High-throughput Experimental Methods Research Articles

Ni- and Ni/Pd-Catalyzed Reductive Coupling of Lignin-Derived Aromatics to Access Biobased Plasticizers.

Lignin-derived aromatic chemicals offer a compelling alternative to petrochemical feedstocks, and new applications are the focus of extensive interest. 4-Hydroxybenzoic acid (H), vanillic acid (G), and syringic acid (S) are readily obtained via oxidative depolymerization of hardwood lignin substrates. Here, we explore the use of these compounds to access biaryl dicarboxylate esters that represent biobased, less toxic alternatives to phthalate plasticizers. Chemical and electrochemical methods are developed for catalytic reductive coupling of sulfonate derivatives of H, G, and S to access all possible homo- and cross-coupling products. A conventional NiCl2/bipyridine catalyst is able to access the H-H and G-G products, but new catalysts are identified to afford the more challenging coupling products, including a NiCl2/bisphosphine catalyst for S-S and a NiCl2/phenanthroline/PdCl2/phosphine cocatalyst system for H-G, H-S, and G-S. High-throughput experimentation methods with a chemical reductant (Zn powder) are shown to provide an efficient screening platform for identification of new catalysts, while electrochemical methods can access improved yields and/or facilitate implementation on larger scale. Plasticizer tests are performed with poly(vinyl chloride), using esters of the 4,4'-biaryl dicarboxylate products. The H-G and G-G derivatives, in particular, exhibit performance advantages relative to an established petroleum-based phthalate ester plasticizer.

View-Aware Collaborative Learning for Survival Prediction and Subgroup Identification.

Advances of high throughput experimental methods have led to the availability of more diverse omic datasets in clinical analysis applications. Different types of omic data reveal different cellular aspects and contribute to the understanding of disease progression from these aspects. While survival prediction and subgroup identification are two important research problems in clinical analysis, their performance can be further boosted by taking advantages of multiple omics data through multi-view learning. However, these two tasks are generally studied separately, and the possibility that they could reinforce each other by collaborative learning has not been adequately considered. In light of this, we propose a View-aware Collaborative Learning (VaCoL) method to jointly boost the performance of survival prediction and subgroup identification by integration of multiple omics data. Specifically, survival analysis and affinity learning, which respectively perform survival prediction and subgroup identification, are integrated into a unified optimization framework to learn the two tasks in a collaborative way. In addition, by considering the diversity of different types of data, we make use of the log-rank test statistic to evaluate the importance of different views. As a result, the proposed approach can adaptively learn the optimal weight for each view during training. Empirical results on several real datasets show that our method is able to significantly improve the performance of survival prediction and subgroup identification. A detailed model analysis study is also provided to show the effectiveness of the proposed collaborative learning and view-weight learning approaches.

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants.

Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors. In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins. We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.

The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.

Deciphering the mechanical code of the genome and epigenome.

Diverse DNA-deforming processes are impacted by the local mechanical and structural properties of DNA, which in turn depend on local sequence and epigenetic modifications. Deciphering this mechanical code (that is, this dependence) has been challenging due to the lack of high-throughput experimental methods. Here we present a comprehensive characterization of the mechanical code. Utilizing high-throughput measurements of DNA bendability via loop-seq, we quantitatively established how the occurrence and spatial distribution of dinucleotides, tetranucleotides and methylated CpG impact DNA bendability. We used our measurements to develop a physical model for the sequence and methylation dependence of DNA bendability. We validated the model by performing loop-seq on mouse genomic sequences around transcription start sites and CTCF-binding sites. We applied our model to test the predictions of all-atom molecular dynamics simulations and to demonstrate that sequence and epigenetic modifications can mechanically encode regulatory information in diverse contexts.

Data-Driven Discovery of Robust Materials for Photocatalytic Energy Conversion

The solar–to–chemical energy conversion of Earth-abundant resources like water or greenhouse gas pollutants like CO2 promises an alternate energy source that is clean, renewable, and environmentally friendly. The eventual large-scale application of such photo-based energy conversion devices can be realized through the discovery of novel photocatalytic materials that are efficient, selective, and robust. In the past decade, the Materials Genome Initiative has led to a major leap in the development of materials databases, both computational and experimental. Hundreds of photocatalysts have recently been discovered for various chemical reactions, such as water splitting and carbon dioxide reduction, employing these databases and/or data informatics, machine learning, and high-throughput computational and experimental methods. In this article, we review these data-driven photocatalyst discoveries, emphasizing the methods and techniques developed in the last few years to determine the (photo)electrochemical stability of photocatalysts, leading to the discovery of photocatalysts that remain robust and durable under operational conditions.

