Chittibabu Guda

Structural Genomics of the Thermotoga maritima Proteome Implemented in a High-throughput Structure Determination Pipeline

Structural genomics is emerging as a principal approach to define protein structure–function relationships. To apply this approach on a genomic scale, novel methods and technologies must be developed to determine large numbers of structures. We describe the design and implementation of a high-throughput structural genomics pipeline and its application to the proteome of the thermophilic bacterium Thermotoga maritima. By using this pipeline, we successfully cloned and attempted expression of 1,376 of the predicted 1,877 genes (73%) and have identified crystallization conditions for 432 proteins, comprising 23% of the T. maritima proteome. Representative structures from TM0423 glycerol dehydrogenase and TM0449 thymidylate synthase-complementing protein are presented as examples of final outputs from the pipeline.

CZH proteins: a new family of Rho-GEFs.

The Rho family of small GTPases are important regulators of multiple cellular activities and, most notably, reorganization of the actin cytoskeleton. Dbl-homology (DH)-domain-containing proteins are the classical guanine nucleotide exchange factors (GEFs) responsible for activation of Rho GTPases. However, members of a newly discovered family can also act as Rho-GEFs. These CZH proteins include: CDM (Ced-5, Dock180 and Myoblast city) proteins, which activate Rac; and zizimin proteins, which activate Cdc42. The family contains 11 mammalian proteins and has members in many other eukaryotes. The GEF activity is carried out by a novel, DH-unrelated domain named the DOCKER, CZH2 or DHR2 domain. CZH proteins have been implicated in cell migration, phagocytosis of apoptotic cells, T-cell activation and neurite outgrowth, and probably arose relatively early in eukaryotic evolution.

CE-MC: a multiple protein structure alignment server.

CE-MC server (http://cemc.sdsc.edu) provides a web-based facility for the alignment of multiple protein structures based on C-alpha coordinate distances, using combinatorial extension (CE) and Monte Carlo (MC) optimization methods. Alignments are possible for user-selected PDB (Protein Data Bank) chains as well as for user-uploaded structures or the combination of the two. The whole process of generating multiple structure alignments involves three distinct steps, i.e. all-to-all pairwise alignment using the CE algorithm, iterative global optimization of a multiple alignment using the MC algorithm and formatting MC results using the JOY program. The server can be used to get multiple alignments for up to 25 protein structural chains with the flexibility of uploading multiple coordinate files and performing multiple structure alignment for user-selected PDB chains. For large-scale jobs and local installation of the CE-MC program, users can download the source code and precompiled binaries from the web server.

MITOPRED: a web server for the prediction of mitochondrial proteins

MITOPRED web server enables prediction of nucleus-encoded mitochondrial proteins in all eukaryotic species. Predictions are made using a new algorithm based primarily on Pfam domain occurrence patterns in mitochondrial and non-mitochondrial locations. Pre-calculated predictions are instantly accessible for proteomes of Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, Homo sapiens, Mus musculus and Arabidopsis species as well as all the eukaryotic sequences in the Swiss-Prot and TrEMBL databases. Queries, at different confidence levels, can be made through four distinct options: (i) entering Swiss-Prot/TrEMBL accession numbers; (ii) uploading a local file with such accession numbers; (iii) entering protein sequences; (iv) uploading a local file containing protein sequences in FASTA format. Automated updates are scheduled for the pre-calculated prediction database so as to provide access to the most current data. The server, its documentation and the data are available from http://mitopred.sdsc.edu.

ngLOC: an n-gram-based Bayesian method for estimating the subcellular proteomes of eukaryotes

We present a method called ngLOC, an n-gram-based Bayesian classifier that predicts the localization of a protein sequence over ten distinct subcellular organelles. A tenfold cross-validation result shows an accuracy of 89% for sequences localized to a single organelle, and 82% for those localized to multiple organelles. An enhanced version of ngLOC was developed to estimate the subcellular proteomes of eight eukaryotic organisms: yeast, nematode, fruitfly, mosquito, zebrafish, chicken, mouse, and human.We present a method called ngLOC, an n-gram-based Bayesian classifier that predicts the localization of a protein sequence over ten distinct subcellular organelles. A tenfold cross-validation result shows an accuracy of 89% for sequences localized to a single organelle, and 82% for those localized to multiple organelles. An enhanced version of ngLOC was developed to estimate the subcellular proteomes of eight eukaryotic organisms: yeast, nematode, fruitfly, mosquito, zebrafish, chicken, mouse, and human.

