Bio-Layer Interferometry (BLI) Detection Service

BLI can be applied to certain protein-small molecule binding assays. However, small molecules feature low molecular weights and only generate limited signal shifts upon binding, which imposes stricter requirements on experimental design. We routinely evaluate parameters including the immobilized target protein load, anticipated affinity range of compounds, and background noise of the buffer system. Detectability can be improved by increasing analyte concentration gradients, optimizing sensor selection, and minimizing non-specific adsorption. For projects with extremely weak signals, we also recommend complementary validation via orthogonal techniques such as MST or SPR.

Unstable KD values commonly stem from inaccurate sample concentrations, protein aggregation or low active protein fractions, mass transport limitations caused by excessively high immobilization density, and false signals from non-specific adsorption. To enhance fitting reliability and deliver stable, reproducible kinetic parameters, we typically optimize immobilization levels, run blank controls with reference signal subtraction, adjust the range of concentration gradients, and implement strict sample quality control protocols (including clarification by centrifugation and repeated freeze-thaw avoidance).

Yes, BLI is widely adopted for competitive binding assays and epitope binning. Especially in antibody research and development, it can rapidly identify competitive interactions among multiple antibodies, supporting customers in formulating antibody combination strategies. For projects involving bispecific antibody design, cocktail therapeutics or antibody combination development, epitope binning data delivers high-value insights to guide key decision-making.

Common interfering factors for BLI measurements include elevated background from non-specific adsorption, abnormal signals induced by protein aggregation, baseline drift arising from mismatched buffer formulations, and low binding efficiency due to improper sensor selection. We generally improve data stability and mitigate drift and false positive interference via multiple approaches: optimizing buffer compositions (adjusting salt concentration, adding low levels of surfactants or BSA), regulating sample quality (preventing aggregation, clarifying samples by filtration and centrifugation), and selecting compatible sensors alongside appropriate immobilization strategies.