Biomedical subjects

Martin Madera

Publications and source records attributed to Martin Madera.

10 recordsLinked to original sources

PubMed2007

New developments in the InterPro database.

InterPro is an integrated resource for protein families, domains and functional sites, which integrates the following protein signature databases: PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The latter two new member databases have been integrated since the last publication in this journal. There have been several new developments in InterPro, including an additional reading field, new database links, extensions to the web interface and additional match XML files. InterPro has always provided matches to UniProtKB proteins on the website and in the match XML file on the FTP site. Additional matches to proteins in UniParc (UniProt archive) are now available for download in the new match XML files only. The latest InterPro release (13.0) contains more than 13 000 entries, covering over 78% of all proteins in UniProtKB. The database is available for text- and sequence-based searches via a webserver (http://www.ebi.ac.uk/interpro), and for download by anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro). The InterProScan search tool is now also available via a web service at http://www.ebi.ac.uk/Tools/webservices/WSInterProScan.html.

Databases, Protein↗

PubMed2006

The SUPERFAMILY database in 2007: families and functions.

The SUPERFAMILY database provides protein domain assignments, at the SCOP 'superfamily' level, for the predicted protein sequences in over 400 completed genomes. A superfamily groups together domains of different families which have a common evolutionary ancestor based on structural, functional and sequence data. SUPERFAMILY domain assignments are generated using an expert curated set of profile hidden Markov models. All models and structural assignments are available for browsing and download from http://supfam.org. The web interface includes services such as domain architectures and alignment details for all protein assignments, searchable domain combinations, domain occurrence network visualization, detection of over- or under-represented superfamilies for a given genome by comparison with other genomes, assignment of manually submitted sequences and keyword searches. In this update we describe the SUPERFAMILY database and outline two major developments: (i) incorporation of family level assignments and (ii) a superfamily-level functional annotation. The SUPERFAMILY database can be used for general protein evolution and superfamily-specific studies, genomic annotation, and structural genomics target suggestion and assessment.

Databases, Protein↗

PubMed2006

Discrimination of non-protein-coding transcripts from protein-coding mRNA.

Several recent studies indicate that mammals and other organisms produce large numbers of RNA transcripts that do not correspond to known genes. It has been suggested that these transcripts do not encode proteins, but may instead function as RNAs. However, discrimination of coding and non-coding transcripts is not straightforward, and different laboratories have used different methods, whose ability to perform this discrimination is unclear. In this study, we examine ten bioinformatic methods that assess protein-coding potential and compare their ability and congruency in the discrimination of non-coding from coding sequences, based on four underlying principles: open reading frame size, sequence similarity to known proteins or protein domains, statistical models of protein-coding sequence, and synonymous versus non-synonymous substitution rates. Despite these different approaches, the methods show broad concordance, suggesting that coding and non-coding transcripts can, in general, be reliably discriminated, and that many of the recently discovered extra-genic transcripts are indeed non-coding. Comparison of the methods indicates reasons for unreliable predictions, and approaches to increase confidence further. Conversely and surprisingly, our analyses also provide evidence that as much as approximately 10% of entries in the manually curated protein database Swiss-Prot are erroneous translations of actually non-coding transcripts.

Algorithms↗

PubMed2006

A database of bacterial lipoproteins (DOLOP) with functional assignments to predicted lipoproteins.

Lipid modification of the N-terminal Cys residue (N-acyl-S-diacylglyceryl-Cys) has been found to be an essential, ubiquitous, and unique bacterial posttranslational modification. Such a modification allows anchoring of even highly hydrophilic proteins to the membrane which carry out a variety of functions important for bacteria, including pathogenesis. Hence, being able to identify such proteins is of great value. To this end, we have created a comprehensive database of bacterial lipoproteins, called DOLOP, which contains information and links to molecular details for about 278 distinct lipoproteins and predicted lipoproteins from 234 completely sequenced bacterial genomes. The website also features a tool that applies a predictive algorithm to identify the presence or absence of the lipoprotein signal sequence in a user-given sequence. The experimentally verified lipoproteins have been classified into different functional classes and more importantly functional domain assignments using hidden Markov models from the SUPERFAMILY database that have been provided for the predicted lipoproteins. Other features include the following: primary sequence analysis, signal sequence analysis, and search facility and information exchange facility to allow researchers to exchange results on newly characterized lipoproteins. The website, along with additional information on the biosynthetic pathway, statistics on predicted lipoproteins, and related figures, is available at http://www.mrc-lmb.cam.ac.uk/genomes/dolop/.

