Search

The CX and DPX web-based servers at http://pongor.itk.ppke.hu/bioinfoservices are dedicated to the analysis of protein 3D structures submitted by the users as Protein Data Bank (PDB) files. CX computes an atomic protrusion index, cx that makes it possible to highlight the protruding atoms within a protein 3D structure. DPX calculates a depth index, dpx for buried atoms, and allows one to visualize the distribution of buried residues. CX and DPX visualize 3D structures colored according to the calculated indices and return PDB files that can be visualized using standard programs. A combined server site, the Protein Core Workbench allows visualization of dpx, cx, solvent-accessible area as well as the number of atomic contacts as 3D plots and 1D sequence plots. Online visualization of the 3D structures and 1D sequence plots are available in all three servers. Mirror sites are available at http://hydra.icgeb.trieste.it/protein/ .

Motivation: Tandem mass spectrometry has become a standard tool for identifying post-translational modifications (PTMs) of proteins. Algorithmic searches for PTMs from tandem mass spectrum data (MS/MS) tend to be hampered by noisy data as well as by a combinatorial explosion of search space. This leads to high uncertainty and long search-execution times., Results: To address this issue, we present PTMTreeSearch, a new algorithm that uses a large database of known PTMs to identify PTMs from MS/MS data. For a given peptide sequence, PTMTreeSearch builds a computational tree wherein each path from the root to the leaves is labeled with the amino acids of a peptide sequence. Branches then represent PTMs. Various empirical tree pruning rules have been designed to decrease the search-execution time by eliminating biologically unlikely solutions. PTMTreeSearch first identifies a relatively small set of high confidence PTM types, and in a second stage, performs a more exhaustive search on this restricted set using relaxed search parameter settings. An analysis of experimental data shows that using the same criteria for false discovery, PTMTreeSearch annotates more peptides than the current state-of-the-art methods and PTM identification algorithms, and achieves this at roughly the same execution time. PTMTreeSearch is implemented as a plugable scoring function in the X!Tandem search engine., Availability: The source code of PTMTreeSearch and a demo server application can be found at http://net.icgeb.org/ptmtreesearch

Identification and elimination of noise peaks in mass spectra from large proteomics data streams simultaneously improves the accuracy of peptide identification and significantly decreases the size of the data. There are a number of peak filtering strategies that can achieve this goal. Here we present a simple algorithm wherein the number of highest intensity peaks retained for further analysis is proportional to the mass of the precursor ion. We show that this technique provides an improvement over other intensity based strategies, especially for low mass precursors.

Motivation: Identification of proteins by mass spectrometry-based proteomics requires automated interpretation of peptide tandem mass spectrometry spectra. The effectiveness of peptide identification can be greatly improved by filtering out extraneous noise peaks before the subsequent database searching steps., Results: Here we present a novel chemical rule-based filtering algorithm, termed CRF, which makes use of the predictable patterns (rules) of collision-induced peptide fragmentation. The algorithm selects peak pairs that obey the common fragmentation rules within plausible limits of mass tolerance as well as peak intensity and produces spectra that can be subsequently submitted to any search engine. CRF increases the positive predictive value and decreases the number of random matches and thus improves performance by 15-20% in terms of peptide annotation using search engines, such as X!Tandem. Importantly, the algorithm also achieves data compression rates of ∼75%., Availability: The MATLAB source code and a web server are available at http://hydrax.icgeb.trieste.it/CRFilter/., Supplementary Information: Supplementary data are available at Bioinformatics online.

SBASE is a project initiated to detect known domain types and predicting domain architectures using sequence similarity searching (Simon et al., Protein Seq Data Anal, 5: 39-42, 1992, Pongor et al, Nucl. Acids. Res. 21:3111-3115, 1992). The current approach uses a curated collection of domain sequences - the SBASE domain library - and standard similarity search algorithms, followed by postprocessing which is based on a simple statistics of the domain similarity network (http://hydra.icgeb.trieste.it/sbase/). It is especially useful in detecting rare, atypical examples of known domain types which are sometimes missed even by more sophisticated methodologies. This approach does not require multiple alignment or machine learning techniques, and can be a useful complement to other domain detection methodologies. This article gives an overview of the project history as well as of the concepts and principles developed within this the project.

