A high-throughput pipeline for design and selection of peptides targeting the SARS-Cov-2 Spike protein

Redox cycling-based signal amplification at alkanethiol modified nanoporous gold interdigitated microelectrodes

Interdigitated electrodes (IDEs) enable electrochemical signal enhancement through repeated reduction and oxidation of the analyte molecule. Porosity on these electrodes is often used to lower the impedance background. However, their high capacitive current and signal interferences with oxygen reduction limit electrochemical detection ability. We present utilization of alkanethiol modification on nanoporous gold (NPG) electrodes to lower their background capacitance and chemically passivate them from interferences due to oxygen reduction, while maintaining their fast electron transfer rates, as validated by lower separation between anodic and cathodic peaks (ΔE) and lower charge transfer resistance (Rct) values in comparison to planar gold electrodes. Redox amplification based on this modification enables sensitive detection of various small molecules, including pyocyanin, p-aminophenol, and selective detection of dopamine in the presence of ascorbic acid. Alkanethiol NPG arrays are applied as a multiplexed sensor testbed within a well plate to screen binding of various peptide receptors to the SARS COV2 S-protein by using a sandwich assay for conversion of PAPP (4-aminophenyl phosphate) to PAP (p-aminophenol), by the action of AP (alkaline phosphatase), which is validated against optical ELISA screens of the peptides. Such arrays are especially of interest in small volume analytical settings with complex samples, wherein optical methods are unsuitable.

Peptide libraries: from epitope mapping to in-depth high-throughput analysis

Peptide arrays are a valuable instrument in the characterization of protein-protein interactions (PPIs) and immunogenic regions. New methods were developed to exploit the high-throughput potential of peptide arrays to obtain more in-depth information, replacing traditional resource-intensive experiments. Here, we discuss the recent advances in peptide-array-based technologies and the remaining challenges.

Study of V2CTx-MXene Based Immunosensor for Sensitive Label-Free Impedimetric Detection of SARS-CoV-2 Spike Protein

Rapid and reliable immunosensing is undoubtedly one of the priorities in the efficient management and combat against a pandemic, as society has experienced with the SARS-CoV-2 outbreak; simple and cost-effective sensing strategies are at the forefront of these efforts. In this regard, 2D-layered MXenes hold great potential for electrochemical biosensing due to their attractive physicochemical properties. Herein, we present a V2CTx MXene-based sensing layer as an integral part of a label-free immunosensor for sensitive and selective detection of the SARS-CoV-2 spike protein. The sensor was fabricated on a supporting screen-printed carbon electrode using Nafion as an immobilizing agent for MXene and glutaraldehyde, the latter enabling effective binding of protein A for further site-oriented immobilization of anti-SARS-CoV-2 antibodies. A thorough structural analysis of the sensor architecture was carried out, and several key parameters affecting the fabrication and analytical performance of the immunosensor were investigated and optimized. The immunosensor showed excellent electroanalytical performance in combination with an impedimetric approach and exhibited a low detection limit of only 45 fM SARS-CoV-2 spike protein. Its practical applicability was successfully demonstrated by measuring the spike protein in a spiked artificial nasopharyngeal fluid sample.

Site-specific protein labeling strategies for super-resolution microscopy

Super-resolution microscopy (SRM) has transformed our understanding of proteins' subcellular organization and revealed cellular details down to nanometers, far beyond conventional microscopy. While localization precision is independent of the number of fluorophores attached to a biomolecule, labeling density is a decisive factor for resolving complex biological structures. The average distance between adjacent fluorophores should be less than half the desired spatial resolution for optimal clarity. While this was not a major limitation in recent decades, the success of modern microscopy approaching molecular resolution down to the single-digit nanometer range will depend heavily on advancements in fluorescence labeling. This review highlights recent advances and challenges in labeling strategies for SRM, focusing on site-specific labeling technologies. These advancements are crucial for improving SRM precision and expanding our understanding of molecular interactions.

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein-peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide-protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides.

A miniprotein receptor electrochemical biosensor chip based on quantum dots

Recently protein binders have emerged as a promising substitute for antibodies due to their high specificity and low cost. Herein, we demonstrate an electrochemical biosensor chip through the electronic labelling strategy using lead sulfide (PbS) colloidal quantum dots (CQDs) and the unnatural SARS-CoV-2 spike miniprotein receptor LCB. The unnatural receptor can be utilized as a molecular probe for the construction of CQD-based electrochemical biosensor chips, through which the specific binding of LCB and the spike protein is transduced to sensor electrical signals. The biosensor exhibits a good linear response in the concentration range of 10 pg mL-1 to 1 μg mL-1 (13.94 fM to 1.394 nM) with the limit of detection (LOD) being 3.31 pg mL-1 (4.607 fM for the three-electrode system) and 9.58 fg mL-1 (0.013 fM for the HEMT device). Due to the high sensitivity of the electrochemical biosensor, it was also used to study the binding kinetics between the unnatural receptor LCB and spike protein, which has achieved comparable results as those obtained with commercial equipment. To the best of our knowledge, this is the first example of using a computationally designed miniprotein receptor based on electrochemical methods, and it is the first kinetic assay performed with an electrochemical assay alone. The miniprotein receptor electrochemical biosensor based on QDs is desirable for fabricating high-throughput, large-area, wafer-scale biochips.