Bacterial Proteins↗

PubMed2005

InterPro, progress and status in 2005.

InterPro, an integrated documentation resource of protein families, domains and functional sites, was created to integrate the major protein signature databases. Currently, it includes PROSITE, Pfam, PRINTS, ProDom, SMART, TIGRFAMs, PIRSF and SUPERFAMILY. Signatures are manually integrated into InterPro entries that are curated to provide biological and functional information. Annotation is provided in an abstract, Gene Ontology mapping and links to specialized databases. New features of InterPro include extended protein match views, taxonomic range information and protein 3D structure data. One of the new match views is the InterPro Domain Architecture view, which shows the domain composition of protein matches. Two new entry types were introduced to better describe InterPro entries: these are active site and binding site. PIRSF and the structure-based SUPERFAMILY are the latest member databases to join InterPro, and CATH and PANTHER are soon to be integrated. InterPro release 8.0 contains 11 007 entries, representing 2573 domains, 8166 families, 201 repeats, 26 active sites, 21 binding sites and 20 post-translational modification sites. InterPro covers over 78% of all proteins in the Swiss-Prot and TrEMBL components of UniProt. The database is available for text- and sequence-based searches via a webserver (http://www.ebi.ac.uk/interpro), and for download by anonymous FTP (ftp://ftp.ebi.ac.uk/pub/databases/interpro).

Databases, Protein↗

PubMed2004

VH gene segments in the mouse and human genomes.

We have examined the mouse genome sequence to determine its VH gene segment repertoire. In all, 141 segments are mapped to a 3 Mb region of chromosome 12. There is evidence that 92 of these are functional in the mouse strain used for the genome sequence, C57BL/6J; 12 are functional in other mouse strains, and 37 are pseudogenes. The mouse VH gene segment repertoire is therefore twice the size of that in humans. The mouse and human loci bear no large-scale similarity to each other. The 104 functional segments belong to one of the 15 known sequence subgroups, which have been further clustered into eight sets here. Seven of these sets, comprising 101 sequences, are related to five of the human VH families and have the same canonical structures in their hypervariable regions. Duplication of members of one set in the distal half of the locus is mainly responsible for the larger size of the mouse repertoire. Phylogenetic analysis of the VH segments indicates that most of the sequences in the human and mouse VH loci have arisen subsequent to the divergence of the two organisms from their common ancestor.

Amino Acid Sequence↗

PubMed2004

The SUPERFAMILY database in 2004: additions and improvements.

The SUPERFAMILY database provides structural assignments to protein sequences and a framework for analysis of the results. At the core of the database is a library of profile Hidden Markov Models that represent all proteins of known structure. The library is based on the SCOP classification of proteins: each model corresponds to a SCOP domain and aims to represent an entire superfamily. We have applied the library to predicted proteins from all completely sequenced genomes (currently 154), the Swiss-Prot and TrEMBL databases and other sequence collections. Close to 60% of all proteins have at least one match, and one half of all residues are covered by assignments. All models and full results are available for download and online browsing at http://supfam.org. Users can study the distribution of their superfamily of interest across all completely sequenced genomes, investigate with which other superfamilies it combines and retrieve proteins in which it occurs. Alternatively, concentrating on a particular genome as a whole, it is possible first, to find out its superfamily composition, and secondly, to compare it with that of other genomes to detect superfamilies that are over- or under-represented. In addition, the webserver provides the following standard services: sequence search; keyword search for genomes, superfamilies and sequence identifiers; and multiple alignment of genomic, PDB and custom sequences.