Current protein sequence databases employ different classification schemes that often provide conflicting annotations, especially for poorly characterized proteins. ProGMap (Protein Group Mappings, http://www.bioinformatics.nl/progmap) is a web-tool designed to help researchers and database annotators to assess the coherence of protein groups defined in various databases and thereby facilitate the annotation of newly sequenced proteins. ProGMap is based on a non-redundant dataset of over 6.6 million protein sequences which is mapped to 240,000 protein group descriptions collected from UniProt, RefSeq, Ensembl, COG, KOG, OrthoMCL-DB, HomoloGene, TRIBES and PIRSF. ProGMap combines the underlying classification schemes via a network of links constructed by a fast and fully automated mapping approach originally developed for document classification. The web interface enables queries to be made using sequence identifiers, gene symbols, protein functions or amino acid and nucleotide sequences. For the latter query type BLAST similarity search and QuickMatch identity search services have been incorporated, for finding sequences similar (or identical) to a query sequence. ProGMap is meant to help users of high throughput methodologies who deal with partially annotated genomic data.

Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and random sampling was used to construct model datasets, suitable for algorithm comparison.

Identification of problematic protein classes (domain types, protein families) that are difficult to predict from sequence is a key issue in genome annotation. ROC (Receiver Operating Characteristic) analysis is routinely used for the evaluation of protein similarities, however its results - the area under curve (AUC) values - are differentially biased for the various protein classes that are highly different in size. We show the bias can be compensated for by adjusting the length of the top list in a class-dependent fashion, so that the number of negatives within the top list will be equal to (or proportional with) the size of the positive class. Using this balanced protocol the problematic classes can be identified by their AUC values, or by a scatter diagram in which the AUC values are plotted against positive/negative ratio of the top list. The use of likelihood-ratio scoring (Kaján et al, Bioinformatics,22, 2865-2869, 2007) the bias caused by class imbalance can be further decreased.

Unlabelled: PRIDE-NMR is a fast novel method to relate known protein folds to NMR distance restraints. It can be used to obtain a first guess about a structure being determined, as well as to estimate the completeness or verify the correctness of NOE data., Availability: The PRIDE-NMR server is available at http://www.icgeb.org/pride

We present two efficient network propagation algorithms that operate on a binary tree, i.e., a sparse-edged substitute of an entire similarity network. TreeProp-N is based on passing increments between nodes while TreeProp-E employs propagation to the edges of the tree. Both algorithms improve protein classification efficiency.

This report describes an approach for the study of the biology of methylarginine proteins based on the generation of immunological reagents capable of recognizing the methylarginine status of cellular proteins. Two forms of an immunizing peptide were prepared based upon an amino acid sequence motif found most prevalently among verified dimethylarginine-containing proteins. One form of the peptide was constructed with 7 arginine residues alternating with 8 glycine residues. None of the arginines used in the synthesis were methylated. The alternative form of the peptide was synthesized with the identical repeating GRG sequence, but with asymmetrical dimethylarginine at each arginine residue. A methylarginine-specific antiserum was generated using the latter peptide. ELISA and western blotting of glycine arginine-rich peptides, each synthesized with or without asymmetric dimethylarginine, demonstrate the methyl specificity of the antiserum. The methylarginine-specific antibody co-localizes with the highly methylated native nucleolin protein conspicuously concentrated in the nucleolus. The methylarginine-specific antiserum recognizes a GRG peptide and bacterially expressed RBP16 only after incubation of the peptide or RBP16 with recombinant protein arginine methyltransferase 1, or cell extracts, respectively. Proteins isolated from cells in different developmental states exhibit different patterns of reactivity observed by western blots. Finally, the methylarginine-specific reagent interacts specifically with the methylarginine of cellular hnRNPA1 and human fragile X mental retardation protein expressed in cultured PC12 cells. An immunological reagent capable of detecting the methylarginine status of cellular methylproteins will facilitate the cellular and molecular analysis of protein arginine methylation in a wide variety of research and biomedical applications.

