Histone Sequence Database: new histone fold family members.

Searches of the major public protein databases with core and linker chicken and human histone sequences have resulted in the compilation of an annotated set of histone protein sequences. In addition, new database searches with two distinct motif search algorithms have identified several members of the histone fold family, including human DRAP1 and yeast CSE4. Database resources include information on conflicts between similar sequence entries in different source databases, multiple sequence alignments, links to the Entrez integrated information retrieval system, structures for histone and histone fold proteins, and the ability to visualize structural data through Cn3D. The database currently contains >1000 protein sequences, which are searchable by protein type, accession number, organism name, or any other free text appearing in the definition line of the entry. All sequences and alignments in this database are available through the World Wide Web at http://www.nhgri.nih. gov/DIR/GTB/HISTONES or http://www.ncbi.nlm.nih. gov/Baxevani/HISTONES     

Histone Sequence Database: a compilation of highly-conserved nucleoprotein sequences.

By searching the current protein sequence databases using sequences from human and chicken histones H1/H5, H2A, H2B, H3 and H4, a database of aligned histone protein sequences with statistically significant sequence similarity to the search sequence was constructed. In addition, a nucleotide sequence database of the corresponding coding regions for these proteins has been assembled. The region of each of the core histones containing the histone fold motif is identified in the protein alignments. The database contains >1300 protein and nucleotide sequences. All sequences and alignments in this database are available through the World Wide Web at http://www.ncbi.nlm.nih.gov/Baxevani/HISTO NES.     

The Histone Database: a comprehensive resource for histones and histone fold-containing proteins

The Histone Database is a curated and searchable collection of full-length sequences and structures of histones and nonhistone proteins containing histone-like folds, compiled from major public databases. Several new histone fold-containing proteins have been identified, including the huntingtin-interacting protein HYPM. Additionally, based on the recent crystal structure of the Son of Sevenless protein, an interpretation of the sequence analysis of the histone fold domain is presented. The database contains an updated collection of multiple sequence alignments for the four core histones (H2A, H2B, H3, and H4) and the linker histones (H1/H5) from a total of 975 organisms. The database also contains information on the human histone gene complement and provides links to three-dimensional structures of histone and histone fold-containing proteins. The Histone Database is a comprehensive bioinformatics resource for the study of structure and function of histones and histone fold-containing proteins. The database is available at http://research.nhgri.nih.gov/histones/.     

The histone database: a comprehensive WWW resource for histones and histone fold-containing proteins

The Histone Database (HDB) is an annotated and searchable collection of all full-length sequences and structures of histone and non-histone proteins containing the histone fold motif. These sequences are both eukaryotic and archaeal in origin. Several new histone fold-containing proteins have been identified, including Spt7p, and a few false positives have been removed from the earlier version of HDB. Database contents include compilations of post-translational modifications for each of the core and linker histones, as well as genomic information in the form of map loci for the human histone gene complement, with the genetic loci linked to Online Mendelian Inheritance in Man (OMIM). Conflicts between similar sequence entries from a number of source databases are also documented. Newly added to the HDB are multiple sequence alignments in which predicted functions of histone fold amino acid residues are annotated. The database is freely accessible through the WWW at http://genome.nhgri.nih.gov/histones/     

PASS2: a semi-automated database of Protein Alignments Organised as Structural Superfamilies

PASS2 is a nearly automated version of CAMPASS and contains sequence alignments of proteins grouped at the level of superfamilies. This database has been created to fall in correspondence with SCOP database (1.53 release) and currently consists of 110 multi-member superfamilies and 613 superfamilies corresponding to single members. In multi-member superfamilies, protein chains with no more than 25% sequence identity have been considered for the alignment and hence the database aims to address sequence alignments which represent 26 219 protein domains under the SCOP 1.53 release. Structure-based sequence alignments have been obtained by COMPARER and the initial equivalences are provided automatically from a MALIGN alignment and subsequently augmented using STAMP4.0. The final sequence alignments have been annotated for the structural features using JOY4.0. Several interesting links are provided to other related databases and genome sequence relatives. Availability of reliable sequence alignments of distantly related proteins, despite poor sequence identity and single-member superfamilies, permit better sampling of structures in libraries for fold recognition of new sequences and for the understanding of protein structure–function relationships of individual superfamilies. The database can be queried by keywords and also by sequence search, interfaced by PSI-BLAST methods. Structure-annotated sequence alignments and several structural accessory files can be retrieved for all the superfamilies including the user-input sequence. The database can be accessed from http://www.ncbs.res.in/%7Efaculty/mini/campass/pass.html.     

