SCOP Classification Research Articles

Analyzing the aromatic-aromatic interactions in proteins: A2ID 2.0

The Aromatic-Aromatic Interactions Database (A2ID) is a comprehensive repository dedicated to documenting aromatic-aromatic (π-π) networks observed in experimentally determined protein structures. The first version of A2ID was reported in 2011 [Int J Biol Macromol, 2011, 48, 540]. It has undergone a series of significant updates, leading to its current version, which focuses on the identification and analysis of 3,444,619 π-π networks from proteins. The geometrical parameters such as centroid-centroid distances (r) and interplanar angles (ϕ) were used to identify and characterize π-π networks. It was observed that among the 84,500 proteins with at least one aromatic π-π network, about 92.50 % of the instances are found to be either 2π (77.34 %) or 3π (15.23 %) networks. The analysis of interacting amino acid pairs in 2π networks indicated a dominance of PHE residues followed by TYR. The updated version of A2ID incorporates analysis of π-π networks based on SCOP2 and ECOD classifiers, in addition to the existing SCOP, CATH, and EC classifications. This expanded scope allows researchers to explore the characteristics and functional implications of π-π networks in protein structures from multiple perspectives. The current version of A2ID along with its extensive dataset and detailed geometric information is publicly accessible using https://acds.neist.res.in/a2idv2.

MoonProt 3.0: an update of the moonlighting proteins database.

MoonProt 3.0 (http://moonlightingproteins.org) is an updated open-access database storing expert-curated annotations for moonlighting proteins. Moonlighting proteins have two or more physiologically relevant distinct biochemical or biophysical functions performed by a single polypeptide chain. Here, we describe an expansion in the database since our previous report in the Database Issue of Nucleic Acids Research in 2018. For this release, the number of proteins annotated has been expanded to over 500 proteins and dozens of protein annotations have been updated with additional information, including more structures in the Protein Data Bank, compared with version 2.0. The new entries include more examples from humans, plants and archaea, more proteins involved in disease and proteins with different combinations of functions. More kinds of information about the proteins and the species in which they have multiple functions has been added, including CATH and SCOP classification of structure, known and predicted disorder, predicted transmembrane helices, type of organism, relationship of the protein to disease, and relationship of organism to cause of disease.

MANET 3.0: Hierarchy and modularity in evolving metabolic networks.

Enzyme recruitment is a fundamental evolutionary driver of modern metabolism. We see evidence of recruitment at work in the metabolic Molecular Ancestry Networks (MANET) database, an online resource that integrates data from KEGG, SCOP and structural phylogenomic reconstruction. The database, which was introduced in 2006, traces the deep history of the structural domains of enzymes in metabolic pathways. Here we release version 3.0 of MANET, which updates data from KEGG and SCOP, links enzyme and PDB information with PDBsum, and traces evolutionary information of domains defined at fold family level of SCOP classification in metabolic subnetwork diagrams. Compared to SCOP folds used in the previous versions, fold families are cohesive units of functional similarity that are highly conserved at sequence level and offer a 10-fold increase of data entries. We surveyed enzymatic, functional and catalytic site distributions among superkingdoms showing that ancient enzymatic innovations followed a biphasic temporal pattern of diversification typical of module innovation. We grouped enzymatic activities of MANET into a hierarchical system of subnetworks and mesonetworks matching KEGG classification. The evolutionary growth of these modules of metabolic activity was studied using bipartite networks and their one-mode projections at enzyme, subnetwork and mesonetwork levels of organization. Evolving metabolic networks revealed patterns of enzyme sharing that transcended mesonetwork boundaries and supported the patchwork model of metabolic evolution. We also explored the scale-freeness, randomness and small-world properties of evolving networks as possible organizing principles of network growth and diversification. The network structure shows an increase in hierarchical modularity and scale-free behavior as metabolic networks unfold in evolutionary time. Remarkably, this evolutionary constraint on structure was stronger at lower levels of metabolic organization. Evolving metabolic structure reveals a ‘principle of granularity’, an evolutionary increase of the cohesiveness of lower-level parts of a hierarchical system. MANET is available at http://manet.illinois.edu.

A component analysis of the free energies of folding of 35 proteins: A consensus view on the thermodynamics of folding at the molecular level.

