Current Protein Data Bank Research Articles

Deciphering the Cofilin Oligomers via Intermolecular Disulfide Bond Formation: A Coarse-Grained Molecular Dynamics Approach to Understanding Cofilin's Regulation on Actin Filaments.

Cofilin, a key actin-binding protein, orchestrates the dynamics of the actomyosin network through its actin-severing activity and by promoting the recycling of actin monomers. Recent experiments suggest that cofilin forms functionally distinct oligomers via thiol post-translational modifications (PTMs) that promote actin nucleation and assembly. Despite these advances, the structural conformations of cofilin oligomers that modulate actin activity remain elusive because there are combinatorial ways to oxidize thiols in cysteines to form disulfide bonds rapidly. This study employs molecular dynamics simulations to investigate human cofilin 1 as a case study for exploring cofilin dimers via disulfide bond formation. Utilizing a biasing scheme in simulations, we focus on analyzing dimer conformations conducive to disulfide bond formation. Additionally, we explore potential PTMs arising from the examined conformational ensemble. Using the free energy profiling, our simulations unveil a range of probable cofilin dimer structures not represented in current Protein Data Bank entries. These candidate dimers are characterized by their distinct population distributions and relative free energies. Of particular note is a dimer featuring an interface between cysteines 139 and 147 residues, which demonstrates stable free energy characteristics and intriguingly symmetrical geometry. In contrast, the experimentally proposed dimer structure exhibits a less stable free energy profile. We also evaluate frustration quantification based on the energy landscape theory in the protein-protein interactions at the dimer interfaces. Notably, the 39-39 dimer configuration emerges as a promising candidate for forming cofilin tetramers, as substantiated by frustration analysis. Additionally, docking simulations with actin filaments further evaluate the stability of these cofilin dimer-actin complexes. Our findings thus offer a computational framework for understanding the role of thiol PTM of cofilin proteins in regulating oligomerization, and the subsequent cofilin-mediated actin dynamics in the actomyosin network.

Information quantity for secondary structure propensities of protein subsequences in the Protein Data Bank.

Elucidating the principles of sequence–structure relationships of proteins is a long-standing issue in biology. The nature of a short segment of a protein is determined by both the subsequence of the segment itself and its environment. For example, a type of subsequence, the so-called chameleon sequences, can form different secondary structures depending on its environments. Chameleon sequences are considered to have a weak tendency to form a specific structure. Although many chameleon sequences have been identified, they are only a small part of all possible subsequences in the proteome. The strength of the tendency to take a specific structure for each subsequence has not been fully quantified. In this study, we comprehensively analyzed subsequences consisting of four to nine amino acid residues, or N-gram (4≤N≤9), observed in non-redundant sequences in the Protein Data Bank (PDB). Tendencies to form a specific structure in terms of the secondary structure and accessible surface area are quantified as information quantities for each N-gram. Although the majority of observed subsequences have low information quantity due to lack of samples in the current PDB, thousands of N-grams with strong tendencies, including known structural motifs, were found. In addition, machine learning partially predicted the tendency of unknown N-grams, and thus, this technique helps to extract knowledge from the limited number of samples in the PDB.

Structural Versatility of Hepatitis C Virus Proteins: Implications for the Design of Novel Anti-HCV Intervention Strategies.

HCV-linked pathologies represent worldwide health threats. Over the years, an enormous number of independent studies have been devoted to the understanding of the molecular bases of HCV infection. A significant amount of these investigations has been focused on the structural characterization of the virus proteins with the aim of developing structure-based innovative therapeutic approaches. An analysis of the current Protein Data Bank content unravels that the structural biology of the virus has hitherto covered a large fraction of the HCV proteins (75%). The present review recapitulates the state-of-the-art of structural characterizations of HCV individual proteins with a specific focus on their structural versatility/flexibility. This survey indicates there is accumulating evidence that structural flexibility is a common feature among HCV proteins. This versatility can be detected at different structural level i.e. occurrence of alternative oligomeric states and/or of local and global flexibility. Somewhat surprisingly, some disordered or highly flexible regions of HCV proteins, such as the core and the antigenic fragment 412-423 of E2, present highly conserved sequences among the virus genotypes. The overall versatility of HCV proteins plays an important role in host protein recognition, drug resistance mechanisms, and virus escape from the host immunogenic system. Of particular relevance is the emerging idea that HCV uses local structural flexibility as an alternative tool to sequence variability to evade the immune response of the host organism. We believe that concepts emerged from this survey will be important for the development of anti-HCV vaccines that are eagerly needed.

