Peter Murray‐Rust · 1992
Abstract As part of the UK SERC Protein Engineering Club, a database project was funded, whose aim was to coordinate the storage and searching of protein sequence and structure (ISIS). This chapter describes the BIPED database, where protein structures from the Brookhaven databank have been organised in relational mode. The database has been developed at Birkbeck College and is available on-line via the SEQNET facility of Daresbury Laboratory. It is expected that academic distribution will begin shortly and there are further plans for commercial distribution and continued support.
Cited by 3
Gardner, S, Thornton, JM · UCL Discovery (University College London) · 1998
Cited by 0
Hamid Nikbakht, Payam Mahmoudian, Bahram Goliaei · 2015
Abstract: Protein Secondary Structure Database (PSSD) is a database that incorporates sequences of secondary structure elements of all proteins which their three dimensional structures are defined by experimental methods such as NMR-Spectroscopy or X-Ray Crystallography and their structural data exists in Brookhaven protein databank. Dictionary of Secondary Structure of Proteins (DSSP) criteria have been used to define both ends of each structural element. At present PSSD includes 290,709 alpha helices, 418,362 beta strands, 571,176 turns and 118,109 helices
Cited by 0
Ken Nishikawa, Swindells Mb · PubMed · 1994
Cited by 0
Stephen P. Gardner, Janet M. Thornton · Acta Crystallographica Section D Biological Crystallography · 1998
The validation, enrichment and organization of the data stored in PDB files is essential for those data to be used accurately and efficiently for modelling, experimental design and the determination of molecular interactions. The Iditis protein structure database has been designed to allow the widest possible range of queries to be performed across all available protein structures. The Iditis database is the most comprehensive protein structure resource currently available, and contains over 500 fields of information describing all publicly deposited protein structures. A custom-written database engine and graphical user interface provide a natural and simple environment for the construction of searches for complex sequence- and structure-based motifs. Extensions and specialized interfaces allow the data generated by the database to used in conjunction with a wide range of applications.
Cited by 7Open access
Michael J.E. Sternberg, Suhail A. Islam · Biochemical Society Transactions · 1989
Conference Article| October 01 1989 A relational database of protein structure MICHAEL J. E. STERNBERG; MICHAEL J. E. STERNBERG 1Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, P.O. Box 123, Lincoln's Inn Fields, London WC2A 3PX, U.K. Search for other works by this author on: This Site PubMed Google Scholar SUHAIL A. ISLAM SUHAIL A. ISLAM 1Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, P.O. Box 123, Lincoln's Inn Fields, London WC2A 3PX, U.K. Search for other works by this author on: This Site PubMed Google Scholar Biochem Soc Trans (1989) 17 (5): 845–847. https://doi.org/10.1042/bst0170845 Article history Received: April 18 1989 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Cite Icon Cite Get Permissions Citation MICHAEL J. E. STERNBERG, SUHAIL A. ISLAM; A relational database of protein structure. Biochem Soc Trans 1 October 1989; 17 (5): 845–847. doi: https://doi.org/10.1042/bst0170845 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsBiochemical Society Transactions Search Advanced Search Keywords: ISIS, integrated sequence/integrated structure data resource, SQL, structured query language, c.p.u., central processor unit This content is only available as a PDF. © 1989 Biochemical Society1989 Article PDF first page preview Close Modal You do not currently have access to this content.
Cited by 2
Tony Yeung · Digital Archive @ GSU · 2022
In recent years, the field of bioinformatics has exploded in a scale that is unprecedented. The amount of data generated from different genome projects demands a new and efficient way of information storage and retrieval. The analysis and management of the protein structure information has become one of the main focuses. It is well-known that a protein?s functions differ depending on its structure?s position in 3-dimensional space. Due to the fact that protein structures are exceedingly large, complex, and multi-dimensional, there is a need for a data model that can fulfill the requirements of storing protein structures in accordance to its spatial arrangement and topological relationships and, at the same time, provide tools to analyze the information stored. With the emergence of spatial database, first used in the field of Geographical Information Systems, the data model for protein structure could be based on the geographic model, as they share several similar uncanny traits. The geometry of proteins can be modeled using the spatial types provided in a spatial database. In a similar way, special geometry queries used for geographical analysis can also be used to provide information for analysis on the structure of the proteins. This thesis will explore the mechanics of extracting structural information for a protein from a flat file (PDB), storing that information into a spatial data model based on a spatial data model, and making analysis using geometric operators provided by the spatial database. The database used is Oracle 9i. Most features are provided by the Oracle Spatial package. Queries using the ideas aforementioned will be demonstrated.