What factors favor protein folding? This is a textbook question. Parsing the experimental free energies of folding/unfolding into diverse enthalpic and entropic components of solute and solvent favoring or disfavoring folding is not an easy task. In this study, we present a computational protocol for estimating the free energy contributors to protein folding semi-quantitatively using ensembles of unfolded and native states generated via molecular dynamics simulations. We tested the methodology on 35 proteins with diverse structural motifs and sizes and found that the calculated free energies correlate well with experiment (correlation coefficient ∼ 0.85), enabling us to develop a consensus view of the energetics of folding. As a more sensitive test of the methodology, we also investigated the free energies of folding of an additional 33 single point mutants and obtained a correlation coefficient of 0.8. A notable observation is that the folding free energy components appear to carry signatures of the fold (SCOP classification) of the protein. © 2017 Wiley Periodicals, Inc.

The early history and emergence of molecular functions and modular scale-free network behavior.

The formation of protein structural domains requires that biochemical functions, defined by conserved amino acid sequence motifs, be embedded into a structural scaffold. Here we trace domain history onto a bipartite network of elementary functional loop sequences and domain structures defined at the fold superfamily level of SCOP classification. The resulting ‘elementary functionome’ network and its loop motif and structural domain graph projections create evolutionary ‘waterfalls’ describing the emergence of primordial functions. Waterfalls reveal how ancient loops are shared by domain structures in two initial waves of functional innovation that involve founder ‘p-loop’ and ‘winged helix’ domain structures. They also uncover a dynamics of modular motif embedding in domain structures that is ongoing, which transfers ‘preferential’ cooption properties of ancient loops to emerging domains. Remarkably, we find that the emergence of molecular functions induces hierarchical modularity and power law behavior in network evolution as the network of motifs and structures expand metabolic pathways and translation.

Customised fragments libraries for protein structure prediction based on structural class annotations.

BackgroundSince experimental techniques are time and cost consuming, in silico protein structure prediction is essential to produce conformations of protein targets. When homologous structures are not available, fragment-based protein structure prediction has become the approach of choice. However, it still has many issues including poor performance when targets’ lengths are above 100 residues, excessive running times and sub-optimal energy functions. Taking advantage of the reliable performance of structural class prediction software, we propose to address some of the limitations of fragment-based methods by integrating structural constraints in their fragment selection process.ResultsUsing Rosetta, a state-of-the-art fragment-based protein structure prediction package, we evaluated our proposed pipeline on 70 former CASP targets containing up to 150 amino acids. Using either CATH or SCOP-based structural class annotations, enhancement of structure prediction performance is highly significant in terms of both GDT_TS (at least +2.6, p-values < 0.0005) and RMSD (−0.4, p-values < 0.005). Although CATH and SCOP classifications are different, they perform similarly. Moreover, proteins from all structural classes benefit from the proposed methodology. Further analysis also shows that methods relying on class-based fragments produce conformations which are more relevant to user and converge quicker towards the best model as estimated by GDT_TS (up to 10% in average). This substantiates our hypothesis that usage of structurally relevant templates conducts to not only reducing the size of the conformation space to be explored, but also focusing on a more relevant area.ConclusionsSince our methodology produces models the quality of which is up to 7% higher in average than those generated by a standard fragment-based predictor, we believe it should be considered before conducting any fragment-based protein structure prediction. Despite such progress, ab initio prediction remains a challenging task, especially for proteins of average and large sizes. Apart from improving search strategies and energy functions, integration of additional constraints seems a promising route, especially if they can be accurately predicted from sequence alone.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0576-2) contains supplementary material, which is available to authorized users.

Classification of α-Helical Membrane Proteins Using Predicted Helix Architectures

Despite significant methodological advances in protein structure determination high-resolution structures of membrane proteins are still rare, leaving sequence-based predictions as the only option for exploring the structural variability of membrane proteins at large scale. Here, a new structural classification approach for α-helical membrane proteins is introduced based on the similarity of predicted helix interaction patterns. Its application to proteins with known 3D structure showed that it is able to reliably detect structurally similar proteins even in the absence of any sequence similarity, reproducing the SCOP and CATH classifications with a sensitivity of 65% at a specificity of 90%. We applied the new approach to enhance our comprehensive structural classification of α-helical membrane proteins (CAMPS), which is primarily based on sequence and topology similarity, in order to find protein clusters that describe the same fold in the absence of sequence similarity. The total of 151 helix architectures were delineated for proteins with more than four transmembrane segments. Interestingly, we observed that proteins with 8 and more transmembrane helices correspond to fewer different architectures than proteins with up to 7 helices, suggesting that in large membrane proteins the evolutionary tendency to re-use already available folds is more pronounced.