Histone Sequence Database: sequences, structures, post-translational modifications and genetic loci.

The Histone Sequence Database is an annotated and searchable collection of all available histone and histone fold sequences and structures. Particular emphasis has been placed on documenting conflicts between similar sequence entries from a number of source databases, conflicts that are not necessarily documented in the source databases themselves. New additions to the database include compilations of post-translational modifications for each of the core and linker histones, as well as genomic information in the form of map loci for the human histone gene complement, with the genetic loci linked to Online Mendelian Inheritance in Man (OMIM). The database is freely accessible through the World Wide Web at either http://genome.nhgri.nih.gov/histones/ or http://www.ncbi.nlm.nih. gov/Baxevani/HISTONES     

Filling-in Void and Sparse Regions in Protein Sequence Space by Protein-Like Artificial Sequences Enables Remarkable Enhancement in Remote Homology Detection Capability

Protein functional annotation relies on the identification of accurate relationships, sequence divergence being a key factor. This is especially evident when distant protein relationships are demonstrated only with three-dimensional structures. To address this challenge, we describe a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like “linker” sequences. For this, we represented SCOP domain families, integrated with sequence homologues, as multiple profiles and performed HMM-HMM alignments between related domain families. Where convincing alignments were achieved, we applied a roulette wheel-based method to design 3,611,010 protein-like sequences corresponding to 374 SCOP folds. To analyze their ability to link proteins in homology searches, we used 3024 queries to search two databases, one containing only natural sequences and another one additionally containing designed sequences. Our results showed that augmented database searches showed up to 30% improvement in fold coverage for over 74% of the folds, with 52 folds achieving all theoretically possible connections. Although sequences could not be designed between some families, the availability of designed sequences between other families within the fold established the sequence continuum to demonstrate 373 difficult relationships. Ultimately, as a practical and realistic extension, we demonstrate that such protein-like sequences can be “plugged-into” routine and generic sequence database searches to empower not only remote homology detection but also fold recognition. Our richly statistically supported findings show that complementary searches in both databases will increase the effectiveness of sequence-based searches in recognizing all homologues sharing a common fold.     

Iterative sequence/secondary structure search for protein homologs: comparison with amino acid sequence alignments and application to fold recognition in genome databases

MOTIVATION: Sequence alignment techniques have been developed into extremely powerful tools for identifying the folding families and function of proteins in newly sequenced genomes. For a sufficiently low sequence identity it is necessary to incorporate additional structural information to positively detect homologous proteins. We have carried out an extensive analysis of the effectiveness of incorporating secondary structure information directly into the alignments for fold recognition and identification of distant protein homologs. A secondary structure similarity matrix based on a database of three-dimensionally aligned proteins was first constructed. An iterative application of dynamic programming was used which incorporates linear combinations of amino acid and secondary structure sequence similarity scores. Initially, only primary sequence information is used. Subsequently contributions from secondary structure are phased in and new homologous proteins are positively identified if their scores are consistent with the predetermined error rate. RESULTS: We used the SCOP40 database, where only PDB sequences that have 40% homology or less are included, to calibrate homology detection by the combined amino acid and secondary structure sequence alignments. Combining predicted secondary structure with sequence information results in a 8-15% increase in homology detection within SCOP40 relative to the pairwise alignments using only amino acid sequence data at an error rate of 0.01 errors per query; a 35% increase is observed when the actual secondary structure sequences are used. Incorporating predicted secondary structure information in the analysis of six small genomes yields an improvement in the homology detection of approximately 20% over SSEARCH pairwise alignments, but no improvement in the total number of homologs detected over PSI-BLAST, at an error rate of 0.01 errors per query. However, because the pairwise alignments based on combinations of amino acid and secondary structure similarity are different from those produced by PSI-BLAST and the error rates can be calibrated, it is possible to combine the results of both searches. An additional 25% relative improvement in the number of genes identified at an error rate of 0.01 is observed when the data is pooled in this way. Similarly for the SCOP40 dataset, PSI-BLAST detected 15% of all possible homologs, whereas the pooled results increased the total number of homologs detected to 19%. These results are compared with recent reports of homology detection using sequence profiling methods. AVAILABILITY: Secondary structure alignment homepage at http://lutece.rutgers.edu/ssas CONTACT: anders@rutchem.rutgers.edu; ronlevy@lutece.rutgers.edu Supplementary Information: Genome sequence/structure alignment results at http://lutece.rutgers.edu/ss_fold_predictions.     