Glycan Reader is improved to recognize most sugar types and chemical modifications in the Protein Data Bank

Glycans play a central role in many essential biological processes. Glycan Reader was originally developed to simplify the reading of Protein Data Bank (PDB) files containing glycans through the automatic detection and annotation of sugars and glycosidic linkages between sugar units and to proteins, all based on atomic coordinates and connectivity information. Carbohydrates can have various chemical modifications at different positions, making their chemical space much diverse. Unfortunately, current PDB files do not provide exact annotations for most carbohydrate derivatives and more than 50% of PDB glycan chains have at least one carbohydrate derivative that could not be correctly recognized by the original Glycan Reader. Glycan Reader has been improved and now identifies most sugar types and chemical modifications (including various glycolipids) in the PDB, and both PDB and PDBx/mmCIF formats are supported. CHARMM-GUI Glycan Reader is updated to generate the simulation system and input of various glycoconjugates with most sugar types and chemical modifications. It also offers a new functionality to edit the glycan structures through addition/deletion/modification of glycosylation types, sugar types, chemical modifications, glycosidic linkages, and anomeric states. The simulation system and input files can be used for CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Glycan Fragment Database in GlycanStructure.Org is also updated to provide an intuitive glycan sequence search tool for complex glycan structures with various chemical modifications in the PDB. http://www.charmm-gui.org/input/glycan and http://www.glycanstructure.org. wonpil@lehigh.edu. Supplementary data are available at Bioinformatics online.

PKNOT v.2: the protein KNOT web server

Knotted proteins have recently received lots of attention due to their interesting topological novelty as well as its puzzling folding mechanisms. We previously published a pKNOT server, which provides a structural database of knotted proteins, analysis tools for detecting and analyzing knotted regions from structures as well as a Java-based 3D graphics viewer for visualizing knotted structures. However, there lacks a convenient platform performing similar tasks directly from ‘protein sequences’. In the current version of the web server, referred to as pKNOT v.2, we implement a homology modeling tool such that the server can now accept protein sequences in addition to 3D structures or Protein Data Bank (PDB) IDs and return knot analysis. In addition, we have updated the database of knotted proteins from the current PDB with a combination of automatic and manual procedure. We believe that the updated pKNOT server with its extended functionalities will provide better service to biologists interested in the research of knotted proteins. The pKNOT v.2 is available from http://pknot.life.nctu.edu.tw/.

Super: a web server to rapidly screen superposable oligopeptide fragments from the protein data bank

Searching for well-fitting 3D oligopeptide fragments within a large collection of protein structures is an important task central to many analyses involving protein structures. This article reports a new web server, Super, dedicated to the task of rapidly screening the protein data bank (PDB) to identify all fragments that superpose with a query under a prespecified threshold of root-mean-square deviation (RMSD). Super relies on efficiently computing a mathematical bound on the commonly used structural similarity measure, RMSD of superposition. This allows the server to filter out a large proportion of fragments that are unrelated to the query; >99% of the total number of fragments in some cases. For a typical query, Super scans the current PDB containing over 80 500 structures (with ∼40 million potential oligopeptide fragments to match) in under a minute. Super web server is freely accessible from: http://lcb.infotech.monash.edu.au/super.

