Interpretation of results from the competitive Biacore procedure for characterizing immunochemical interactions in solution

Easy and Rapid Approach to Obtaining the Binding Affinity of Biomolecular Interactions Based on the Deep Learning Boost

Recently, the deep learning (DL) dimension of artificial intelligence has received much attention from biochemical researchers and thus has gradually become the key approach adopted in the area of biosensing applications. Studies have shown that the use of DL techniques for sensing can not only shorten the time of data analysis but also significantly increase the accuracy of data analysis and prediction, resulting in the performance improvement of biosensing systems in comparison to conventional methods. However, obtaining reliable equilibrium and rate constants of biomolecular interactions during the detection process remains difficult and time-consuming to date. In this study, we propose a transformed model based on the deep transfer learning and sequence-to-sequence autoencoder that can successfully transfer the SPR sensorgram to the protein-binding constants, that is, the association rate constant (k_a) and dissociation rate constant (k_d), which provide crucial information to understand the mechanisms of drug action and the functional structures of biomolecules. Experimentally, we first trained and tested the pre-trained model using the Langmuir model which generated ideal SPR sensorgrams and then we fine-tuned the pre-trained model through the augmented SPR sensorgrams which were synthesized by using the synthesized minority oversampling technique (SMOTE) through the moderate-scale experiment. Next, the fine-tuned model was inputted with a short experimental SPR sensorgram that only needs 110 s, and the sensorgram was directly transformed into a reconstructed ideal sensorgram. Finally, the binding kinetic constants, that is, k_a and k_d, as outputs, were obtained through fitting the reconstructed ideal sensorgram. The results showed that the prediction errors of k_a and k_d obtained by our model were less than 12 and 24%, respectively. Based on the convenience, accuracy, and reliability of the proposed DL approach, we believe our strategy significantly boosts the feasibility to monitor the binding affinity of antibodies online during production.

A Fiber-Based SPR Aptasensor for the In Vitro Detection of Inflammation Biomarkers

It is widely accepted that the abnormal concentrations of different inflammation biomarkers can be used for the early diagnosis of cardiovascular disease (CVD). Currently, many reported strategies, which require extra report tags or bulky detection equipment, are not portable enough for onsite inflammation biomarker detection. In this work, a fiber-based surface plasmon resonance (SPR) biosensor decorated with DNA aptamers, which were specific to two typical inflammation biomarkers, C-reactive protein (CRP) and cardiac troponin I (cTn-I), was developed. By optimizing the surface concentration of the DNA aptamer, the proposed sensor could achieve a limit of detection (LOD) of 1.7 nM (0.204 μg/mL) and 2.5 nM (57.5 ng/mL) to CRP and cTn-I, respectively. Additionally, this biosensor could also be used to detect other biomarkers by immobilizing corresponding specific DNA aptamers. Integrated with a miniaturized spectral analysis device, the proposed sensor could be applied for constructing a portable instrument to provide the point of care testing (POCT) for CVD patients.