Protein-Peptide Complex Prediction through Fragment Interaction Patterns

The number of protein-peptide interactions in a cell isso large that experimental determination of all these complex structures would be a daunting task. Although homology modeling and refinement protocols have vastly improved the number and quality of predicted structural models, ab initio methods are still challenged by both the large number of possible docking sites and the conformational space accessible to flexible peptides. We present a method that addresses these challenges by sampling the entire accessible surface of a protein with a reduced conformational space of interacting backbone fragment pairs from unrelated structures. We demonstrate its potential by predicting ab initio the bound structure for a variety of protein-peptide complexes. In addition, we show the potential of our method forthe discovery of domain interaction sites and domain-domain docking.

Origin and evolution of protein fold designs inferred from phylogenomic analysis of CATH domain structures in proteomes.

The spatial arrangements of secondary structures in proteins, irrespective of their connectivity, depict the overall shape and organization of protein domains. These features have been used in the CATH and SCOP classifications to hierarchically partition fold space and define the architectural make up of proteins. Here we use phylogenomic methods and a census of CATH structures in hundreds of genomes to study the origin and diversification of protein architectures (A) and their associated topologies (T) and superfamilies (H). Phylogenies that describe the evolution of domain structures and proteomes were reconstructed from the structural census and used to generate timelines of domain discovery. Phylogenies of CATH domains at T and H levels of structural abstraction and associated chronologies revealed patterns of reductive evolution, the early rise of Archaea, three epochs in the evolution of the protein world, and patterns of structural sharing between superkingdoms. Phylogenies of proteomes confirmed the early appearance of Archaea. While these findings are in agreement with previous phylogenomic studies based on the SCOP classification, phylogenies unveiled sharing patterns between Archaea and Eukarya that are recent and can explain the canonical bacterial rooting typically recovered from sequence analysis. Phylogenies of CATH domains at A level uncovered general patterns of architectural origin and diversification. The tree of A structures showed that ancient structural designs such as the 3-layer (αβα) sandwich (3.40) or the orthogonal bundle (1.10) are comparatively simpler in their makeup and are involved in basic cellular functions. In contrast, modern structural designs such as prisms, propellers, 2-solenoid, super-roll, clam, trefoil and box are not widely distributed and were probably adopted to perform specialized functions. Our timelines therefore uncover a universal tendency towards protein structural complexity that is remarkable.

Measuring and comparing structural fluctuation patterns in large protein datasets

The function of a protein depends not only on its structure but also on its dynamics. This is at the basis of a large body of experimental and theoretical work on protein dynamics. Further insight into the dynamics-function relationship can be gained by studying the evolutionary divergence of protein motions. To investigate this, we need appropriate comparative dynamics methods. The most used dynamical similarity score is the correlation between the root mean square fluctuations (RMSF) of aligned residues. Despite its usefulness, RMSF is in general less evolutionarily conserved than the native structure. A fundamental issue is whether RMSF is not as conserved as structure because dynamics is less conserved or because RMSF is not the best property to use to study its conservation. We performed a systematic assessment of several scores that quantify the (dis)similarity between protein fluctuation patterns. We show that the best scores perform as well as or better than structural dissimilarity, as assessed by their consistency with the SCOP classification. We conclude that to uncover the full extent of the evolutionary conservation of protein fluctuation patterns, it is important to measure the directions of fluctuations and their correlations between sites. Nathalie.Reuter@mbi.uib.no Supplementary data are available at Bioinformatics Online.

