Search tips
Search criteria

Results 1-20 (20)

Clipboard (0)

Select a Filter Below

Year of Publication
1.  The Discovery of Novel 10,11-Dihydro-5H-dibenz[b,f]azepine SIRT2 Inhibitors 
MedChemComm  2012;10.1039/C2MD00290F.
Isoform selective inhibitors of the sirtuins (NAD+-dependent histone deacetylases) should enable an in depth study of the molecular biology underpinning these targets and how they are deregulated in diseases such as cancer and neurodegeneration. Herein, we present the discovery of structurally novel SIRT2 inhibitors. Hit molecule 8 was discovered through the chemical synthesis and biological characterization of a small-molecule compound library based around the 10,11-dihydro-5H-dibenz[b,f]azepine scaffold. In vitro screening assays revealed compound 8 to have an IC50 of 18 μM against SIRT2 and to exhibit more than 30-fold selectivity compared to SIRT1. Cellular assays, performed on MCF-7 cells, confirmed the in vitro selectivity and showed hit 8 to have antiproliferative activity at a concentration of 30 μM. Computational studies were performed to predict the SIRT2 binding mode and to rationalise the observed selectivity.
PMCID: PMC3856871  PMID: 24340169
2.  CombFunc: predicting protein function using heterogeneous data sources 
Nucleic Acids Research  2012;40(Web Server issue):W466-W470.
Only a small fraction of known proteins have been functionally characterized, making protein function prediction essential to propose annotations for uncharacterized proteins. In recent years many function prediction methods have been developed using various sources of biological data from protein sequence and structure to gene expression data. Here we present the CombFunc web server, which makes Gene Ontology (GO)-based protein function predictions. CombFunc incorporates ConFunc, our existing function prediction method, with other approaches for function prediction that use protein sequence, gene expression and protein–protein interaction data. In benchmarking on a set of 1686 proteins CombFunc obtains precision and recall of 0.71 and 0.64 respectively for gene ontology molecular function terms. For biological process GO terms precision of 0.74 and recall of 0.41 is obtained. CombFunc is available at
PMCID: PMC3394346  PMID: 22641853
3.  Genome3D: exploiting structure to help users understand their sequences 
Nucleic Acids Research  2014;43(Database issue):D382-D386.
Genome3D ( is a collaborative resource that provides predicted domain annotations and structural models for key sequences. Since introducing Genome3D in a previous NAR paper, we have substantially extended and improved the resource. We have annotated representatives from Pfam families to improve coverage of diverse sequences and added a fast sequence search to the website to allow users to find Genome3D-annotated sequences similar to their own. We have improved and extended the Genome3D data, enlarging the source data set from three model organisms to 10, and adding VIVACE, a resource new to Genome3D. We have analysed and updated Genome3D's SCOP/CATH mapping. Finally, we have improved the superposition tools, which now give users a more powerful interface for investigating similarities and differences between structural models.
PMCID: PMC4384030  PMID: 25348407
4.  Prediction of ligand binding sites using homologous structures and conservation at CASP8 
Proteins  2009;77(Suppl 9):147-151.
The Critical Assessment of protein Structure Prediction experiment (CASP) is a blind assessment of the prediction of protein structure and related topics including function prediction. We present our results in the function/binding site prediction category. Our approach to identify binding sites combined the use of the predicted structure of the targets with both residue conservation and the location of ligands bound to homologous structures. We obtained average coverage of 83% and 56% accuracy. Analysis of our predictions suggests that overprediction reduces the accuracy obtained due to large areas of conservation around the binding site that do not bind the ligand. In some proteins such conserved residues may have a functional role. A server version of our method will soon be available.
PMCID: PMC2814558  PMID: 19626715
bioinformatics; binding site; CASP; function prediction; structural biology
5.  SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features 
Journal of Molecular Biology  2014;426(14):2692-2701.
