DNA Flow-Stretch Assays for Studies of Protein-DNA Interactions at the Single-Molecule Level

Protein-DNA interactions are the core of the cell’s molecular machinery. For a long time, conventional biochemical methods served as a powerful investigatory basis of protein-DNA interactions and target search mechanisms. Currently single-molecule (SM) techniques have emerged as a complementary tool for studying these interactions and have revealed plenty of previously obscured mechanistic details. In comparison to the traditional ones, SM methods allow direct monitoring of individual biomolecules. Therefore, SM methods reveal reactions that are otherwise hidden by the ensemble averaging observed in conventional bulk-type methods. SM biophysical techniques employing various nanobiotechnology methods for immobilization of studied molecules grant the possibility to monitor individual reaction trajectories of biomolecules. Next-generation in vitro SM biophysics approaches enabling high-throughput studies are characterized by much greater complexity than the ones developed previously. Currently, several high-throughput DNA flow-stretch assays have been published and have shown many benefits for mechanistic target search studies of various DNA-binding proteins, such as CRISPR-Cas, Argonaute, various ATP-fueled helicases and translocases, and others. This review focuses on SM techniques employing surface-immobilized and relatively long DNA molecules for studying protein-DNA interaction mechanisms.

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.

A harmonized global land evaporation dataset from model-based products covering 1980–2017

Land evaporation (ET) plays a crucial role in the hydrological and energy cycle. However, the widely used model-based products, even though helpful, are still subject to great uncertainties due to imperfect model parameterizations and forcing data. The lack of available observed data has further complicated estimation. Hence, there is an urgency to define the global proxy land ET with lower uncertainties for climate-induced hydrology and energy change. This study has combined three existing model-based products – the fifth-generation ECMWF reanalysis (ERA5), Global Land Data Assimilation System Version 2 (GLDAS2), and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) – to obtain a single framework of a long-term (1980–2017) daily ET product at a spatial resolution of 0.25∘. Here, we use the reliability ensemble averaging (REA) method, which minimizes errors using reference data, to combine the three products over regions with high consistencies between the products using the coefficient of variation (CV). The Global Land Evaporation Amsterdam Model Version 3.2a (GLEAM3.2a) and flux tower observation data were selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well over a range of vegetation cover scenarios. The merged product also captured the trend of land evaporation over different areas well, showing the significant decreasing trend in the Amazon Plain in South America and Congo Basin in central Africa and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to demonstrating a good performance, the REA method also successfully converged the models based on the reliability of the inputs. The resulting REA data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).

Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility

The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat myriad diseases. In this work we examine the computation of alchemical relative binding free energies with an eye to assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2 and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2 and NAMD3. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between packages of 0.5 $kcal/mol$ The correlation between packages is very good with the lowest Spearman's, Pearson's and Kendall's tau correlation coefficient between two packages being 0.91, 0.89 and 0.74 respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.

Mechanistic Insights From Single-Molecule Studies of Repair of Double Strand Breaks

DNA double strand breaks (DSBs) are among some of the most deleterious forms of DNA damage. Left unrepaired, they are detrimental to genome stability, leading to high risk of cancer. Two major mechanisms are responsible for the repair of DSBs, homologous recombination (HR) and nonhomologous end joining (NHEJ). The complex nature of both pathways, involving a myriad of protein factors functioning in a highly coordinated manner at distinct stages of repair, lend themselves to detailed mechanistic studies using the latest single-molecule techniques. In avoiding ensemble averaging effects inherent to traditional biochemical or genetic methods, single-molecule studies have painted an increasingly detailed picture for every step of the DSB repair processes.

