Structures In Protein Data Bank Research Articles

Conformational Variability Prediction of Influenza Virus Hemagglutinins with Amino Acid Mutations Using Supersecondary Structure Code.

Supersecondary structure code (SSSC), which is represented as a conformation term using the letters "H," "S," "T," and "D" for each amino acid peptide unit, can be utilized to look up supersecondary structure motifs like a helix-hairpin-helix (HhH) motif as character strings from the Protein Data Bank (PDB) structure files. The deep neural network-based conformational variability prediction system of protein structures (SSSCPreds) can simultaneously predict locations of protein flexibility or rigidity and the shapes of those regions with high accuracy. The sequence flexibility/rigidity map obtained from SSSCPreds has the prediction accuracy enough to discuss the correlation with the sequence-to-phenotype ones by mutations. In this chapter, the protocol of conformational variability prediction methods using SSSC, including the analysis of influenza virus hemagglutinins with amino acid mutations, is described. The conformational variability pattern of hemagglutinins for human influenza A H1N1pdm extremely resembles that of avian influenza A H5N1, except for the furin cleavage site of H5N1. The transition of virus variants is visually understandable from the maps, including the sharply increased flexibility with the insight into the recent unseasonal influenza epidemics in Japan. The prediction accuracy of conformational variability is low for proteins at pH5 with very few measurement conditions, so this is the limitation of the conformational variability prediction method.

The Role of Vimentin Peptide Citrullination in the Structure and Dynamics of HLA-DRB1 Rheumatoid Arthritis Risk-Associated Alleles.

Citrullination, a post-translational modification (PTM), plays a critical role in rheumatoid arthritis (RA) by triggering immune responses to citrullinated self-antigens. Some HLA-DRB1 genes encode molecules with the shared epitope (QKRAA/QRRAA) sequence in the peptide-binding groove which preferentially presents citrulline-modified peptides, like vimentin, that intensifies the immune response in RA. In this study, we used computational approaches to evaluate intermolecular interactions between vimentin peptide-ligands (with/without PTM) and HLA-DRB1 alleles associated with a significantly increased risk for RA development. Crystal structures for HLA-DRB1*04:01, *04:04, and *04:05 bound to citrullinated peptides (PDB ID: 4MCY, 4MD5, 6BIR) were retrieved from the Protein Data Bank and non-citrullinated 3D structures were generated by mutating citrulline to arginine. The pHLA complexes were submitted to four rounds (50 ns each) of molecular dynamic simulations (MD) with Gromacs v.2022. Our results show that citrulline strengthens the interaction between vimentin and the HLA-DRB1 molecules, therefore impacting both the peptide affinity to the HLAs and pHLA stability; it also induces more intermolecular hydrogen bond formation during MD in the pHLA. Citrulline prevents repulsion between amino acid 71β and the P4-residue of native vimentin. Thus, vimentin citrullination seems to affect pHLA binding and dynamics, which may influence RA-related immune responses.

CATH v4.4: major expansion of CATH by experimental and predicted structural data.

CATH (https://www.cathdb.info) is a structural classification database that assigns domains to the structures in the Protein Data Bank (PDB) and AlphaFold Protein Structure Database (AFDB) and adds layers of biological information, including homology and functional annotation. This article covers developments in the CATH classification since 2021. We report the significant expansion of structural information (180-fold) for CATH superfamilies through classification of PDB domains and predicted domain structures from the Encyclopedia of Domains (TED) resource. TED provides information on predicted domains in AFDB. CATH v4.4 represents an expansion of ∼64 844 experimentally determined domain structures from PDB. We also present a mapping of ∼90 million predicted domains from TED to CATH superfamilies. New PDB and TED data increases the number of superfamilies from 5841 to 6573, folds from 1349 to 2078 and architectures from 41 to 77. TED data comprises predicted structures, so these new folds and architectures remain hypothetical until experimentally confirmed. CATH also classifies domains into functional families (FunFams) within a superfamily. We have updated sequences in FunFams by scanning FunFam-HMMs against UniProt release 2024_02, giving a 276% increase in FunFams coverage. The mapping of TED structural domains has resulted in a 4-fold increase in FunFams with structural information.