Protein classification by machine learning algorithms is now widely used in structural and functional annotation of proteins. The Protein Classification Benchmark collection (http://hydra.icgeb.trieste.it/benchmark) was created in order to provide standard datasets on which the performance of machine learning methods can be compared. It is primarily meant for method developers and users interested in comparing methods under standardized conditions. The collection contains datasets of sequences and structures, and each set is subdivided into positive/negative, training/test sets in several ways. There is a total of 6405 classification tasks, 3297 on protein sequences, 3095 on protein structures and 10 on protein coding regions in DNA. Typical tasks include the classification of structural domains in the SCOP and CATH databases based on their sequences or structures, as well as various functional and taxonomic classification problems. In the case of hierarchical classification schemes, the classification tasks can be defined at various levels of the hierarchy (such as classes, folds, superfamilies, etc.). For each dataset there are distance matrices available that contain all vs. all comparison of the data, based on various sequence or structure comparison methods, as well as a set of classification performance measures computed with various classifier algorithms.

Motivation: Likelihood ratio approximants (LRA) have been widely used for model comparison in statistics. The present study was undertaken in order to explore their utility as a scoring (ranking) function in the classification of protein sequences., Results: We used a simple LRA-based on the maximal similarity (or minimal distance) scores of the two top ranking sequence classes. The scoring methods (Smith-Waterman, BLAST, local alignment kernel and compression based distances) were compared on datasets designed to test sequence similarities between proteins distantly related in terms of structure or evolution. It was found that LRA-based scoring can significantly outperform simple scoring methods.

Motivation: Distance measures built on the notion of text compression have been used for the comparison and classification of entire genomes and mitochondrial genomes. The present study was undertaken in order to explore their utility in the classification of protein sequences., Results: We constructed compression-based distance measures (CBMs) using the Lempel-Zlv and the PPMZ compression algorithms and compared their performance with that of the Smith-Waterman algorithm and BLAST, using nearest neighbour or support vector machine classification schemes. The datasets included a subset of the SCOP protein structure database to test distant protein similarities, a 3-phosphoglycerate-kinase sequences selected from archaean, bacterial and eukaryotic species as well as low and high-complexity sequence segments of the human proteome, CBMs values show a dependence on the length and the complexity of the sequences compared. In classification tasks CBMs performed especially well on distantly related proteins where the performance of a combined measure, constructed from a CBM and a BLAST score, approached or even slightly exceeded that of the Smith-Waterman algorithm and two hidden Markov model-based algorithms.

We calculated profiles for mean residue depth, contact order, and number of contacts in the native structure of a series of proteins for which folding has been studied extensively, the chymotrypsin inhibitor 2, the SH3 module from the src tyrosine kinase, the small ribonuclease barnase, the bacterial immunity protein Im7, and apomyoglobin. We compared these profiles with experimental data from equilibrium or pulse labeling hydrogen-deuterium exchange obtained from NMR and phi values obtained from the protein engineering approach. We find a good qualitative agreement between the hierarchy of formation of topological elements during the folding process and the ranking of secondary structure elements in terms of residue depth. Residues that are most deeply buried in the core of the native protein usually belong to stretches of secondary structure elements that are formed early in the folding pathway. Residue depth can thus provide a useful and simple tool for the design of folding experiments., (Copyright 2005 Wiley-Liss, Inc.)

Unlabelled: An improved version of the PRIDE (PRobaility of IDEntity) fold prediction algorithm has been developed, based on more solid statistical basis, fast search capabilities and efficient input structure processing. The new algorithm is effective in identifying protein structures at the 'H' level of the CATH hierarchy., Availability: The new algorithm is integrated into the PRIDE2 web servers at http://pride.szbk.u-szeged.hu and http://www.icgeb.org/pride., Supplementary Information: Detailed documentation and performance evaluation is available in the description section of the PRIDE2 web server.

Background: The process of oxidative folding combines the formation of native disulfide bond with conformational folding resulting in the native three-dimensional fold. Oxidative folding pathways can be described in terms of disulfide intermediate species (DIS) which can also be isolated and characterized. Each DIS corresponds to a family of folding states (conformations) that the given DIS can adopt in three dimensions., Results: The oxidative folding space can be represented as a network of DIS states interconnected by disulfide interchange reactions that can either create/abolish or rearrange disulfide bridges. We propose a simple 3D representation wherein the states having the same number of disulfide bridges are placed on separate planes. In this representation, the shuffling transitions are within the planes, and the redox edges connect adjacent planes. In a number of experimentally studied cases (bovine pancreatic trypsin inhibitor, insulin-like growth factor and epidermal growth factor), the observed intermediates appear as part of contiguous oxidative folding pathways., Conclusions: Such networks can be used to visualize folding pathways in terms of the experimentally observed intermediates. A simple visualization template written for the Tulip package http://www.tulip-software.org/ can be obtained from V.A.