DNA reference alignment benchmarks based on tertiary structure of encoded proteins

MOTIVATION: Multiple sequence alignments (MSAs) are at the heart of bioinformatics analysis. Recently, a number of multiple protein sequence alignment benchmarks (i.e. BAliBASE, OXBench, PREFAB and SMART) have been released to evaluate new and existing MSA applications. These databases have been well received by researchers and help to quantitatively evaluate MSA programs on protein sequences. Unfortunately, analogous DNA benchmarks are not available, making evaluation of MSA programs difficult for DNA sequences. RESULTS: This work presents the first known multiple DNA sequence alignment benchmarks that are (1) comprised of protein-coding portions of DNA (2) based on biological features such as the tertiary structure of encoded proteins. These reference DNA databases contain a total of 3545 alignments, comprising of 68 581 sequences. Two versions of the database are available: mdsa_100s and mdsa_all. The mdsa_100s version contains the alignments of the data sets that TBLASTN found 100% sequence identity for each sequence. The mdsa_all version includes all hits with an E-value score above the threshold of 0.001. A primary use of these databases is to benchmark the performance of MSA applications on DNA data sets. The first such case study is included in the Supplementary Material.     

The PAS fold. A redefinition of the PAS domain based upon structural prediction.

In the postgenomic era it is essential that protein sequences are annotated correctly in order to help in the assignment of their putative functions. Over 1300 proteins in current protein sequence databases are predicted to contain a PAS domain based upon amino acid sequence alignments. One of the problems with the current annotation of the PAS domain is that this domain exhibits limited similarity at the amino acid sequence level. It is therefore essential, when using proteins with low-sequence similarities, to apply profile hidden Markov model searches for the PAS domain-containing proteins, as for the PFAM database. From recent 3D X-ray and NMR structures, however, PAS domains appear to have a conserved 3D fold as shown here by structural alignment of the six representative 3D-structures from the PDB database. Large-scale modelling of the PAS sequences from the PFAM database against the 3D-structures of these six structural prototypes was performed. All 3D models generated (> 5700) were evaluated using prosaii. We conclude from our large-scale modelling studies that the PAS and PAC motifs (which are separately defined in the PFAM database) are directly linked and that these two motifs form the PAS fold. The existing subdivision in PAS and PAC motifs, as used by the PFAM and SMART databases, appears to be caused by major differences in sequences in the region connecting these two motifs. This region, as has been shown by Gardner and coworkers for human PAS kinase (Amezcua, C.A., Harper, S.M., Rutter, J. & Gardner, K.H. (2002) Structure 10, 1349-1361, [1]), is very flexible and adopts different conformations depending on the bound ligand. Some PAS sequences present in the PFAM database did not produce a good structural model, even after realignment using a structure-based alignment method, suggesting that these representatives are unlikely to have a fold resembling any of the structural prototypes of the PAS domain superfamily.     

VIDA: a virus database system for the organization of animal virus genome open reading frames

VIDA is a new virus database that organizes open reading frames (ORFs) from partial and complete genomic sequences from animal viruses. Currently VIDA includes all sequences from GenBank for Herpesviridae, Coronaviridae and Arteriviridae. The ORFs are organized into homologous protein families, which are identified on the basis of sequence similarity relationships. Conserved sequence regions of potential functional importance are identified and can be retrieved as sequence alignments. We use a controlled taxonomical and functional classification for all the proteins and protein families in the database. When available, protein structures that are related to the families have also been included. The database is available for online search and sequence information retrieval at http://www.biochem.ucl.ac.uk/bsm/virus_database/ VIDA.html.     

ProClass protein family database

ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PIR superfamilies and PROSITE patterns. By combining global similarities and functional motifs into a single classification scheme, ProClass helps to reveal domain and family relationships and classify multi-domain proteins. The database currently consists of >155 000 sequence entries retrieved from both PIR-International and SWISS-PROT databases. Approximately 92 000 or 60% of the ProClass entries are classified into approximately 6000 families, including a large number of new members detected by our GeneFIND family identification system. The ProClass motif collection contains approximately 72 000 motif sequences and >1300 multiple alignments for all PROSITE patterns, including >21 000 matches not listed in PROSITE and mostly detected from unique PIR sequences. To maximize family information retrieval, the database provides links to various protein family, domain, alignment and structural class databases. With its high classification rate and comprehensive family relationships, ProClass can be used to support full-scale genomic annotation. The database, now being implemented in an object-relational database management system, is available for online sequence search and record retrieval from our WWW server at http://pir.georgetown.edu/gfserver/proclass.html     

Hidden Markov models that use predicted secondary structures for fold recognition.