Intra-Chain 3D Segment Swapping Spawns the Evolution of New Multidomain Protein Architectures

Multidomain proteins form in evolution through the concatenation of domains, but structural domains may comprise multiple segments of the chain. In this work, we demonstrate that new multidomain architectures can evolve by an apparent three-dimensional swap of segments between structurally similar domains within a single-chain monomer. By a comprehensive structural search of the current Protein Data Bank (PDB), we identified 32 well-defined segment-swapped proteins (SSPs) belonging to 18 structural families. Nearly 13% of all multidomain proteins in the PDB may have a segment-swapped evolutionary precursor as estimated by more permissive searching criteria. The formation of SSPs can be explained by two principal evolutionary mechanisms: (i) domain swapping and fusion (DSF) and (ii) circular permutation (CP). By large-scale comparative analyses using structural alignment and hidden Markov model methods, it was found that the majority of SSPs have evolved via the DSF mechanism, and a much smaller fraction, via CP. Functional analyses further revealed that segment swapping, which results in two linkers connecting the domains, may impart directed flexibility to multidomain proteins and contributes to the development of new functions. Thus, inter-domain segment swapping represents a novel general mechanism by which new protein folds and multidomain architectures arise in evolution, and SSPs have structural and functional properties that make them worth defining as a separate group.

Searching Protein 3-D Structures for Optimal Structure Alignment Using Intelligent Algorithms and Data Structures

In this paper, we present a novel algorithm for measuring protein similarity based on their 3-D structure (protein tertiary structure). The algorithm used a suffix tree for discovering common parts of main chains of all proteins appearing in the current research collaboratory for structural bioinformatics protein data bank (PDB). By identifying these common parts, we build a vector model and use some classical information retrieval (IR) algorithms based on the vector model to measure the similarity between proteins--all to all protein similarity. For the calculation of protein similarity, we use term frequency × inverse document frequency ( tf × idf ) term weighing schema and cosine similarity measure. The goal of this paper is to introduce new protein similarity metric based on suffix trees and IR methods. Whole current PDB database was used to demonstrate very good time complexity of the algorithm as well as high precision. We have chosen the structural classification of proteins (SCOP) database for verification of the precision of our algorithm because it is maintained primarily by humans. The next success of this paper would be the ability to determine SCOP categories of proteins not included in the latest version of the SCOP database (v. 1.75) with nearly 100% precision.

Geometric similarity between protein–RNA interfaces

A new method is described to measure the geometric similarity between protein-RNA interfaces quantitatively. The method is based on a procedure that dissects the interface geometry in terms of the spatial relationships between individual amino acid nucleotide pairs. Using this technique, we performed an all-on-all comparison of 586 protein-RNA interfaces deposited in the current Protein Data Bank, as the result, an interface-interface similarity score matrix was obtained. Based upon this matrix, hierarchical clustering was carried out which yielded a complete clustering tree for the 586 protein-RNA interfaces. By investigating the organizing behavior of the clustering tree and the SCOP classification of protein partners in complexes, a geometrically nonredundant, diverse data set (representative data set) consisting of 45 distinct protein-RNA interfaces was extracted for the purpose of studying protein-RNA interactions, RNA regulations, and drug design. We classified protein-RNA interfaces into three types. In type I, the families and interface structural classes of the protein partners, as well as the interface geometries are all similar. In type II, the interface geometries and the interface structural classes are similar, whereas the protein families are different. In type III, only the interface geometries are similar but the protein families and the interface structural classes are distinct. Furthermore, we also show two new RNA recognition themes derived from the representative data set.

