Phage display: an ideal platform for coupling protein to nucleic acid

Extensive antibody search with whole spectrum black-box optimization

In designing functional biological sequences with machine learning, the activity predictor tends to be inaccurate due to shortage of data. Top ranked sequences are thus unlikely to contain effective ones. This paper proposes to take prediction stability into account to provide domain experts with a reasonable list of sequences to choose from. In our approach, multiple prediction models are trained by subsampling the training set and the multi-objective optimization problem, where one objective is the average activity and the other is the standard deviation, is solved. The Pareto front represents a list of sequences with the whole spectrum of activity and stability. Using this method, we designed VHH (Variable domain of Heavy chain of Heavy chain) antibodies based on the dataset obtained from deep mutational screening. To solve multi-objective optimization, we employed our sequence design software MOQA that uses quantum annealing. By applying several selection criteria to 19,778 designed sequences, five sequences were selected for wet-lab validation. One sequence, 16 mutations away from the closest training sequence, was successfully expressed and found to possess desired binding specificity. Our whole spectrum approach provides a balanced way of dealing with the prediction uncertainty, and can possibly be applied to extensive search of functional sequences.

Emerging Landscape of Nanobodies and Their Neutralizing Applications against SARS-CoV-2 Virus

The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the coronavirus disease 2019 (COVID-19) has significantly altered people's way of life. Despite widespread knowledge of vaccination, mask use, and avoidance of close contact, COVID-19 is still spreading around the world. Numerous research teams are examining the SARS-CoV-2 infection process to discover strategies to identify, prevent, and treat COVID-19 to limit the spread of this chronic coronavirus illness and restore lives to normalcy. Nanobodies have advantages over polyclonal and monoclonal antibodies (Ab) and Ab fragments, including reduced size, high stability, simplicity in manufacture, compatibility with genetic engineering methods, and lack of solubility and aggregation issues. Recent studies have shown that nanobodies that target the SARS-CoV-2 receptor-binding domain and disrupt ACE2 interactions are helpful in the prevention and treatment of SARS-CoV-2-infected animal models, despite the lack of evidence in human patients. The creation and evaluation of nanobodies, as well as their diagnostic and therapeutic applications against COVID-19, are discussed in this paper.

Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning

Despite the advances in surface-display systems for directed evolution, variants with high affinity are not always enriched due to undesirable biases that increase target-unrelated variants during biopanning. Here, our goal was to design a library containing improved variants from the information of the "weakly enriched" library where functional variants were weakly enriched. Deep sequencing for the previous biopanning result, where no functional antibody mimetics were experimentally identified, revealed that weak enrichment was partly due to undesirable biases during phage infection and amplification steps. The clustering analysis of the deep sequencing data from appropriate steps revealed no distinct sequence patterns, but a Bayesian machine learning model trained with the selected deep sequencing data supplied nine clusters with distinct sequence patterns. Phage libraries were designed on the basis of the sequence patterns identified, and four improved variants with target-specific affinity (EC₅₀ = 80-277 nM) were identified by biopanning. The selection and use of deep sequencing data without undesirable bias enabled us to extract the information on prospective variants. In summary, the use of appropriate deep sequencing data and machine learning with the sequence data has the possibility of finding sequence space where functional variants are enriched.

Screening of autoantibodies as biomarkers in the serum of renal cancer patients based on human proteome microarray

The autoantibody in patients' serum can act as a biomarker for diagnosing cancer, and the differences in autoantibodies are significantly correlated with the changes in their target proteins. In this study, 16 renal cancer (RC) patients were assigned to the disease group, and 16 healthy people were assigned to the healthy control (HC) group. The human proteome microarray consisting of>19,500 proteins was used to examine the differences in IgG and IgM autoantibodies in sera between RC and HC. The comparative analysis of the microarray results shows that 101 types of IgG and 25 types of IgM autoantibodies are significantly higher in RC than in HC. Highly responsive autoantibodies can be candidate biomarkers (e.g., anti-KCNAB2 IgG and anti-RCN1 IgM). Extensive enzyme-linked immunosorbent assay (ELISA) was performed to screen sera in 72 RC patients and 66 healthy volunteers to verify the effectiveness of the new autoantibodies. The AUCs of anti-KCNAB2 IgG and anti-GAPDH IgG were 0.833 and 0.753, respectively. KCNAB2 achieves high protein expression, and its high mRNA level is confirmed to be an unfavorable prognostic marker in clear cell renal cell carcinoma (ccRCC) tissues. This study suggests that the high-throughput human proteome microarray can effectively screen autoantibodies in serum as candidate biomarkers, and their corresponding target proteins can lay a basis for the in-depth investigation into renal cancer.

Characterization of the Binding Behavior of Specific Cobalt and Nickel Ion-Binding Peptides Identified by Phage Surface Display

In recent years, the application focus of phage surface display (PSD) technology has been extended to the identification of metal ion-selective peptides. In previous studies, two phage clones—a nickel-binding one with the peptide motif CNAKHHPRCGGG and a cobalt-binding one with the peptide motif CTQMLGQLCGGG—were isolated, and their binding ability to metal-loaded NTA agarose beads was investigated. Here, the free cyclic peptides are characterized by UV/VIS spectroscopy with respect to their binding capacity for the respective target ion and in crossover experiments for the other ion by isothermal titration calorimetry (ITC) in different buffer systems. This revealed differences in selectivity and affinity. The cobalt-specific peptide is very sensitive to different buffers; it has a 20-fold higher affinity for cobalt and nickel under suitable conditions. The nickel-specific peptide binds more moderately and robustly in different buffers but only selectively to nickel.

Current advances in antibody-based serum biomarker studies: From protein microarray to phage display

PURPOSE

This review aims to summarize the technological advances in the field of antibody-based biomarker studies by proteome microarray and phage display. In addition, the possible development directions of this field are also discussed.

EXPERIMENTAL DESIGN

We have focused on the antibody profiling by proteome microarray and phage display, including the technological advances, the tools/resources constructed, and the characteristics of both platforms.

RESULTS

With the help of tools/resources and technological advances in proteome microarray and phage display, the efficiency of profiling antibody-based biomarkers in serum samples has been greatly improved.

CONCLUSIONS

In the past few years, proteome microarray and phage display, especially the latter one, have already demonstrated their capacity and efficiency for biomarker identification. In the near future, we believe that more antibody-based biomarkers could be identified, and some of them could eventually be developed into real clinical applications.