Computational generation and characterization of IsdA-binding aptamers with single-molecule FRET analysis.

Staphylococcus aureus is a major foodborne bacterial pathogen. Early detection of S. aureus is crucial to prevent infections and ensure food quality. The iron-regulated surface determinant protein A (IsdA) of S. aureus is a unique surface protein necessary for sourcing vital iron from host cells for the survival and colonization of the bacteria. The function, structure, and location of the IsdA protein make it an important protein for biosensing applications relating to the pathogen. Here, we report an in-silico approach to develop and validate high-affinity binding aptamers for the IsdA protein detection using custom-designed in-silico tools and single-molecule Fluorescence Resonance Energy Transfer (smFRET) measurements. We utilized in-silico oligonucleotide screening methods and metadynamics-based methods to generate 10 aptamer candidates and characterized them based on the Dissociation Free Energy (DFE) of the IsdA-aptamer complexes. Three of the aptamer candidates were shortlisted for smFRET experimental analysis of binding properties. Limits of detection in the low picomolar range were observed for the aptamers, and the results correlated well with the DFE calculations, indicating the potential of the in-silico approach to support aptamer discovery. This study showcases a computational SELEX method in combination with single-molecule binding studies deciphering effective aptamers against S. aureus IsdA, protein. The established approach demonstrates the ability to expedite aptamer discovery that has the potential to cut costs and predict binding efficacy. The application can be extended to designing aptamers for various protein targets, enhancing molecular recognition, and facilitating the development of high-affinity aptamers for multiple uses.

FRET-Based Single-Molecule Detection of Pathogen Protein IsdA Using Computationally Selected Aptamers.

Iron-regulated surface determinant protein A (IsdA) is a key surface protein found in the foodborne bacteria─Staphylococcus aureus (S. aureus)─which is known to be critical for bacterial survival and colonization. S. aureus is pathogenic and has been linked to foodborne diseases; thus, early detection is critical to prevent diseases caused by this bacterium. Despite IsdA being a specific marker for S. aureus and several detection methods have been developed for sensitive detection of this bacteria such as cell culture, nucleic acids amplification, and other colorimetric and electrochemical methods, the detection of S. aureus through IsdA is underdeveloped. Here, by combining computational generation of target-guided aptamers and fluorescence resonance energy transfer (FRET)-based single-molecule analysis, we presented a widely applicable and robust detection method for IsdA. Three different RNA aptamers specific to the IsdA protein were identified and their ability to switch a FRET construct to a high-FRET state in the presence of protein was verified. The presented approach demonstrated the detection of IsdA down to picomolar levels (×10-12 M, equivalent to ∼1.1 femtomoles IsdA) with a dynamic range extending to ∼40 nM. The FRET-based single-molecule technique that we reported here is capable of detecting the foodborne pathogen protein IsdA with high sensitivity and specificity and has a broader application in the food industry and aptamer-based sensing field by enabling quantitative detection of a wide range of pathogen proteins.

Neuroblastoma GD2 Expression and Computational Analysis of Aptamer-Based Bioaffinity Targeting.

Neuroblastoma (NB) is a neuroectodermal embryonic cancer that originates from primordial neural crest cells, and amongst pediatric cancers with high mortality rates. NB is categorized into high-, intermediate-, and low-risk cases. A significant proportion of high-risk patients who achieve remission have a minimal residual disease (MRD) that causes relapse. Whilst there exists a myriad of advanced treatment options for NB, it is still characterized by a high relapse rate, resulting in a reduced chance of survival. Disialoganglioside (GD2) is a lipo-ganglioside containing a fatty acid derivative of sphingosine that is coupled to a monosaccharide and a sialic acid. Amongst pediatric solid tumors, NB tumor cells are known to express GD2; hence, it represents a unique antigen for subclinical NB MRD detection and analysis with implications in determining a response for treatment. This article discusses NB MRD expression and analytical assays for GD2 detection and quantification as well as computational approaches for GD2 characterization based on high-throughput image processing and genomic data analysis.

Oncological Ligand-Target Binding Systems and Developmental Approaches for Cancer Theranostics.

Targeted treatment of cancer hinges on the identification of specific intracellular molecular receptors on cancer cells to stimulate apoptosis for eventually inhibiting growth; the development of novel ligands to target biomarkers expressed by the cancer cells; and the creation of novel multifunctional carrier systems for targeted delivery of anticancer drugs to specific malignant sites. There are numerous receptors, antigens, and biomarkers that have been discovered as oncological targets (oncotargets) for cancer diagnosis and treatment applications. Oncotargets are critically important to navigate active anticancer drug ingredients to specific disease sites with no/minimal effect on surrounding normal cells. In silico techniques relating to genomics, proteomics, and bioinformatics have catalyzed the discovery of oncotargets for various cancer types. Effective oncotargeting requires high-affinity probes engineered for specific binding of receptors associated with the malignancy. Computational methods such as structural modeling and molecular dynamic (MD) simulations offer opportunities to structurally design novel ligands and optimize binding affinity for specific oncotargets. This article proposes a streamlined approach for the development of ligand-oncotarget bioaffinity systems via integrated structural modeling and MD simulations, making use of proteomics, genomic, and X-ray crystallographic resources, to support targeted diagnosis and treatment of cancers and tumors.

Biophysical analysis of SARS-CoV-2 transmission and theranostic development via N protein computational characterization.

Recently, SARS-CoV-2 has been identified as the causative factor of viral infection called COVID-19 that belongs to the zoonotic beta coronavirus family known to cause respiratory disorders or viral pneumonia, followed by an extensive attack on organs that express angiotensin-converting enzyme II (ACE2). Human transmission of this virus occurs via respiratory droplets from symptomatic and asymptomatic patients, which are released into the environment after sneezing or coughing. These droplets are capable of staying in the air as aerosols or surfaces and can be transmitted to persons through inhalation or contact with contaminated surfaces. Thus, there is an urgent need for advanced theranostic solutions to control the spread of COVID-19 infection. The development of such fit-for-purpose technologies hinges on a proper understanding of the transmission, incubation, and structural characteristics of the virus in the external environment and within the host. Hence, this article describes the development of an intrinsic model to describe the incubation characteristics of the virus under varying environmental factors. It also discusses on the evaluation of SARS-CoV-2 structural nucleocapsid protein properties via computational approaches to generate high-affinity binding probes for effective diagnosis and targeted treatment applications by specific targeting of viruses. In addition, this article provides useful insights on the transmission behavior of the virus and creates new opportunities for theranostics development.

Phytosynthesized nanoparticles as a potential cancer therapeutic agent.

Plants are the well-known sources for the hyper-accumulation and reduction of metallic ions. Analysis of various plant extracts has justified the presence of different types of phytochemicals that possess the stabilization and reduction functionalities of precursors to form nanoparticles. Such characteristics make plants as an attractive source for synthesizing eco-friendly nanoparticles (NPs) with potentially less toxicity to the body. Recently, phytosynthesized nanoparticles have been explored for targeted inhibition and diagnosis of cancer cells without affecting non-cancerous healthy cells. The aim of this review is to discuss the characteristic performance of NPs synthesized from various plant sources for the diagnosis and inhibition of cancer. The mode of action of phytosynthesized nanoparticles for anti-cancer applications are also discussed.