PDA: an automatic and comprehensive analysis program for protein-DNA complex structures

BackgroundKnowledge of protein-DNA interactions at the structural-level can provide insights into the mechanisms of protein-DNA recognition and gene regulation. Although over 1400 protein-DNA complex structures have been deposited into Protein Data Bank (PDB), the structural details of protein-DNA interactions are generally not available. In addition, current approaches to comparison of protein-DNA complexes are mainly based on protein sequence similarity while the DNA sequences are not taken into account. With the number of experimentally-determined protein-DNA complex structures increasing, there is a need for an automatic program to analyze the protein-DNA complex structures and to provide comprehensive structural information for the benefit of the whole research community.ResultsWe developed an automatic and comprehensive protein-DNA complex structure analysis program, PDA (for protein-DNA complex structure analyzer). PDA takes PDB files as inputs and performs structural analysis that includes 1) whole protein-DNA complex structure restoration, especially the reconstruction of double-stranded DNA structures; 2) an efficient new approach for DNA base-pair detection; 3) systematic annotation of protein-DNA interactions; and 4) extraction of DNA subsequences involved in protein-DNA interactions and identification of protein-DNA binding units. Protein-DNA complex structures in current PDB were processed and analyzed with our PDA program and the analysis results were stored in a database. A dataset useful for studying protein-DNA interactions involved in gene regulation was generated using both protein and DNA sequences as well as the contact information of the complexes. WebPDA was developed to provide a web interface for using PDA and for data retrieval.ConclusionPDA is a computational tool for structural annotations of protein-DNA complexes. It provides a useful resource for investigating protein-DNA interactions. Data from the PDA analysis can also facilitate the classification of protein-DNA complexes and provide insights into rational design of benchmarks. The PDA program is freely available at .

Solution structure of the extraterminal domain of the bromodomain‐containing protein BRD4

BRD4, which is a member of the BET (bromodomains and extraterminal) protein family, interacts preferentially with acetylated chromatin and possesses multiple cellular functions in meiosis, embryonic development, the cell cycle, and transcription. BRD4 and its family members contain two bromodomains known to bind acetylated lysine, and a conserved ET domain whose function is unclear. Here we show the solution structure of the ET domain of mouse BRD4, which provides the first three-dimensional structure of an ET domain in the BET family. We determined the NMR structure of BRD4-ET with a root-mean-square deviation of 0.41 A for the backbone atoms in the structured region of residues 608-676 on the basis of 1793 upper distance limits derived from NOE intensities measured in three-dimensional NOESY spectra. The structure of the BRD4-ET domain comprises three alpha-helices and a characteristic loop region of an irregular but well-defined structure. A DALI search revealed no close structural homologs in the current Protein Data Bank. The BRD4-ET structure has an acidic patch that forms a continuous ridge with a hydrophobic cleft, which may interact with other proteins and/or DNA.

Crystal structure of VC1805, a conserved hypothetical protein from a Vibrio cholerae pathogenicity island, reveals homology to human p32