Atom depth, originally defined as the distance between a protein atom and the nearest water molecule surrounding a protein, is a simple but valuable geometrical descriptor of the protein interior. It can be easily computed from the 3D structure of a protein, thus complementing the information provided by the calculation of the solvent accessible surface area and buried surface area. Depth has been found to be correlated with several molecular, residue and atomic properties, such as average protein domain size, protein stability, free energy of formation of protein complexes, amino acid type hydrophobicity, residue conservation and hydrogen/deuterium amide proton exchange rates.

Atom depth, defined as the distance (dpx, A) of a nonhydrogen atom from its closest solvent-accessible protein neighbor, provides a simple but precise description of the protein interior. Mean residue depths can be easily computed and are very sensitive to structural features. From the analysis of the average and maximum atom depths of a set of 136 protein structures, we derive a limit of approximately 200 residues for protein and protein domain size. The average and maximum atom depths in a protein are related to its size but not to the fold type. From the same set of structures, we calculated the mean residue depths for the 20 amino acid types, and show that they correlate well with hydrophobicity scales. We show that dpx values can be used to partition atoms in discrete layers according to their depth and to identify atoms that, although buried, are potential targets for posttranslational modifications like phosphorylation. Finally, we find a correlation between highly conserved residues in structural neighbors of the same fold type, and their mean residue depth in the reference structure.

Summary: In order to obtain an accurate description of the protein interior, we describe a simple and fast algorithm that measures the depth of each atom in a protein (dpx), defined as its distance (A) from the closest solvent accessible atom. The program reads a PDB file containing the atomic solvent accessibility in the B-factor field, and writes a file in the same format, where the B-factor field now contains the dpx value. Output structure files can be thus directly displayed with molecular graphics programs like RASMOL, MOLMOL, Swiss-PDB View and colored according to dpx values., Availability: The algorithm is implemented in a standalone program written in C and its source is freely available at ftp.icgeb.trieste.it/pub/DPX or on request from the authors.

SBASE (http://www.icgeb.trieste.it/sbase) is an on-line collection of protein domain sequences and related computational tools designed to facilitate detection of domain homologies based on simple database search. The 10th 'jubilee release' of the SBASE library of protein domain sequences contains 1 052 904 protein sequence segments annotated by structure, function, ligand-binding or cellular topology, clustered into over 6000 domain groups. Domain identification and functional prediction are based on a comparison of BLAST search outputs with a knowledge base of biologically significant similarities extracted from known domain groups. The knowledge base is generated automatically for each domain group from the comparison of within-group ('self') and out-of-group ('non-self') similarities. This is a memory-based approach wherein group-specific similarity functions are automatically learned from the database.

Starting with the Protein Data Bank (PDB) as a common ancestor, the evolution of structural databases has been driven by the rapprochement of the structural world and the practical applications. The result is an impressive number of secondary structural databases that is welcomed by structural biologists and bioinformaticians but runs the risk of producing an embarrassment of riches among non-specialist users. Given that any profit depends on the number of customers, efficient interfaces between many structural data banks must be available to make their contents easily accessible. Increasing the information content of central structural repositories might be the best way to guide users through the many, sometimes overlapping databases.

Quantitation of protein 3-D structure similarity is crucial in such fields as evolutionary studies, structural modeling and prediction of biological function. There are various approaches, many of which are tailored to specific problems. This review summarizes the recent developments in this field with particular interest in two main areas: i) improvements to and statistical interpretation of the root-man-square distance between equivalent atoms, rmsd; and ii) methods of protein structural classification based on geometrical features. Special attention is given to fast methods capable of analyzing large structural databases.

