Taner Z. Sen

GOR V server for protein secondary structure prediction

SUMMARY We have created the GOR V web server for protein secondary structure prediction. The GOR V algorithm combines information theory, Bayesian statistics and evolutionary information. In its fifth version, the GOR method reached (with the full jack-knife procedure) an accuracy of prediction Q3 of 73.5%. Although GOR V has been among the most successful methods, its online unavailability has been a deterrent to its popularity. Here, we remedy this situation by creating the GOR V server.

The Locus Lookup tool at MaizeGDB

SUMMARY Methods to automatically integrate sequence information with physical and genetic maps are scarce. The Locus Lookup tool enables researchers to define windows of genomic sequence likely to contain loci of interest where only genetic or physical mapping associations are reported. Using the Locus Lookup tool, researchers will be able to locate specific genes more efficiently that will ultimately help them develop a better maize plant. With the availability of the well-documented source code, the tool can be easily adapted to other biological systems. AVAILABILITY The Locus Lookup tool is available on the web at http://maizegdb.org/cgi-bin/locus_lookup.cgi. It is implemented in PHP, Oracle and Apache, with all major browsers supported. Source code is freely available for download at http://ftp.maizegdb.org/open_source/locus_lookup/.

MaizeGDB: curation and outreach go hand-in-hand

First released in 1991 with the name MaizeDB, the Maize Genetics and Genomics Database, now MaizeGDB, celebrates its 20th anniversary this year. MaizeGDB has transitioned from a focus on comprehensive curation of the literature, genetic maps and stocks to a paradigm that accommodates the recent release of a reference maize genome sequence, multiple diverse maize genomes and sequence-based gene expression data sets. The MaizeGDB Team is relatively small, and relies heavily on the research community to provide data, nomenclature standards and most importantly, to recommend future directions, priorities and strategies. Key aspects of MaizeGDBs intimate interaction with the community are the co-location of curators with maize research groups in multiple locations across the USA as well as coordination with MaizeGDB’s close partner, the Maize Genetics Cooperation—Stock Center. In this report, we describe how the MaizeGDB Team currently interacts with the maize research community and our plan for future interactions that will support updates to the functional and structural annotation of the B73 reference genome.

Maize Metabolic Network Construction and Transcriptome Analysis

A framework for understanding the synthesis and catalysis of metabolites and other biochemicals by proteins is crucial for unraveling the physiology of cells. To create such a framework for Zea mays L. subsp. mays (maize), we developed MaizeCyc, a metabolic network of enzyme catalysts, proteins, carbohydrates, lipids, amino acids, secondary plant products, and other metabolites by annotating the genes identified in the maize reference genome sequenced from the B73 variety. MaizeCyc version 2.0.2 is a collection of 391 maize pathways involving 8889 enzyme mapped to 2110 reactions and 1468 metabolites. We used MaizeCyc to describe the development and function of maize organs including leaf, root, anther, embryo, and endosperm by exploring the recently published microarray‐based maize gene expression atlas. We found that 1062 differentially expressed metabolic genes mapped to 524 unique enzymatic reactions associated with 310 pathways. The MaizeCyc pathway database was created by running a library of evidences collected from the maize genome annotation, gene‐based phylogeny trees, and comparison to known genes and pathways from rice (Oryza sativa L.) and Arabidopsis thaliana (L.) Heynh. against the PathoLogic module of Pathway Tools. The network and the database that were also developed as a community resource are freely accessible online at http://maizecyc.maizegdb.org to facilitate analysis and promote studies on metabolic genes in maize.

MaizeGDB update: new tools, data and interface for the maize model organism database

MaizeGDB is a highly curated, community-oriented database and informatics service to researchers focused on the crop plant and model organism Zea mays ssp. mays. Although some form of the maize community database has existed over the last 25 years, there have only been two major releases. In 1991, the original maize genetics database MaizeDB was created. In 2003, the combined contents of MaizeDB and the sequence data from ZmDB were made accessible as a single resource named MaizeGDB. Over the next decade, MaizeGDB became more sequence driven while still maintaining traditional maize genetics datasets. This enabled the project to meet the continued growing and evolving needs of the maize research community, yet the interface and underlying infrastructure remained unchanged. In 2015, the MaizeGDB team completed a multi-year effort to update the MaizeGDB resource by reorganizing existing data, upgrading hardware and infrastructure, creating new tools, incorporating new data types (including diversity data, expression data, gene models, and metabolic pathways), and developing and deploying a modern interface. In addition to coordinating a data resource, the MaizeGDB team coordinates activities and provides technical support to the maize research community. MaizeGDB is accessible online at http://www.maizegdb.org.