Restoring protein glycosylation with GlycoShape

Despite ground-breaking innovations in experimental structural biology and protein structure prediction techniques, capturing the structure of the glycans that functionalize proteins remains a challenge. Here we introduce GlycoShape (https://glycoshape.org), an open-access glycan structure database and toolbox designed to restore glycoproteins to their native and functional form in seconds. The GlycoShape database counts over 500 unique glycans so far, covering the human glycome and augmented by elements from a wide range of organisms, obtained from 1 ms of cumulative sampling from molecular dynamics simulations. These structures can be linked to proteins with a robust algorithm named Re-Glyco, directly compatible with structural data in open-access repositories, such as the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and AlphaFold Protein Structure Database, or own. The quality, performance and broad applicability of GlycoShape is demonstrated by its ability to predict N-glycosylation occupancy, scoring a 93% agreement with experiment, based on screening all proteins in the PDB with a corresponding glycoproteomics profile, for a total of 4,259 N-glycosylation sequons.

Prediction of protein-ligand binding sites modulating activity of MAST protein kinases

Aim. Identification of the protein-ligand binding sites, that may be the target of compounds, affecting individual human protein kinases of the MAST family (MAST1, 2, 3, 4 and MASTL / GWL). Methods. Literature and database search. Comparison of protein and ligand structures. Protein structure modeling, structural superimposition, etc. Results. The structural alignment demonstrates significant similarity of catalytic domains in MAST1, 2, 3, 4 and MASTL (GWL). It justifies transferring of reference ligands from PDB structures to human MASTs, discovering potential sites of ligand binding. 13 sites of ligand-binding were specified based on refrence ligands, transferred from RCSB Protein Data Bank structures, and differences in sites amino acid composition of MAST family members were discovered. Сonclusions. Based on the differences in the amino acid composition of the studied pockets in MAST1, 2, 3, 4 and MASTL (GWL), the sites B, C, D, E, F, were selected for further study and virtual screening for new selective inhibitors of individual members of MAST protein kinase family.

Mut-Map: Comprehensive Computational Pipeline for Structural Mapping and Analysis of Cancer-Associated Mutations.

Understanding the functional impact of genetic mutations on protein structures is essential for advancing cancer research and developing targeted therapies. The main challenge lies in accurately mapping these mutations to protein structures and analysing their effects on protein function. To address this, Mut-Map (https://genemutation.org/) is a comprehensive computational pipeline designed to integrate mutation data from the Catalogue Of Somatic Mutations In Cancer database with protein structural data from the Protein Data Bank and AlphaFold models. The pipeline begins by taking a UniProt ID and proceeds through mapping corresponding Protein Data Bank structures, renumbering residues, and assessing disorder percentages. It then overlays mutation data, categorizes mutations based on structural context, and visualizes them using advanced tools like MolStar. This approach allows for a detailed analysis of how mutations may disrupt protein function by affecting key regions such as DNA interfaces, ligand-binding sites, and dimer interactions. To validate the pipeline, a case study on the TP53 gene, a critical tumour suppressor often mutated in cancers, was conducted. The analysis highlighted the most frequent mutations occurring at the DNA-binding interface, providing insights into their potential role in cancer progression. Mut-Map offers a powerful resource for elucidating the structural implications of cancer-associated mutations, paving the way for more targeted therapeutic strategies and advancing our understanding of protein structure-function relationships.

Evaluation of AlphaFold 3's Protein-Protein Complexes for Predicting Binding Free Energy Changes upon Mutation.

AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug discovery and protein engineering, marking a major step toward comprehensive, automated protein structure prediction. However, independent validation of AF3's predictions is necessary. In this work, we evaluate AF3 complex structures using the SKEMPI 2.0 database which involves 317 protein-protein complexes and 8338 mutations. AF3 complex structures when applied to the most advanced TDL model, MT-TopLap (MultiTask-Topological Laplacian), give rise to a very good Pearson correlation coefficient of 0.86 for predicting protein-protein binding free energy changes upon mutation, which is slightly less than the 0.88 achieved earlier with the Protein Data Bank (PDB) structures. Nonetheless, AF3 complex structures led to a 8.6% increase in the prediction RMSE compared to original PDB complex structures. Additionally, some of AF3's complex structures have large errors, which were not captured in its ipTM performance metric. Finally, it is found that AF3's complex structures are not reliable for intrinsically flexible regions or domains.

An efficient and simple approach for synthesizing indazole compounds using palladium-catalyzed Suzuki-Miyaura cross-coupling.

