Peptide-Modified Surfaces for Enzyme Immobilization

BACKGROUND: Chemistry and particularly enzymology at surfaces is a topic of rapidly growing interest, both in terms of its role in biological systems and its application in biocatalysis. Existing protein immobilization approaches, including noncovalent or covalent attachments to solid supports, have difficulties in controlling protein orientation, reducing nonspecific absorption and preventing protein denaturation. New strategies for enzyme immobilization are needed that allow the precise control over orientation and position and thereby provide optimized activity. METHODOLOGY/PRINCIPAL FINDINGS: A method is presented for utilizing peptide ligands to immobilize enzymes on surfaces with improved enzyme activity and stability. The appropriate peptide ligands have been rapidly selected from high-density arrays and when desirable, the peptide sequences were further optimized by single-point variant screening to enhance both the affinity and activity of the bound enzyme. For proof of concept, the peptides that bound to β-galactosidase and optimized its activity were covalently attached to surfaces for the purpose of capturing target enzymes. Compared to conventional methods, enzymes immobilized on peptide-modified surfaces exhibited higher specific activity and stability, as well as controlled protein orientation. CONCLUSIONS/SIGNIFICANCE: A simple method for immobilizing enzymes through specific interactions with peptides anchored on surfaces has been developed. This approach will be applicable to the immobilization of a wide variety of enzymes on surfaces with optimized orientation, location and performance, and provides a potential mechanism for the patterned self-assembly of multiple enzymes on surfaces

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Purpose – Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps. Design/methodology/approach – This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing. Findings – Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction. Research limitations/implications – The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations. Practical implications – The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications. Originality/value – This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning

Temporal and structural patterns of hepatitis B virus integrations in hepatocellular carcinoma

Chronic infection of hepatitis B virus (HBV) is the major cause of hepatocellular carcinoma (HCC). Notably, 90% of HBV-positive HCC cases exhibit detectable HBV integrations, hinting at the potential early entanglement of these viral integrations in tumorigenesis and their subsequent oncogenic implications. Nevertheless, the precise chronology of integration events during HCC tumorigenesis, alongside their sequential structural patterns, has remained elusive thus far. In this study, we applied whole-genome sequencing to multiple biopsies extracted from six HBV-positive HCC cases. Through this approach, we identified point mutations and viral integrations, offering a blueprint for the intricate tumor phylogeny of these samples. The emergent narrative paints a rich tapestry of diverse evolutionary trajectories characterizing the analyzed tumors. We uncovered oncogenic integration events in some samples that appear to happen before and during the initiation stage of tumor development based on their locations in reconstituted trajectories. Furthermore, we conducted additional long-read sequencing of selected samples and unveiled integration-bridged chromosome rearrangements and tandem repeats of the HBV sequence within integrations. In summary, this study revealed premalignant oncogenic and sequential complex integrations and highlighted the contributions of HBV integrations to HCC development and genome instability

Thermodynamic Additivity of Sequence Variations: An Algorithm for Creating High Affinity Peptides Without Large Libraries or Structural Information

BACKGROUND: There is a significant need for affinity reagents with high target affinity/specificity that can be developed rapidly and inexpensively. Existing affinity reagent development approaches, including protein mutagenesis, directed evolution, and fragment-based design utilize large libraries and/or require structural information thereby adding time and expense. Until now, no systematic approach to affinity reagent development existed that could produce nanomolar affinity from small chemically synthesized peptide libraries without the aid of structural information. METHODOLOGY/PRINCIPAL FINDINGS: Based on the principle of additivity, we have developed an algorithm for generating high affinity peptide ligands. In this algorithm, point-variations in a lead sequence are screened and combined in a systematic manner to achieve additive binding energies. To demonstrate this approach, low-affinity lead peptides for multiple protein targets were identified from sparse random sequence space and optimized to high affinity in just two chemical steps. In one example, a TNF-α binding peptide with K(d) = 90 nM and high target specificity was generated. The changes in binding energy associated with each variation were generally additive upon combining variations, validating the basis of the algorithm. Interestingly, cooperativity between point-variations was not observed, and in a few specific cases, combinations were less than energetically additive. CONCLUSIONS/SIGNIFICANCE: By using this additivity algorithm, peptide ligands with high affinity for protein targets were generated. With this algorithm, one of the highest affinity TNF-α binding peptides reported to date was produced. Most importantly, high affinity was achieved from small, chemically-synthesized libraries without the need for structural information at any time during the process. This is significantly different than protein mutagenesis, directed evolution, or fragment-based design approaches, which rely on large libraries and/or structural guidance. With this algorithm, high affinity/specificity peptide ligands can be developed rapidly, inexpensively, and in an entirely chemical manner

Microarray Selection of Cooperative Peptides for Modulating Enzyme Activities

Recently, peptide microarrays have been used to distinguish proteins, antibodies, viruses, and bacteria based on their binding to random sequence peptides. We reported on the use of peptide arrays to identify enzyme modulators that involve screening an array of 10,000 defined and addressable peptides on a microarray. Primary peptides were first selected to inhibit the enzyme at low μM concentrations. Then, new peptides were found to only bind strongly with the enzyme–inhibitor complex, but not the native enzyme. These new peptides served as secondary inhibitors that enhanced the inhibition of the enzyme together with the primary peptides. Without the primary peptides, the secondary effect peptides had little effect on the enzyme activity. Conversely, we also selected peptides that recovered the activities of inhibited enzyme–peptide complex. The selection of cooperative peptide pairs will provide a versatile toolkit for modulating enzyme functions, which may potentially be applied to drug discovery and biocatalysis