Functional clustering of yeast proteins from the protein-protein interaction network.

BackgroundThe abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins.ResultsIn the present work, individual clusters identified by an eigenmode analysis of the connectivity matrix of the protein-protein interaction network in yeast are investigated for possible functional relationships among the members of the cluster. With our functional clustering we have successfully predicted several new protein-protein interactions that indeed have been reported recently.ConclusionEigenmode analysis of the entire connectivity matrix yields both a global and a detailed view of the network. We have shown that the eigenmode clustering not only is guided by the number of proteins with which each protein interacts, but also leads to functional clustering that can be applied to predict new protein interactions.

MaizeGDB becomes ‘sequence-centric’

MaizeGDB is the maize research community’s central repository for genetic and genomic information about the crop plant and research model Zea mays ssp. mays. The MaizeGDB team endeavors to meet research needs as they evolve based on researcher feedback and guidance. Recent work has focused on better integrating existing data with sequence information as it becomes available for the B73, Mo17 and Palomero Toluqueño genomes. Major endeavors along these lines include the implementation of a genome browser to graphically represent genome sequences; implementation of POPcorn, a portal ancillary to MaizeGDB that offers access to independent maize projects and will allow BLAST similarity searches of participating projects’ data sets from a single point; and a joint MaizeGDB/PlantGDB project to involve the maize community in genome annotation. In addition to summarizing recent achievements and future plans, this article also discusses specific examples of community involvement in setting priorities and design aspects of MaizeGDB, which should be of interest to other database and resource providers seeking to better engage their users. MaizeGDB is accessible online at http://www.maizegdb.org. Database URL: http://www.maizegdb.org

Consensus Data Mining (CDM) Protein Secondary Structure Prediction Server

One of the challenges in protein secondary structure prediction is to overcome the cross-validated 80% prediction accuracy barrier. Here, we propose a novel approach to surpass this barrier. Instead of using a single algorithm that relies on a limited data set for training, we combine two complementary methods having different strengths: Fragment Database Mining (FDM) and GOR V. FDM harnesses the availability of the known protein structures in the Protein Data Bank and provides highly accurate secondary structure predictions when sequentially similar structural fragments are identified. In contrast, the GOR V algorithm is based on information theory, Bayesian statistics, and PSI-BLAST multiple sequence alignments to predict the secondary structure of residues inside a sliding window along a protein chain. A combination of these two different methods benefits from the large number of structures in the PDB and significantly improves the secondary structure prediction accuracy, resulting in Q3 ranging from 67.5 to 93.2%, depending on the availability of highly similar fragments in the Protein Data Bank.

Endoglucanases: insights into thermostability for biofuel applications.

Obtaining bioethanol from cellulosic biomass involves numerous steps, among which the enzymatic conversion of the polymer to individual sugar units has been a main focus of the biotechnology industry. Among the cellulases that break down the polymeric cellulose are endoglucanases that act synergistically for subsequent hydrolytic reactions. The endoglucanases that have garnered relatively more attention are those that can withstand high temperatures, i.e., are thermostable. Although our understanding of thermostability in endoglucanases is incomplete, some molecular features that are responsible for increased thermostability have been recently identified. This review focuses on the investigations of endoglucanases and their implications for biofuel applications.

Thermostability in endoglucanases is fold-specific

BackgroundEndoglucanases are usually considered to be synergistically involved in the initial stages of cellulose breakdown-an essential step in the bioprocessing of lignocellulosic plant materials into bioethanol. Despite their economic importance, we currently lack a basic understanding of how some endoglucanases can sustain their ability to function at elevated temperatures required for bioprocessing, while others cannot. In this study, we present a detailed comparative analysis of both thermophilic and mesophilic endoglucanases in order to gain insights into origins of thermostability. We analyzed the sequences and structures for sets of endoglucanase proteins drawn from the Carbohydrate-Active enZymes (CAZy) database.ResultsOur results demonstrate that thermophilic endoglucanases and their mesophilic counterparts differ significantly in their amino acid compositions. Strikingly, these compositional differences are specific to protein folds and enzyme families, and lead to differences in intramolecular interactions in a fold-dependent fashion.ConclusionsHere, we provide fold-specific guidelines to control thermostability in endoglucanases that will aid in making production of biofuels from plant biomass more efficient.