A series of indazole derivatives (6a-6i and 7a-7i) has been synthesized using Suzuki Miyaura cross-coupling with a palladium catalyst from readily available starting materials. An efficient and reliable methodology was employed for the synthesis, and the compounds were thoroughly characterized using 1H NMR, 13C NMR, FT-IR, and HRMS analysis to confirm their structural integrity and purity. Density function theory (DFT) computation identified four compounds (6g, 6h, 7g, and 7h) with significant energy band gaps. Additionally, the molecular electrostatic potential study highlighted the distinct electrical characteristics of these indazole molecules. Subsequent molecular docking investigations were carried out using the AUTODOCK method with two separate protein data bank (PDB) structures (6FEW, 4WA9) involved in renal cancer pathways. The results showed that eight substances PDB: 6FEW (6g, 6h, 7g, and 7h) and PDB: 4WA9 (6a, 6c, and 7c, 7g) had the highest binding energies, indicating their potential as therapeutic agents for treating kidney cancer.

CASTpFold: Computed Atlas of Surface Topography of the universe of protein Folds.

Geometric and topological properties of protein structures, including surface pockets, interior cavitiesand cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins(CASTp) is a widely used web server for locating, delineating, and measuring these geometric and topological properties of protein structures. Recent developments in AI-based protein structure prediction such as AlphaFold2 (AF2) have significantly expanded our knowledge on protein structures. Here we present CASTpFold, a continuation of CASTp that provides accurate and comprehensive identifications and quantifications of protein topography. It now provides (i) results on an expanded database of proteins, including the Protein Data Bank(PDB) and non-singleton representative structures of AlphaFold2 structures, covering 183 million AF2 structures; (ii) functional pockets prediction with corresponding Gene Ontology (GO) terms or Enzyme Commission (EC) numbers for AF2-predicted structuresand (iii) pocket similarity search function for surface and protein-protein interface pockets. The CASTpFold web server is freely accessible at https://cfold.bme.uic.edu/castpfold/.

From PDB files to protein features: a comparative analysis of PDB bind and STCRDAB datasets.

Understanding protein structures is crucial for various bioinformatics research, including drug discovery, disease diagnosis, and evolutionary studies. Protein structure classification is a critical aspect of structural biology, where supervised machine learning algorithms classify structures based on data from databases such as Protein Data Bank (PDB). However, the challenge lies in designing numerical embeddings for protein structures without losing essential information. Although some effort has been made in the literature, researchers have not effectively and rigorously combined the structural and sequence-based features for efficient protein classification to the best of our knowledge. To this end, we propose numerical embeddings that extract relevant features for protein sequences fetched from PDB structures from popular datasets such as PDB Bind and STCRDAB. The features are physicochemical properties such as aromaticity, instability index, flexibility, Grand Average of Hydropathy (GRAVY), isoelectric point, charge at pH, secondary structure fracture, molar extinction coefficient, and molecular weight. We also incorporate scaling features for the sliding windows (e.g., k-mers), which include Kyte and Doolittle (KD) hydropathy scale, Eisenberg hydrophobicity scale, Hydrophilicity scale, Flexibility of the amino acids, and Hydropathy scale. Multiple-feature selection aims to improve the accuracy of protein classification models. The results showed that the selected features significantly improved the predictive performance of existing embeddings.

RCSB Protein Data Bank: supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures.

The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules (e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.

Abstract 2348: A global multiscale map of protein assemblies from integration of protein interactions and images

Abstract Cells regulate growth and function through a hierarchical structure of subcellular protein assemblies, in which alterations can result in cellular dysfunction leading to disease such as cancer. Much of this structure remains uncharted, resulting in recent efforts to map subcellular organization at different physical scales. Here, we report a global architectural map of human cancer cell protein assemblies spanning 10−9 to 10−5 nm, based on integration of near-proteome-wide affinity purification mass spectrometry-based protein interactions and immunofluorescent imaging in U-2 OS osteosarcoma cancer cells. The U-2 OS multi-scale integrated cell map places >5000 proteins into 270 distinct subcellular protein assemblies across biological scales, representing approximately half of expressed proteins. There are known organelles and protein complexes recovered in the map, as well as 152 putative assemblies, such as a putative interferon signaling complex consisting of a serine protease and STAT transcription factors. The map also incorporates 128 previously uncharacterized proteins into protein assemblies, such as C18orf21 association with a canonical RNAse complex. Protein subsystems in the U-2 OS map were evaluated for the potential for downstream integrative structural modeling, where available structural data (e.g. crosslinking, Protein Data Bank structures, AlphaFold predictions) are combined in a Bayesian framework to model the physical structures of protein complexes. Using this framework, we created an integrated structural model of new proteins interacting with the Rag-Ragulator complex, which regulates MAPK and mTOR signaling. In summary, the global proteome architecture map provides a resource for cellular biology discovery and the systematic determination of protein functions and structures. Citation Format: Leah V. Schaffer, Mengzhou Hu, Edward L. Huttlin, Gege Qian, Abantika Pal, Neelesh Soni, Andrew Latham, Laura Pontano Vaites, Trang Le, Yue Qin, Christopher Churas, Dexter Pratt, Ignacia Echeverria, Andrej Sali, J Wade Harper, Steven P. Gygi, Emma Lundberg, Trey Ideker. A global multiscale map of protein assemblies from integration of protein interactions and images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2348.

