High-throughput Experimental Methods Research Articles

A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data.

Characterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-seq and RNAcompete, usually suffer from the false negative issue. Here, we develop a deep boosting based machine learning approach, called DeBooster, to accurately model the binding sequence preferences and identify the corresponding binding targets of RBPs from CLIP-seq data. Comprehensive validation tests have shown that DeBooster can outperform other state-of-the-art approaches in RBP target prediction. In addition, we have demonstrated that DeBooster may provide new insights into understanding the regulatory functions of RBPs, including the binding effects of the RNA helicase MOV10 on mRNA degradation, the potentially different ADAR1 binding behaviors related to its editing activity, as well as the antagonizing effect of RBP binding on miRNA repression. Moreover, DeBooster may provide an effective index to investigate the effect of pathogenic mutations in RBP binding sites, especially those related to splicing events. We expect that DeBooster will be widely applied to analyze large-scale CLIP-seq experimental data and can provide a practically useful tool for novel biological discoveries in understanding the regulatory mechanisms of RBPs. The source code of DeBooster can be downloaded from http://github.com/dongfanghong/deepboost.

BEESEM: estimation of binding energy models using HT-SELEX data.

Characterizing the binding specificities of transcription factors (TFs) is crucial to the study of gene expression regulation. Recently developed high-throughput experimental methods, including protein binding microarrays (PBM) and high-throughput SELEX (HT-SELEX), have enabled rapid measurements of the specificities for hundreds of TFs. However, few studies have developed efficient algorithms for estimating binding motifs based on HT-SELEX data. Also the simple method of constructing a position weight matrix (PWM) by comparing the frequency of the preferred sequence with single-nucleotide variants has the risk of generating motifs with higher information content than the true binding specificity. We developed an algorithm called BEESEM that builds on a comprehensive biophysical model of protein-DNA interactions, which is trained using the expectation maximization method. BEESEM is capable of selecting the optimal motif length and calculating the confidence intervals of estimated parameters. By comparing BEESEM with the published motifs estimated using the same HT-SELEX data, we demonstrate that BEESEM provides significant improvements. We also evaluate several motif discovery algorithms on independent PBM and ChIP-seq data. BEESEM provides significantly better fits to in vitro data, but its performance is similar to some other methods on in vivo data under the criterion of the area under the receiver operating characteristic curve (AUROC). This highlights the limitations of the purely rank-based AUROC criterion. Using quantitative binding data to assess models, however, demonstrates that BEESEM improves on prior models. Freely available on the web at http://stormo.wustl.edu/resources.html . stormo@wustl.edu. Supplementary data are available at Bioinformatics online.

Microscale High-Throughput Experimentation as an Enabling Technology in Drug Discovery: Application in the Discovery of (Piperidinyl)pyridinyl-1H-benzimidazole Diacylglycerol Acyltransferase 1 Inhibitors.

Miniaturization and parallel processing play an important role in the evolution of many technologies. We demonstrate the application of miniaturized high-throughput experimentation methods to resolve synthetic chemistry challenges on the frontlines of a lead optimization effort to develop diacylglycerol acyltransferase (DGAT1) inhibitors. Reactions were performed on ∼1 mg scale using glass microvials providing a miniaturized high-throughput experimentation capability that was used to study a challenging SNAr reaction. The availability of robust synthetic chemistry conditions discovered in these miniaturized investigations enabled the development of structure-activity relationships that ultimately led to the discovery of soluble, selective, and potent inhibitors of DGAT1.

Bioinformatics Analysis of Protein Phosphorylation in Plant Systems Biology Using P3DB.