Motivation: A simple and fast algorithm is described that calculates a measure of protrusion (cx) for atoms in protein structures, directly useable with the common molecular graphics programs., Results: A sphere of predetermined radius is centered around each non-hydrogen atom, and the volume occupied by the protein and the free volume within the sphere (internal and external volumes, respectively) are calculated. Atoms in protruding regions have a high ratio (cx) between the external and the internal volume. The program reads a PDB file, and writes the output in the same format, with cx values in the B factor field. Output structure files can be directly displayed with standard molecular graphics programs like RASMOL, MOLMOL, Swiss-PDB Viewer and colored according to cx values. We show the potential use of this program in the analysis of two protein-protein complexes and in the prediction of limited proteolysis sites in native proteins., Availability: The algorithm is implemented in a standalone program written in C and its source is freely available at ftp.icgeb.trieste.it/pub/CX or on request from the authors.

The distribution of the C(alpha)-C(alpha) distances between residues separated by three to 30 amino acid residues is highly characteristic of protein folds and makes it possible to identify them from a straightforward comparison of the distance histograms. The comparison is carried out by contingency table analysis and yields a probability of identity (PRIDE score), with values between zero and 1. For closely related structures, PRIDE is highly correlated with the root-mean-square distance between C(alpha) atoms, but it provides a correct classification even for unrelated structures for which a structural alignment is not meaningful. For example, an analysis of the CATH database of fold structures showed that 98.8% of the folds fall into the correct CATH homologous superfamily category, based on the highest PRIDE score obtained. Structural alignment and secondary-structure assignment are not necessary for the calculation of PRIDE, which is fast enough to allow the scanning of large databases., (Copyright 2002 Academic Press.)

SBASE (http://www.icgeb.trieste.it/sbase) is an online resource of protein domain sequences designed to facilitate detection of domain homologies based on a simple database search. The ninth release of the SBASE library of protein domain sequences contains 320 000 annotated structural, functional, ligand-binding and topogenic segments of proteins clustered into over 3481 domain groups and 483 protein families. Domain identification and functional prediction are based on a comparison of BLAST search outputs with a knowledge base of within-group ('self') and out-of-group ('non-self') similarities of the known domain groups. This is a memory-based approach wherein class-specific similarity functions are automatically learned from the database [Stanfill,C. and Waltz,D. (1986) COMMUN: ACM, 29, 1213-1228].

The degree of similarity of two protein three-dimensional structures is usually measured with the root-mean-square distance between equivalent atom pairs. Such a similarity measure depends on the dimension of the proteins, that is, on the number of equivalent atom pairs. The present communication presents a simple procedure to make the root-mean-square distances between pairs of three-dimensional structures independent of their dimensions. This normalization may be useful in evolutionary and fold classification studies as well as in simple comparisons between different structural models.

SBASE 8.0 is the eighth release of the SBASE library of protein domain sequences that contains 294 898 annotated structural, functional, ligand-binding and topogenic segments of proteins, cross-referenced to most major sequence databases and sequence pattern collections. The entries are clustered into over 2005 statistically validated domain groups (SBASE-A) and 595 non-validated groups (SBASE-B), provided with several WWW-based search and browsing facilities for online use. A domain-search facility was developed, based on non-parametric pattern recognition methods, including artificial neural networks. SBASE 8.0 is freely available by anonymous 'ftp' file transfer from ftp.icgeb.trieste.it. Automated searching of SBASE can be carried out with the WWW servers http://www.icgeb.trieste.it/sbase/ and http://sbase.abc. hu/sbase/.

SBASE 7.0 is the seventh release of the SBASE protein domain library sequences that contains 237 937 annotated structural, functional, ligand-binding and topogenic segments of proteins, cross-referenced to all major sequence databases and sequence pattern collections. The entries are clustered into over 1811 groups and are provided with two WWW-based search facilities for on-line use. SBASE 7.0 is freely available by anonymous 'ftp' file transfer from ftp.icgeb. trieste.it. Automated searching of SBASE with BLAST can be carried out with the WWW servers http://www.icgeb.trieste.it/sbase/and http://sbase.abc.hu/sbase/

Results: A WWW server for protein domain homology prediction, based on BLAST search and a simple data-mining algorithm (Hegyi,H. and Pongor,S. (1993) Comput. Appl. Biosci., 9, 371-372), was constructed providing a tabulated list and a graphic plot of similarities., Availability: http://www.icgeb.trieste.it/domain. Mirror site is available at http://sbase.abc.hu/domain. A standalone programme will be available on request., Supplementary Information: A series of help files is available at the above addresses.

