SCNProDB: A database for the identification of soybean cyst nematode proteins

Soybean cyst nematode (Heterodera glycines, SCN) is the most destructive pathogen of soybean around the world. Crop rotation and resistant cultivars are used to mitigate the damage of SCN, but these approaches are not completely successful because of the varied SCN populations. Thus, the limitations of these practices with soybean dictate investigation of other avenues of protection of soybean against SCN, perhaps through genetically engineering of broad resistance to SCN. For better understanding of the consequences of genetic manipulation, elucidation of SCN protein composition at the subunit level is necessary. We have conducted studies to determine the composition of SCN proteins using a proteomics approach in our laboratory using twodimensional polyacrylamide gel electrophoresis (2D-PAGE) to separate SCN proteins and to characterize the proteins further using mass spectrometry. Our analysis resulted in the identification of several hundred proteins. In this investigation, we developed a web based database (SCNProDB) containing protein information obtained from our previous published studies. This database will be useful to scientists who wish to develop SCN resistant soybean varieties through genetic manipulation and breeding efforts. The database is freely accessible from: http://bioinformatics.towson.edu/Soybean_SCN_proteins_2D_Gel_DB/Gel1.aspx     

SoyProDB: A database for the identification of soybean seed proteins

Soybean continues to serve as a rich and inexpensive source of protein for humans and animals. A substantial amount of information has been reported on the genotypic variation and beneficial genetic manipulation of soybeans. For better understanding of the consequences of genetic manipulation, elucidation of soybean protein composition is necessary, because of its direct relationship to phenotype. We have conducted studies to determine the composition of storage, allergen and anti-nutritional proteins in cultivated soybean using a combined proteomics approach. Two-dimensional polyacrylamide gel electrophoresis (2DPAGE) was implemented for the separation of proteins along with matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) and liquid chromatography mass spectrometry (LC-MS/MS) for the identification of proteins. Our analysis resulted in the identification of several proteins, and a web based database named soybean protein database (SoyProDB) was subsequently built to house and allow scientists to search the data. This database will be useful to scientists who wish to genetically alter soybean with higher quality storage proteins, and also helpful for consumers to get a greater understanding about proteins that compose soy products available in the market. The database is freely accessible.

Availability

http://bioinformatics.towson.edu/Soybean_Seed_Proteins_2D_Gel_DB/Home.aspx     

An efficient extraction method to enhance analysis of low abundant proteins from soybean seed

Large amounts of the major storage proteins, β-conglycinin and glycinin, in soybean (Glycine max) seeds hinder the isolation and characterization of less abundant seed proteins. We investigated whether isopropanol extraction could facilitate resolution of the low abundant proteins, different from the main storage protein fractions, in one-dimensional polyacrylamide gel electrophoresis (1D-PAGE) and two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). 1D-PAGE of proteins extracted by different concentrations (10%, 20%, 30%, 40%, 50%, 60%, 70% and 80%) of isopropanol showed that greater than 30% isopropanol was suitable for preferential enrichment of low abundant proteins. Analysis of 2D-PAGE showed that proteins which were less abundant or absent by the conventional extraction procedure were clearly seen in the 40% isopropanol extracts. Increasing isopropanol concentration above 40% resulted in a decrease in the number of less abundant protein spots. We have identified a total of 107 protein spots using matrix-assisted laser desorption/ionization time of flight mass spectrophotometry (MALDI-TOF-MS) and liquid chromatography-mass spectrometry (LC-MS/MS). Our results suggest that extraction of soybean seed powder with 40% isopropanol enriches lower abundance proteins and is a suitable method for 2D-PAGE separation and identification. This methodology could potentially allow the extraction and characterization of low abundant proteins of other legume seeds containing highly abundant storage proteins.     

RKN Lethal DB: A database for the identification of Root Knot Nematode (Meloidogyne spp.) candidate lethal genes

Root Knot nematode (RKN; Meloidogyne spp.) is one of the most devastating parasites that infect the roots of hundreds of plant species. RKN cannot live independently from their hosts and are the biggest contributors to the loss of the world''s primary foods. RNAi gene silencing studies have demonstrated that there are fewer galls and galls are smaller when RNAi constructs targeted to silence certain RKN genes are expressed in plant roots. We conducted a comparative genomics analysis, comparing RKN genes of six species: Meloidogyne Arenaria, Meloidogyne Chitwoodi, Meloidogyne Hapla, Meloidogyne Incognita, Meloidogyne Javanica, and Meloidogyne Paranaensis to that of the free living nematode Caenorhabditis elegans, to identify candidate genes that will be lethal to RKN when silenced or mutated. Our analysis yielded a number of such candidate lethal genes in RKN, some of which have been tested and proven to be effective in soybean roots. A web based database was built to house and allow scientists to search the data. This database will be useful to scientists seeking to identify candidate genes as targets for gene silencing to confer resistance in plants to RKN.