Whole-genome and exome sequencing studies reveal many genetic variants between individuals, some of which are linked to disease. Many of these variants lead to single amino acid variants (SAVs), and accurate prediction of their phenotypic impact is important. Incorporating sequence conservation and network-level features, we have developed a method, SuSPect (Disease-Susceptibility-based SAV Phenotype Prediction), for predicting how likely SAVs are to be associated with disease. SuSPect performs significantly better than other available batch methods on the VariBench benchmarking dataset, with a balanced accuracy of 82%. SuSPect is available at The Web site has been implemented in Perl and SQLite and is compatible with modern browsers. An SQLite database of possible missense variants in the human proteome is available to download at
Graphical abstract
•Bioinformatics approaches are key for identification of disease-causing variants.•SAV phenotype prediction can be improved using network information.•A method including these features, SuSPect, outperforms tested methods.•SuSPect is available to use at
PMCID: PMC4087249  PMID: 24810707
MCC, Matthews correlation coefficient; SVM, support vector machine; PPI, protein–protein interaction; SAV, single amino acid variant; MSA, multiple sequence alignment; PSSM, position-specific scoring matrix; RSA, relative solvent accessibility; RBF, radial basis function; protein–protein interaction; nsSNP; missense mutation; SuSPect; SAV
6.  A large-scale evaluation of computational protein function prediction 
Radivojac, Predrag | Clark, Wyatt T | Ronnen Oron, Tal | Schnoes, Alexandra M | Wittkop, Tobias | Sokolov, Artem | Graim, Kiley | Funk, Christopher | Verspoor, Karin | Ben-Hur, Asa | Pandey, Gaurav | Yunes, Jeffrey M | Talwalkar, Ameet S | Repo, Susanna | Souza, Michael L | Piovesan, Damiano | Casadio, Rita | Wang, Zheng | Cheng, Jianlin | Fang, Hai | Gough, Julian | Koskinen, Patrik | Törönen, Petri | Nokso-Koivisto, Jussi | Holm, Liisa | Cozzetto, Domenico | Buchan, Daniel W A | Bryson, Kevin | Jones, David T | Limaye, Bhakti | Inamdar, Harshal | Datta, Avik | Manjari, Sunitha K | Joshi, Rajendra | Chitale, Meghana | Kihara, Daisuke | Lisewski, Andreas M | Erdin, Serkan | Venner, Eric | Lichtarge, Olivier | Rentzsch, Robert | Yang, Haixuan | Romero, Alfonso E | Bhat, Prajwal | Paccanaro, Alberto | Hamp, Tobias | Kassner, Rebecca | Seemayer, Stefan | Vicedo, Esmeralda | Schaefer, Christian | Achten, Dominik | Auer, Florian | Böhm, Ariane | Braun, Tatjana | Hecht, Maximilian | Heron, Mark | Hönigschmid, Peter | Hopf, Thomas | Kaufmann, Stefanie | Kiening, Michael | Krompass, Denis | Landerer, Cedric | Mahlich, Yannick | Roos, Manfred | Björne, Jari | Salakoski, Tapio | Wong, Andrew | Shatkay, Hagit | Gatzmann, Fanny | Sommer, Ingolf | Wass, Mark N | Sternberg, Michael J E | Škunca, Nives | Supek, Fran | Bošnjak, Matko | Panov, Panče | Džeroski, Sašo | Šmuc, Tomislav | Kourmpetis, Yiannis A I | van Dijk, Aalt D J | ter Braak, Cajo J F | Zhou, Yuanpeng | Gong, Qingtian | Dong, Xinran | Tian, Weidong | Falda, Marco | Fontana, Paolo | Lavezzo, Enrico | Di Camillo, Barbara | Toppo, Stefano | Lan, Liang | Djuric, Nemanja | Guo, Yuhong | Vucetic, Slobodan | Bairoch, Amos | Linial, Michal | Babbitt, Patricia C | Brenner, Steven E | Orengo, Christine | Rost, Burkhard | Mooney, Sean D | Friedberg, Iddo
Nature methods  2013;10(3):221-227.
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools.
PMCID: PMC3584181  PMID: 23353650
7.  Protein flexibility, not disorder, is intrinsic to molecular recognition 
An ‘intrinsically disordered protein’ (IDP) is assumed to be unfolded in the cell and perform its biological function in that state. We contend that most intrinsically disordered proteins are in fact proteins waiting for a partner (PWPs), parts of a multi-component complex that do not fold correctly in the absence of other components. Flexibility, not disorder, is an intrinsic property of proteins, exemplified by X-ray structures of many enzymes and protein-protein complexes. Disorder is often observed with purified proteins in vitro and sometimes also in crystals, where it is difficult to distinguish from flexibility. In the crowded environment of the cell, disorder is not compatible with the known mechanisms of protein-protein recognition, and, foremost, with its specificity. The self-assembly of multi-component complexes may, nevertheless, involve the specific recognition of nascent polypeptide chains that are incompletely folded, but then disorder is transient, and it must remain under the control of molecular chaperones and of the quality control apparatus that obviates the toxic effects it can have on the cell.
PMCID: PMC3542771  PMID: 23361309
8.  Gene Function Hypotheses for the Campylobacter jejuni Glycome Generated by a Logic-Based Approach 
Journal of Molecular Biology  2013;425(1):186-197.
Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning—the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system.
Graphical Abstract
► A challenge in systems biology modelling is to integrate different data sources. ► A logic-based approach was used to hypothesise gene function for the C. jejuni glycome. ► Gene knockout and serotype strain data were integrated with pathway information. ► Functions were hypothesised for capsule polysaccharide biosynthetic pathway genes. ► Logic-based learning proposed hypotheses for the behaviour of a biological system.
PMCID: PMC3546167  PMID: 23103756
ILP, inductive logic programming; CPS, capsular polysaccharide; HR-MAS, high-resolution magic angle spinning; CE-ESMS, capillary electrophoresis coupled to electrospray mass spectrometry; BBSRC, Biotechnology and Biological Sciences Research Council; systems biology; Campylobacter jejuni; machine learning; capsular polysaccharide; pathway modelling
9.  Genome3D: a UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains 
Nucleic Acids Research  2012;41(Database issue):D499-D507.
Genome3D, available at, is a new collaborative project that integrates UK-based structural resources to provide a unique perspective on sequence–structure–function relationships. Leading structure prediction resources (DomSerf, FUGUE, Gene3D, pDomTHREADER, Phyre and SUPERFAMILY) provide annotations for UniProt sequences to indicate the locations of structural domains (structural annotations) and their 3D structures (structural models). Structural annotations and 3D model predictions are currently available for three model genomes (Homo sapiens, E. coli and baker’s yeast), and the project will extend to other genomes in the near future. As these resources exploit different strategies for predicting structures, the main aim of Genome3D is to enable comparisons between all the resources so that biologists can see where predictions agree and are therefore more trusted. Furthermore, as these methods differ in whether they build their predictions using CATH or SCOP, Genome3D also contains the first official mapping between these two databases. This has identified pairs of similar superfamilies from the two resources at various degrees of consensus (532 bronze pairs, 527 silver pairs and 370 gold pairs).
PMCID: PMC3531217  PMID: 23203986
10.  Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma 
Chambers, John C | Zhang, Weihua | Sehmi, Joban | Li, Xinzhong | Wass, Mark N | Van der Harst, Pim | Holm, Hilma | Sanna, Serena | Kavousi, Maryam | Baumeister, Sebastian E | Coin, Lachlan J | Deng, Guohong | Gieger, Christian | Heard-Costa, Nancy L | Hottenga, Jouke-Jan | Kühnel, Brigitte | Kumar, Vinod | Lagou, Vasiliki | Liang, Liming | Luan, Jian’an | Vidal, Pedro Marques | Leach, Irene Mateo | O’Reilly, Paul F | Peden, John F | Rahmioglu, Nilufer | Soininen, Pasi | Speliotes, Elizabeth K | Yuan, Xin | Thorleifsson, Gudmar | Alizadeh, Behrooz Z | Atwood, Larry D | Borecki, Ingrid B | Brown, Morris J | Charoen, Pimphen | Cucca, Francesco | Das, Debashish | de Geus, Eco J C | Dixon, Anna L | Döring, Angela | Ehret, Georg | Eyjolfsson, Gudmundur I | Farrall, Martin | Forouhi, Nita G | Friedrich, Nele | Goessling, Wolfram | Gudbjartsson, Daniel F | Harris, Tamara B | Hartikainen, Anna-Liisa | Heath, Simon | Hirschfield, Gideon M | Hofman, Albert | Homuth, Georg | Hyppönen, Elina | Janssen, Harry L A | Johnson, Toby | Kangas, Antti J | Kema, Ido P | Kühn, Jens P | Lai, Sandra | Lathrop, Mark | Lerch, Markus M | Li, Yun | Liang, T Jake | Lin, Jing-Ping | Loos, Ruth J F | Martin, Nicholas G | Moffatt, Miriam F | Montgomery, Grant W | Munroe, Patricia B | Musunuru, Kiran | Nakamura, Yusuke | O’Donnell, Christopher J | Olafsson, Isleifur | Penninx, Brenda W | Pouta, Anneli | Prins, Bram P | Prokopenko, Inga | Puls, Ralf | Ruokonen, Aimo | Savolainen, Markku J | Schlessinger, David | Schouten, Jeoffrey N L | Seedorf, Udo | Sen-Chowdhry, Srijita | Siminovitch, Katherine A | Smit, Johannes H | Spector, Timothy D | Tan, Wenting | Teslovich, Tanya M | Tukiainen, Taru | Uitterlinden, Andre G | Van der Klauw, Melanie M | Vasan, Ramachandran S | Wallace, Chris | Wallaschofski, Henri | Wichmann, H-Erich | Willemsen, Gonneke | Würtz, Peter | Xu, Chun | Yerges-Armstrong, Laura M | Abecasis, Goncalo R | Ahmadi, Kourosh R | Boomsma, Dorret I | Caulfield, Mark | Cookson, William O | van Duijn, Cornelia M | Froguel, Philippe | Matsuda, Koichi | McCarthy, Mark I | Meisinger, Christa | Mooser, Vincent | Pietiläinen, Kirsi H | Schumann, Gunter | Snieder, Harold | Sternberg, Michael J E | Stolk, Ronald P | Thomas, Howard C | Thorsteinsdottir, Unnur | Uda, Manuela | Waeber, Gérard | Wareham, Nicholas J | Waterworth, Dawn M | Watkins, Hugh | Whitfield, John B | Witteman, Jacqueline C M | Wolffenbuttel, Bruce H R | Fox, Caroline S | Ala-Korpela, Mika | Stefansson, Kari | Vollenweider, Peter | Völzke, Henry | Schadt, Eric E | Scott, James | Järvelin, Marjo-Riitta | Elliott, Paul | Kooner, Jaspal S
Nature genetics  2011;43(11):1131-1138.
Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10−8 to P = 10−190). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function.
PMCID: PMC3482372  PMID: 22001757
11.  PINALOG: a novel approach to align protein interaction networks—implications for complex detection and function prediction 
Bioinformatics  2012;28(9):1239-1245.
Motivation: Analysis of protein–protein interaction networks (PPINs) at the system level has become increasingly important in understanding biological processes. Comparison of the interactomes of different species not only provides a better understanding of species evolution but also helps with detecting conserved functional components and in function prediction.
Method and Results: Here we report a PPIN alignment method, called PINALOG, which combines information from protein sequence, function and network topology. Alignment of human and yeast PPINs reveals several conserved subnetworks between them that participate in similar biological processes, notably the proteasome and transcription related processes. PINALOG has been tested for its power in protein complex prediction as well as function prediction. Comparison with PSI-BLAST in predicting protein function in the twilight zone also shows that PINALOG is valuable in predicting protein function.
Availability and implementation: The PINALOG web-server is freely available from The PINALOG program and associated data are available from the Download section of the web-server.
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3338015  PMID: 22419782
12.  Genome-wide association study identifies variants in TMPRSS6 associated with hemoglobin levels 
Nature genetics  2009;41(11):1170-1172.
We carried out a genome-wide association study of hemoglobin levels in 16,001 individuals of European and Indian Asian ancestry. The most closely associated SNP (rs855791) results in nonsynonymous (V736A) change in the serine protease domain of TMPRSS6 and a blood hemoglobin concentration 0.13 (95% CI 0.09–0.17) g/dl lower per copy of allele A (P = 1.6 × 10−13). Our findings suggest that TMPRSS6, a regulator of hepcidin synthesis and iron handling, is crucial in hemoglobin level maintenance.
PMCID: PMC3178047  PMID: 19820698
13.  3DLigandSite: predicting ligand-binding sites using similar structures 
Nucleic Acids Research  2010;38(Web Server issue):W469-W473.
3DLigandSite is a web server for the prediction of ligand-binding sites. It is based upon successful manual methods used in the eighth round of the Critical Assessment of techniques for protein Structure Prediction (CASP8). 3DLigandSite utilizes protein-structure prediction to provide structural models for proteins that have not been solved. Ligands bound to structures similar to the query are superimposed onto the model and used to predict the binding site. In benchmarking against the CASP8 targets 3DLigandSite obtains a Matthew’s correlation co-efficient (MCC) of 0.64, and coverage and accuracy of 71 and 60%, respectively, similar results to our manual performance in CASP8. In further benchmarking using a large set of protein structures, 3DLigandSite obtains an MCC of 0.68. The web server enables users to submit either a query sequence or structure. Predictions are visually displayed via an interactive Jmol applet. 3DLigandSite is available for use at
PMCID: PMC2896164  PMID: 20513649
14.  Protein Folding Requires Crowd Control in a Simulated Cell 
Journal of Molecular Biology  2010;397(5):1329-1338.