The sixth release of the SBASE protein domain library sequences contains 130 703 annotated and crossreferenced entries corresponding to structural, functional, ligand-binding and topogenic segments of proteins. The entries were grouped based on standard names (2312 groups) and futher classified on the basis of the BLAST similarity (2463 clusters). Automated searching with BLAST and a new sequence-plot representation of local domain similarities are available at the WWW-server http://www.icgeb.trieste.it/sbase. A mirror site is at http://sbase.abc.hu/sbase. The database is freely available by anonymous 'ftp' file transfer from ftp.icgeb.trieste.it

SBASE 5.0 is the fifth release of SBASE, a collection of annotated protein domain sequences that represent various structural, functional, ligand-binding and topogenic segments of proteins. SBASE was designed to facilitate the detection of functional homologies and can be searched with standard database-search programs. The present release contains over 79863 entries provided with standardized names and is cross-referenced to all major sequence databases and sequence pattern collections. The information is assigned to individual domains rather than to entire protein sequences, thus SBASE contains substantially more cross-references and links than do the protein sequence databases. The entries are clustered into >16 000 groups in order to facilitate the detection of distant similarities. SBASE 5.0 is freely available by anonymous 'ftp' file transfer from . Automated searching of SBASE with BLAST can be carried out with the WWW-server . and with the electronic mail server which now also provides a graphic representation of the homologies. A related WWW-server and e-mail server predicts SBASE domain homologies on the basis of SWISS-PROT searches.

SBASE 4.0 is the fourth release of SBASE, a collection of annotated protein domain sequences that represent various structural, functional, ligand binding and topogenic segments of proteins. SBASE was designed to facilitate the detection of functional homologies and can be searched with standard database search tools, such as FASTA and BLAST3. The present release contains 61 137 entries provided with standardized names and cross-referenced to all major protein, nucleic acid and sequence pattern collections. The entries are clustered into 13 155 groups in order to facilitate detection of distant similarities. SBASE 4.0 is freely available by anonymous ftp file transfer from ftp.icgeb.trieste.it. Individual records can be retrieved with the gopher server at icgeb.trieste.it and with a World Wide Web server at http://www.icgeb.trieste.it. Automated searching of SBASE with BLAST can be carried out with the electronic mail server sbase@icgeb.trieste.it, which now also provides a graphic representation of the homologies. A related mail server, domain@hubi.abc.hu, assigns SBASE domain homologies on the basis of SWISS-PROT searches.

SBASE 3.0 is the third release of SBASE, a collection of annotated protein domain sequences. SBASE entries represent various structural, functional, ligand-binding and topogenic segments of proteins as defined by their publishing authors. SBASE can be used for establishing domain homologies using different database-search tools such as FASTA [Lipman and Pearson (1985) Science, 227, 1436-1441], and BLAST3 [Altschul and Lipman (1990) Proc. Natl. Acad. Sci. USA, 87, 5509-5513] which is especially useful in the case of loosely defined domain types for which efficient consensus patterns can not be established. The present release contains 41,749 entries provided with standardized names and cross-referenced to the major protein and nucleic acid databanks as well as to the PROSITE catalogue of protein sequence patterns. The entries are clustered into 2285 groups using the BLAST algorithm for computing similarity measures. SBASE 3.0 is freely available on request to the authors or by anonymous 'ftp' file transfer from < ftp.icgeb.trieste.it >. Individual records can be retrieved with the gopher server at < icgeb.trieste.it > and with a www-server at < http:@www.icgeb.trieste.it >. Automated searching of SBASE by BLAST can be carried out with the electronic mail server < sbase@icgeb.trieste.it >. Another mail server < domain@hubi.abc.hu > assigns SBASE domain homologies on the basis of SWISS-PROT searches. A comparison of pertinent search strategies is presented.