Protein phosphorylation is one of the most pervasive protein post-translational modification events in plant cells. It is involved in many plant biological processes, such as plant growth, organ development, and plant immunology, by regulating or switching signaling and metabolic pathways. High-throughput experimental methods like mass spectrometry can easily characterize hundreds to thousands of phosphorylation events in a single experiment. With the increasing volume of the data sets, Plant Protein Phosphorylation DataBase (P3DB, http://p3db.org ) provides a comprehensive, systematic, and interactive online platform to deposit, query, analyze, and visualize these phosphorylation events in many plant species. It stores the protein phosphorylation sites in the context of identified mass spectra, phosphopeptides, and phosphoproteins contributed from various plant proteome studies. In addition, P3DB associates these plant phosphorylation sites to protein physicochemical information in the protein charts and tertiary structures, while various protein annotations from hierarchical kinase phosphatase families, protein domains, and gene ontology are also added into the database. P3DB not only provides rich information, but also interconnects and provides visualization of the data in networks, in systems biology context. Currently, P3DB includes the KiC (Kinase Client) assay network, the protein-protein interaction network, the kinase-substrate network, the phosphatase-substrate network, and the protein domain co-occurrence network. All of these are available to query for and visualize existing phosphorylation events. Although P3DB only hosts experimentally identified phosphorylation data, it provides a plant phosphorylation prediction model for any unknown queries on the fly. P3DB is an entry point to the plant phosphorylation community to deposit and visualize any customized data sets within this systems biology framework. Nowadays, P3DB has become one of the major bioinformatics platforms of protein phosphorylation in plant biology.

Stability of a Transient Protein Complex in a Charged Aqueous Droplet with Variable pH.

Electrospray ionization mass spectrometry (ESI-MS) has the potential to become a high-throughput robust experimental method for the detection of protein-protein equilibrium constants. Poorly understood processes that affect the stability of weak noncovalent protein complexes in the intervening droplet environment are a significant factor that precludes the advancement of the method. We use molecular dynamics to study the stability of a ubiquitin and ubiquitin-associated domain complex (RCSB PDB code 2MRO ) in an aqueous droplet with changing size and charge concentration. We present evidence that a weak protein complex changes conformation and may dissociate in shrinking droplets. Then, the droplets containing these dissociated proteins divide. Our findings suggest that in some cases ESI-MS does not measure the correct association constants. The study intends to stimulate research for systematic development of experimental protocols that stabilize weakly bound protein interfaces in droplets.

Prediction of Protein-Protein Interactions by Evidence Combining Methods.

Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

Sequence-Based Prediction of RNA-Binding Residues in Proteins.

Identifying individual residues in the interfaces of protein-RNA complexes is important for understanding the molecular determinants of protein-RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein-RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein-RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner.

High-throughput discovery and characterization of multicomponent bulk metallic glass alloys

This work describes a high-throughput experimental method to characterize compositional trends in the glass forming ability and mechanical behavior of a ternary metallic alloy system. Continuously-graded composition libraries of Cu-Zr-Ti ternary alloys were produced by laser deposition, and continuous regions of glass-forming compositions were rapidly identified through an optical microscopy technique. By varying the laser processing parameters and thereby the cooling rate of the melt within each library, the composition with the greatest glass-forming ability within the range studied was determined to be Cu51.7Zr36.7Ti11.6. An alternative deposition scheme was applied to fabricate libraries containing a large array of discrete compositions. Instrumented nanoindentation was performed on the discrete libraries to establish compositional trends in the measured properties. The indentation modulus was observed to be strongly correlated with the Ti-content over the entire region of study, while the hardness was more sensitive to Cu for high Zr-contents and to Ti at lower Zr-contents. These trends could inform the design of new metallic glass alloys possessing an optimized balance of both ductility and glass forming ability.

The Implicitome: A Resource for Rationalizing Gene-Disease Associations.

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.

Inference of protein-protein interaction networks from multiple heterogeneous data.