Animals↗

PubMed2003

Integrated mapping, chromosomal sequencing and sequence analysis of Cryptosporidium parvum.

The apicomplexan Cryptosporidium parvum is one of the most prevalent protozoan parasites of humans. We report the physical mapping of the genome of the Iowa isolate, sequencing and analysis of chromosome 6, and approximately 0.9 Mbp of sequence sampled from the remainder of the genome. To construct a robust physical map, we devised a novel and general strategy, enabling accurate placement of clones regardless of clone artefacts. Analysis reveals a compact genome, unusually rich in membrane proteins. As in Plasmodium falciparum, the mean size of the predicted proteins is larger than that in other sequenced eukaryotes. We find several predicted proteins of interest as potential therapeutic targets, including one exhibiting similarity to the chloroquine resistance protein of Plasmodium. Coding sequence analysis argues against the conventional phylogenetic position of Cryptosporidium and supports an earlier suggestion that this genus arose from an early branching within the Apicomplexa. In agreement with this, we find no significant synteny and surprisingly little protein similarity with Plasmodium. Finally, we find two unusual and abundant repeats throughout the genome. Among sequenced genomes, one motif is abundant only in C. parvum, whereas the other is shared with (but has previously gone unnoticed in) all known genomes of the Coccidia and Haemosporida. These motifs appear to be unique in their structure, distribution and sequences.

Animals↗

PubMed2003

Determination of inorganic corrosion inhibitors in heat transfer systems by ion chromatography.

In water-based heating and cooling circuits monoethylene glycol is frequently used as an anti-freezing agent. For corrosion protection inhibitors based on nitrite, molybdate or amines are commonly added. The determination of nitrite is usually performed by ion chromatography (IC) using an IonPac AS14 analytical column for the anion separation and a suppressed conductivity detection. Local overheating in some circuits causes degradation of ethylene glycol and leads to the formation of some organic acids. Under such chemical conditions the correct quantification of nitrite becomes a more complex analytical task due to the interference of the organic acids. This problem was solved using the IonPac AS9-HC separation column. In heat transfer systems, where nitrite is not stable, molybdate can be used as an inhibitor for corrosion protection. In these cases photometric methods are recommended for monitoring the molybdate concentration. However, due to the dark brown colour and turbidity of aged glycol solutions photometric methods were not applicable. Thus the use of IC offered a reliable alternative for the determination of molybdate, also in aged glycol solutions, using IonPac AS9-HC or AS14 columns for separation.

Chromatography, Ion Exchange↗

PubMed2002

A comparison of profile hidden Markov model procedures for remote homology detection.

Profile hidden Markov models (HMMs) are amongst the most successful procedures for detecting remote homology between proteins. There are two popular profile HMM programs, HMMER and SAM. Little is known about their performance relative to each other and to the recently improved version of PSI-BLAST. Here we compare the two programs to each other and to non-HMM methods, to determine their relative performance and the features that are important for their success. The quality of the multiple sequence alignments used to build models was the most important factor affecting the overall performance of profile HMMs. The SAM T99 procedure is needed to produce high quality alignments automatically, and the lack of an equivalent component in HMMER makes it less complete as a package. Using the default options and parameters as would be expected of an inexpert user, it was found that from identical alignments SAM consistently produces better models than HMMER and that the relative performance of the model-scoring components varies. On average, HMMER was found to be between one and three times faster than SAM when searching databases larger than 2000 sequences, SAM being faster on smaller ones. Both methods were shown to have effective low complexity and repeat sequence masking using their null models, and the accuracy of their E-values was comparable. It was found that the SAM T99 iterative database search procedure performs better than the most recent version of PSI-BLAST, but that scoring of PSI-BLAST profiles is more than 30 times faster than scoring of SAM models.

Amino Acid Sequence↗