Availability

The database can be accessed from http://bioinformatics.towson.edu/RKN/     

Seed Pro-Nutra Care: A tool for characterization of seed storage proteins and database of bioactive peptides having potential health benefits

Seed storage proteins, the major food proteins, possess unique physicochemical characteristics which determine their nutritional importance and influence their utilization by humans. Here, we describe a database driven tool named Seed Pro-Nutra Care which comprises a systematic compendium of seed storage proteins and their bioactive peptides influencing several vital organ systems for maintenance of health. Seed Pro-Nutra Careis an integrated resource on seed storage protein. This resource help in the (I) Characterization of proteins whether they belong to seed storage protein group or not. (II) Identification the bioactive peptides with their sequences using peptide name (III) Determination of physico chemical properties of seed storage proteins. (IV) Epitope identification and mapping (V) Allergenicity prediction and characterization. Seed Pro-Nutra Care is a compilation of data on bioactive peptides present in seed storage proteins from our own collections and other published and unpublished sources. The database provides an information resource of a variety of seed related biological information and its use for nutritional and biomedical application.

Availability

http://www.gbpuat-cbsh.ac.in/departments/bi/database/seed_pro_nutra_care/     

Comparison of protein solubilization methods suitable for proteomic analysis of soybean seed proteins

Extraction of soybean seed proteins for two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry analysis is challenging and inconsistent. In this study, we compared four different protein extraction/solubilization methods-urea, thiourea/urea, phenol, and a modified trichloroacetic acid (TCA)/acetone-to determine their efficacy in separating soybean seed proteins by 2D-PAGE. In all four methods, seed storage proteins were well separated by 2D-PAGE with minor variations in the intensity of the spots. The thiourea/urea and TCA methods showed higher protein resolution and spot intensity of all proteins compared with the other two methods. In addition, several less abundant and high molecular weight proteins were clearly resolved and strongly detected using the thiourea/urea and TCA methods. Protein spots obtained from the TCA method were subjected to mass spectrometry analysis to test their quality and compatibility. Fifteen protein spots were selected, digested with trypsin, and analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and liquid chromatography mass spectrometry (LC-MS). The proteins identified were beta-conglycinin, glycinin, Kunitz trypsin inhibitor, alcohol dehydrogenase, Gly m Bd 28K allergen, and sucrose binding proteins. These results suggest that the thiourea/urea and TCA methods are efficient and reliable methods for 2D separation of soybean seed proteins and subsequent identification by mass spectrometry.     

Using the Fast Fourier Transform to Accelerate the Computational Search for RNA Conformational Switches

Using complex roots of unity and the Fast Fourier Transform, we design a new thermodynamics-based algorithm, FFTbor, that computes the Boltzmann probability that secondary structures differ by base pairs from an arbitrary initial structure of a given RNA sequence. The algorithm, which runs in quartic time and quadratic space , is used to determine the correlation between kinetic folding speed and the ruggedness of the energy landscape, and to predict the location of riboswitch expression platform candidates. A web server is available at http://bioinformatics.bc.edu/clotelab/FFTbor/.     

Computing folding pathways between RNA secondary structures

Given an RNA sequence and two designated secondary structures A, B, we describe a new algorithm that computes a nearly optimal folding pathway from A to B. The algorithm, RNAtabupath, employs a tabu semi-greedy heuristic, known to be an effective search strategy in combinatorial optimization. Folding pathways, sometimes called routes or trajectories, are computed by RNAtabupath in a fraction of the time required by the barriers program of Vienna RNA Package. We benchmark RNAtabupath with other algorithms to compute low energy folding pathways between experimentally known structures of several conformational switches. The RNApathfinder web server, source code for algorithms to compute and analyze pathways and supplementary data are available at http://bioinformatics.bc.edu/clotelab/RNApathfinder.     

Prediction of S-Glutathionylation Sites Based on Protein Sequences

S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.     

HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures

Hydrogen constitutes nearly half of all atoms in proteins and their positions are essential for analyzing hydrogen-bonding interactions and refining atomic-level structures. However, most protein structures determined by experiments or computer prediction lack hydrogen coordinates. We present a new algorithm, HAAD, to predict the positions of hydrogen atoms based on the positions of heavy atoms. The algorithm is built on the basic rules of orbital hybridization followed by the optimization of steric repulsion and electrostatic interactions. We tested the algorithm using three independent data sets: ultra-high-resolution X-ray structures, structures determined by neutron diffraction, and NOE proton-proton distances. Compared with the widely used programs CHARMM and REDUCE, HAAD has a significantly higher accuracy, with the average RMSD of the predicted hydrogen atoms to the X-ray and neutron diffraction structures decreased by 26% and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have more matches with the NOE restraints and fewer clashes with heavy atoms. The average CPU cost by HAAD is 18 and 8 times lower than that of CHARMM and REDUCE, respectively. The significant advantage of HAAD in both the accuracy and the speed of the hydrogen additions should make HAAD a useful tool for the detailed study of protein structure and function. Both an executable and the source code of HAAD are freely available at http://zhang.bioinformatics.ku.edu/HAAD.     