pTARGET: a web server for predicting protein subcellular localization

The pTARGET web server enables prediction of nine distinct protein subcellular localizations in eukaryotic non-plant species. Predictions are made using a new algorithm [C. Guda and S. Subramaniam (2005) pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes. Bioinformatics, 21, 3963–3969], which is primarily based on the occurrence patterns of location-specific protein functional domains in different subcellular locations. We have implemented a relational database, PreCalcDB, to store pre-computed prediction results for all eukaryotic non-plant protein sequences in the public domain that includes about 770 000 entries. Queries can be made by entering protein sequences or by uploading a file containing up to 5000 protein sequences in FASTA format. Prediction results for queries with matching entries in the PreCalcDB will be retrieved instantly; while for the missing ones new predictions will be computed and sent by email. Pre-computed predictions can also be downloaded for complete proteomes of Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, Mus musculus and Homo sapiens. The server, its documentation and the data are accessible from .

LMPD: LIPID MAPS proteome database

The LIPID MAPS Proteome Database (LMPD) is an object-relational database of lipid-associated protein sequences and annotations. The initial release contains 2959 records, representing human and mouse proteins involved in lipid metabolism. UniProt IDs were obtained based on keyword search of KEGG and GO databases, and this LMPD protein list was then enhanced with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources. We also assigned associations with general lipid categories, based on GO and KEGG annotations. Users may search LMPD by database ID or keyword, and filter by species and/or lipid class associations; from the search results, one can then access a compilation of data relevant to each protein of interest, cross-linked to external databases. The LIPID MAPS Proteome Database (LMPD) is publicly available from the LIPID MAPS Consortium website (). The direct URL is .

A new algorithm for the alignment of multiple protein structures using Monte Carlo optimization.

We have developed a new algorithm for the alignment of multiple protein structures based on a Monte Carlo optimization technique. The algorithm uses pair-wise structural alignments as a starting point. Four different types of moves were designed to generate random changes in the alignment. A distance-based score is calculated for each trial move and moves are accepted or rejected based on the improvement in the alignment score until the alignment is converged. Initial tests on 66 protein structural families show promising results, the score increases by 69% on average. The increase in score is accompanied by an increase (12%) in the number of residue positions incorporated into the alignment. Two specific families, protein kinases and aspartic proteinases were tested and compared against curated alignments from HOMSTRAD and manual alignments. This algorithm has improved the overall number of aligned residues while preserving key catalytic residues. Further refinement of the method and its application to generate multiple alignments for all protein families in the PDB, is currently in progress.

Hyper expression of an environmentally friendly synthetic polymer gene

SummaryBiodegradable polymers offer an environmentally friendly alternative to petroleum-based polymers. Applications of protein based polymers include the use of these compounds in the fields of medicine, molecular-based energy conversions, the manufacture of unique fibers, coatings and biodegradable plastics. We report here expression of a synthetic gene G-(VPGVG) 119-VPGV coding for the EG-120mer (elastomer) in E. coli. Polymer expression is observed in uninduced cells grown in terrific broth in polyacrylamide gels negatively stained with CuCl2. Electron micrographs reveal formation of inclusion bodies in uninduced cells occupying upto 80–90% of the cell volume under optimal growth conditions. To the best of our knowledge this report represents the first demonstration of hyper expression of a synthetic gene (with no natural analog) in E. coli.

A Comparison of Variant Calling Pipelines Using Genome in a Bottle as a Reference

High-throughput sequencing, especially of exomes, is a popular diagnostic tool, but it is difficult to determine which tools are the best at analyzing this data. In this study, we use the NIST Genome in a Bottle results as a novel resource for validation of our exome analysis pipeline. We use six different aligners and five different variant callers to determine which pipeline, of the 30 total, performs the best on a human exome that was used to help generate the list of variants detected by the Genome in a Bottle Consortium. Of these 30 pipelines, we found that Novoalign in conjunction with GATK UnifiedGenotyper exhibited the highest sensitivity while maintaining a low number of false positives for SNVs. However, it is apparent that indels are still difficult for any pipeline to handle with none of the tools achieving an average sensitivity higher than 33% or a Positive Predictive Value (PPV) higher than 53%. Lastly, as expected, it was found that aligners can play as vital a role in variant detection as variant callers themselves.