High-throughput experimental study on the microstructural and compositional variations of mechanical properties for AlCoCrFeNi high entropy alloys

To efficiently establish the relationship among microstructure, composition and mechanical properties of the AlCoCrFeNi high entropy alloys, a high-throughput experimental method has been developed combining diffusion couple with the nanoindentation technique. In this paper, B2(A2) denotes that A2 (disordered bcc) particles are coherently dispersed in the B2 (ordered bcc) matrix, and vice versa for A2(B2). A B2(A2)-A2(B2)-fcc sandwich microstructure was produced by annealing an Al1.1CoCrFeNi/CoCrFeNi diffusion couple. The following nanoindentation measurements found that the hardness of the A2(B2) structure is slightly lower than that of B2(A2), but the elastic modulus and ductility behave differently. The fcc region, on the other hand, has a larger ductility than both B2(A2) and A2(B2). In addition, the elastic modulus of fcc decreases with the increasing Al content, whereas the hardness does not significantly change with composition.

Research Progress in High-Throughput Screening of CO2 Reduction Catalysts

The conversion and utilization of carbon dioxide (CO2) have dual significance for reducing carbon emissions and solving energy demand. Catalytic reduction of CO2 is a promising way to convert and utilize CO2. However, high-performance catalysts with excellent catalytic activity, selectivity and stability are currently lacking. High-throughput methods offer an effective way to screen high-performance CO2 reduction catalysts. Here, recent advances in high-throughput screening of electrocatalysts for CO2 reduction are reviewed. First, the mechanism of CO2 reduction reaction by electrocatalysis and potential catalyst candidates are introduced. Second, high-throughput computational methods developed to accelerate catalyst screening are presented, such as density functional theory and machine learning. Then, high-throughput experimental methods are outlined, including experimental design, high-throughput synthesis, in situ characterization and high-throughput testing. Finally, future directions of high-throughput screening of CO2 reduction electrocatalysts are outlooked. This review will be a valuable reference for future research on high-throughput screening of CO2 electrocatalysts.

Design of super-hard high-entropy ceramics coatings via machine learning

High-entropy ceramic coatings have some unique physical and mechanical properties, such as high hardness, good corrosion resistance and excellent thermal stability. However, since they can contain five or more metal elements, their composition is quite complex. Combined with machine learning and high-throughput experimental methods, ultra-hard high-entropy ceramic coatings were screened in a short period of time. The hardness of coatings is predicted using a random forest algorithm based on its composition and processing parameters. The uncertainty of machine learning prediction is further reduced by active learning. After three iterations, a new high-entropy ceramic coating (AlCrNbTaTi)N with a hardness of 40.1 GPa has been successfully prepared, which is 9% higher than the optimal hardness of the original quinary system. This paper demonstrates that machine learning combined with high-throughput experimental methods can effectively accelerate design and composition optimization of multicomponent materials.

Large-scale comparative review and assessment of computational methods for phage virion proteins identification.

Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the in silico identification of PVPs in conjunction with traditional experimental approaches. As a result, a revised and more thorough assessment is extremely desirable. With this purpose in mind, we first conduct a thorough survey and evaluation of a vast array of 13 state-of-the-art PVP predictors. Among these PVP predictors, they can be classified into three groups according to the types of machine learning (ML) algorithms employed (i.e. traditional ML-based methods, ensemble-based methods and deep learning-based methods). Subsequently, we explored which factors are important for building more accurate and stable predictors and this included training/independent datasets, feature encoding algorithms, feature selection methods, core algorithms, performance evaluation metrics/strategies and web servers. Finally, we provide insights and future perspectives for the design and development of new and more effective computational approaches for the detection and characterization of PVPs.

(Invited) Reaction Kinetics and Gas Permeability Properties of Thin Film Ionomers for High-Temperature Polymer Electrolyte Membrane Systems

Polymer electrolyte membranes and binders are important materials to the performance and efficiency of numerous electrochemical processes such as electrolyzers and fuel cells. This talk presents the impetus behind the re-emergence of high-temperature polymer electrolyte membrane (HT-PEM) fuel cells. New polymer architectures based upon polycation-acid anion interactions have resulted in superior HT-PEMs in terms of ionic conductivity and stability over the classic phosphoric acid imbibed polybenzimidazole (PBI). Despite the advent of more functional membranes, gas reactant transport and reaction kinetic limitations in electrode layers still stymie the power density of HT-PEM fuel cells. To address these problems, our lab has leveraged high-throughput experimental methods to study the electrochemical properties of thin film high-temperature polymer electrolytes. These methods include a new class of interdigitated electrodes featuring nanoscale electrocatalysts from block copolymer templating. Furthermore, the new HT-PEMs and binders developed in our lab are effective for carrying out challenging hydrogen separations from reforming effluents.