There are many proteins that share the same fold but have no clear sequence similarity. To predict the structure of these proteins, so called "protein fold recognition methods" have been developed. During the last few years, improvements of protein fold recognition methods have been achieved through the use of predicted secondary structures (Rice and Eisenberg, J Mol Biol 1997;267:1026-1038), as well as by using multiple sequence alignments in the form of hidden Markov models (HMM) (Karplus et al., Proteins Suppl 1997;1:134-139). To test the performance of different fold recognition methods, we have developed a rigorous benchmark where representatives for all proteins of known structure are matched against each other. Using this benchmark, we have compared the performance of automatically-created hidden Markov models with standard-sequence-search methods. Further, we combine the use of predicted secondary structures and multiple sequence alignments into a combined method that performs better than methods that do not use this combination of information. Using only single sequences, the correct fold of a protein was detected for 10% of the test cases in our benchmark. Including multiple sequence information increased this number to 16%, and when predicted secondary structure information was included as well, the fold was correctly identified in 20% of the cases. Moreover, if the correct secondary structure was used, 27% of the proteins could be correctly matched to a fold. For comparison, blast2, fasta, and ssearch identifies the fold correctly in 13-17% of the cases. Thus, standard pairwise sequence search methods perform almost as well as hidden Markov models in our benchmark. This is probably because the automatically-created multiple sequence alignments used in this study do not contain enough diversity and because the current generation of hidden Markov models do not perform very well when built from a few sequences.     

Large-scale comparison of protein sequence alignment algorithms with structure alignments

Sequence alignment programs such as BLAST and PSI-BLAST are used routinely in pairwise, profile-based, or intermediate-sequence-search (ISS) methods to detect remote homologies for the purposes of fold assignment and comparative modeling. Yet, the sequence alignment quality of these methods at low sequence identity is not known. We have used the CE structure alignment program (Shindyalov and Bourne, Prot Eng 1998;11:739) to derive sequence alignments for all superfamily and family-level related proteins in the SCOP domain database. CE aligns structures and their sequences based on distances within each protein, rather than on interprotein distances. We compared BLAST, PSI-BLAST, CLUSTALW, and ISS alignments with the CE structural alignments. We found that global alignments with CLUSTALW were very poor at low sequence identity (<25%), as judged by the CE alignments. We used PSI-BLAST to search the nonredundant sequence database (nr) with every sequence in SCOP using up to four iterations. The resulting matrix was used to search a database of SCOP sequences. PSI-BLAST is only slightly better than BLAST in alignment accuracy on a per-residue basis, but PSI-BLAST matrix alignments are much longer than BLAST's, and so align correctly a larger fraction of the total number of aligned residues in the structure alignments. Any two SCOP sequences in the same superfamily that shared a hit or hits in the nr PSI-BLAST searches were identified as linked by the shared intermediate sequence. We examined the quality of the longest SCOP-query/ SCOP-hit alignment via an intermediate sequence, and found that ISS produced longer alignments than PSI-BLAST searches alone, of nearly comparable per-residue quality. At 10-15% sequence identity, BLAST correctly aligns 28%, PSI-BLAST 40%, and ISS 46% of residues according to the structure alignments. We also compared CE structure alignments with FSSP structure alignments generated by the DALI program. In contrast to the sequence methods, CE and structure alignments from the FSSP database identically align 75% of residue pairs at the 10-15% level of sequence identity, indicating that there is substantial room for improvement in these sequence alignment methods. BLAST produced alignments for 8% of the 10,665 nonimmunoglobulin SCOP superfamily sequence pairs (nearly all <25% sequence identity), PSI-BLAST matched 17% and the double-PSI-BLAST ISS method aligned 38% with E-values <10.0. The results indicate that intermediate sequences may be useful not only in fold assignment but also in achieving more complete sequence alignments for comparative modeling.     

Towards a reliable objective function for multiple sequence alignments.

Multiple sequence alignment is a fundamental tool in a number of different domains in modern molecular biology, including functional and evolutionary studies of a protein family. Multiple alignments also play an essential role in the new integrated systems for genome annotation and analysis. Thus, the development of new multiple alignment scores and statistics is essential, in the spirit of the work dedicated to the evaluation of pairwise sequence alignments for database searching techniques. We present here norMD, a new objective scoring function for multiple sequence alignments. NorMD combines the advantages of the column-scoring techniques with the sensitivity of methods incorporating residue similarity scores. In addition, norMD incorporates ab initio sequence information, such as the number, length and similarity of the sequences to be aligned. The sensitivity and reliability of the norMD objective function is demonstrated using structural alignments in the SCOP and BAliBASE databases. The norMD scores are then applied to the multiple alignments of the complete sequences (MACS) detected by BlastP with E-value<10, for a set of 734 hypothetical proteins encoded by the Vibrio cholerae genome. Unrelated or badly aligned sequences were automatically removed from the MACS, leaving a high-quality multiple alignment which could be reliably exploited in a subsequent functional and/or structural annotation process. After removal of unreliable sequences, 176 (24 %) of the alignments contained at least one sequence with a functional annotation. 103 of these new matches were supported by significant hits to the Interpro domain and motif database.     

