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Biomedical subjects

Chittibabu Guda

Publications and source records attributed to Chittibabu Guda.

At least 19 recordsLinked to original sources
PubMed2007

Mitoproteome: human heart mitochondrial protein sequence database.

The human mitochondrial proteome database has been developed by deriving data from a combination of public repositories and experimental and computational prediction methods. The experimental data is derived from highly purified mitochondria from human heart tissue, whereas predictions have been performed by MITOPRED, a genome-scale method for the prediction of nucleus-encoded mitochondrial proteins. Mitochondrial protein sequences from different sources have been clustered to generate a nonredundant dataset. Annotations related to the protein function, structure, disease association, pathways, and so on are collected from a number of public databases using commonly used UNIX and Perl scripts. This chapter provides a detailed description of various data sources and methods used to download, curate, parse, and generate meaningful annotations from primary as well as derived databases.

Computational Biology↗
PubMed2006

Conserved motifs in voltage-sensing and pore-forming modules of voltage-gated ion channel proteins.

Voltage-gated ion channels (VGCs) mediate selective diffusion of ions across cell membranes to enable many vital cellular processes. Three-dimensional structure data are lacking for VGC proteins; hence, to better understand their function, there is a need to identify the conserved motifs using sequence analysis methods. In this study, we have used a profile-to-profile alignment method to identify several new conserved motifs specific to each transmembrane segment (TMS) of the voltage-sensing and the pore-forming modules of Ca2+, Na+, and K+ channel subfamilies. For Ca2+ and Na+, the functional theme of motif conservation is similar in all segments while they differ with those of the K+ channel proteins. Nevertheless, the conservation is strikingly similar in the S4 segment of the voltage-sensing module across all subfamilies. In each subfamily and for each TMS, we have identified conserved motifs/residues and correlated their functional significance and disease associations in human, using mutational data from the literature.

Amino Acid Motifs↗
PubMed2006

Tracing the origin of functional and conserved domains in the human proteome: implications for protein evolution at the modular level.

BACKGROUND: The functional repertoire of the human proteome is an incremental collection of functions accomplished by protein domains evolved along the Homo sapiens lineage. Therefore, knowledge on the origin of these functionalities provides a better understanding of the domain and protein evolution in human. The lack of proper comprehension about such origin has impelled us to study the evolutionary origin of human proteome in a unique way as detailed in this study. RESULTS: This study reports a unique approach for understanding the evolution of human proteome by tracing the origin of its constituting domains hierarchically, along the Homo sapiens lineage. The uniqueness of this method lies in subtractive searching of functional and conserved domains in the human proteome resulting in higher efficiency of detecting their origins. From these analyses the nature of protein evolution and trends in domain evolution can be observed in the context of the entire human proteome data. The method adopted here also helps delineate the degree of divergence of functional families occurred during the course of evolution. CONCLUSION: This approach to trace the evolutionary origin of functional domains in the human proteome facilitates better understanding of their functional versatility as well as provides insights into the functionality of hypothetical proteins present in the human proteome. This work elucidates the origin of functional and conserved domains in human proteins, their distribution along the Homo sapiens lineage, occurrence frequency of different domain combinations and proteome-wide patterns of their distribution, providing insights into the evolutionary solution to the increased complexity of the human proteome.

Animals↗
PubMed2006

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 http://bioinformatics.albany.edu/~ptarget.

Algorithms↗
PubMed2006

Complete chloroplast genome sequences of Solanum bulbocastanum, Solanum lycopersicum and comparative analyses with other Solanaceae genomes.

Despite the agricultural importance of both potato and tomato, very little is known about their chloroplast genomes. Analysis of the complete sequences of tomato, potato, tobacco, and Atropa chloroplast genomes reveals significant insertions and deletions within certain coding regions or regulatory sequences (e.g., deletion of repeated sequences within 16S rRNA, ycf2 or ribosomal binding sites in ycf2). RNA, photosynthesis, and atp synthase genes are the least divergent and the most divergent genes are clpP, cemA, ccsA, and matK. Repeat analyses identified 33-45 direct and inverted repeats >or=30 bp with a sequence identity of at least 90%; all but five of the repeats shared by all four Solanaceae genomes are located in the same genes or intergenic regions, suggesting a functional role. A comprehensive genome-wide analysis of all coding sequences and intergenic spacer regions was done for the first time in chloroplast genomes. Only four spacer regions are fully conserved (100% sequence identity) among all genomes; deletions or insertions within some intergenic spacer regions result in less than 25% sequence identity, underscoring the importance of choosing appropriate intergenic spacers for plastid transformation and providing valuable new information for phylogenetic utility of the chloroplast intergenic spacer regions. Comparison of coding sequences with expressed sequence tags showed considerable amount of variation, resulting in amino acid changes; none of the C-to-U conversions observed in potato and tomato were conserved in tobacco and Atropa. It is possible that there has been a loss of conserved editing sites in potato and tomato.

Base Sequence↗
PubMed2006

DMAPS: a database of multiple alignments for protein structures.

The database of multiple alignments for protein structures (DMAPS) provides instant access to pre-computed multiple structure alignments for all protein structure families in the Protein Data Bank (PDB). Protein structure families have been obtained from four distinct classification methods including SCOP, CATH, ENZYME and CE, and multiple structure alignments have been built for all families containing at least three members, using CE-MC software. Currently, multiple structure alignments are available for 3050 SCOP-, 3087 CATH-, 664 ENZYME- and 1707 CE-based families. A web-based query system has been developed to retrieve multiple alignments for these families using the PDB chain ID of any member of a family. Multiple alignments can be viewed or downloaded in six different formats, including JOY/html, TEXT, FASTA, PDB (superimposed coordinates), JOY/postscript and JOY/rtf. DMAPS is accessible online at http://bioinformatics.albany.edu/~dmaps.

