Protein-DNA interactions: a novel approach to improve the fluorescence stability of DNA/Ag nanoclusters

Evaluation of DNA-protein complex structures using the deep learning method

Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental methods for this purpose are costly and technically demanding. Consequently, there is an urgent need for computational techniques to identify the structures of DNA-protein complexes. Despite technological advancements, accurately identifying DNA-protein complexes through computational methods still poses a challenge. Our team has developed a cutting-edge deep-learning approach called DDPScore that assesses DNA-protein complex structures. DDPScore utilizes a 4D convolutional neural network to overcome limited training data. This approach effectively captures local and global features while comprehensively considering the conformational changes arising from the flexibility during the DNA-protein docking process. DDPScore consistently outperformed the available methods in comprehensive DNA-protein complex docking evaluations, even for the flexible docking challenges. DDPScore has a wide range of applications in predicting and designing structures of DNA-protein complexes.

Structural predictions of protein-DNA binding: MELD-DNA

Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein-DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein-DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein-DNA interactions).

A rolling circle amplification based platform for ultrasensitive detection of heparin

Heparin has a variety of pharmacological uses, including applications for anti-tumor metastasis, anti-inflammatory and anti-viral activities and is widely used as a clinical anticoagulant. Due to its widespread applications in the clinical procedures, monitoring heparin levels is critically important to ensure the safe use of heparin and to prevent overdose and complications, such as hemorrhage and thrombocytopenia. However, traditional heparin detection relies on the measurements of the activated clotting time or activated partial thromboplastin time, which are not sufficiently reliable or accurate measurements for certain clinical settings. In this work, we describe a dumbbell probe-aided strategy for ultrasensitive and isothermal detection of heparin based on a uniquely strong protamine-heparin interaction and rolling circle amplification driven signal amplification. The detection limit for heparin is 12.5 ng mL-1 (0.83 nM), which is much lower than the therapeutic level of heparin in cardiovascular surgery (17-67 μM) and in postoperative and long-term treatment (1.7-10 μM). Additionally, the proposed sensing platform works well for heparin monitoring in human plasma samples. This simple and ultrasensitive heparin biosensor has potential application in diagnostics, therapeutics, and in biological research.

Progress in electrochemiluminescence of nanoclusters: how to improve the quantum yield of nanoclusters

Classification of nanoclusters and methods to improve their quantum yield and applications.

A near-infrared BSA coated DNA-AgNCs for cellular imaging

Near-infrared silver nanoclusters, have potential applications in the field of biosensing and biological imaging. However, less stability of most DNA-AgNCs limits their application. To obtain stable near-infrared fluorescence DNA-AgNCs for biological imaging, a new kind of near-infrared fluorescent DNA-Ag nanoclusters was constructed using the C3A rich aptamer as a synthesis template, GAG as the enhancer. In particular, a new DNA-AgNCs-Trp@BSA was obtained based on the self-assembly of bovine serum albumin (BSA) and tryptophan loaded DNA-AgNCs by hydrophobic interaction. This self-assembly method can be used to stabilize DNAn-Ag (n = 1-3) nanoclusters. Hence, the near-infrared fluorescence DNA-AgNCs-Trp@BSA was applied in cellular imaging of HepG-2 cells.

Hydrogels Based on Ag+ -Modulated Assembly of 5'-Adenosine Monophosphate for Enriching Biomolecules

Supramolecular hydrogels obtained by combining 5'-adenosine monophosphate (AMP) with Ag⁺ were fabricated in this work. Their gelation capability was enhanced by increasing the concentration of Ag⁺ or decreasing the pH. The gels are very sensitive to light, which endows them with potential applications as visible-light photosensitive materials. Coordination between the nucleobase of AMP and Ag⁺ , as well as π-π stacking of nucleobases, are considered to be the main driving forces for self-assembly. The hydrogels successfully achieved the encapsulation and enrichment of biomolecules. Hydrogen bonding between the amino group of guest molecules and silver nanoparticles along the nanofibers drives the enrichment and is considered to be a crucial interaction.

Red-emitted electrochemiluminescence by yellow fluorescent thioglycol/glutathione dual thiolate co-coated Au nanoclusters

This study reports the occurrence of a special red-emitted anodic electrochemiluminescence (ECL) emission at +1.4 V (vs. Ag/AgCl) on a glass carbon electrode (GCE) after the addition of thioglycol (TG) to surface-unsaturated glutathione (GSH)-coated Au nanoclusters (NCs), with an emission peak at ∼630 nm. Compared to the ECL at a potential of +1.8 V (vs. Ag/AgCl) and an emission peak at 580 nm (corresponding to fluorescence) for only GSH-coated Au NCs, this ECL emission not only exhibits a lower ECL potential but also shows a significantly red-shifted emission wavelength up to ∼50 nm. We demonstrated that the formation of TG/GSH dual ligand-coated Au NCs is responsible for the red-shifted ECL emission. Other common thiol compounds cannot result in similar effect on GCE, and no ECL is observed on other electrodes such as indium tin oxide and platinum electrodes. This finding offers a great possibility to design novel feasible ECL systems for different complicated applications.

Aggregation-induced structure transition of protein-stabilized zinc/copper nanoclusters for amplified chemiluminescence

A stable, water-soluble fluorescent Zn/Cu nanocluster (NC) capped with a model protein, bovine serum albumin (BSA), was synthesized and applied to the reaction of hydrogen peroxide and sodium hydrogen carbonate. A significantly amplified chemiluminescence (CL) from the accelerated decomposition of peroxymonocarbonate (HCO4(-)) by the nanosluster was observed. The CL reaction led to a structure change of BSA and aggregation of Zn/Cu NCs. In the presence of H2O2, Zn/Cu-S bonding between BSA scaffolds and the encapsulated Zn/Cu@BSA NC was oxidized to form a disulfide product. Zn/Cu@BSA NCs were prone to aggregate to form larger nanoparticles without the protection of scaffolds. It is revealed that the strong CL emission was initiated from the catalysis of Zn/Cu@BSA NC and the surface plasmon coupling of the formed Zn/Cu nanoparticles in a single chemical reaction. This amplified CL was successfully exploited for selective sensing of hydrogen peroxide in environmental samples.