Protein-protein interaction (PPI) prediction is a central task in achieving a better understanding of cellular and intracellular processes. Because high-throughput experimental methods are both expensive and time-consuming, and are also known of suffering from the problems of incompleteness and noise, many computational methods have been developed, with varied degrees of success. However, the inference of PPI network from multiple heterogeneous data sources remains a great challenge. In this work, we developed a novel method based on approximate Bayesian computation and modified differential evolution sampling (ABC-DEP) and regularized laplacian (RL) kernel. The method enables inference of PPI networks from topological properties and multiple heterogeneous features including gene expression and Pfam domain profiles, in forms of weighted kernels. The optimal weights are obtained by ABC-DEP, and the kernel fusion built based on optimal weights serves as input to RL to infer missing or new edges in the PPI network. Detailed comparisons with control methods have been made, and the results show that the accuracy of PPI prediction measured by AUC is increased by up to 23 %, as compared to a baseline without using optimal weights. The method can provide insights into the relations between PPIs and various feature kernels and demonstrates strong capability of predicting faraway interactions that cannot be well detected by traditional RL method.

Investigation of the phase space in lead-free (K x Na1-x )1-y Li y (Nb1-z Ta z )O3 ferroelectric ceramics

A library of ceramic compounds based on the lead-free (KxNa1−x)1−yLiy(Nb1−zTaz)O3 solid solution has been synthesized and characterized using high-throughput experimentation (HTE) method. The phase space previously reported by Saito and Takao has been expanded to {{x, 0.1, 1.0}, {y, 0, 0.1}, {z, 0, 0.2}}, and new phase boundaries are observed. The relative density values show that with the appropriate sintering temperature, ∼92% of the theoretical density can be reached. The relative permittivity values show that with increasing amount of K+ and Ta5+, the dielectric constant values increase. The effect of density on the dielectric constant values is however minimal. Resistivity values ranging from 109 to 1013 Ω·cm are obtained for the samples. The piezoelectric charge coefficient values for selected compositions show that higher values are obtained close to the phase boundaries rather than away from them. The properties for the ceramic library using the HTE method are generally 15%–20% less than from the conventional method. This method is therefore more suited for screening of sample compositions than for producing samples with high piezoelectric properties.

Genome-Wide Assessment of Outer Membrane Vesicle Production in Escherichia coli.

The production of outer membrane vesicles by Gram-negative bacteria has been well documented; however, the mechanism behind the biogenesis of these vesicles remains unclear. Here a high-throughput experimental method and systems-scale analysis was conducted to determine vesiculation values for the whole genome knockout library of Escherichia coli mutant strains (Keio collection). The resultant dataset quantitatively recapitulates previously observed phenotypes and implicates nearly 150 new genes in the process of vesiculation. Gene functional and biochemical pathway analyses suggest that mutations that truncate outer membrane structures such as lipopolysaccharide and enterobacterial common antigen lead to hypervesiculation, whereas mutants in oxidative stress response pathways result in lower levels. This study expands and refines the current knowledge regarding the cellular pathways required for outer membrane vesiculation in E. coli.

Highlights from the 5th Symposium on Biological Data Visualization: Part 1.