Cited by 0Open access
Schravesande, W.E.W., Verhage, Adriaan, Cligge, M.V. et al. · Universiteit van Amsterdam · 2025
# Custom Virus database A custom foldseek target database was created, including all protein sequences derived from plant-infecting viruses currently found in the NCBI RefSeq database. In total, 8,191 protein sequences were extracted and used as template for protein structure predictions. Colabfold v1.5.2 (using localcolabfold), which is based upon AlphaFold v2.3.1(40), was used for protein model prediction. Setting: --random-seed 101 --num-seeds 3 --use-dropout --num-models 1 --num-recycle 8 --recycle-early-stop-tolerance 0.5No templates were used during the protein model prediction. The uniref30_2302 and colabfold_envdb_202108 databases were used to generate the multiple sequence alignments (https://colabfold.mmseqs.com/)The predicted structures were filtered based on the pLDDT value, resulting in a set of 7545 protein structures with a pLDDT ≥ 50.<br>## Filesmodelling_stats.txt < Tab seperated file containing the modelling statistics for each structure predictionpdb_files/all < folder containing all pdb files resulting from the structure predictionpdb_files/pLDDT50 < folder containing all pdb files resulting from the structure prediction having a pLDDT score of 50 or higherVIRAL_PROTEIN_PLANT_REFSEQ.fasta < fasta file contain all protein sequences extracted from plant infecting viral genomes uploaded in the NCBI RefSeq database
Cited by 0Open access
Qiangfeng Cliff Zhang, Donald Petrey, José Ignacio Garzón et al. · Nucleic Acids Research · 2012
PrePPI (http://bhapp.c2b2.columbia.edu/PrePPI) is a database that combines predicted and experimentally determined protein-protein interactions (PPIs) using a Bayesian framework. Predicted interactions are assigned probabilities of being correct, which are derived from calculated likelihood ratios (LRs) by combining structural, functional, evolutionary and expression information, with the most important contribution coming from structure. Experimentally determined interactions are compiled from a set of public databases that manually collect PPIs from the literature and are also assigned LRs. A final probability is then assigned to every interaction by combining the LRs for both predicted and experimentally determined interactions. The current version of PrePPI contains ∼2 million PPIs that have a probability more than ∼0.1 of which ∼60 000 PPIs for yeast and ∼370 000 PPIs for human are considered high confidence (probability > 0.5). The PrePPI database constitutes an integrated resource that enables users to examine aggregate information on PPIs, including both known and potentially novel interactions, and that provides structural models for many of the PPIs.
Cited by 257Open access
Bouras, George · Zenodo (CERN European Organization for Nuclear Research) · 2025
Repository containing 1,802,498 protein structures consituting all proteins in Phold DB 3.16M that are not in Phold Search DB 1.36M. All Phold Search DB 1.36M structures can be found at https://doi.org/10.5281/zenodo.16739199. These are all efam and enVhog proteins that were not able to be assigned PHROGs and have no annotated functional label. Specifically, the large `final_combined_phold_db_3M16_efam_envhog_no_phrog.tar.xz` tarball consists of: * 158,898 extremely conservative efam proteins <3000AA without an assigned PHROG group are in `efam`. These have been chunked into batches of 5000 `.pdb` files for file-system sanity * 1,638,667 enVhog proteins <3000AA without an assigned PHROG group are in `envhog`. These have also been chunked into batches of 5000 files. There is an additional smaller tarball `envhogs_renamed_3000_plus.tar.gz` containing * 4933 `.pdb` files of enVhog proteins 3000+ AA have been fragmented as described in the Phold manuscript (i.e. proteins were chunked into equal fragments under 3000AA so a 5000AA protein has two 2500AA chunks, a 8100AA protein has three 2700AA chunks). These fragments are indicated as `_1` `_2` etc. These are in a separate top level tarball (`envhogs_renamed_3000_plus.tar.gz`) All structures are named as they appear in the Phold DB 3.16M # Recombining the large tarball Due to the large size of uploading the tarball `final_combined_phold_db_3M16_efam_envhog_no_phrog.tar.xz1`, I had significant difficulties in uploading it to Zenodo. Accordingly, I split it into ten smaller chunks as follows (using Linux split command): `split --bytes=5G --suffix-length=3 --numeric-suffixes final_combined_phold_db_3M16_efam_envhog_no_phrog.tar.xz final_combined_phold_db_3M16_efam_envhog_no_phrog.tar.xz_part_` To recombine into the original larger tarball, download all the chunks in this record and combine `cat final_combined_phold_db_3M16_efam_envhog_no_phrog.tar.xz_part_??? > final_combined_phold_db_3M16_efam_envhog_no_phrog.tar.xz`
Cited by 0Open access
Bouras, George · Zenodo (CERN European Organization for Nuclear Research) · 2026
Tarball containing 1,366,602 protein structures consituting the Phold Search Database 1.36M. All protein structures are `.pdb` format (as per the output for ColabFold/AlphaFold2 or ESMFold). The overall tarball contains one `.tar.gz` tarball per PHROG (from 1-38880), containing all constituent proteins for each PHROG. This will include all original PHROG, enVhog and efam proteins that have been assigned to the PHROG. This was done primarily for organisation and to ensure untarring the top level tarball won't destroy easily-stress filesystems. The overall tarball also contains a separate tarball with PHROG singletons, and one for each of Phold's special databases (anti-CRISPRdb, CARD, DefenseFinder, DGR Reverse Transcriptases, Netflax Toxin-Antitoxins and VFDB) as separate tarballs. 10 March 2026 update - adds defensefinder.tar.gz for all defensefinder structures (missed in the original tarball)
Cited by 0Open access
Marc A. Martí-renom, Valentin A. Ilyin, Andrej Sali · 2001
Cited by 0
Jinn‐Moon Yang · Nucleic Acids Research · 2006
As more protein structures become available and structural genomics efforts provide structural models in a genome-wide strategy, there is a growing need for fast and accurate methods for discovering homologous proteins and evolutionary classifications of newly determined structures. We have developed 3D-BLAST, in part, to address these issues. 3D-BLAST is as fast as BLAST and calculates the statistical significance (E-value) of an alignment to indicate the reliability of the prediction. Using this method, we first identified 23 states of the structural alphabet that represent pattern profiles of the backbone fragments and then used them to represent protein structure databases as structural alphabet sequence databases (SADB). Our method enhanced BLAST as a search method, using a new structural alphabet substitution matrix (SASM) to find the longest common substructures with high-scoring structured segment pairs from an SADB database. Using personal computers with Intel Pentium4 (2.8 GHz) processors, our method searched more than 10 000 protein structures in 1.3 s and achieved a good agreement with search results from detailed structure alignment methods. [3D-BLAST is available at http://3d-blast.life.nctu.edu.tw].