The severe diarrhoeal disease cholera is caused by the gram-negative bacterium Vibrio cholerae and continues to be a major cause of morbidity and mortality. Like many Vibrio species V. cholerae inhabits an aquatic ecosystem, and most V. cholerae isolates do not possess the ability to cause cholera. Of more than 200 known serotypes, only O1 and O139 serogroups are highly pathogenic and acknowledged to cause epidemic disease.1 The O1 serogroup can be divided into two groups: classical and El Tor, with the first cholera pandemic, beginning in Asia in 1817, and the subsequent five pandemics probably caused by the classical biotype. The seventh and present pandemic began in 1961 caused by the El Tor biotype.2 In 1992 a novel O-serogroup, O-139, emerged to cause epidemic cholera.3 All V. cholerae O1 and O139 serogroups encode the major virulence factors cholera toxin and toxin coregulated pilus, the latter being encoded on a pathogenicity island, named Vibrio Pathogenicity Island-I (VPI-1).4 Several other genomic regions have been identified that occur mainly among epidemic O1 and O139 serogroup isolates, including the so-called Vibrio seventh pandemic island-I (VSP-I) encoding ORFs VC0175 to VC0185, VSP-II encoding ORFs VC0490 to VC0516 and VPI-2 encoding ORFs VC1758 to VC1809.5–7 A microarray analysis of V. cholerae El Tor isolates was used to identify VSP-I and VSP-II encompassing genes that are possibly responsible for the unique characteristics of the seventh pandemic (El Tor) strains.5 An evolutionary genetic analysis of clinical and environmental isolates suggested that pandemic strains arose from a common O1 serogroup progenitor through the successive acquisition of new virulence regions.8 VPI-2 is a 57.3 kb region which consists of 52 ORFs present in all toxigenic O1 and O139 serogroup isolates, but lacking in non-O1 and non-O139 nontoxigenic isolates.6 VPI-2 encodes a type-I restriction modification system, a nan-nag region of genes involved in sialic acid metabolism that may play a nutritional role,9 a sialidase/neuraminidase known to convert intestinal higher-order gangliosides to GM1,10 and a region with homology to Mu phage. In addition, VPI-2 contains several genes that code for hypothetical proteins. One of these proteins, VC1805, is a 148 amino acid protein with no function revealed so far through sequence analysis. VC1805 is also encoded in the reduced 20 kb VPI-2 region present among most O139 serogroup isolates that are missing ORFs VC1761 to VC1788.6 A paralogue of VC1805 exists within VSP-II, VC0508 a 147 residue protein that shares 59% amino acid sequence identity with VC1805. A search of the sequence database using PSI-BLAST reveals homologues of VC1805 in several Vibrio species: V. vulnificus, V. splendidus, V. alginolyticus and V. fischeri, and orthologues in Altermonas macleodii, Aeromonas hydrophilia, and some Shewanella species. In addition, PSI-BLAST reveals that the adjacent hypothetical protein VC1804, with 104 residues, is a homologue of VC1805 sharing 26% sequence identity. Similarly the hypothetical protein VC0509 is a homologue of the adjacent protein VC0508. The four proteins VC0508, VC0509, VC1804, and VC1805 are therefore likely to share the same protein fold and be functionally related. As part of a structural genomics approach to understanding the function of hypothetical proteins within V. cholerae genomic islands we have determined the crystal structure of VC1805 to a resolution of 2.1 A using heavy atom isomorphous replacement. The structure reveals a similarity to the human mitochondrial protein p32 that is known to have several binding partners, including the human complement system protein C1q. This study shows that VC1805 does bind C1q, suggesting potential biological roles for the protein and its homologues.

Functional Linkages Can Reveal Protein Complexes for Structure Determination

In the study of protein complexes, is there a computational method for inferring which combinations of proteins in an organism are likely to form a crystallizable complex? Here we attempt to answer this question, using the Protein Data Bank (PDB) to assess the usefulness of inferred functional protein linkages from the Prolinks database. We find that of the 242 nonredundant prokaryotic protein complexes shared between the current PDB and Prolinks, 44% (107/242) contain proteins linked at high confidence by one or more methods of computed functional linkages. Similarly, high-confidence linkages detect 47% of known Escherichia coli protein complexes, with 45% accuracy. Together these findings suggest that functional linkages will be useful in defining protein complexes for structural studies, including for structural genomics. We offer a database of inferred linkages corresponding to likely protein complexes for some 629,952 pairs of proteins in 154 prokaryotes and archaea.

NQ-Flipper: recognition and correction of erroneous asparagine and glutamine side-chain rotamers in protein structures

The current Protein Data Bank (PDB) contains about 40 000 protein structures with approximately half a million incorrect atom positions resulting from erroneously assigned asparagine (Asn) and glutamine (Gln) rotamers. These errors affect applications in protein structure analysis, modeling and docking and therefore the detection, correction and prevention of such errors is highly desirable. We present NQ-Flipper, a web service based on mean force potentials to automatically detect and correct erroneous Asn and Gln rotamers. The service accepts protein structure files formatted in PDB style or PDB codes. For an Asn/Gln side-chain amide NQ-Flipper computes the total interaction energy with the surrounding atoms as the sum of pairwise atom–atom interaction energies. The energy difference between the original and the alternative rotamers identifies the correct configuration of the amide group. The web service lists the interaction energies of all Asn/Gln residues found in a PDB file and shows the structure and offending residues in an interactive 3D viewer. The corrected protein structure is available for download in various compression formats. The web service is accessible at http://flipper.services.came.sbg.ac.at

Deubiquitination, a New Function of the Severe Acute Respiratory Syndrome Coronavirus Papain-Like Protease?