High-throughput and high-resolution experimental methods in biology pose enormous challenges for current biological data visualization approaches. To address these challenges, researchers in the visualization and bioinformatics communities need to engage in the design, implementation, application, and evaluation of novel visualization techniques and tools that provide insight into large and highly complex data sets. BioVis 2015 - the fifth Symposium on Biological Data Visualization - brought together researchers from the visualization, bioinformatics, and biology communities to establish an interdisciplinary dialogue and promote the sharing of expertise between both meeting participants and the communities at large. The meeting educated, inspired, and engaged visualization researchers in problems in biological data visualization as well as bioinformatics and biology researchers in state-of-the-art visualization research. The symposium serves as a platform for researchers from these fields to increase the impact of data visualization approaches in biology. The BioVis 2015 symposium is affiliated with ISMB, the Intelligent Systems for Molecular Biology conference, as a Special Interest Group (SIG) and was colocated with ISMB in Dublin, Ireland, July 10-11 2015. Each paper was reviewed by researchers from both the bioinformatics and visualization fields and was evaluated for improvements over state-of-the-art and for scientific soundness. The review process was organized in two review cycles. In the first review cycle, each paper was reviewed by three to four reviewers. In the second review cycle, the primary reviewers checked whether the required revisions for conditionally accepted papers were successfully included. Based on the reviewers' scores, reviews, and recommendations, the BioVis 2015 Paper and Publication Chairs and the BMC Bioinformatics Section Editor together selected those that would be published as a BMC Bioinformatics supplement. The papers from BioVis 2015 appear in two different proceedings: As of the 5th Symposium on Biological Data Visualization: Part 1 in this BMC Bioinformatics supplement and as of the 5th Symposium on Biological Data Visualization: Part 2 in BMC Proceedings (http://www.biomedcentral.com/bmcproc/supplements/9/S6). From the 21 papers submitted to BioVis 2015, 9 papers are published in this BMC Bioinformatics supplement and 5 papers are published in BMC Proceedings. The articles in this supplement cover a wide spectrum of challenging problems in biological data visualization and their solutions. Overall, three main themes arise from the BioVis 2015 articles: omics, proteins, and imaging. In the omics field, Younesy et al. [1] describe VisRseq: a user-friendly interface for biologists to use libraries in R that provides a method for linking R-apps with interactive components. Chelaru et al. [2] expand on the design behind Epiviz, another tool for bringing genome visualization and computational environments together. Hennig et al. [3] describe Pan-Tetris and Aurisano et al. [4] describe BactoGeNIE: both systems are designed for comparing different genomes. The XCluSim tool by L'Yi et al. [5] has a more general application field and aims to provide insight into how different clustering results relate to each other. In the protein field, Stolte et al. [6] give an overview of the design decisions that underlie Aquaria, a visual analytics tool for exploring protein-related data. Finally, three papers are included from the imaging field. Topics range from image generation, as discussed by Abdellah et al. [7], to a method for parameter optimization in image processing by Pretorius et al. [9] (e.g. for cell nuclei detection and colour deconvolution for histology), and all the way to graph-based exploration of histology images in the GRAPHIE system proposed by Ding et al. [8]. The diversity of topics covered in this issue highlights the wide range of challenges in applying existing visualization techniques to biological data. With this analysis and formalization of our collective experiences, we hope to motivate visualization researchers to think about new problems and new approaches to pressing problems in biology.

A Bright Future for Evolutionary Methods in Drug Design

Most medicinal chemists understand that chemical space is extremely large, essentially infinite. Although high-throughput experimental methods allow exploration of drug-like space more rapidly, they are still insufficient to fully exploit the opportunities that such large chemical space offers. Evolutionary methods can synergistically blend automated synthesis and characterization methods with computational design to identify promising regions of chemical space more efficiently. We describe how evolutionary methods are implemented, and provide examples of published drug development research in which these methods have generated molecules with increased efficacy. We anticipate that evolutionary methods will play an important role in future drug discovery.

Fuzzy support vector machine model to predict human death domain protein–protein interactions

Proteins have crucial importance in every living system, and Protein–Protein Interactions (PPIs) play a pivotal role in regulation of virtually all biological processes such as DNA transcription, replication, metabolic cycles and signaling cascades. The PPIs play an important role in the complex process of cell death which mainly occurs via apoptosis and necrosis in eukaryotic cells. Apoptosis is an orderly cellular suicide program critical for the development and homeostasis of multicellular organism. Failure to control apoptosis can have catastrophic consequences. The cascades of amazing reactions carried out by proteins such as Caspase, CARD, NLRP, NOD, FADD, DEDD, POP, Myd88 etc. play important role in the process of cell death. The high throughput experimental methods for determining PPIs are time consuming, expensive and are generating huge amount of PPIs data. Therefore, there is need to develop computational methods to efficiently and accurately predict PPIs. In this work, an attempt has been made to develop a fuzzy support vector machine (F-SVM) model for predicting death domain (DD) PPIs based on sixteen physicochemical, biochemical and structural features of amino acids which are monomers of proteins. First, the protein primary sequences are encoded into sequential features represented by descriptors. Then, the Support Vector Machine and Sequential Minimal Optimization of WEKA software are employed to classify interacting and non-interacting protein pairs. The performance of SVM and F-SVM with various kernel functions has been evaluated and it was observed that libSVM with Polynomial kernel was found to be best with accuracy of 77.94 % via F-SVM which is optimum model in predicting human DD-PPIs. Validation is performed by tenfold cross-validation technique. The F-SVM performance measure is 2.94 % higher than SVM in terms of accuracy with the use of custom designed fuzzy membership function. The results obtained are in agreement with available experimental data. Such models can be useful in providing PPI information of DD proteins which can be useful in understanding the molecular mechanisms involved in death of cells taking place due to aging, programmed cell death and various diseases. It may through some light on the study of cancerous cell and gerontology.

