BACKGROUND: A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners. RESULTS: The method has been tested against the PSI-BLAST program using a set of 3,890 protein sequences from which interaction data was available. For protein sequences that align with at least 40% sequence identity to a known enzyme, the specificity of our method in predicting the first three EC digits increased from 80% to 90% at 80% coverage when compared to PSI-BLAST. CONCLUSION: Our method can also be used in proteins for which homologous sequences with known interacting partners can be detected. Thus, our method could increase 10% the specificity of genome-wide enzyme predictions based on sequence matching by PSI-BLAST alone.

BACKGROUND: Protein aggregation correlates with the development of several debilitating human disorders of growing incidence, such as Alzheimer's and Parkinson's diseases. On the biotechnological side, protein production is often hampered by the accumulation of recombinant proteins into aggregates. Thus, the development of methods to anticipate the aggregation properties of polypeptides is receiving increasing attention. AGGRESCAN is a web-based software for the prediction of aggregation-prone segments in protein sequences, the analysis of the effect of mutations on protein aggregation propensities and the comparison of the aggregation properties of different proteins or protein sets. RESULTS: AGGRESCAN is based on an aggregation-propensity scale for natural amino acids derived from in vivo experiments and on the assumption that short and specific sequence stretches modulate protein aggregation. The algorithm is shown to identify a series of protein fragments involved in the aggregation of disease-related proteins and to predict the effect of genetic mutations on their deposition propensities. It also provides new insights into the differential aggregation properties displayed by globular proteins, natively unfolded polypeptides, amyloidogenic proteins and proteins found in bacterial inclusion bodies. CONCLUSION: By identifying aggregation-prone segments in proteins, AGGRESCAN http://bioinf.uab.es/aggrescan/ shall facilitate (i) the identification of possible therapeutic targets for anti-depositional strategies in conformational diseases and (ii) the anticipation of aggregation phenomena during storage or recombinant production of bioactive polypeptides or polypeptide sets.

MOTIVATION: The detection of function-related local 3D-motifs in protein structures can provide insights towards protein function in absence of sequence or fold similarity. Protein loops are known to play important roles in protein function and several loop classifications have been described, but the automated identification of putative functional 3D-motifs in such classifications has not yet been addressed. This identification can be used on sequence annotations. RESULTS: We evaluated three different scoring methods for their ability to identify known motifs from the PROSITE database in ArchDB. More than 500 new putative function-related motifs not reported in PROSITE were identified. Sequence patterns derived from these motifs were especially useful at predicting precise annotations. The number of reliable sequence annotations could be increased up to 100% with respect to standard BLAST. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.

UNLABELLED: When developing bioinformatical tools dealing with enzymatic activity, metabolism or enzymatic networks, the problem of the lack of a clear nomenclature for biochemical compounds often arises. This problem leads us to develop a small web-based tool (SNOW, Standard NOmenclature Wizard) which may help to find recommended and trivial names or the correct closest spelling for a query compound name, if it exists. AVAILABILITY: Web-based interface available at http://ibb.uab.es/snow/ SUPPLEMENTARY INFORMATION: http://ibb.uab.es/snow/snow_moreinfo.html

Abstract Motivation: The advent of genomics yields thousands of reading frames in search of function. Identification of conserved functional motifs in protein sequences can be helpful for function prediction. Results: A database and a classification of reported DNA-binding protein motifs has been designed. A program (‘TranScout’) has been developed for the detection and evaluation of conserved motifs in prokaryotic and eukaryotic sequences of proteins with a gene regulatory function. The efficiency of the program is shown in a benchmark against a database obtained from SWISS-PROT without the protein sequences used to train the program. All motifs were detected with a mean average sensitivity of 0.98 and a mean average specificity of 0.92. Availability: The program is freely available for use on the internet at http://luz.uab.es/transcout/. The user can find additional information at this site. Contact: [email protected]; [email protected] * To whom correspondence should be addressed. 2 Present address: Laboratorio de Biologı́a Estructural Computacional (GRIB), Universitat Pompeu Fabra, C/Doctor Aiguader, Barcelona 08003, Spain.

MOTIVATION: Genomic sequences from different organisms, even prokaryotic, have plenty of orphan ORFs, making necessary methods for the prediction of protein structure and function. The prediction of the presence of hydrophobic transmembrane (HTM) stretches is a valuable clue for this. RESULTS: The program. TransMem, based on a neural network and running on personal computers (either Apple Macintosh or PC, using Excel worksheets), for the prediction and distribution of amino acid residues in transmembrane segments of integral membrane proteins is reported. The percentage of residue predictive accuracy obtained for the set of proteins tested is 93%, ranging from 99.9% for the best to 71.7% for the worst prediction. The segment-based accuracy is 93.6%; 63.6% of the protein set match any of the predicted and observed segment locations. AVAILABILITY: TransMem is available upon request or by anonymous up: IP address: luz.uab.es, directory/pub/ TransMem. It is also placed on the EMBL file server (ftp:/(/)ftp.ebi.ac.uk/pub/software/mac/TransMem ).