Target Protein Binding Research Articles

Bridged Proteolysis Targeting Chimera (PROTAC) Enables Degradation of Undruggable Targets.

Proteolysis Targeting Chimeras (PROTACs) are attractive therapeutic modalities for degrading disease-causing proteins. While many PROTACs have been developed for numerous protein targets, current small-molecule PROTAC approaches cannot target undruggable proteins that do not have small-molecule binders. Here, we present a novel PROTAC approach, termed bridged PROTAC, which utilizes a small-molecule binder of the target protein's binding partner to recruit the protein complex into close proximity with an E3 ubiquitin ligase to target undruggable proteins. Applying this bridged PROTAC strategy, we discovered MS28, the first-in-class degrader of cyclin D1, which lacks a small-molecule binder. MS28 effectively degrades cyclin D1, with faster degradation kinetics and superior degradation efficiency than CDK4/6, through recruiting the CDK4/6-cyclin D1 complex to the von Hippel-Lindau E3 ligase. MS28 also suppressed the proliferation of cancer cells more effectively than CDK4/6 inhibitors and degraders. Altogether, the bridged PROTAC strategy could provide a generalizable platform for targeting undruggable proteins.

SOI-FET Sensors with Dielectrophoretic Concentration of Viruses and Proteins.

Quick label-free virus screening and highly sensitive analytical tools/techniques are becoming extremely important in a pandemic. In this study, we developed a biosensing device based on the silicon nanoribbon multichannel and dielectrophoretic controlled sensors functionalized with SARS-CoV-2 spike antibodies for the use as a platform for the detection and studding of properties of viruses and their protein components. Replicatively defective viral particles based on vesicular stomatitis viruses and HIV-1 were used as carrier molecules to deliver the target SARS-CoV-2 spike S-proteins to sensory elements. It was shown that fully CMOS-compatible nanoribbon sensors have the subattomolar sensitivity and dynamic range of 4 orders. Specific interaction between S-proteins and antibodies leads to the accumulation of the negative charge on the sensor surface. Nonspecific interactions of the viral particles lead to the positive charge accumulation. It was shown that dielectrophoretic controlled sensors allow to estimate the effective charge of the single virus at the sensor surface and separate it from the charge associated with the binding of target proteins with the sensor surface.

Non-ionic surfactants in drug delivery vehicles: Physicochemical insights with systems of drugs, Igepal CA-630, bovine serum albumin and hen egg-white lysozyme

Most of the synthetically developed drugs are hydrophobic in nature and hence possess low bioavailability. An efficient delivery of such drugs at the specific target site requires usage of suitable drug delivery vehicles. The quantitative aspects of drug partitioning into delivery vehicles, their release and binding of target protein are crucial for developing guidelines aimed at rational drug design. We have examined the partitioning of anti-thyroid, anti-inflammatory and anti-cancer drugs methimazole, naproxen, imatinib, and (s,s)-n,n′-bis(1-hydroxy-2-butyl)-ethylenediamine dihydrochloride in non-ionic self-assemblies of Igepal CA-630. The use of non-ionic surfactants in drug delivery is important because of their mild and harmless nature. The partitioning of drugs into surfactant micelles have been carried out using calorimetric and spectroscopic techniques. The quantitative details of partitioning mechanism have been investigated using parameters of their interaction such as change in Gibbs free-energy, standard molar enthalpy and entropy. These thermodynamic signatures have allowed us to develop relationships between structure-property and energetics which are very important in rational drug design. The results obtained were compared with different class of drugs having divergent nature to understand the key molecular features in the process of drug partitioning.

De novo design of anti-tuberculosis agents using a structure-based deep learning method

Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents targeting Mtb-specific proteins. The ceaseless search for novel antimicrobial agents to combat drug-resistant bacteria can be accelerated by the development of advanced deep learning methods, to explore both existing and uncharted regions of the chemical space. The adaptation of deep learning methods to under-explored pathogens such as Mtb is a challenging aspect, as most of the existing methods rely on the availability of sufficient target-specific ligand data to design novel small molecules with optimized bioactivity. In this work, we report the design of novel anti-tuberculosis agents targeting the Mtb chorismate mutase protein using a structure-based drug design algorithm. The structure-based deep learning method relies on the knowledge of the target protein's binding site structure alone for conditional generation of novel small molecules. The method eliminates the need for curation of a high-quality target-specific small molecule dataset, which remains a challenge even for many druggable targets, including Mtb chorismate mutase. Novel molecules are proposed, that show high complementarity to the target binding site. The graph attention model could identify the probable key binding site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.

