Integration of surface plasmon resonance with mass spectrometry: automated ligand fishing and sample preparation for MALDI MS using a Biacore 3000 biosensor.

Integrating surface plasmon resonance analysis with mass spectrometry allows detection and characterization of molecular interactions to be complemented with identification of interaction partners. We have developed a procedure for Biacore 3000 that automatically performs all steps from ligand fishing and recovery to sample preparation for matrix-assisted laser desorption/ionization (MALDI) mass spectrometry including on-target digestion. In the model system used in this study a signal transduction protein, calmodulin, was selectively captured from brain extract by one of its interaction partners immobilized on a sensor chip. The bound material was eluted, deposited directly onto a MALDI target, and analyzed by mass spectrometry both as an intact protein and after on-target tryptic digestion. The procedure with direct deposition of recovered material on the MALDI target reduces sample losses and, in combination with automatic sample processing, increases the throughput of surface plasmon resonance mass spectrometry analysis.

Optimizing the surface plasmon resonance/mass spectrometry interface for functional proteomics applications: how to avoid and utilize nonspecific adsorption.

A great challenge in functional or interaction proteomics is to map protein networks and establish a functional relationship between expressed proteins and their effects on cellular processes. These cellular processes can be studied by characterizing binding partners to a "bait" protein against a complex background of other molecules present in cells, tissues, or biological fluids. This so-called ligand fishing process can be performed by combining surface plasmon resonance biosensors with MS. This combination generates a unique and automated method to quantify and characterize biomolecular interactions, and identify the interaction partners. A general problem in chip-based affinity separation systems is the large surface-to-volume ratio of the fluidic system. Extreme care, therefore, is required to avoid nonspecific adsorption, resulting in losses of the target protein and carry-over during the affinity purification process, which may lead to unwanted signals in the final MS analysis and a reduction in sensitivity. In this study, carry-over of protein and low-molecular weight substances has been investigated systematically and cleaning strategies are presented. Furthermore, it is demonstrated that by the introduction of colloidal particles as a capturing and transporting agent, the recovery yield of the affinity-purified ligand could be improved nearly twofold.

Kinetic and affinity predictions of a protein-protein interaction using multivariate experimental design.

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.