A new coronavirus has been identified as the infectious agent of severe acute respiratory syndrome (SARS). Although SARS was successfully contained by quarantine measures, the reconstructed itinerary of the virus through 30 countries and its high mortality rate illustrate the global threat that this newly emerging disease represents (17). During the expression of the SARS coronavirus (SCoV) genome, two viral cysteine proteases, a papain-like protease (PLpro) and a chymotrypsin-like protease (3CLpro), process the encoded polyprotein precursor to release most of the proteins required for virus replication. PLpro refers to a domain of nonstructural protein 3, whose boundaries are defined by homology to the papain-like fold (7). The PLpro domain can be regarded as the catalytic core behind PLpro-mediated cleavages, even though processing by PLpros has been reported to be modulated by additional amino acid residues outside of these boundaries (15, 18). The SCoV utilizes a single PLpro, whereas most coronaviruses contain two paralogous enzymes, termed PL1pro and PL2pro (14). PLpros in general are not as well characterized as 3CLpros and have not generated as much interest as pharmaceutical targets. However, further structure-to-function annotations might refocus the attention on PLpros. For this purpose, we mined the current Protein Data Bank (PDB) content using the Structure Prediction Meta Server (http://www.bioinfo.pl/meta ), which assembles state-of-the-art fold recognition methods and provides a consensus sequence-to-structure hyperscore with the 3D-Jury method (4). The only highly reliable prediction (3D-Jury score > 100) obtained for the SCoV PLpro sequence from K1632 to E1847 was the structure of the catalytic core domain of the herpesvirus-associated ubiquitin-specific protease (HAUSP), also known as USP7 (8).

The protein structure prediction problem could be solved using the current PDB library.

For single-domain proteins, we examine the completeness of the structures in the current Protein Data Bank (PDB) library for use in full-length model construction of unknown sequences. To address this issue, we employ a comprehensive benchmark set of 1,489 medium-size proteins that cover the PDB at the level of 35% sequence identity and identify templates by structure alignment. With homologous proteins excluded, we can always find similar folds to native with an average rms deviation (RMSD) from native of 2.5 A with approximately 82% alignment coverage. These template structures often contain a significant number of insertions/deletions. The tasser algorithm was applied to build full-length models, where continuous fragments are excised from the top-scoring templates and reassembled under the guide of an optimized force field, which includes consensus restraints taken from the templates and knowledge-based statistical potentials. For almost all targets (except for 2/1,489), the resultant full-length models have an RMSD to native below 6 A (97% of them below 4 A). On average, the RMSD of full-length models is 2.25 A, with aligned regions improved from 2.5 A to 1.88 A, comparable with the accuracy of low-resolution experimental structures. Furthermore, starting from state-of-the-art structural alignments, we demonstrate a methodology that can consistently bring template-based alignments closer to native. These results are highly suggestive that the protein-folding problem can in principle be solved based on the current PDB library by developing efficient fold recognition algorithms that can recover such initial alignments.