A solid-phase screening method for identification of glycan-binding cells

Glycan immobilization and epitope recognition on a solid support is useful for functional glycoproteomics and analysis of protein-protein interactions. However, the lack of high-throughput experimental methods for the examination of cell-glycan interactions means that identifying glycan-binding cells remains a challenge. Herein, we describe a tool which enables the screening of glycanbinding cells that specifically recognize the glycan epitopes released from the membrane glycoproteins of cells and tissues. Biotinylated pig kidney N-glycans were immobilized on streptavidin-conjugated solid supports and then incubated with human immune cell lines. U937 cells discriminated between the α-galactose (α-Gal) and non-Gal pig N-glycans on the solid support. This screening strategy could aid exploration of cell-protein or cell-cell interactions.

Systems biology approaches for advancing the discovery of effective drug combinations.

Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations.Graphical abstractSpectrum of Systems Biology Approaches for Drug Combinations.

Detecting protein complexes in protein interaction networks using a ranking algorithm with a refined merging procedure.

BackgroundDeveloping suitable methods for the identification of protein complexes remains an active research area. It is important since it allows better understanding of cellular functions as well as malfunctions and it consequently leads to producing more effective cures for diseases. In this context, various computational approaches were introduced to complement high-throughput experimental methods which typically involve large datasets, are expensive in terms of time and cost, and are usually subject to spurious interactions.ResultsIn this paper, we propose ProRank+, a method which detects protein complexes in protein interaction networks. The presented approach is mainly based on a ranking algorithm which sorts proteins according to their importance in the interaction network, and a merging procedure which refines the detected complexes in terms of their protein members. ProRank + was compared to several state-of-the-art approaches in order to show its effectiveness. It was able to detect more protein complexes with higher quality scores.ConclusionsThe experimental results achieved by ProRank + show its ability to detect protein complexes in protein interaction networks. Eventually, the method could potentially identify previously-undiscovered protein complexes.The datasets and source codes are freely available for academic purposes at http://faculty.uaeu.ac.ae/nzaki/Research.htm.

Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.

Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions. However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection.

Texture transformations in Ag thin films

A thickness-dependent texture transformation during annealing of initially (111) fiber-textured face-centered-cubic metal thin films is phenomenologically well known: sufficiently thin films retain the (111) texture, while sufficiently thick films transform to a (100) fiber texture. This transformation has been explained based on minimization of strain and interface energies, but recent work calls into question the roles of both of these driving forces. A high-throughput experimental method for the study of this texture transformation has been developed and applied to thin silver films with and without Ti adhesion layers. More than 150 individual samples spanning a range of thicknesses and interface conditions were prepared in a single deposition run. The texture evolution of these samples was characterized using X-ray diffraction as a function of time and temperature during annealing. The transformation proceeds despite the fact that the stresses are too low according to the strain/interface energy model. For films with Ti adhesion layers, the transformation kinetics and extent of transformation depend on the film thickness in a surprising way with intermediate thickness films showing initially fast transformations and stable mixed textures, while thicker films show an incubation time but transform fully. The results are consistent with reduction in defect energy (e.g. dislocations or point defects) as the driving force for secondary grain growth in an environment in which only (100) recrystallization nuclei can form. The driving force increases with film thickness so the nonmonotonic variation in transformation rate implies that the density of (100) nuclei decreases with thickness.