CytokineDB and CytReD@CROM

Motivations. The cytokines family is composed by many proteins that need to bind to specific receptors on the cell surface to perform their biological function. This binding can stimulate both the expression of receptors for cytokines and the production of other cytokines that in turn act on other target cells. On the whole, the totality of the cytokines and of their interactions in and around biological cells is defined with the 'cytokinome" term. Often these molecules are involved in cancer-related chronic inflammation and play a pivotal role in promoting tumorigenesis and metastatic processes. Therefore, we have developed i) CytokineDB that is an annotated database that collects biological information regarding the cytokines family in human and ii) CytReD (Cytokine Receptor Database) that collects biological information regarding the human cytokine receptor families and their related ligands and can be used by researchers as well as physicians and clinicians to identify what cytokines are reported in the literature as significant in a given disease. Methods. Some databases were used to collect gene and protein data regarding the human cytokine family: Pubmed and OMIM for biological activity, ENSEMBL for gene records, SRS retrieval system for searching DNA and protein sequences, PDB for three-dimensional structures, and FoldIndex for the prediction of disorder propensity of the cytokine receptors. Search form of CytokineDB is based on a CGI script written in PERL language. CytReD is developed using a dynamic content management system Drupal version 6.17 and scripting language PHP version 5.3.2. JMOL visualization package is embedded for cytokine receptors three dimensional structure in detailed information page. Results. In CytokineDB the human cytokine family was subdivided in 12 sub-families and the user can click on the image near to the name of each cytokine family and have a short description of the structures of these families. In each subgroup, all the known cytokines were inserted. The user can select a sub-family and choose the protein of which want have information. The output page for each cytokine reports the cell type, where the protein is located, Entrez Gene, the target receptors and cells, the main effects, the description of biological activity, the references, EMBL code, Ensembl protein_coding gene, the number of transcripts, the number and the code of associated peptides, the number and the code of exons, the chromosome location, CCDS, RefSeq DNA, RefSeq peptide, Protein ID, UniProt code, Sequence isoforms, amino acid sequence, PDB code, CATH and SCOP classifications, and structural features. In CytReD the user can search by selecting from four options: cytokine receptor name, ligands, cytokine Family or disease. The result page is divided into description, sequences and accession codes and other biological information along with the references. Description provides information about the biological description, the name of ligands linked to CytokineDB database, the related synonyms, cell type on which the protein is expressed, the role and the diseases in which it is involved linked to PharmaGKB database. The sequences and accession codes section contains the nucleotide sequence, protein sequence and its isoforms. In particular, the accession codes are linked to the other important databases like Uniprot, Protein Databank, Entrez gene. In the biological information section there are information related to chromosome localization, Ensemble protein coding gene, the codes of transcripts, associated peptides, references, CATH and SCOP classifications, and quantitative analysis like number of residues, isoelectric field, number of positively and negatively charged residues, unfoldability index, and mean hydropathicity value(GRAVY). CytokineDB and CytReD are part of a broader project aimed to develop tools and portals able to be useful supports for a reliable predictive medicine.

Characterizing protein motions from structure

To clarify the extent structure plays in determining protein dynamics, a comparative study is made using three models that characterize native state dynamics of single domain proteins starting from known structures taken from four distinct SCOP classifications. A geometrical simulation using the framework rigidity optimized dynamics algorithm (FRODA) based on rigid cluster decomposition is compared to the commonly employed elastic network model (specifically the Anisotropic Network Model ANM) and molecular dynamics (MD) simulation. The essential dynamics are quantified by a mode subspace constructed from ANM and a principal component analysis (PCA) on FRODA and MD trajectories. Aggregate conformational ensembles are constructed to provide a basis for quantitative comparisons between FRODA runs using different parameter settings to critically assess how the predictions of essential dynamics depend on a priori arbitrary user-defined distance constraint rules. We established a range of physicality for these parameters. Surprisingly, FRODA maintains greater intra-consistent results than obtained from MD trajectories, comparable to ANM. Additionally, a mode subspace is constructed from PCA on an exemplar set of myoglobin structures from the Protein Data Bank. Significant overlap across the three model subspaces and the experimentally derived subspace is found. While FRODA provides the most robust sampling and characterization of the native basin, all three models give similar dynamical information of a native state, further demonstrating that structure is the key determinant of dynamics.