A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.

Biopanning of specific peptide for SARS-CoV-2 nucleocapsid protein and enzyme-linked immunosorbent assay-based antigen assay

The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide which triggered serious public health issues. The search for rapid and accurate diagnosis, effective prevention, and treatment is urgent. The nucleocapsid protein (NP) of SARS-CoV-2 is one of the main structural proteins expressed and most abundant in the virus, and is considered a diagnostic marker for the accurate and sensitive detection of SARS-CoV-2. Herein, we report the screening of specific peptides from the pIII phage library that bind to SARS-CoV-2 NP. The phage monoclone expressing cyclic peptide N1 (peptide sequence, ACGTKPTKFC, with C&C bridged by disulfide bonding) specifically recognizes SARS-CoV-2 NP. Molecular docking studies reveal that the identified peptide is bound to the "pocket" region on the SARS-CoV-2 NP N-terminal domain mainly by forming a hydrogen bonding network and through hydrophobic interaction. Peptide N1 with the C-terminal linker was synthesized as the capture probe for SARS-CoV-2 NP in ELISA. The peptide-based ELISA was capable of assaying SARS-CoV-2 NP at concentrations as low as 61 pg/mL (∼1.2 pM). Furthermore, the as-proposed method could detect the SARS-CoV-2 virus at limits as low as 50 TCID₅₀ (median tissue culture infective dose)/mL. This study demonstrates that selected peptides are powerful biomolecular tools for SARS-CoV-2 detection, providing a new and inexpensive method of rapidly screening infections as well as rapidly diagnosing coronavirus disease 2019 patients.

Discovery of Peptidic Ligands against the SARS-CoV-2 Spike Protein and Their Use in the Development of a Highly Sensitive Personal Use Colorimetric COVID-19 Biosensor

In addition to efficacious vaccines and antiviral therapeutics, reliable and flexible in-home personal use diagnostics for the detection of viral antigens are needed for effective control of the COVID-19 pandemic. Despite the approval of several PCR-based and affinity-based in-home COVID-19 testing kits, many of them suffer from problems such as a high false-negative rate, long waiting time, and short storage period. Using the enabling one-bead-one-compound (OBOC) combinatorial technology, several peptidic ligands with a nanomolar binding affinity toward the SARS-CoV-2 spike protein (S-protein) were successfully discovered. Taking advantage of the high surface area of porous nanofibers, immobilization of these ligands on nanofibrous membranes allows the development of personal use sensors that can achieve low nanomolar sensitivity in the detection of the S-protein in saliva. This simple biosensor employing naked-eye reading exhibits detection sensitivity comparable to some of the current FDA-approved home detection kits. Furthermore, the ligand used in the biosensor was found to detect the S-protein derived from both the original strain and the Delta variant. The workflow reported here may enable us to rapidly respond to the development of home-based biosensors against future viral outbreaks.

Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing

Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of in-silico peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of in-silico peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics.

Pattern enrichment analysis for phage selection of stapled peptide ligands

Phage display is the most widely used technique to discover de novo peptides that bind to target proteins. However, it is associated with some challenges such as compositional bias. In this study, to overcome these difficulties, we devised a 'pattern enrichment analysis.' In this method, two samples (one obtained by affinity selection, the other simply amplified without selection) are prepared, and the two sequence datasets read on next-generation sequencer are compared to find the three-residue pattern most enriched in the selected sample. This allows us to compare two sequence datasets with high coverage and facilitates the identification of peptide sequences and the key residues for binding. We also demonstrated that this approach in the combination with structured peptide libraries allowed spatial mapping of the enriched sequence patterns. Here, we prepared a phage library displaying chemically stapled helical peptides with the X1C2X3X4X5X6X7X8C9X10 sequence, where X is any amino acid. To validate our method, we performed screening against the HDM2 protein. The results showed that the hydrophobic residues (Phe, Tyr, Trp and Leu) that are key to interactions with HDM2 were clearly identified by the pattern enrichment analysis. We also performed selection targeting the SARS-CoV-2 spike RBD in the same manner. The results showed that similar patterns were enriched among the hit peptides that inhibited the protein-protein interaction.