Robust deep learning-based protein sequence design using ProteinMPNN.

Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo-electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.

Non-steroidal anti-inflammatory drugs ketorolac and etodolac can augment the treatment against pneumococcal meningitis by targeting penicillin-binding proteins.

Streptococcus pneumoniae is the principal etiological agent of acute bacterial meningitis (ABM) which has fatal outcome in children and elderly. Due to poor blood-brain barrier (BBB) permeation, conventional β-lactam antibiotics fail to establish the requisite bactericidal concentration in central nervous system leading to resistance in meningeal infections. The present study intended to identify potential therapeutic alternatives against Streptococcal meningitis. Virtual screening, pharmacokinetics/pharmacodynamics (PK/PD) and anti-bacterial evaluations were employed to screen potential drugs. Molecular docking and structural dynamics simulations were performed to analyze the binding affinity and interaction stability of the drugs against the conventional Penicillin binding protein (PBP) targets. Screened drugs were also checked for interactions with other possible Streptococcal targets and relevant host targets. Non-steroidal anti-inflammatory drugs (NSAIDs) ketorolac and etodolac exhibiting high BBB-permeation and anti-bacterial potency were identified. Ketorolac and etodolac possessed uniform binding affinities against PBP1A, PBP2X, PBP2B and PBP3 with low inhibition constants (<50μM). Against PBP2B and PBP3, higher binding affinities were observed for ketorolac (-6.45 and -6Kcal/mol respectively) and etodolac (-6.36 and -6.55Kcal/mol respectively) than penicillin (-5.95 and -5.85Kcal/mol respectively) and cefotaxime (-5.08 and -5.07Kcal/mol respectively). The binding affinities were contributed by conventional H-bonds and non-canonical interactions with active site residues of PBPs. Structural dynamics simulations further indicated the overall stability of the drug-bound complexes through minimal overall average root-mean square fluctuations (RMSFs) (<1.0Å). The average binding affinities of Ketorolac and Etodolac with PBPs were marginally higher than other Streptococcal targets and comparable to their conventional inflammatory targets. Pharmacological and structural profiles indicated that ketorolac and etodolac can potentially subdue the cause and effects of streptococcal meningitis and hence encourage experimental validations.

Impact of CTLA-4 checkpoint antibodies on ligand binding and Transendocytosis.

Anti-CTLA-4 antibodies have pioneered the field of tumour immunotherapy. However, despite impressive clinical response data, the mechanism by which anti-CTLA-4 antibodies work is still controversial. Two major checkpoint antibodies (ipilimumab and tremelimumab) have been trialled clinically. Both have high affinity binding to CTLA-4 and occupy the ligand binding site, however recently it has been suggested that in some settings such antibodies may not block ligand-CTLA-4 interactions. Here we evaluated blocking capabilities of these antibodies in a variety of settings using both soluble and cell bound target proteins. We found that when ligands (CD80 or CD86) were expressed on cells, soluble CTLA-4-Ig bound in line with affinity expectations and that this interaction was effectively disrupted by both ipilimumab and tremelimumab antibodies. Similarly, cellular CTLA-4 binding to soluble ligands was comparably prevented. We further tested the ability of these antibodies to block transendocytosis, whereby CTLA-4 captures ligands from target cells during a cognate cell-cell interaction. Once again ipilimumab and tremelimumab were similar in preventing removal of ligand by transendocytosis. Furthermore, even once transendocytosis was ongoing and cell contact was fully established, the addition of these antibodies could prevent further ligand transfer. Together these data indicate that the above checkpoint inhibitors performed in-line with predictions based on affinity and binding site data and are capable of blocking CTLA-4-ligand interactions in a wide range of settings tested.

Shedding Light on the Drug-Target Prediction of the Anti-Inflammatory Peptide TnP with Bioinformatics Tools.