Signatures of spatial organizations – From the universe of proteins to the universe in general

AbstractPattern generation and recognition in 3-D space have been of wide interest in fields varying from atomic and molecular structures to organization of celestial objects. Our thoughts towards investigating patterns (and elucidation of geometrical rules) followed by a known number of points with specific or specified coordinates are guided by the assumption/belief that there must be some thermodynamic and/or kinetic basis leading to the organization of coordinates of the known points. In terms of biomolecular structures, the DNA double helix is one of the strongest examples of such structures with coordinates of atoms in the phosphate backbone and the side-chains provide a helical geometrical configuration. It needs to be appreciated (and emphasized) that, regardless of the system in consideration (e.g. molecular structures or celestial objects), the sample space of known points utilized to investigate underlying patterns is comprised of only remnants or subsets of a larger distribution of points that is either too large to sample, or presence of which has been indicated but not conclusively detected, or presence of which is known to have occurred at an earlier time and some data exists to show disappearance of some points in the sample space due to certain events leading to their deletion. For example, it is now well established that all DNA is not double helical and there are a variety of forms of DNA structures. In this work, I show conclusively it is possible to decipher the origin of apparently random points in space that assemble to give a diversity of structures. I have developed spatial maps for distinguishing different clusters of coordinates originating from well defined geometries. Utilizing the maps, I mathematically prove the existence of a recently discovered universal spatial organization of naturally occurring folded proteins, regardless of their fold or function as categorized by the SCOP classification system. Development of the spatial maps in this work could prove to be a major step in not just advancing our understanding of biomolecular structures, but also arrangement of spatial objects that are apparent in their present coordinate space following some major reorganizing events in the history of our universe.

BAR-PLUS: the Bologna Annotation Resource Plus for functional and structural annotation of protein sequences

We introduce BAR-PLUS (BAR+), a web server for functional and structural annotation of protein sequences. BAR+ is based on a large-scale genome cross comparison and a non-hierarchical clustering procedure characterized by a metric that ensures a reliable transfer of features within clusters. In this version, the method takes advantage of a large-scale pairwise sequence comparison of 13 495 736 protein chains also including 988 complete proteomes. Available sequence annotation is derived from UniProtKB, GO, Pfam and PDB. When PDB templates are present within a cluster (with or without their SCOP classification), profile Hidden Markov Models (HMMs) are computed on the basis of sequence to structure alignment and are cluster-associated (Cluster-HMM). Therefrom, a library of 10 858 HMMs is made available for aligning even distantly related sequences for structural modelling. The server also provides pairwise query sequence–structural target alignments computed from the correspondent Cluster-HMM. BAR+ in its present version allows three main categories of annotation: PDB [with or without SCOP (*)] and GO and/or Pfam; PDB (*) without GO and/or Pfam; GO and/or Pfam without PDB (*) and no annotation. Each category can further comprise clusters where GO and Pfam functional annotations are or are not statistically significant. BAR+ is available at http://bar.biocomp.unibo.it/bar2.0.

A Model Comparison for Characterizing Protein Motions from Structure

To understand the extent structure plays in determining protein dynamics, a comparative study is made using three computational models that characterize native state dynamics starting from known protein structures taken from four distinct SCOP classifications. A geometrical simulation [1] (FRODA) is performed based on an initial rigid cluster decomposition using FIRST [2], and the results are compared to the commonly employed elastic network model (ANM) and molecular dynamics (MD) simulations. The essential dynamics is quantified by a direct analysis of a mode subspace constructed from ANM and a principal component analysis (PCA) on both the FRODA and MD trajectories using root mean square inner (RMSIP) product and principal angles (PA). Relative subspace sizes and overlaps are visualized using the projection of displacement vectors on the model modes. Additionally, a mode subspace is constructed from PCA on an exemplar set of X-ray crystal structures in order to determine similarly with respect to the generated ensembles. Our quantitative analysis reveals there is significant overlap across the three model subspaces and the model independent subspace. The subspaces generated from all three models were found to have high overlap for all four SCOP classes of proteins investigated, although FRODA provided the most robust sampling of the native basin. These results indicate that structure is the key determinant for native state dynamics. This work is supported by NIH grant 1R21HL093531.[1] Wells S, Menor S, Hespenheide B, Thorpe MF: Constrained geometric simulation of diffusive motion in proteins. Phys Biol, 2:S127-S136 (2005).[2] Jacobs DJ, Rader A, Kuhn LA, Thorpe MF: Graph Theory Predictions of Protein Flexibility. Proteins, 44:150-65 (2001).