SBASE 2.0 is the second release of SBASE, a collection of annotated protein domain sequences. SBASE entries represent various structural, functional, ligand-binding and topogenic segments of proteins [Pongor, S. et al. (1993) Prot. Eng., in press]. This release contains 34,518 entries provided with standardized names and it is cross-referenced to the major protein and nucleic acid databanks as well as to the PROSITE catalog of protein sequence patterns [Bairoch, A. (1992) Nucl. Acids Res., 20 suppl, 2013-2018]. SBASE can be used for establishing domain homologies using different database-search tools such as FASTA [Lipman and Pearson (1985) Science, 227, 1436-1441], FASTDB [Brutlag et al. (1990) Comp. Appl. Biosci., 6, 237-245] or BLAST3 [Altschul and Lipman (1990) Proc. Natl. Acad. Sci. USA, 87, 5509-5513] which is especially useful in the case of loosely defined domain types for which efficient consensus patterns can not be established. SBASE 2.0 and a set of search and retrieval tools are freely available on request to the authors or by anonymous 'ftp' file transfer from mean value of ftp.icgeb.trieste.it.

SBASE is a database of annotated protein domain sequences representing various structural, functional, ligand binding and topogenic segments of proteins. The current release of SBASE contains 27,211 entries which are provided with standardized names in order to facilitate retrieval. SBASE is cross-referenced to the major protein and nucleic acid databanks as well as to the PROSITE catalog of protein sequence patterns [Bairoch, A. (1992) Nucleic Acids Res., 20, Suppl., 2013-2118]. SBASE can be used to establish domain homologies through database search using programs such as FASTA [Lipman and Pearson (1985) Science, 227, 1436-1441], FASTDB [Brutlag et al. (1990) Comp. Appl. Biosci., 6, 237-245] or BLAST3 [Altschul and Lipman (1990) Proc. Natl. Acad. Sci. USA, 87, 5509-5513], which is especially useful in the case of loosely defined domain types for which efficient consensus patterns cannot be established. The use of SBASE is illustrated on the DNA binding protein Brain-4. The database and a set of search and retrieval tools are freely available on request to the authors or by anonymous 'ftp' file transfer from < ftp.icgeb.trieste.it >.

Proteins exposed to glucose over long periods are known to undergo physicochemical changes including crosslinking and formation of brown fluorescent pigments of poorly characterized structure. Acid hydrolysis of both browned poly(L-lysine) and browned bovine serum albumin is found to release a major fluorescent chromophore, which after alkalinization is extractable into organic solvents and which can be purified by silica gel chromatography. The fluorescence properties of this compound very closely resemble those of the bulk browned polypeptides. By NMR, mass spectroscopy, and chemical derivatization, this compound is assigned the structure 2-(2-furoyl)-4(5)-(2-furanyl)-1H-imidazole (FFI). Confirmation was obtained by independent chemical synthesis from furylglyoxal and ammonia. The incorporation of two peptide-derived amine nitrogens and two glucose residues in FFI strongly suggests that peptide-bound FFI precursors are implicated in the crosslinking of proteins by glucose in vivo. This reaction has potential implications in the understanding of glucose-mediated protein modifications and their role in the complications of diabetes and aging.

A rapid non hydrolytic method was developed for the estimation of methionine residues in peptides and proteins. The method is based on the gas chromatographic determination of methylthiocyanate liberated in the reaction of cyanogen bromide with the methionine residues. In contrast to the amino acid analyzer technique this method allows selective determination of methionine in the presence of methionine sulphoxide and sulphone. It was tested with both purified proteins and protein-containing crude samples (legume seeds) and was compared with different analyzer techniques.

It has been reported that the C-terminus of the second conserved region (C2) of the envelope glycoprotein gp120, encompassing peptide RSANFTDNAKTIIVQLNESVEIN (NTM), is important for infectivity and neutralization of the human immunodeficiency virus type 1 (HIV-1). It was also demonstrated that human natural anti-vasoactive intestinal peptide (VIP) antibodies reactive with this gp120 region play an important role in control of HIV disease progression. The bioinformatic analysis based on the time-frequency signal processing revealed non-obvious similarities between NTM and VIP. When tested against a battery of sera from 46 AIDS patients, these peptides, in spite of a significant difference in their primary structures, showed a similar reactivity profiles (r = 0.83). Presented results point out that similarity in the periodical pattern of some physicochemical properties in primary structures of peptides plays a significant role in determination of their immunological crossreactivity. Based on these findings, we propose this bioinformatic criterion be used for design of VIP/NTM peptide mimetics for prevention and treatment of HIV disease. [ABSTRACT FROM AUTHOR]