Databases, Protein↗
PubMed2006

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 (http://www.lipidmaps.org/). The direct URL is http://www.lipidmaps.org/data/proteome/index.cgi.

Animals↗
PubMed2006

Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis.

Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. In this study, we use Machine Learning and Exploratory Data Analysis (EDA) techniques to examine and characterize amino acid sequences of human proteins localized in nine cellular compartments. A dataset of 3,749 protein sequences representing human proteins was extracted from the SWISS-PROT database. Feature vectors were created to capture specific amino acid sequence characteristics. Relative to a Support Vector Machine, a Multi-layer Perceptron, and a Naive Bayes classifier, the C4.5 Decision Tree algorithm was the most consistent performer across all nine compartments in reliably predicting the subcellular localization of proteins based on their amino acid sequences (average Precision=0.88; average Sensitivity=0.86). Furthermore, EDA graphics characterized essential features of proteins in each compartment. As examples, proteins localized on the plasma membrane had higher proportions of hydrophobic amino acids; cytoplasmic proteins had higher proportions of neutral amino acids; and mitochondrial proteins had higher proportions of neutral amino acids and lower proportions of polar amino acids. These data showed that the C4.5 classifier and EDA tools can be effective for characterizing and predicting the subcellular localization of human proteins based on their amino acid sequences.

Algorithms↗
PubMed2005

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.

Animals↗
PubMed2005

pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes.

MOTIVATION: There is a scarcity of efficient computational methods for predicting protein subcellular localization in eukaryotes. Currently available methods are inadequate for genome-scale predictions with several limitations. Here, we present a new prediction method, pTARGET that can predict proteins targeted to nine different subcellular locations in the eukaryotic animal species. RESULTS: The nine subcellular locations predicted by pTARGET include cytoplasm, endoplasmic reticulum, extracellular/secretory, golgi, lysosomes, mitochondria, nucleus, plasma membrane and peroxisomes. Predictions are based on the location-specific protein functional domains and the amino acid compositional differences across different subcellular locations. Overall, this method can predict 68-87% of the true positives at accuracy rates of 96-99%. Comparison of the prediction performance against PSORT showed that pTARGET prediction rates are higher by 11-60% in 6 of the 8 locations tested. Besides, the pTARGET method is robust enough for genome-scale prediction of protein subcellular localizations since, it does not rely on the presence of signal or target peptides. AVAILABILITY: A public web server based on the pTARGET method is accessible at the URL http://bioinformatics.albany.edu/~ptarget. Datasets used for developing pTARGET can be downloaded from this web server. Source code will be available on request from the corresponding author.

Algorithms↗
PubMed2004

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.

Algorithms↗
PubMed2004

SledgeHMMER: a web server for batch searching the Pfam database.

The SledgeHMMER web server is intended for genome-scale searching of the Pfam database without having to install this database and the HMMER software locally. The server implements a parallelized version of hmmpfam, the program used for searching the Pfam HMM database. Pfam search results have been calculated for the entire Swiss-Prot and TrEmbl database sequences (approximately 1.2 million) on 256 processors of IA64-based teragrid machines. The Pfam database can be searched in local, glocal or merged mode, using either gathering or E-value thresholds. Query sequences are first matched against the pre-calculated entries to retrieve results, and those without matches are processed through a new search process. Results are emailed in a space-delimited tabular format upon completion of the search. While most other Pfam-searching web servers set a limit of one sequence per query, this server processes batch sequences with no limit on the number of input sequences. The web server and downloadable data are accessible from http://SledgeHmmer.sdsc.edu.

Algorithms↗
PubMed2004

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.

Algorithms↗
PubMed2004

MITOPRED: a genome-scale method for prediction of nucleus-encoded mitochondrial proteins.

MOTIVATION: Currently available methods for the prediction of subcellular location of mitochondrial proteins rely largely on the presence of mitochondrial targeting signals in the protein sequences. However, a large fraction of mitochondrial proteins lack such signals, making those tools ineffective for genome-scale prediction of mitochondria-targeted proteins. Here, we propose a method for genome-scale prediction of nucleus-encoded mitochondrial proteins. The new method, MITOPRED, is based on the Pfam domain occurrence patterns and the amino acid compositional differences between mitochondrial and non-mitochondrial proteins. RESULTS: MITOPRED could predict mitochondrial proteins with 100% specificity at a 44% sensitivity rate and with 67% specificity at 99% sensitivity. Additionally, it was sufficiently robust to predict mitochondrial proteins across different eukaryotic species with similar accuracy. Based on Matthews correlation coefficient measure, the prediction performance of MITOPRED is clearly superior (0.73) to those of the two popular methods TargetP (0.51) and PSORT (0.53). Using this method, we predicted the nucleus-encoded mitochondrial proteins from six complete genomes (three invertebrate, two vertebrate and one plant species) and estimated the total number in each genome. In human, our method estimated the existence of 1362 mitochondrial proteins corresponding to 4.8% of the total proteome. AVAILABILITY: MITOPRED program is freely accessible at http://mitopred.sdsc.edu. Source code is available on request from the authors. SUPPLEMENTARY INFORMATION: Training data sets are also available at http://mitopred.sdsc.edu

Algorithms↗