Synthesis and biological evaluation of 3‐hydroxypyrazoles as aquaporin 9 inhibitors

AbstractA series of 3‐hydroxypyrazole derivatives have been synthesized by a base‐promoted reaction of nitro‐substituted donor–acceptor cyclopropanes with hydrazines. The synthesized compounds have been investigated for their ability to inhibit aquaporin 9 (AQP9) in rat Leydig cells (LC‐540). The protein data bank structure for AQP9 was predicted using homology modeling; and the protein–ligand interaction for the synthesized hydroxyl pyrazole derivatives were analyzed using molecular modeling and docking studies. The results of in silico analyses showed that compound 5b had a higher binding affinity with AQP9 than other compounds. Further, in vitro studies conducted in LC‐540 cells confirmed that compound 5b effectively inhibits AQP9. Hence, compound 5b may be used as an inhibitor in enhancing our understanding of AQP9 function, and in the treatment of several diseases.

Ensemble-based energetic differences between Protein Data Bank structures and AlphaFold2 models

Ensemble-based energetic differences between Protein Data Bank structures and AlphaFold2 models

Computational Studies on 6-Pyruvoyl Tetrahydropterin Synthase (6-PTPS) of Plasmodium falciparum

6-Pyruvoyl tetrahydropterin synthase (6-PTPS) is a lyase involved in the synthesis of tetrahydrobiopterin. In Plasmodium species where dihydroneopterin aldolase (DHNA) is absent, it acts in the folate biosynthetic pathway necessary for the growth and survival of the parasite. The 6-pyruvoyl tetrahydropterin synthase of Plasmodium falciparum ( PfPTPS) has been identified as a potential antimalarial drug target. This study identified potential inhibitors of PfPTPS using molecular docking techniques. Molecular docking and virtual screening of 62 compounds including the control to the deposited Protein Data Bank (PDB) structure was carried out using AutoDock Vina in PyRx. Five of the compounds, N,N-dimethyl- N’-[4-oxo-6-(2,2,5-trimethyl-1,3-dioxolan-4-yl)-3H-pteridin-2-yl]methanimidamide (140296439), 2-amino-6-[(1R)-3-cyclohexyl-1-hydroxypropyl]-3H-pteridin-4-one (140296495), 2-(2,3-dihydroxypropyl)-8,9-dihydro-6H-pyrimido[2,1-b]pteridine-7,11-dione (144380406), 2-(dimethylamino)-6-[(2,2-dimethyl-1,3-dioxolan-4-yl)-hydroxymethyl]-3H-pteridin-4-one (135573878), and [1-acetyloxy-1-(2-methyl-4-oxo-3H-pteridin-6-yl)propan-2-yl] acetate (136075207), showed better binding affinity than the control ligand, biopterin (135449517), and were selected and screened. Three conformers of 140296439 with the binding energy of −7.2, −7.1, and −7.0 kcal/mol along with 140296495 were better than the control at −5.7 kcal/mol. In silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies predicted good pharmacokinetic properties of all the compounds while reporting a high risk of irritant toxicity in 140296439 and 144380406. The study highlights the five compounds, 140296439, 140296495, 144380406, 135573878 and 136075207, as potential inhibitors of PfPTPS and possible compounds for antimalarial drug development.

Artificial Intelligence-based database forprediction ofprotein structure andtheir alterations inocular diseases.

The aim of the study is to establish an online database for predicting protein structures altered in ocular diseases by Alphafold2 and RoseTTAFold algorithms. Totally, 726 genes of multiple ocular diseases were collected for protein structure prediction. Both Alphafold2 and RoseTTAFold algorithms were built locally using the open-source codebases. A dataset with 48 protein structures from Protein Data Bank (PDB) was adopted for algorithm set-up validation. A website was built to match ocular genes with the corresponding predicted tertiary protein structures for each amino acid sequence. The predicted local distance difference test-Cα (pLDDT) and template modeling (TM) scores of the validation protein structure and the selected ocular genes were evaluated. Molecular dynamics and molecular docking simulations were performed to demonstrate the applications of the predicted structures. For the validation dataset, 70.8% of the predicted protein structures showed pLDDT greater than 90. Compared to the PDB structures, 100% of the AlphaFold2-predicted structures and 97.9% of the RoseTTAFold-predicted structure showed TM score greater than 0.5. Totally, 1329 amino acid sequences of 430 ocular disease-related genes have been predicted, of which 75.9% showed pLDDT greater than 70 for the wildtype sequences and 76.1% for the variant sequences. Small molecule docking and molecular dynamics simulations revealed that the predicted protein structures with higher confidence scores showed similar molecular characteristics with the structures from PDB. We have developed an ocular protein structure database (EyeProdb) for ocular disease, which is released for the public and will facilitate the biological investigations and structure-based drug development for ocular diseases. Database URL: http://eyeprodb.jsiec.org.