Phase-Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries.

Exploring the vast compositional space offered by multicomponent systems or high entropy materials using the traditional route of materials discovery, one experiment at a time, is prohibitive in terms of cost and required time. Consequently, the development of high-throughput experimental methods, aided by machine learning and theoretical predictions will facilitate the search for multicomponent materials in their compositional variety. In this study, high entropy oxides are fabricated and characterized using automated high-throughput techniques. For intuitive visualization, a graphical phase-property diagram correlating the crystal structure, the chemical composition, and the band gap are introduced. Interpretable machine learning models are trained for automated data analysis and to speed up data comprehension. The establishment of materials libraries of multicomponent systems correlated with their properties (as in the present work), together with machine learning-based data analysis and theoretical approaches are opening pathways toward virtual development of novel materials for both functional and structural applications.

High‐throughput experimental methods for investigating biomolecular condensates

BackgroundThe concept of biomolecular condensate was put forward recently to emphasize the ability of certain cellular compartments to concentrate molecules and comprise proteins and nucleic acids with specific biological functions, from ribosome genesis to RNA splicing. Due to their unique role in biological processes, it is crucial to investigate their compositions, which is a primary determinant of condensate properties.ResultsSince a wide range of macromolecules comprise biomolecular condensates, it is necessary for researchers to investigate them using high‐throughput methodologies while low‐throughput experiments are not efficient enough. These high‐throughput methods usually purify interacting protein libraries from condensates before being scanned in mass spectrometry. It is possible to extract organelles as a whole for specific condensates for further analysis, however, most condensates do not have a distinguishable marker or are sensitive to shear force to be extracted as a whole. Affinity tagging allows a comprehensive view of interacting proteins of target molecule yet only proteins with strong bonds may be pulled down. Proximity labeling serves as a complementary method to label more dynamic proteins with weaker interactions, increasing sensitivity while decreasing specificity. Image‐based fluorescent screening takes another path by scanning images automatically to illustrate the condensing state of biomolecules within membraneless organelles, which is a unique feature unlike the previous mass spectrometry‐based methods.ConclusionThis review presents a rough glimpse into high‐throughput methodologies for biomolecular condensate investigation to encourage usage of bioinformatic tools by researchers in relevant fields.

BERT-m7G: A Transformer Architecture Based on BERT and Stacking Ensemble to Identify RNA N7-Methylguanosine Sites from Sequence Information

As one of the most prevalent posttranscriptional modifications of RNA, N7-methylguanosine (m7G) plays an essential role in the regulation of gene expression. Accurate identification of m7G sites in the transcriptome is invaluable for better revealing their potential functional mechanisms. Although high-throughput experimental methods can locate m7G sites precisely, they are overpriced and time-consuming. Hence, it is imperative to design an efficient computational method that can accurately identify the m7G sites. In this study, we propose a novel method via incorporating BERT-based multilingual model in bioinformatics to represent the information of RNA sequences. Firstly, we treat RNA sequences as natural sentences and then employ bidirectional encoder representations from transformers (BERT) model to transform them into fixed-length numerical matrices. Secondly, a feature selection scheme based on the elastic net method is constructed to eliminate redundant features and retain important features. Finally, the selected feature subset is input into a stacking ensemble classifier to predict m7G sites, and the hyperparameters of the classifier are tuned with tree-structured Parzen estimator (TPE) approach. By 10-fold cross-validation, the performance of BERT-m7G is measured with an ACC of 95.48% and an MCC of 0.9100. The experimental results indicate that the proposed method significantly outperforms state-of-the-art prediction methods in the identification of m7G modifications.

Automated analysis of bacterial flow cytometry data with FlowGateNIST.

Flow cytometry is commonly used to evaluate the performance of engineered bacteria. With increasing use of high-throughput experimental methods, there is a need for automated analysis methods for flow cytometry data. Here, we describe FlowGateNIST, a Python package for automated analysis of bacterial flow cytometry data. The main components of FlowGateNIST perform automatic gating to differentiate between cells and background events and then between singlet and multiplet events. FlowGateNIST also includes a method for automatic calibration of fluorescence signals using fluorescence calibration beads. FlowGateNIST is open source and freely available with tutorials and example data to facilitate adoption by users with minimal programming experience.