The ASTRAL compendium for protein structure and sequence analysis

The ASTRAL compendium provides several databases and tools to aid in the analysis of protein structures, particularly through the use of their sequences. The SPACI scores included in the system summarize the overall characteristics of a protein structure. A structural alignments database indicates residue equivalencies in superimposed protein domain structures. The PDB sequence-map files provide a linkage between the amino acid sequence of the molecule studied (SEQRES records in a database entry) and the sequence of the atoms experimentally observed in the structure (ATOM records). These maps are combined with information in the SCOPdatabase to provide sequences of protein domains. Selected subsets of the domain database, with varying degrees of similarity measured in several different ways, are also available. ASTRALmay be accessed at http://astral.stanford.edu/     

Filtering redundancies for sequence similarity search programs

Database scanning programs such as BLAST and FASTA are used nowadays by most biologists for the post-genomic processing of DNA or protein sequence information (in particular to retrieve the structure/function of uncharacterized proteins). Unfortunately, their results can be polluted by identical alignments (called redundancies) coming from the same protein or DNA sequences present in different entries of the database. This makes the efficient use of the listed alignments difficult. Pretreatment of databases has been proposed to suppress strictly identical entries. However, there still remain many identical alignments since redundancies may occur locally for entries corresponding to various fragments of the same sequence or for entries corresponding to very homologous sequences but differing at the level of a few residues such as ortholog proteins. In the present work, we show that redundant alignments can be indeed numerous even when working with a pretreated non-redundant data bank, going as high as 60% of the output results according to the query and the bank. Therefore the accuracy and the efficiency of the post-genomic work will be greatly increased if these redundancies are removed. To solve this up to now unaddressed problem, we have developed an algorithm that allows for the efficient and safe suppression of all the redundancies with no loss of information. This algorithm is based on various filtering steps that we describe here in the context of the Automat similarity search program, and such an algorithm should also be added to the other similarity search programs (BLAST, FASTA, etc...).     

A data bank merging related protein structures and sequences.

A data collection which merges protein structural and sequence information is described. Structural superpositions amongst proteins with similar main-chain fold were performed or collected from the literature. Sequences taken from the protein primary structure databases were associated with the multiple structural alignments providing they were at least 50% homologous in residue identity to one of the structural sequences and at least 50% of the structural sequence residues were alignable. Such restrictions allow reasonable confidence that the primary sequences share the conformation of the tertiary structural templates, except in the less conserved loop regions. Multiple structural superpositions were collected for 38 familial groups containing a total of 209 tertiary structures; 45 structures had no superposable mates and were used individually. Other information is also provided as main-chain and side-chain conformational angles, secondary structural assignments and the like. Wedding the primary and tertiary structural data resulted in an 8-fold increase of data bank sequence entries over those associated with the known three-dimensional architectures alone.     

A database of unique protein sequence identifiers for proteome studies

In proteome studies, identification of proteins requires searching protein sequence databases. The public protein sequence databases (e.g., NCBInr, UniProt) each contain millions of entries, and private databases add thousands more. Although much of the sequence information in these databases is redundant, each database uses distinct identifiers for the identical protein sequence and often contains unique annotation information. Users of one database obtain a database-specific sequence identifier that is often difficult to reconcile with the identifiers from a different database. When multiple databases are used for searches or the databases being searched are updated frequently, interpreting the protein identifications and associated annotations can be problematic. We have developed a database of unique protein sequence identifiers called Sequence Globally Unique Identifiers (SEGUID) derived from primary protein sequences. These identifiers serve as a common link between multiple sequence databases and are resilient to annotation changes in either public or private databases throughout the lifetime of a given protein sequence. The SEGUID Database can be downloaded (http://bioinformatics.anl.gov/SEGUID/) or easily generated at any site with access to primary protein sequence databases. Since SEGUIDs are stable, predictions based on the primary sequence information (e.g., pI, Mr) can be calculated just once; we have generated approximately 500 different calculations for more than 2.5 million sequences. SEGUIDs are used to integrate MS and 2-DE data with bioinformatics information and provide the opportunity to search multiple protein sequence databases, thereby providing a higher probability of finding the most valid protein identifications.