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins.

Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.

PDBminer to Find and Annotate Protein Structures for Computational Analysis.

Computational methods relying on protein structure strongly depend on the structure selected for investigation. Typical sources of protein structures include experimental structures available at the Protein Data Bank (PDB) and high-quality in silico model structures, such as those available at the AlphaFold Protein Structure Database. Either option has significant advantages and drawbacks, and exploring the wealth of available structures to identify the most suitable ones for specific applications can be a daunting task. We provide an open-source software package, PDBminer, with the purpose of making structure identification and selection easier, faster, and less error prone. PDBminer searches the AlphaFold Database and the PDB for available structures of interest and provides an up-to-date, quality-ranked table of structures applicable for further use. PDBminer provides an overview of the available protein structures to one or more input proteins, parallelizing the runs if multiple cores are specified. The output table reports the coverage of the protein structures aligned to the UniProt sequence, overcoming numbering differences in PDB structures and providing information regarding model quality, protein complexes, ligands, and nucleic acid chain binding. The PDBminer2coverage and PDBminer2network tools assist in visualizing the results. PDBminer can be applied to overcome the tedious task of choosing a PDB structure without losing the wealth of additional information available in the PDB. Here, we showcase the main functionalities of the package on the p53 tumor suppressor protein. The package is available at http://github.com/ELELAB/PDBminer.

Conservation of knotted and slipknotted topology in transmembrane transporters

The existence of nontrivial topology is well accepted in globular proteins but not in membrane proteins. Our comprehensive topological analysis of the Protein Data Bank structures reveals 18 families of transmembrane proteins with nontrivial topology, showing that they constitute a significant number of membrane proteins. Moreover, we found that they comprise one of the largest groups of secondary active transporters. We classified them based on their knotted fingerprint into four groups: three slipknotted and one knotted. Unexpectedly, we found that the same protein can possess two distinct slipknot motifs that correspond to its outward- and inward-open conformational state. Based on the analysis of structures and knotted fingerprints, we show that slipknot topology is directly involved in the conformational transition and substrate transfer. Therefore, entanglement can be used to classify proteins and to find their structure-function relationship. Furthermore, based on the topological analysis of the transmembrane protein structures predicted by AlphaFold, we identified new potentially slipknotted protein families.

CC+ : A searchable database of validated coiled coils in PDB structures and AlphaFold2 models.

α-Helical coiled coils are common tertiary and quaternary elements of protein structure. In coiled coils, two or more α helices wrap around each other to form bundles. This apparently simple structural motif can generate many architectures and topologies. Coiled coil-forming sequences can be predicted from heptad repeats of hydrophobic and polar residues, hpphppp, although this is not always reliable. Alternatively, coiled-coil structures can be identified using the program SOCKET, which finds knobs-into-holes (KIH) packing between side chains of neighboring helices. SOCKET also classifies coiled-coil architecture and topology, thus allowing sequence-to-structure relationships to be garnered. In 2009, we used SOCKET to create a relational database of coiled-coil structures, CC+ , from the RCSB Protein Data Bank (PDB). Here, we report an update of CC+ following an update of SOCKET (to Socket2) and the recent explosion of structural data and the success of AlphaFold2 in predicting protein structures from genome sequences. With the most-stringent SOCKET parameters, CC+ contains ≈12,000 coiled-coil assemblies from experimentally determined structures, and ≈120,000 potential coiled-coil structures within single-chain models predicted by AlphaFold2 across 48 proteomes. CC+ allows these and other less-stringently defined coiled coils to be searched at various levels of structure, sequence, and side-chain interactions. The identified coiled coils can be viewed directly from CC+ using the Socket2 application, and their associated data can be downloaded for further analyses. CC+ is available freely at http://coiledcoils.chm.bris.ac.uk/CCPlus/Home.html. It will be updated automatically. We envisage that CC+ could be used to understand coiled-coil assemblies and their sequence-to-structure relationships, and to aid protein design and engineering.