Single-Atom Nanomaterials in Electrochemical Sensors Applications

In recent years, the development of highly sensitive sensors has become a popular research topic. Some functional nanomaterials occupy an important position in the sensing field by virtue of their unique structures and catalytic properties, but there are still problems such as low sensitivity and poor specificity. Single-atom nanomaterials (SANs) show significant advantages in amplifying sensing signals and improving sensor interference resistance due to their high atomic utilization, structural simplicity, and homogeneity. They are expected to achieve high sensitivity and high specificity monitoring by modulating the active sites. In this review, the recent progress on SANs for electrochemical sensing applications was summarized. We first briefly summarize the features and advantages of single-atom catalysts. Then recent advances in the regulation of reaction sites in noble and non-noble metal-based SANs, including the introduction of defects in the carrier, other metal atoms, and ligand atoms, were highlighted. After that, the SANs for the construction of electrochemical, electrochemiluminescent (ECL), and photoelectrochemical (PEC) sensors and their applications in biochemical and environmental analysis were demonstrated. Finally, the future research aspect of SANs-based electrochemical sensing and the challenges of the SANs design and structure-properties revelation were illustrated, giving guidance on sensitive and accurate biosensing toward clinic diagnostic and environmental analysis

Characterization of DNA Origami Nanostructures for Size and Concentration by Dynamic Light Scattering and Nanoparticle Tracking Analysis

Nucleic acids self-assembly has rapidly advanced to produce multi-dimensional nanostructures with precise sizes and shapes. DNA nanostructures hold great potential for a wide range of applications, including biocatalysis, smart materials, molecular diagnosis, and therapeutics. Here, we present a study of using dynamic light scattering (DLS) and nanoparticles tracking analysis (NTA) to analyze DNA origami nanostructures for their size distribution and particles concentrations. Compared to DLS, NTA demonstrated higher resolution of size measurement with a smaller FWHM and was well suited for characterizing multimerization of DNA nanostructures. We future used intercalation dye to enhance the fluorescence signals of DNA origami to increase the detection sensitivity. By optimizing intercalation dyes and the dye-to-DNA origami ratio, fluorescent NTA was able to accurately quantify the concentration of dye-intercalated DNA nanostructures, closely matching with values obtained by UV absorbance at 260 nm. This optimized fluorescent NTA method offers an alternative approach for determining the concentration of DNA nanostructures based on their size distribution, in addition to commonly used UV absorbance quantification. This detailed information of size and concentration is not only crucial for production and quality control but could also provide mechanistic insights in various applications of DNA nanomaterials

Clinical Outcomes of Transcatheter Aortic Valve Replacement in Nonagenarians: A Systematic Review and Meta-Analysis

Objectives. To compare the incidence of mortality and complications between nonagenarians and younger patients undergoing transcatheter aortic valve replacement (TAVR). Background. TAVR has become an alternative treatment for nonagenarian patients with severe aortic stenosis. Previous studies have reported conflicting results regarding the clinical outcomes between nonagenarians and younger patients who underwent TAVR. Methods. We searched PubMed, EMBASE, and Cochrane Library databases with predefined criteria from the inception dates to July 8, 2018. The primary clinical endpoint was 30-day and 1-year all-cause mortalities. Secondary outcomes were considered the rates of stroke, myocardial infarction, any bleeding, any acute kidney injury, any vascular complications, new pacemaker implantation, and conversion to surgical aortic valve replacement. Results. A total of 5 eligible studies with 25,371 patients were included in this meta-analysis. Compared with younger patients who underwent TAVR, nonagenarians had a significantly higher mean Society of Thoracic Surgeons score (STS score) (MD, 2.80; 95%CI: 2.58, 3.30; P<0.00001) and logistic European System for Cardiac Operative Risk Evaluation (logistic EuroSCORE) (MD, 2.72; 95%CI: 1.01, 4.43; P=0.002). Nonagenarians were associated with significantly higher 30-day mortality (6.2% vs. 3.7%; OR, 1.73; 95%CI: 1.49, 2.00) and 1-year mortality (15.5% vs. 11.8%; OR, 1.39; 95%CI: 1.26, 1.53), without significant statistical heterogeneity. Nonagenarians were associated with significantly increased rates of major or life-threatening bleeding, vascular complications and stroke of 20%, 35%, and 32%, respectively. There were no significant differences in the rate of myocardial infarction, stage 2 or 3 acute kidney injury, new pacemaker implantation, or conversion to surgical aortic valve replacement. Conclusions. Nonagenarians showed worse clinical outcomes than younger patients after TAVR, while the incidence of mortality was acceptable. TAVR remains an option for nonagenarian patients with severe aortic stenosis and should be comprehensively evaluated by the heart valve team