Estimating Regionalized Hydrological Impacts of Climate Change Over Europe by Performance-Based Weighting of CORDEX Projections

Ensemble projections of future changes in discharge over Europe show large variation. Several methods for performance-based weighting exist that have the potential to increase the robustness of the change signal. Here we use future projections of an ensemble of three hydrological models forced with climate datasets from the Coordinated Downscaling Experiment - European Domain (EURO-CORDEX). The experiment is set-up for nine river basins spread over Europe that hold different climate and catchment characteristics. We evaluate the ensemble consistency and apply two weighting approaches; the Climate model Weighting by Independence and Performance (ClimWIP) that focuses on meteorological variables and the Reliability Ensemble Averaging (REA) in our study applied to discharge statistics per basin. For basins with a strong climate signal, in Southern and Northern Europe, the consistency in the set of projections is large. For rivers in Central Europe the differences between models become more pronounced. Both weighting approaches assign high weights to single General Circulation Models (GCMs). The ClimWIP method results in ensemble mean weighted changes that differ only slightly from the non-weighted mean. The REA method influences the weighted mean more, but the weights highly vary from basin to basin. We see that high weights obtained through past good performance can provide deviating projections for the future. It is not apparent that the GCM signal dominates the overall change signal, i.e., there is no strong intra GCM consistency. However, both weighting methods favored projections from the same GCM.

Fusion Method to Estimate Heart Rate from Facial Videos Based on RPPG and RBCG

Remote sensing of vital signs has been developed to improve the measurement environment by using a camera without a skin-contact sensor. The camera-based method is based on two concepts, namely color and motion. The color-based method, remote photoplethysmography (RPPG), measures the color variation of the face generated by reflectance of blood, whereas the motion-based method, remote ballistocardiography (RBCG), measures the subtle motion of the head generated by heartbeat. The main challenge of remote sensing is overcoming the noise of illumination variance and motion artifacts. The studies on remote sensing have focused on the blind source separation (BSS) method for RGB colors or motions of multiple facial points to overcome the noise. However, they have still been limited in their real-world applications. This study hypothesized that BSS-based combining of colors and the motions can improve the accuracy and feasibility of remote sensing in daily life. Thus, this study proposed a fusion method to estimate heart rate based on RPPG and RBCG by the BSS methods such as ensemble averaging (EA), principal component analysis (PCA), and independent component analysis (ICA). The proposed method was verified by comparing it with previous RPPG and RBCG from three datasets according to illumination variance and motion artifacts. The three main contributions of this study are as follows: (1) the proposed method based on RPPG and RBCG improved the remote sensing with the benefits of each measurement; (2) the proposed method was demonstrated by comparing it with previous methods; and (3) the proposed method was tested in various measurement conditions for more practical applications.

Higher spin wormholes from modular bootstrap

We investigate the connection between spacetime wormholes and ensemble averaging in the context of higher spin AdS3/CFT2. Using techniques from modular bootstrap combined with some holographic inputs, we evaluate the partition function of a Euclidean wormhole in AdS3 higher spin gravity. The fixed spin sectors of the dual CFT2 exhibit features that starkly go beyond conventional random matrix ensembles: power-law ramps in the spectral form factor and potentials with a double-well/crest underlying the level statistics.

Molecular Brightness Approach for FRET Analysis of Donor-Linker-Acceptor Constructs at the Single Molecule Level: A Concept

In this report, we have developed a simple approach using single-detector fluorescence autocorrelation spectroscopy (FCS) to investigate the Förster resonance energy transfer (FRET) of genetically encoded, freely diffusing crTC2.1 (mTurquoise2.1–linker–mCitrine) at the single molecule level. We hypothesize that the molecular brightness of the freely diffusing donor (mTurquoise2.1) in the presence of the acceptor (mCitrine) is lower than that of the donor alone due to FRET. To test this hypothesis, the fluorescence fluctuation signal and number of molecules of freely diffusing construct were measured using FCS to calculate the molecular brightness of the donor, excited at 405 nm and detected at 475/50 nm, in the presence and absence of the acceptor. Our results indicate that the molecular brightness of cleaved crTC2.1 in a buffer is larger than that of the intact counterpart under 405-nm excitation. The energy transfer efficiency at the single molecule level is larger and more spread in values as compared with the ensemble-averaging time-resolved fluorescence measurements. In contrast, the molecular brightness of the intact crTC2.1, under 488 nm excitation of the acceptor (531/40 nm detection), is the same or slightly larger than that of the cleaved counterpart. These FCS-FRET measurements on freely diffusing donor-acceptor pairs are independent of the precise time constants associated with autocorrelation curves due to the presence of potential photophysical processes. Ultimately, when used in living cells, the proposed approach would only require a low expression level of these genetically encoded constructs, helping to limit potential interference with the cell machinery.