Leveraging the therapeutic, biological, and self-assembling potential of peptides for the treatment of viral infections

Peptides and peptide-based materials have an increasing role in the treatment of viral infections through their use as active pharmaceutical ingredients, targeting moieties, excipients, carriers, or structural components in drug delivery systems. The discovery of peptide-based therapeutic compounds, coupled with the development of new stabilization and formulation strategies, has led to a resurgence of antiviral peptide therapeutics over the past two decades. The ability of peptides to bind cell receptors and to facilitate membrane penetration and subsequent intracellular trafficking enables their use in various antiviral systems for improved targeting efficiency and treatment efficacy. Importantly, the self-assembly of peptides into well-defined nanostructures provides a vast library of discrete constructs and supramolecular biomaterials for systemic and local delivery of antiviral agents. We review here the recent progress in exploiting the therapeutic, biological, and self-assembling potential of peptides, peptide conjugates, and their supramolecular assemblies in treating human viral infections, with an emphasis on the treatment strategies for Human Immunodeficiency Virus (HIV).

Rational Design of Peptides Derived from Odorant-Binding Proteins for SARS-CoV-2-Related Volatile Organic Compounds Recognition

Peptides are promising molecular-binding elements and have attracted great interest in novel biosensor development. In this study, a series of peptides derived from odorant-binding proteins (OBPs) were rationally designed for recognition of SARS-CoV-2-related volatile organic compounds (VOCs). Ethanol, nonanal, benzaldehyde, acetic acid, and acetone were selected as representative VOCs in the exhaled breath during the COVID-19 infection. Computational docking and prediction tools were utilized for OBPs peptide characterization and analysis. Multiple parameters, including the docking model, binding affinity, sequence specification, and structural folding, were investigated. The results demonstrated a rational, rapid, and efficient approach for designing breath-borne VOC-recognition peptides, which could further improve the biosensor performance for pioneering COVID-19 screening and many other applications.

Secondary Structures of the Transmembrane Domain of SARS-CoV-2 Spike Protein in Detergent Micelles

Spike protein of SARS-CoV-2 contains a single-span transmembrane (TM) domain and plays roles in receptor binding, viral attachment and viral entry to the host cells. The TM domain of spike protein is critical for viral infectivity. Herein, the TM domain of spike protein of SARS-CoV-2 was reconstituted in detergent micelles and subjected to structural analysis using solution NMR spectroscopy. The results demonstrate that the TM domain of the protein forms a helical structure in detergent micelles. An unstructured linker is identified between the TM helix and heptapeptide repeat 2 region. The linker is due to the proline residue at position 1213. Side chains of the three tryptophan residues preceding to and within the TM helix important for the function of S-protein might adopt multiple conformations which may be critical for their function. The side chain of W1212 was shown to be exposed to solvent and the side chains of residues W1214 and W1217 are buried in micelles. Relaxation study shows that the TM helix is rigid in solution while several residues have exchanges. The secondary structure and dynamics of the TM domain in this study provide insights into the function of the TM domain of spike protein.

Microarray-Based Detection of Antibodies against SARS-CoV-2 Proteins, Common Respiratory Viruses and Type I Interferons

A microarray-based assay to detect IgG and IgM antibodies against betacoronaviruses (SARS-CoV-2, SARS, MERS, OC43, and HKU1), other respiratory viruses and type I interferons (IFN-Is) was developed. This multiplex assay was applied to track antibody cross-reactivity due to previous contact with similar viruses and to identify antibodies against IFN-Is as the markers for severe COVID-19. In total, 278 serum samples from convalescent plasma donors, COVID-19 patients in the intensive care unit (ICU) and patients who recovered from mild/moderate COVID-19, vaccine recipients, prepandemic and pandemic patients with autoimmune endocrine disorders, and a heterogeneous prepandemic cohort including healthy individuals and chronically ill patients were analyzed. The anti-SARS-CoV-2 microarray results agreed well with the ELISA results. Regarding ICU patients, autoantibodies against IFN-Is were detected in 10.5% of samples, and 10.5% of samples were found to simultaneously contain IgM antibodies against more than two different viruses. Cross-reactivity between IgG against the SARS-CoV-2 nucleocapsid and IgG against the OC43 and HKU1 spike proteins was observed, resulting in positive signals for the SARS-CoV-2 nucleocapsid in prepandemic samples from patients with autoimmune endocrine disorders. The presence of IgG against the SARS-CoV-2 nucleocapsid in the absence of IgG against the SARS-CoV-2 spike RBD should be interpreted with caution.