Characterizing the regularity of tetrahedral packing motifs in protein tertiary structure

While protein secondary structure is well understood, representing the repetitive nature of tertiary packing in proteins remains difficult. We have developed a construct called the relative packing group (RPG) that applies the clique concept from graph theory as a natural basis for defining the packing motifs in proteins. An RPG is defined as a clique of residues, where every member contacts all others as determined by the Delaunay tessellation. Geometrically similar RPGs define a regular element of tertiary structure or tertiary motif (TerMo). This intuitive construct provides a simple approach to characterize general repetitive elements of tertiary structure. A dataset of over 4 million tetrahedral RPGs was clustered using different criteria to characterize the various aspects of regular tertiary structure in TerMos. Grouping this data within the SCOP classification levels of Family, Superfamily, Fold, Class and PDB showed that similar packing is shared across different folds. Classification of RPGs based on residue sequence locality reveals topological preferences according to protein sizes and secondary structure. We find that larger proteins favor RPGs with three local residues packed against a non-local residue. Classifying by secondary structure, helices prefer mostly local residues, sheets favor at least two local residues, while turns and coil populate with more local residues. To depict these TerMos, we have developed 2 complementary and intuitive representations: (i) Dirichlet process mixture density estimation of the torsion angle distributions and (ii) kernel density estimation of the Cartesian coordinate distribution. The TerMo library and representations software are available upon request.

RefProtDom: a protein database with improved domain boundaries and homology relationships

Summary: RefProtDom provides a set of divergent query domains, originally selected from Pfam, and full-length proteins containing their homologous domains, with diverse architectures, for evaluating pair-wise and iterative sequence similarity searches. Pfam homology and domain boundary annotations in the target library were supplemented using local and semi-global searches, PSI-BLAST searches, and SCOP and CATH classifications.Availability: RefProtDom is available from http://faculty.virginia.edu/wrpearson/fasta/PUBS/gonzalez09aContact: miledywgonzalez@gmail.com; pearson@virginia.edu

Automatic structure classification of small proteins using random forest

BackgroundRandom forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs.ResultsRandom forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP Class, Fold, Super-family or Family levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC) ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases.ConclusionsThe utility of random forest in classifying domains from the place-holder classes of SCOP to the true Class, Fold, Super-family or Family levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.

The C-terminal α–α superhelix of Pat is required for mRNA decapping in metazoa

Pat proteins regulate the transition of mRNAs from a state that is translationally active to one that is repressed, committing targeted mRNAs to degradation. Pat proteins contain a conserved N-terminal sequence, a proline-rich region, a Mid domain and a C-terminal domain (Pat-C). We show that Pat-C is essential for the interaction with mRNA decapping factors (i.e. DCP2, EDC4 and LSm1–7), whereas the P-rich region and Mid domain have distinct functions in modulating these interactions. DCP2 and EDC4 binding is enhanced by the P-rich region and does not require LSm1–7. LSm1–7 binding is assisted by the Mid domain and is reduced by the P-rich region. Structural analysis revealed that Pat-C folds into an α–α superhelix, exposing conserved and basic residues on one side of the domain. This conserved and basic surface is required for RNA, DCP2, EDC4 and LSm1–7 binding. The multiplicity of interactions mediated by Pat-C suggests that certain of these interactions are mutually exclusive and, therefore, that Pat proteins switch decapping partners allowing transitions between sequential steps in the mRNA decapping pathway.

GIS: a comprehensive source for protein structure similarities

A web service for analysis of protein structures that are sequentially or non-sequentially similar was generated. Recently, the non-sequential structure alignment algorithm GANGSTA+ was introduced. GANGSTA+ can detect non-sequential structural analogs for proteins stated to possess novel folds. Since GANGSTA+ ignores the polypeptide chain connectivity of secondary structure elements (i.e. α-helices and β-strands), it is able to detect structural similarities also between proteins whose sequences were reshuffled during evolution. GANGSTA+ was applied in an all-against-all comparison on the ASTRAL40 database (SCOP version 1.75), which consists of >10 000 protein domains yielding about 55 × 106 possible protein structure alignments. Here, we provide the resulting protein structure alignments as a public web-based service, named GANGSTA+ Internet Services (GIS). We also allow to browse the ASTRAL40 database of protein structures with GANGSTA+ relative to an externally given protein structure using different constraints to select specific results. GIS allows us to analyze protein structure families according to the SCOP classification scheme. Additionally, users can upload their own protein structures for pairwise protein structure comparison, alignment against all protein structures of the ASTRAL40 database (SCOP version 1.75) or symmetry analysis. GIS is publicly available at http://agknapp.chemie.fu-berlin.de/gplus.