Determination of Seed Storage Proteins and Total Isoflavones in Wild and Cultivated Soybeans

Two soybean components namely, storage proteins and isoflavone content in a wild and three cultivated soybean genotypes were characterized and compared. The storage proteins, β-conglycinin and glycinin were separated by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), and two major storage proteins and their subunits were characterized using mass spectrometry. The three isoflavones, aglycon and the nine conjugated forms were separated by HPLC (high performance liquid chromatography) and identified by comparison of retention time, ultraviolet and mass spectral analyses. Comparison between the number of 2D-PAGE protein spots of the storage protein subunits and HPLC area of twelve isoflavones was also evaluated. The analysis of proteins and isoflavones from the wild genotype and the three cultivated genotypes suggested possible interactions between proteins and isoflavones. The same wild genotype, which showed significant statistical differences in β-conglycinin and glycinin protein profiles also revealed considerable reduction in total isoflavones (> 55%) content.     

Generating Triangulated Macromolecular Surfaces by Euclidean Distance Transform

Macromolecular surfaces are fundamental representations of their three-dimensional geometric shape. Accurate calculation of protein surfaces is of critical importance in the protein structural and functional studies including ligand-protein docking and virtual screening. In contrast to analytical or parametric representation of macromolecular surfaces, triangulated mesh surfaces have been proved to be easy to describe, visualize and manipulate by computer programs. Here, we develop a new algorithm of EDTSurf for generating three major macromolecular surfaces of van der Waals surface, solvent-accessible surface and molecular surface, using the technique of fast Euclidean Distance Transform (EDT). The triangulated surfaces are constructed directly from volumetric solids by a Vertex-Connected Marching Cube algorithm that forms triangles from grid points. Compared to the analytical result, the relative error of the surface calculations by EDTSurf is <2–4% depending on the grid resolution, which is 1.5–4 times lower than the methods in the literature; and yet, the algorithm is faster and costs less computer memory than the comparative methods. The improvements in both accuracy and speed of the macromolecular surface determination should make EDTSurf a useful tool for the detailed study of protein docking and structure predictions. Both source code and the executable program of EDTSurf are freely available at http://zhang.bioinformatics.ku.edu/EDTSurf.     

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be considered as pre-training for various applications of deep learning in bioinformatics. The related data is available at Life Language Processing Website: http://llp.berkeley.edu and Harvard Dataverse: http://dx.doi.org/10.7910/DVN/JMFHTN.     

Determination of Optimal Protein Quantity Required to Identify Abundant and Less Abundant Soybean Seed Proteins by 2D-PAGE and MS

Optimizing the amounts of proteins required to separate and characterize both abundant and less abundant proteins by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) is critical for conducting proteomic research. In this study, we tested five different levels of soybean seed proteins (75, 100, 125, 150, and 200 μg) by 2D-PAGE. Following 2D-PAGE and spot excision, proteins were identified by mass spectrometry analysis. The number of visible protein spots was increased with an increase in the amount of protein loaded. The intensity of highly abundant proteins [β-conglycinin β-homotrimer and glycinin G4 (A5A4B3) precursors] increased linearly between 75 and 125 μg, whereas the proglycinin G3 (A1ab1b) homotrimer showed linearity between 75 and 150 μg. The spot intensity of less abundant proteins, glycinin G2 (A2b1a) precursor and proglycinin G3 (A1ab1b) homotrimer, increased linearly with an increase in the amount of protein through 200 μg, whereas spot intensity of β-conglycinin β-homotrimer and the allergen Gly m bd 28K increased linearly until 150 μg and did not increase further at 200 μg. These results suggest that 150 μg protein was a suitable amount for the separation of abundant proteins, and 200 μg protein was suitable for the separation of less abundant proteins prepared from soybean seeds. Mention of trade name, proprietary product or vendor does not constitute a guarantee or warranty of the product by the U.S. Department of Agriculture or imply its approval to the exclusion of other products or vendors that also may be suitable.     

FANSe2: A Robust and Cost-Efficient Alignment Tool for Quantitative Next-Generation Sequencing Applications

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy. Its sensitivity and accuracy are higher than the BWT-based algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. FANSe2 showed remarkably better consistency to the microarray than most other algorithms in terms of gene expression quantifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. With three normal office computers, we demonstrated that FANSe2 mapped an RNA-seq dataset generated from an entire Illunima HiSeq 2000 flowcell (8 lanes, 608 M reads) to masked human genome within 4.1 hours with higher sensitivity than Bowtie/Bowtie2. FANSe2 thus provides robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications. FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/.     

Network Properties of the Ensemble of RNA Structures

We describe the first dynamic programming algorithm that computes the expected degree for the network, or graph G = (V, E) of all secondary structures of a given RNA sequence a = a ₁, …, a _n. Here, the nodes V correspond to all secondary structures of a, while an edge exists between nodes s, t if the secondary structure t can be obtained from s by adding, removing or shifting a base pair. Since secondary structure kinetics programs implement the Gillespie algorithm, which simulates a random walk on the network of secondary structures, the expected network degree may provide a better understanding of kinetics of RNA folding when allowing defect diffusion, helix zippering, and related conformation transformations. We determine the correlation between expected network degree, contact order, conformational entropy, and expected number of native contacts for a benchmarking dataset of RNAs. Source code is available at http://bioinformatics.bc.edu/clotelab/RNAexpNumNbors.     

Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm

A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into -values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx.