UniqueProt is a practical and easy to use web service designed to create representative, unbiased data sets of protein sequences. The largest possible representative sets are found through a simple greedy algorithm using the HSSP-value to establish sequence similarity. UniqueProt is not a real clustering program in the sense that the 'representatives' are not at the centres of well-defined clusters since the definition of such clusters is problem-specific. Overall, UniqueProt is a reasonable fast solution for bias in data sets. The service is accessible at http://cubic.bioc.columbia.edu/services/uniqueprot; a command-line version for Linux is downloadable from this web site.
Experimental high-throughput studies of protein–protein interactions are beginning to provide enough data for comprehensive computational studies. Today, about ten large data sets, each with thousands of interacting pairs, coarsely sample the interactions in fly, human, worm, and yeast. Another about 55,000 pairs of interacting proteins have been identified by more careful, detailed biochemical experiments. Most interactions are experimentally observed in prokaryotes and simple eukaryotes; very few interactions are observed in higher eukaryotes such as mammals. It is commonly assumed that pathways in mammals can be inferred through homology to model organisms, e.g. the experimental observation that two yeast proteins interact is transferred to infer that the two corresponding proteins in human also interact. Two pairs for which the interaction is conserved are often described as interologs. The goal of this investigation was a large-scale comprehensive analysis of such inferences, i.e. of the evolutionary conservation of interologs. Here, we introduced a novel score for measuring the overlap between protein–protein interaction data sets. This measure appeared to reflect the overall quality of the data and was the basis for our two surprising results from our large-scale analysis. Firstly, homology-based inferences of physical protein–protein interactions appeared far less successful than expected. In fact, such inferences were accurate only for extremely high levels of sequence similarity. Secondly, and most surprisingly, the identification of interacting partners through sequence similarity was significantly more reliable for protein pairs within the same organism than for pairs between species. Our analysis underlined that the discrepancies between different datasets are large, even when using the same type of experiment on the same organism. This reality considerably constrains the power of homology-based transfer of interactions. In particular, the experimental probing of interactions in distant model organisms has to be undertaken with some caution. More comprehensive images of protein–protein networks will require the combination of many high-throughput methods, including in silico inferences and predictions. http://www.rostlab.org/results/2006/ppi_homology/
The nuclear matrix (NM) is a structure resulting from the aggregation of proteins and RNA in the nucleus of eukaryotic cells; it is the 'sticky bit' that remains after aggressive DNAse digestion and salt extraction protocols. Owing to the important role of the NM in DNA replication, DNA transcription and RNA splicing, the expression pattern of NM proteins has become an important early indicator for numerous cancers/ tumors. Recent descriptions of the NM structure distinguish between a network-like 'internal nuclear matrix' (INM) and a 'nuclear shell' that connects the INM to the inner and outer nuclear membranes. A cautious NM preparation protocol reveals a coat of proteins on top of the INM; these proteins are usually referred to as the 'nuclear matrix-associated proteins'. Here, we describe a new database (NMPdb at http://www.rostlab.org/db/NMPdb/) that currently contains details of 398 NM proteins. We collected these data through a semi-automated analysis of over 3000 scientific articles in PubMed. We could match these 398 proteins to 302 protein sequences in UniProt or GenBank. Our NMPdb repository annotates these links along with the following annotations: organism, cell type, PubMed identifier, sequence-based predictions of structural and functional features and for some entries the explicit sequence segment that is responsible for localization (nuclear matrix targeting signal).
http://cubic.bioc.columbia.edu/services/nlprot/
Automatically extracting protein names from the literature and linking these names to the associated entries in sequence databases is becoming increasingly important for annotating biological databases. NLProt is a novel system that combines dictionary- and rule-based filtering with several support vector machines (SVMs) to tag protein names in PubMed abstracts. When considering partially tagged names as errors, NLProt still reached a precision of 75% at a recall of 76%. By many criteria our system outperformed other tagging methods significantly; in particular, it proved very reliable even for novel names. Names encountered particularly frequently in Drosophila, such as white, wing and bizarre, constitute an obvious limitation of NLProt. Our method is available both as an Internet server and as a program for download (http://cubic.bioc.columbia.edu/services/NLProt/). Input can be PubMed/MEDLINE identifiers, authors, titles and journals, as well as collections of abstracts, or entire papers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.