Double-Site Substitution of Ce into (Ba, Sr)MnO3 Perovskites for Solar Thermochemical Hydrogen Production

Solar thermochemical hydrogen production (STCH) is a renewable alternative to hydrogen produced using fossil fuels. While serial bulk experimental methods can accurately measure STCH performance, screening chemically complex materials systems for new promising candidates is more challenging. Here we identify double-site Ce-substituted (Ba,Sr)MnO3 oxide perovskites as promising STCH candidates using a combination of bulk synthesis and high-throughput thin film experiments. The Ce substitution on the B-site in 10H-BaMnO3 and on the A-site in 4P-SrMnO3 lead to 2-3x higher hydrogen production compared to CeO2, but these bulk single-site substituted perovskites suffer from incomplete reoxidation. Double-site Ce substitution on both A- and B-site in (Ba,Sr)MnO3 thin films increases Ce solubility and extends the stability of 10H and 4P structures, which is promising for their thermochemical reversibility. This study demonstrates a high-throughput experimental method for screening complex oxide materials for STCH applications.

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

BackgroundThe topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.ResultsWe evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification.ConclusionsSCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

Protein–protein interaction prediction based on ordinal regression and recurrent convolutional neural networks

BackgroundProtein protein interactions (PPIs) are essential to most of the biological processes. The prediction of PPIs is beneficial to the understanding of protein functions and thus is helpful to pathological analysis, disease diagnosis and drug design etc. As the amount of protein data is growing fast in the post genomic era, high-throughput experimental methods are expensive and time-consuming for the prediction of PPIs. Thus, computational methods have attracted researcher’s attention in recent years. A large number of computational methods have been proposed based on different protein sequence encoders.ResultsNotably, the confidence score of a protein sequence pair could be regarded as a kind of measurement to PPIs. The higher the confidence score for one protein pair is, the more likely the protein pair interacts. Thus in this paper, a deep learning framework, called ordinal regression and recurrent convolutional neural network (OR-RCNN) method, is introduced to predict PPIs from the perspective of confidence score. It mainly contains two parts: the encoder part of protein sequence pair and the prediction part of PPIs by confidence score. In the first part, two recurrent convolutional neural networks (RCNNs) with shared parameters are applied to construct two protein sequence embedding vectors, which can automatically extract robust local features and sequential information from the protein pairs. Based on it, the two embedding vectors are encoded into one novel embedding vector by element-wise multiplication. By taking the ordinal information behind confidence score into consideration, ordinal regression is used to construct multiple sub-classifiers in the second part. The results of multiple sub-classifiers are aggregated to obtain the final confidence score. Following that, the existence of PPIs is determined by the confidence score. We set a threshold theta, and say the interaction exists between the protein pair if its confidence score is bigger than theta.ConclusionsWe applied our method to predict PPIs on data sets S. cerevisiae and Homo sapiens. Through experimental verification, our method outperforms state-of-the-art PPI prediction models.

Time-Resolved Force Measurements to Determine Positioning Tolerances for Impulse-Based Indentations

High-throughput experimentation methods determine characteristic values, which are correlated with material properties by means of mathematical models. Here, an indentation method based on laser-induced shock waves is presented, which predicts the material properties, such as hardness and tensile strength, by the induced plastic deformation in the substrate material. The shock wave pushes a spherical indenter inside a substrate material. For reproducible indentations, the applied load is of importance. To compare different processes and process parameters, the measured plastic deformation is normalized by the applied load. However, eccentric irradiation leads to altered beam profiles on the surface of spherical indenters and the angle of incidence is changed. Thus, the influence of eccentric irradiation is studied with an adapted time-resolved force measurement setup to determine the required positioning tolerances. The spherical indenter is placed inside a cylindrical pressure cell to increase the laser-induced shock pressure. From the validated time-resolved force measurement method we derive that deviations from the indentation forces are acceptable, when the lateral deviation of the beam center, which depends only on the alignment of the setup, does not exceed ± 0.4 mm. A vertical displacement from the focus position between -3.0 mm and + 2.0 mm still leads to acceptable deviations from the indentation force.

Design of Low-Cost and High-Strength Titanium Alloys Using Pseudo-Spinodal Mechanism through Diffusion Couple Technology and CALPHAD.

The high cost of development and raw materials have been obstacles to the widespread use of titanium alloys. In the present study, the high-throughput experimental method of diffusion couple combined with CALPHAD calculation was used to design and prepare the low-cost and high-strength Ti-Al-Cr system titanium alloy. The results showed that ultra-fine α phase was obtained in Ti-6Al-10.9Cr alloy designed through the pseudo-spinodal mechanism, and it has a high yield strength of 1437 ± 7 MPa. Furthermore, application of the 3D strength model of Ti-6Al-xCr alloy showed that the strength of the alloy depended on the volume fraction and thickness of the α phase. The large number of α/β interfaces produced by ultra-fine α phase greatly improved the strength of the alloy but limited its ductility. Thus, we have demonstrated that the pseudo-spinodal mechanism combined with high-throughput diffusion couple technology and CALPHAD was an efficient method to design low-cost and high-strength titanium alloys.