The PDB is a Covering Set of Small Protein Structures

Structure comparisons of all representative proteins have been done. Employing the relative root mean square deviation (RMSD) from native enables the assessment of the statistical significance of structure alignments of different lengths in terms of a Z-score. Two conclusions emerge: first, proteins with their native fold can be distinguished by their Z-score. Second and somewhat surprising, all small proteins up to 100 residues in length have significant structure alignments to other proteins in a different secondary structure and fold class; i.e. 24.0% of them have 60% coverage by a template protein with a RMSD below 3.5 Å and 6.0% have 70% coverage. If the restriction that we align proteins only having different secondary structure types is removed, then in a representative benchmark set of proteins of 200 residues or smaller, 93% can be aligned to a single template structure (with average sequence identity of 9.8%), with a RMSD less than 4 Å, and 79% average coverage. In this sense, the current Protein Data Bank (PDB) is almost a covering set of small protein structures. The length of the aligned region (relative to the whole protein length) does not differ among the top hit proteins, indicating that protein structure space is highly dense. For larger proteins, non-related proteins can cover a significant portion of the structure. Moreover, these top hit proteins are aligned to different parts of the target protein, so that almost the entire molecule can be covered when combined. The number of proteins required to cover a target protein is very small, e.g. the top ten hit proteins can give 90% coverage below a RMSD of 3.5 Å for proteins up to 320 residues long. These results give a new view of the nature of protein structure space, and its implications for protein structure prediction are discussed.

Analysis of gammabeta, betagamma, gammagamma, betabeta multiple turns in proteins.

The number of gamma-turns in a representative protein dataset selected from the current Protein Data Bank has increased almost seven times during the past decade. Eighty percent classic gamma-turns and 57% inverse gamma-turns are associated as multiple turns with either another y-turn or a beta-turn. We refer to these as multiple turns of the (gammabeta)1,2,3 or (betagamma)1,2,3 type, depending upon whether the gamma-turn is before or after the beta-turn along the protein chain, respectively. However, for multiple turns involving only gamma-turns, we follow the nomenclature analogous to that proposed earlier for the multiple (or double) beta-turns. Fifty-eight per cent beta-turns are associated as multiple turns with another beta-turn. We extracted multiple turns from the protein dataset and classified them on the basis of individual gamma- or beta-turn types and the number of overlapping residues. Furthermore, we evaluated the amino acid positional potentials and determined the statistically significant amino acid preferences, hydrogen bond/side-chain interaction preferences in the multiple turns and secondary structure preferences for residues immediately flanking these turns. The results of our analysis would be useful in the modeling, prediction or design of multiple turns in proteins. The amino acid sequence corresponding to the multiple turn, position in the protein chain, PDB Code/chain in which multiple turn is present and the individual turn types constituting the multiple turns are available from our website and this information would also be integrated in our Database of Structural Motifs in Proteins (http://www.cdfd.org.in/dsmp.html).

CIF Applications. VIII. pdb2cif: translating PDB entries into mmCIF format

pdb2cif is a new version of an awk script originally written by P. E. Bourne in 1993 to translate from the 1992 Protein Data Bank (PDB) format to the then-emerging macromolecular Crystallographic Information File (mmCIF) definition. This new version of pdb2cif translates from all current PDB formats, including the 1992 PDB format and the 1996 PDB Atomic Coordinate Entry Format, Version 2.0, to the 1997 mmCIF format as defined in the mmCIF dictionary 1.0.00. The program is provided as an m4 script from which both perl and awk versions can be produced. The program identifies mmCIF entities implicitly by sequence homology among PDB SEQRES records. With minor additions to the dictionary, the resultant mmCIF data-sets are substantially compliant with the mmCIF 1.0.00 dictionary.

New guidelines for deposition of nucleic acid structures

Joel L Sussman and Helen M Berman: From January 1, 1996, data for crystal structures of oligonucleotides should be deposited directly with the Nucleic Acid Database (NDB). Once the data are processed they will be forwarded to the Protein Data Bank (PDB) for deposit in the central single archive. This will simplify current procedures and make the data on nucleic acids available more quickly. Protein–nucleic acid complexes and all NMR structures should continue to be deposited at the PDB. All crystal structure data for DNA and RNA will continue to be available from both the NDB and the PDB.To deposit the data, submit the coordinates, structure factors and current PDB deposition form to:deposit@ndbserver.rutgers.edu.A preprint of the related manuscript should be mailed or faxed to:Anke Gelbin, The Nucleic Acid Database, Department of Chemistry, Rutgers, The State University of New Jersey, PO Box 939, Piscataway, NJ 08855-0939, USA.Fax: (908) 445 5958.