SoyProLow: A protein database enriched in low abundant soybean proteins

Soybeans are an important legume crop that contain 2 major storage proteins, β-conglycinin and glycinin, which account about 70- 80% of total seed proteins. These abundant proteins hinder the isolation and characterization of several low abundant proteins in soybean seeds. Several protein extraction methodologies were developed in our laboratory to decrease these abundant storage proteins in seed extracts and to also decrease the amount of ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBisCO), which is normally very abundant in leaf extracts. One of the extraction methodologies used 40% isopropanol and was more effective in depleting soybean storage proteins and enhancing low abundant seed proteins than similar methods using 10-80% isopropanol. Extractions performed with 40% isopropanol decreased the amount of storage proteins and revealed 107 low abundant proteins when using the combined approaches of two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and Mass Spectrometry (MS). The separation of proteins was achieved by iso-electric focusing (IEF) and 2D-PAGE. The proteins were analyzed with MS techniques to provide amino acid sequence. The proteins were identified by comparing their amino acid sequences with those in different databases including NCBI-non redundant, UniprotKB and MSDB databases. In this investigation, previously published results on low abundant soybean seed proteins were used to create an online database (SoyProLow) to provide a data repository that can be used as a reference to identify and characterize low abundance proteins. This database is freely accessible to individuals using similar techniques and can be for the subsequent genetic manipulation to produce value added soybean traits. An intuitive user interface based on dynamic HTML enables users to browse the network and the profiles of the low abundant proteins.

Availability

http://bioinformatics.towson.edu/Soybean_low_abundance_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     

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/     

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/.     

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.     

GMEnzy: A Genetically Modified Enzybiotic Database

GMEs are genetically modified enzybiotics created through molecular engineering approaches to deal with the increasing problem of antibiotic resistance prevalence. We present a fully manually curated database, GMEnzy, which focuses on GMEs and their design strategies, production and purification methods, and biological activity data. GMEnzy collects and integrates all available GMEs and their related information into one web based database. Currently GMEnzy holds 186 GMEs from published literature. The GMEnzy interface is easy to use, and allows users to rapidly retrieve data according to desired search criteria. GMEnzy’s construction will increase the efficiency and convenience of improving these bioactive proteins for specific requirements, and will expand the arsenal available for researches to control drug-resistant pathogens. This database will prove valuable for researchers interested in genetically modified enzybiotics studies. GMEnzy is freely available on the Web at http://biotechlab.fudan.edu.cn/database/gmenzy/.     

Comparative GO: A Web Application for Comparative Gene Ontology and Gene Ontology-Based Gene Selection in Bacteria

Availabilityhttp://turing.ersa.edu.au/BacteriaGO.     

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.     

A Daily-Updated Database and Tools for Comprehensive SARS-CoV-2 Mutation-Annotated Trees

The vast scale of SARS-CoV-2 sequencing data has made it increasingly challenging to comprehensively analyze all available data using existing tools and file formats. To address this, we present a database of SARS-CoV-2 phylogenetic trees inferred with unrestricted public sequences, which we update daily to incorporate new sequences. Our database uses the recently proposed mutation-annotated tree (MAT) format to efficiently encode the tree with branches labeled with parsimony-inferred mutations, as well as Nextstrain clade and Pango lineage labels at clade roots. As of June 9, 2021, our SARS-CoV-2 MAT consists of 834,521 sequences and provides a comprehensive view of the virus’ evolutionary history using public data. We also present matUtils—a command-line utility for rapidly querying, interpreting, and manipulating the MATs. Our daily-updated SARS-CoV-2 MAT database and matUtils software are available at http://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/ and https://github.com/yatisht/usher, respectively.     

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.     

PubstractHelper: A Web-based Text-Mining Tool for Marking Sentences in Abstracts from PubMed Using Multiple User-Defined Keywords

While a huge amount of information about biological literature can be obtained by searching the PubMed database, reading through all the titles and abstracts resulting from such a search for useful information is inefficient. Text mining makes it possible to increase this efficiency. Some websites use text mining to gather information from the PubMed database; however, they are database-oriented, using pre-defined search keywords while lacking a query interface for user-defined search inputs. We present the PubMed Abstract Reading Helper (PubstractHelper) website which combines text mining and reading assistance for an efficient PubMed search. PubstractHelper can accept a maximum of ten groups of keywords, within each group containing up to ten keywords. The principle behind the text-mining function of PubstractHelper is that keywords contained in the same sentence are likely to be related. PubstractHelper highlights sentences with co-occurring keywords in different colors. The user can download the PMID and the abstracts with color markings to be reviewed later. The PubstractHelper website can help users to identify relevant publications based on the presence of related keywords, which should be a handy tool for their research.

Availability

http://bio.yungyun.com.tw/ATM/PubstractHelper.aspx and http://holab.med.ncku.edu.tw/ATM/PubstractHelper.aspx     

Investigating microRNA-Target Interaction-Supported Tissues in Human Cancer Tissues Based on miRNA and Target Gene Expression Profiling

Recent studies have revealed that a small non-coding RNA, microRNA (miRNA) down-regulates its mRNA targets. This effect is regarded as an important role in various biological processes. Many studies have been devoted to predicting miRNA-target interactions. These studies indicate that the interactions may only be functional in some specific tissues, which depend on the characteristics of an miRNA. No systematic methods have been established in the literature to investigate the correlation between miRNA-target interactions and tissue specificity through microarray data. In this study, we propose a method to investigate miRNA-target interaction-supported tissues, which is based on experimentally validated miRNA-target interactions. The tissue specificity results by our method are in accordance with the experimental results in the literature.

Availability and Implementation

Our analysis results are available at http://tsmti.mbc.nctu.edu.tw/ and http://www.stat.nctu.edu.tw/hwang/tsmti.html.     

ProADD: A database on Protein Aggregation Diseases

ProADD, a database for protein aggregation diseases, is developed to organize the data under a single platform to facilitate easy access for researchers. Diseases caused due to protein aggregation and the proteins involved in each of these diseases are integrated. The database helps in classification of proteins involved in the protein aggregation diseases based on sequence and structural analysis. Analysis of proteins can be done to mine patterns prevailing among the aggregating proteins.

Availability

http://bicmku.in/ProADD     

BEID: Database for sequence-structure-function information on antigen-antibody interactions

The B-cell Epitope Interaction Database (BEID; http://datam.i2r.a-star.edu.sg/BEID) is an open-access database describing sequence-structure-function information on immunoglobulin (Ig)-antigen interactions. The current version of the database contains 164 antigens, 126 Ig and 189 Ig-antigen complexes extracted from the Protein Data Bank (PDB). Each entry is manually verified, classified, and analyzed for intermolecular interactions between antigens and the corresponding bound Ig molecules. Ig-antigen interaction information that is stored in BEID includes solvent accessibility, hydrogen bonds, non-hydrogen bonds, gap volume, gap index, interface area and contact residues. The database can be searched with a user-friendly search tool and schematic diagrams for Ig-antigen interactions are available for download in PDF format. The ultimate purpose of BEID is to enhance the understanding of the rules of engagement between antigen and the corresponding bound Ig molecules. It is also a precious data source for developing computational predictors for B-cell epitopes.