Peptide–protein interactions are involved in various fundamental cellular functions, and their identification is crucial for designing efficacious peptide therapeutics. Drug–target interactions can be inferred by in silico prediction using bioinformatics and computational tools. We patented the TnP family of synthetic cyclic peptides, which is in the preclinical stage of developmental studies for chronic inflammatory diseases such as multiple sclerosis. In an experimental autoimmune enceph-alomyelitis model, we found that TnP controls neuroinflammation and prevents demyelination due to its capacity to cross the blood–brain barrier and to act in the central nervous system blocking the migration of inflammatory cells responsible for neuronal degeneration. Therefore, the identification of potential targets for TnP is the objective of this research. In this study, we used bioinformatics and computational approaches, as well as bioactivity databases, to evaluate TnP–target prediction for proteins that were not experimentally tested, specifically predicting the 3D structure of TnP and its biochemical characteristics, TnP–target protein binding and docking properties, and dynamics of TnP competition for the protein/receptor complex interaction, construction of a network of con-nectivity and interactions between molecules as a result of TnP blockade, and analysis of similarities with bioactive molecules. Based on our results, integrins were identified as important key proteins and considered responsible to regulate TnP-governed pharmacological effects. This comprehensive in silico study will help to understand how TnP induces its anti-inflammatory effects and will also facilitate the identification of possible side effects, as it shows its link with multiple biologically important targets in humans.

Chemoproteomics reveals berberine directly binds to PKM2 to inhibit the progression of colorectal cancer

SummaryColorectal cancer is one of the most serious tumors and berberine can inhibit the recurrence and transformation of colorectal adenoma into colorectal cancer. However, the direct binding target proteins of berberine in inhibiting colorectal cancer remain unclear. In this study, the chemical proteomics method was used and demonstrated that berberine is directly bound to pyruvate kinase isozyme type M2 (PKM2) in colorectal cancer cells. The triangular N-O-O triangular structure of berberine contributed to hydrophobic interaction with I119 amino acid residues and π-π interaction with F244 amino acid residues of PKM2 protein. Moreover, berberine was shown to inhibit the reprogramming of glucose metabolism and the phosphorylation of STAT3, down regulate the expression of Bcl-2 and Cyclin D1 genes, ultimately inhibiting the progression of colorectal cancer. This study uncovered the direct binding target protein and mechanism of berberine to improve metabolic reprogramming in colorectal cancer, which is helpful to guide the optimization of berberine.

Quantitative Frameworks for Multivalent Macromolecular Interactions in Biological Linear Lattice Systems.

Multivalent macromolecular interactions underlie dynamic regulation of diverse biological processes in ever-changing cellular states. These interactions often involve binding of multiple proteins to a linear lattice including intrinsically disordered proteins and the chromosomal DNA with many repeating recognition motifs. Quantitative understanding of such multivalent interactions on a linear lattice is crucial for exploring their unique regulatory potentials in the cellular processes. In this review, the distinctive molecular features of the linear lattice system are first discussed with a particular focus on the overlapping nature of potential protein binding sites within a lattice. Then, we introduce two general quantitative frameworks, combinatorial and conditional probability models, dealing with the overlap problem and relating the binding parameters to the experimentally measurable properties of the linear lattice-protein interactions. To this end, we present two specific examples where the quantitative models have been applied and further extended to provide biological insights into specific cellular processes. In the first case, the conditional probability model was extended to highlight the significant impact of nonspecific binding of transcription factors to the chromosomal DNA on gene-specific transcriptional activities. The second case presents the recently developed combinatorial models to unravel the complex organization of target protein binding sites within an intrinsically disordered region (IDR) of a nucleoporin. In particular, these models have suggested a unique function of IDRs as a molecular switch coupling distinct cellular processes. The quantitative models reviewed here are envisioned to further advance for dissection and functional studies of more complex systems including phase-separated biomolecular condensates.

Abstract LB151: Deep learning - based protein therapeutics design to treat HER2+ cancers

Abstract Background: HER2 amplification and activation occurs in approximately 15~25% of all breast cancers and is further indicated in other cancer types with high un-met medical needs, such as cholangiocarcinoma, ovarian and esophageal cancer. Further improvement of current HER2 targeting therapeutics on safety and efficacy remains to be desired. Deep learning - based computational approach, such as AlphaFold, has been successfully applied to the understanding of protein structure. However, its applications to protein therapeutic design remain largely underdeveloped. Method: We have developed an end-to-end protein design engine Apollo, which can be used to design target-binding protein therapeutics in silico. Apollo uses recurrent neural networks (RNNs), transformers and graph convolution neural networks (GCNs) to represent proteins, protein structures and protein-protein interactions. This is further improved by multi-head and hierarchical attention layers. Using generative AI, Apollo can either generate putative binding proteins de novo, or to optimize protein lead molecules. Candidate proteins are evaluated in silico for binding affinity and specificity, secondary and tertiary structure, isoelectric point, solubility, and other drug-like properties. Generated designs are iteratively improved by Apollo via a reinforcement learning loop. Results: Apollo has achieved high prediction accuracy by in silico evaluations, using metrics such as AUC and the confusion matrices. Using Apollo, we then designed mini protein binders of 50~100 aa to target HER2 extracellular domain (ECD). These binders can be produced with high yield and purity using mammalian protein expression systems. From de novo generation, the designed candidates are shown to bind to human HER2 with EC50 in the nanomolar range by ELISA. From optimizations based on lead molecules, the designed candidates have shown improved production yield and thermostability while maintaining binding affinity. Conclusions: We have shown the proof-of-concept success of using machine learning to design protein therapeutics. Citation Format: Ziwei Liang, Tom Murray, Max Bluestone, Chris Zoumadakis, Luke Martin, Alexey Vlasenko, Jeffrey LaFrence, John Lazar, Matthias Denecke, David Longo. Deep learning - based protein therapeutics design to treat HER2+ cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB151.

LukS-PV inhibits hepatocellular carcinoma cells migration by downregulating HDAC6 expression.

BackgroundHepatocellular carcinoma (HCC) is a clinically common malignant tumor worldwide. LukS-PV is the S component of Panton-Valentine leukocidin secreted by Staphylococcus aureus, which has shown anti-cancer activity. Based on previous findings, this study investigated the effects of LukS-PV on HCC migration and the potential molecular mechanisms involving acetylation pathways.MethodsAfter treating HCC cells with different concentrations of LukS-PV, we used scratch assays to determine the mobility of the cancer cells. Western blots were used to determine the expression levels of migration-related proteins. Quantitative proteomic sequencing was used to evaluate proteomic changes in target proteins. Immunoprecipitation and liquid chromatography coupled with tandem mass spectrometry analyses were used to validate the binding of related target proteins.ResultsLukS-PV inhibited HCC cell migration in a concentration-dependent manner. In addition, LukS-PV attenuated the expression of histone deacetylase (HDAC)6, which is highly expressed in HCC cells. Further studies showed that LukS-PV increased the acetylation level of α-tubulin by down-regulating HDAC6, which resulted in the inhibition of HCC cell migration.ConclusionTaken together, our data revealed a vital role of LukS-PV in suppressing HCC cell migration by down-regulating HDAC6 and increasing the acetylation level of α-tubulin.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12885-022-09680-4.

Molecular Docking and Interaction Analysis of Propolis Compounds Against SARS-CoV-2 Receptor

Background: For many people, especially in developing countries, herbal medicine is the most traditional drug choice to treat all diseases including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 infection). Propolis is one of the popular herbal medicine which has various health benefits, particularly antiviral activity. In this molecular docking study, this investigation examined twenty-five kinds of propolis to bind SARS-CoV-2 protein with the main targets of ACE-2 and M-Pro receptors. Method: Propolis ligands were downloaded from PubChem, meanwhile ACE-2 and M-Pro receptors were downloaded from Protein Data Bank. Both ligands and targets were optimized by Pymol. The pharmacokinetic analysis was conducted using SwissADME. Molecular docking was done using PyRx 0.9 and its binding interaction was visualized by Discovery Studio. To predict the potential inhibition, this study compared the ligand-protein complex of propolis to ligands from the previous study. Result: Through the Lipinski rule, only five of twenty-five types of propolis were not qualified for the criterion. The ability to bind protein targets were various between ligands, the highest affinity to ACE-2 receptors were abietic acid, galangin, chrysin, kaempferol and acacetin, respectively. The binding affinity between ligand and M-Pro were seen weaker than ACE-2 receptor, while the strongest were kaempferol, abietic acid, acacetin, galangin and chrysin, respectively. Conclusion: Â Kaempferol is the most potent form of propolis to bind to ACE-2 and M-Pro receptors by assessing the binding affinity and the amount of amino acid residue formation when compared to control ligands. Keywords: ACE-2 receptor, COVID-19, Main protease, Molecular docking, Propolis, SARS-CoV-2

The use of high-affinity polyhistidine binders as masking probes for the selection of an NDM-1 specific aptamer

The emergence of carbapenemase-producing multi-drug resistant Enterobacteriaceae poses a dramatic, world-wide health risk. Limited treatment options and a lack of easy-to-use methods for the detection of infections with multi-drug resistant bacteria leave the health-care system with a fast-growing challenge. Aptamers are single stranded DNA or RNA molecules that bind to their targets with high affinity and specificity and can therefore serve as outstanding detection probes. However, an effective aptamer selection process is often hampered by non-specific binding. When selections are carried out against recombinant proteins, purification tags (e.g. polyhistidine) serve as attractive side targets, which may impede protein target binding. In this study, aptamer selection was carried out against N-terminally hexa-histidine tagged New Delhi metallo-ß-lactamase 1. After 14 selection rounds binding to polyhistidine was detected rather than to New Delhi metallo-ß-lactamase 1. Hence, the selection strategy was changed. As one aptamer candidate showed remarkable binding affinity to polyhistidine, it was used as a masking probe and selection was restarted from selection round 10. Finally, after three consecutive selection rounds, an aptamer with specific binding properties to New Delhi metallo-ß-lactamase 1 was identified. This aptamer may serve as a much-needed detection probe for New Delhi metallo-ß-lactamase 1 expressing Enterobacteriaceae.

Development in Detection Methods for the Expression of Surface-Displayed Proteins.

Directed evolution is a widely-used engineering strategy for improving the stabilities or biochemical functions of proteins by repeated rounds of mutation and selection. A protein of interest is selected as the template and expressed on a molecular display platform such as a bacteriophage for engineering. Initially, the surface-displayed protein template needs to be checked against the desired target via ELISA to examine whether the functions of the displayed template remain intact. The ELISA signal is subject to the protein-target binding affinity. A low-affinity results in a weak ELISA signal which makes it difficult to determine whether the weak signal is because of low affinity or because of poor expression of the protein. Using a methyllysine-binding chromodomain protein Cbx1 that weakly binds to the histone H3K9me3 peptide, we developed and compared three different approaches to increase the signal-to-background ratio of ELISA measurements. We observed that the specific peptide-binding signal was enhanced by increasing the Cbx1 phage concentration on the ELISA plate. The introduction of previously known gain-of-function mutations to the Cbx1 protein significantly increased the ELISA signals. Moreover, we demonstrated that the H3K9me3-specific binding signal was enhanced by fusing Cbx1 with a high-affinity phosphotyrosine-binding protein and by coating the ELISA plate with a mixture of H3K9me3 and phosphotyrosine peptides. This approach also worked with binding to a lower affinity momomethyllysine peptide H3K9me1. These approaches may help improve ELISA experiments when dealing with low-affinity ligand-protein interactions.

DiMeLo-seq: a long-read, single-molecule method for mapping protein-DNA interactions genome wide.

Studies of genome regulation routinely use high-throughput DNA sequencing approaches to determine where specific proteins interact with DNA, and they rely on DNA amplification and short-read sequencing, limiting their quantitative application in complex genomic regions. To address these limitations, we developed Directed Methylation with Long-read sequencing (DiMeLo-seq), which uses antibody-tethered enzymes to methylate DNA near a target protein’s binding sites in situ. These exogenous methylation marks are then detected simultaneously with endogenous CpG methylation on unamplified DNA using long-read, single-molecule sequencing technologies. We optimized and benchmarked DiMeLo-seq by mapping chromatin-binding proteins and histone modifications across the human genome. Furthermore, we identified where centromere protein A (CENP-A) localizes within highly repetitive regions that we re unmappable with short sequencing reads, and we estimated the density of CENP-A molecules along single chromatin fibers. DiMeLo-seq is a versatile method that provides multimodal, genome-wide information for investigating protein-DNA interactions.

Inhibitory Potential of Shen-Shuai-Ling Formulation on Renal Interstitial Fibrosis via Upregulation of PLZF.

Background Renal interstitial fibrosis (RIF) is an important cause of kidney disease, which seriously affects people's health. As a traditional Chinese medicine, Shen-Shuai-Ling Formulation (SSLF) has obvious kidney function. However, the therapeutic effect of SSLF on RIF and its molecular mechanism are still unclear. Methods First, the potential targets and pathways of SSLF for RIF were predicted by network pharmacology, and then, the binding of luteolin and target protein to SSLF was verified by molecular docking and Co-IP experiments. Finally, the effects of SSLF and luteolin on PLZF and (Pro) renin receptor (PRR) were verified by western blot and qPCR experiments. Angiotensin (Ang)-1, Ang-2, and transforming growth factor-β (TGF-β) were the indexes of renal interstitial fibrosis. Results Through the drug-active component-target network diagram, we found that luteolin has the most connections, and promyelocytic leukemia zinc finger (PLZF) is the target protein. GO analysis and KEGG pathway analysis of targets were performed using Cytoscape ClueGO. Molecular docking experiments and Co-IP are used to prove that luteolin and PLZF can be combined. Western blot and qPCR results showed that both SSLF and luteolin significantly upregulated the expression of PLZF and decreased the levels of PRR, Ang-1, Ang-2, and TGF-β. The overexpression of PLZF decreased the expression of PRR, the knockdown of PLZF increased the expression of PRR, and the overexpression of PRR decreased the expression of Ang-1, Ang-2, and TGF-β. Conclusions SSLF inhibits PRR and renal interstitial fibers by the upregulation of PLZF levels.

Analysis of Active Compounds Using Target Protein Cofilin-Cucurbitacins in Cytotoxic Plant Bryonia cretica.

We examined a two-step target protein binding strategy that uses cofilin as the target protein to analyze the active constituents in Bryonia cretica. In the first step, we prepared the target protein, and used it to analyze the compounds binding to it in the second step. We used the methanolic extract of B. cretica as a library of possible active compounds. We conducted LC–MS analysis using information from our previous study. The peaks in the HPLC profile were identified as cucurbitacin D, isocucurbitacin D, and cucurbitacin I. As far as we know, there is no known study of the activity of isocucurbitacin D in this research field. Therefore, we examined the effects of isocucurbitacin D on cell proliferation and cofilin protein in human fibrosarcoma cell line HT1080 to confirm the effectiveness of this strategy. The cytotoxicity assay, the fibrous/globular actin ratio assay, and the immunoblotting analysis revealed that isocucurbitacin D showed a cytotoxic effect with disruption of target protein cofilin. The target protein binding strategy is a direct and straightforward method for finding new drug seeds from crude sources, such as natural plant extracts.

One-step gold nanoparticle size-shift assay using synthetic binding proteins and dynamic light scattering

Gold nanoparticle (AuNPs) coupled dynamic light scattering (DLS) is a biosensing technique combining the light scattering properties of AuNPs with analyte recognition to produce an easy-to-use analytical tool. The principle of the assay is to immobilize bioreceptors specific to the target analyte onto AuNPs to produce nanobiosensors. When the analyte is added to the system, binding of target protein to the immobilized bioreceptors leads to size shift of the functionalised AuNPs. This specific shift in size can be quantified by DLS. It has proven effective in detecting multiple analytes such as metal ions, small chemicals and macromolecules. Previously, antibodies (Abs) have been used as bioreceptors in this technique. However, here we report the use of synthetic binding proteins, termed Affimer reagents, as bioreceptors. Anti-myoglobin Affimers were isolated by phage display and characterized by ELISA and surface plasmon resonance (SPR). Five Affimers showed strong binding to myoglobin and were immobilized onto AuNPs to produce the nanobiosensors for AuNPs coupled DLS assay. The nanobiosensors could selectively detect myoglobin with a limit of detection of 554 fM. AuNP size and concentration were tested and proved to be important factors in the technique.

Therapeutic Implications of Caffeic Acid in Cancer and Neurological Diseases.

Caffeic acid (CA) is found abundantly in fruits, vegetables, tea, coffee, oils, and more. CA and its derivatives have been used for many centuries due to their natural healing and medicinal properties. CA possesses various biological and pharmacological activities, including antioxidant, anti-inflammatory, anticancer, and neuroprotective effects. The potential therapeutic effects of CA are mediated via repression and inhibition of transcription and growth factors. CA possesses potential anticancer and neuroprotective effects in human cell cultures and animal models. However, the biomolecular interactions and pathways of CA have been described highlighting the target binding proteins and signaling molecules. The current review focuses on CA’s chemical, physical, and pharmacological properties, including antioxidant, anti-inflammatory, anticancer, and neuroprotective effects. We further described CA’s characteristics and therapeutic potential and its future directions.