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

Computational analysis of transcriptional circuitries in human embryonic stem cells reveals multiple and independent networks

It has been known that three core transcription factors (TFs), NANOG, OCT4, and SOX2, collaborate to form a transcriptional circuitry to regulate pluripotency and self-renewal of human embryonic stem (ES) cells. Similarly, MYC also plays an important role in regulating pluripotency and self-renewal of human ES cells. However, the precise mechanism by which the transcriptional regulatory networks control the activity of ES cells remains unclear. In this study, we reanalyzed an extended core network, which includes the set of genes that are cobound by the three core TFs and additional TFs that also bind to these cobound genes. Our results show that beyond the core transcriptional network, additional transcriptional networks are potentially important in the regulation of the fate of human ES cells. Several gene families that encode TFs play a key role in the transcriptional circuitry of ES cells. We also demonstrate that MYC acts independently of the core module in the regulation of the fate of human ES cells, consistent with the established argument. We find that TP53 is a key connecting molecule between the core-centered and MYC-centered modules. This study provides additional insights into the underlying regulatory mechanisms involved in the fate determination of human ES cells

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

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

ngLOC is an n-gram-based Bayesian classification method that can predict the localization of a protein sequence over ten distinct subcellular organelles

A Top-Down Approach to Infer and Compare Domain-Domain Interactions across Eight Model Organisms

Knowledge of specific domain-domain interactions (DDIs) is essential to understand the functional significance of protein interaction networks. Despite the availability of an enormous amount of data on protein-protein interactions (PPIs), very little is known about specific DDIs occurring in them. Here, we present a top-down approach to accurately infer functionally relevant DDIs from PPI data. We created a comprehensive, non-redundant dataset of 209,165 experimentally-derived PPIs by combining datasets from five major interaction databases. We introduced an integrated scoring system that uses a novel combination of a set of five orthogonal scoring features covering the probabilistic, evolutionary, evidence-based, spatial and functional properties of interacting domains, which can map the interacting propensity of two domains in many dimensions. This method outperforms similar existing methods both in the accuracy of prediction and in the coverage of domain interaction space. We predicted a set of 52,492 high-confidence DDIs to carry out cross-species comparison of DDI conservation in eight model species including human, mouse, Drosophila, C. elegans, yeast, Plasmodium, E. coli and Arabidopsis. Our results show that only 23% of these DDIs are conserved in at least two species and only 3.8% in at least 4 species, indicating a rather low conservation across species. Pair-wise analysis of DDI conservation revealed a ‘sliding conservation’ pattern between the evolutionarily neighboring species. Our methodology and the high-confidence DDI predictions generated in this study can help to better understand the functional significance of PPIs at the modular level, thus can significantly impact further experimental investigations in systems biology research

Mining functional subgraphs from cancer protein-protein interaction networks

Background: Protein-protein interaction (PPI) networks carry vital information about proteins’ functions. Analysis of PPI networks associated with specific disease systems including cancer helps us in the understanding of the complex biology of diseases. Specifically, identification of similar and frequently occurring patterns (network motifs) across PPI networks will provide useful clues to better understand the biology of the diseases. Results: In this study, we developed a novel pattern-mining algorithm that detects cancer associated functional subgraphs occurring in multiple cancer PPI networks. We constructed nine cancer PPI networks using differentially expressed genes from the Oncomine dataset. From these networks we discovered frequent patterns that occur in all networks and at different size levels. Patterns are abstracted subgraphs with their nodes replaced by node cluster IDs. By using effective canonical labeling and adopting weighted adjacency matrices, we are able to perform graph isomorphism test in polynomial running time. We use a bottom-up pattern growth approach to search for patterns, which allows us to effectively reduce the search space as pattern sizes grow. Validation of the frequent common patterns using GO semantic similarity showed that the discovered subgraphs scored consistently higher than the randomly generated subgraphs at each size level. We further investigated the cancer relevance of a select set of subgraphs using literature-based evidences. Conclusion: Frequent common patterns exist in cancer PPI networks, which can be found through effective pattern mining algorithms. We believe that this work would allow us to identify functionally relevant and coherent subgraphs in cancer networks, which can be advanced to experimental validation to further our understanding of the complex biology of cancer

Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks.

Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis. Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein-protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer-specific protein-protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type. Conclusion. We identified cancer-type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks

mintRULS: Prediction of miRNA-mRNA Target Site Interactions Using Regularized Least Square Method

Identification of miRNA-mRNA interactions is critical to understand the new paradigms in gene regulation. Existing methods show suboptimal performance owing to inappropriate feature selection and limited integration of intuitive biological features of both miRNAs and mRNAs. The present regularized least square-based method, mintRULS, employs features of miRNAs and their target sites using pairwise similarity metrics based on free energy, sequence and repeat identities, and target site accessibility to predict miRNA-target site interactions. We hypothesized that miRNAs sharing similar structural and functional features are more likely to target the same mRNA, and conversely, mRNAs with similar features can be targeted by the same miRNA. Our prediction model achieved an impressive AUC of 0.93 and 0.92 in LOOCV and LmiTOCV settings, respectively. In comparison, other popular tools such as miRDB, TargetScan, MBSTAR, RPmirDIP, and STarMir scored AUCs at 0.73, 0.77, 0.55, 0.84, and 0.67, respectively, in LOOCV setting. Similarly, mintRULS outperformed other methods using metrics such as accuracy, sensitivity, specificity, and MCC. Our method also demonstrated high accuracy when validated against experimentally derived data from condition- and cell-specific studies and expression studies of miRNAs and target genes, both in human and mouse

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