Cited by 119Open access
Kunchur Guruprasad, K. Srikanth, Ala Vasu Babu · International Journal of Biological Macromolecules · 2005
Cited by 4
John S. Garavelli · Nucleic Acids Research · 1999
Because the number of post-translational modifications requiring standardized annotation in the PIR-International Protein Sequence Database was large and steadily increasing, a database of protein structure modifications was constructed in 1993 to assist in producing appropriate feature annotations for covalent binding sites, modified sites and cross-links. In 1995 RESID was publicly released as a PIR-International text database distributed on CD-ROM and accessible through the ATLAS program. In 1998 it was made available on the PIR Web site at http://www-nbrf.georgetown.edu/pir/searchdb++ +.html . The RESID Database includes such information as: systematic and frequently observed alternate names; Chemical s Service registry numbers; atomic formulas and weights; enzyme activities; indicators forN-terminal, C-terminal or peptide chain cross-link modifications; keywords; and literature citations with database cross-references. The RESID Database can be used to predict atomic masses for peptides, and is being enhanced to provide molecular structures for graphical presentation on the PIR Web site using widely available molecular viewing programs.
Cited by 11Open access
Yanchao Wang, Rajshekhar Sunderraman · 2008
Cited by 0
Marc A. Martı́-Renom, Valentin Ilyin, Andrej Săli · Bioinformatics · 2001
http://guitar.rockefeller.edu/DBAli/
Cited by 50Open access
Yuzhen Ye, Adam Godzik · Protein Science · 2004
We have recently developed a flexible protein structure alignment program (FATCAT) that identifies structural similarity, at the same time accounting for flexibility of protein structures. One of the most important applications of a structure alignment method is to aid in functional annotations by identifying similar structures in large structural databases. However, none of the flexible structure alignment methods were applied in this task because of a lack of significance estimation of flexible alignments. In this paper, we developed an estimate of the statistical significance of FATCAT alignment score, allowing us to use it as a database-searching tool. The results reported here show that (1) the distribution of the similarity score of FATCAT alignment between two unrelated protein structures follows the extreme value distribution (EVD), adding one more example to the current collection of EVDs of sequence and structure similarities; (2) introducing flexibility into structure comparison only slightly influences the sensitivity and specificity of identifying similar structures; and (3) the overall performance of FATCAT as a database searching tool is comparable to that of the widely used rigid-body structure comparison programs DALI and CE. Two examples illustrating the advantages of using flexible structure alignments in database searching are also presented. The conformational flexibilities that were detected in the first example may be involved with substrate specificity, and the conformational flexibilities detected in the second example may reflect the evolution of structures by block building.
Cited by 50Open access
Robert F. Service · Science · 2021
DeepMind's AI surveyed the universe of human proteins, making high-confidence predictions for nearly half
Cited by 8
George Bouras · Zenodo (CERN European Organization for Nuclear Research) · 2026
Tarball containing 1,366,602 protein structures consituting the Phold Search Database 1.36M. All protein structures are `.pdb` format (as per the output for ColabFold/AlphaFold2 or ESMFold). The overall tarball contains one `.tar.gz` tarball per PHROG (from 1-38880), containing all constituent proteins for each PHROG. This will include all original PHROG, enVhog and efam proteins that have been assigned to the PHROG. This was done primarily for organisation and to ensure untarring the top level tarball won't destroy easily-stress filesystems. The overall tarball also contains a separate tarball with PHROG singletons, and one for each of Phold's special databases (anti-CRISPRdb, CARD, DefenseFinder, DGR Reverse Transcriptases, Netflax Toxin-Antitoxins and VFDB) as separate tarballs. 10 March 2026 update - adds defensefinder.tar.gz for all defensefinder structures (missed in the original tarball)
Cited by 0Open access