] paper were later re-analyzed using a completely different strategy [25]. The method for describing the mutated single domain antibodies was extended to allow for 3D-QSAR analysis.…”

“…The validity of the model for log(k d ) has been verified in a follow-up study using eight novel peptides [24], of which the k d of seven could be accurately predicted by the model. The second QSAR study included single and double mutated proteins, namely single domain camel antibodies (cAb) binding to lysozyme [18]. In this case, two residues present in the CDR3 loop of cAb were mutated according to the experimental design outlined in Figure 4.…”

“…Two QSAR studies have been performed explicitly to show that kinetic rate constants are beneficial for the QSAR modeling process and for the interpretation of the model [17][18][19]. These two studies were easily designed and dealt with peptides or proteins binding to antibodies.…”

QSAR of multiple mutated antibodies

“…A multivariate QSAR method was applied to model modifications in peptide sequence and buffer composition to predict rates of association and dissociation of peptides interacting with antibodies (Andersson et al, 2001;Choulier et al, 2002). Reliable predictions of binding kinetics and affinity were also obtained for single domain camel antibody mutants binding to a protein antigen, using QSAR (De Genst et al, 2002;Freyhult et al, 2003).…”

SPR identification of mild elution conditions for affinity purification of E6 oncoprotein, using a multivariate experimental design

“…QSAR analysis carried out by SPR allows correct prediction of antigen-antibody binding kinetics in various buffers (Andersson et al, 1999(Andersson et al, , 2001De Genst et al, 2002;Choulier et al, 2002a;Dejaegere et al, 2005). The approach requires kinetic data to be recorded in a set of multivariate conditions (simultaneous variation of several factors) defined by statistical design.…”