Macromolecular crowding has a profound effect upon biochemical processes in the cell. We have computationally studied the effect of crowding upon protein folding for 12 small domains in a simulated cell using a coarse-grained protein model, which is based upon Langevin dynamics, designed to unify the often disjoint goals of protein folding simulation and structure prediction. The model can make predictions of native conformation with accuracy comparable with that of the best current template-free models. It is fast enough to enable a more extensive analysis of crowding than previously attempted, studying several proteins at many crowding levels and further random repetitions designed to more closely approximate the ensemble of conformations. We found that when crowding approaches 40% excluded volume, the maximum level found in the cell, proteins fold to fewer native-like states. Notably, when crowding is increased beyond this level, there is a sudden failure of protein folding: proteins fix upon a structure more quickly and become trapped in extended conformations. These results suggest that the ability of small protein domains to fold without the help of chaperones may be an important factor in limiting the degree of macromolecular crowding in the cell. Here, we discuss the possible implications regarding the relationship between protein expression level, protein size, chaperone activity and aggregation.
PMCID: PMC2891488  PMID: 20149797
TM, template modelling; macromolecular crowding; protein structure prediction; protein misfolding; protein aggregation; protein expression
15.  Including Functional Annotations and Extending the Collection of Structural Classifications of Protein Loops (ArchDB) 
Loops represent an important part of protein structures. The study of loop is critical for two main reasons: First, loops are often involved in protein function, stability and folding. Second, despite improvements in experimental and computational structure prediction methods, modeling the conformation of loops remains problematic. Here, we present a structural classification of loops, ArchDB, a mine of information with application in both mentioned fields: loop structure prediction and function prediction. ArchDB ( is a database of classified protein loop motifs. The current database provides four different classification sets tailored for different purposes. ArchDB-40, a loop classification derived from SCOP40, well suited for modeling common loop motifs. Since features relevant to loop structure or function can be more easily determined on well-populated clusters, we have developed ArchDB-95, a loop classification derived from SCOP95. This new classification set shows a ~40% increase in the number of subclasses, and a large 7-fold increase in the number of putative structure/function-related subclasses. We also present ArchDB-EC, a classification of loop motifs from enzymes, and ArchDB-KI, a manually annotated classification of loop motifs from kinases. Information about ligand contacts and PDB sites has been included in all classification sets. Improvements in our classification scheme are described, as well as several new database features, such as the ability to query by conserved annotations, sequence similarity, or uploading 3D coordinates of a protein. The lengths of classified loops range between 0 and 36 residues long. ArchDB offers an exhaustive sampling of loop structures. Functional information about loops and links with related biological databases are also provided. All this information and the possibility to browse/query the database through a web-server outline an useful tool with application in the comparative study of loops, the analysis of loops involved in protein function and to obtain templates for loop modeling.
PMCID: PMC2789696  PMID: 20066127
function annotation; loop structure classification; loop modeling
17.  The proteome: structure, function and evolution 
This paper reports two studies to model the inter-relationships between protein sequence, structure and function. First, an automated pipeline to provide a structural annotation of proteomes in the major genomes is described. The results are stored in a database at Imperial College, London (3D-GENOMICS) that can be accessed at Analysis of the assignments to structural superfamilies provides evolutionary insights. 3D-GENOMICS is being integrated with related proteome annotation data at University College London and the European Bioinformatics Institute in a project known as e-protein ( The second topic is motivated by the developments in structural genomics projects in which the structure of a protein is determined prior to knowledge of its function. We have developed a new approach PHUNCTIONER that uses the gene ontology (GO) classification to supervise the extraction of the sequence signal responsible for protein function from a structure-based sequence alignment. Using GO we can obtain profiles for a range of specificities described in the ontology. In the region of low sequence similarity (around 15%), our method is more accurate than assignment from the closest structural homologue. The method is also able to identify the specific residues associated with the function of the protein family.
PMCID: PMC1609342  PMID: 16524832
bioinformatics; proteome annotation; protein function
18.  Isolation of a small molecule inhibitor of DNA base excision repair 
Nucleic Acids Research  2005;33(15):4711-4724.
The base excision repair (BER) pathway is essential for the removal of DNA bases damaged by alkylation or oxidation. A key step in BER is the processing of an apurinic/apyrimidinic (AP) site intermediate by an AP endonuclease. The major AP endonuclease in human cells (APE1, also termed HAP1 and Ref-1) accounts for >95% of the total AP endonuclease activity, and is essential for the protection of cells against the toxic effects of several classes of DNA damaging agents. Moreover, APE1 overexpression has been linked to radio- and chemo-resistance in human tumors. Using a newly developed high-throughput screen, several chemical inhibitors of APE1 have been isolated. Amongst these, CRT0044876 was identified as a potent and selective APE1 inhibitor. CRT0044876 inhibits the AP endonuclease, 3′-phosphodiesterase and 3′-phosphatase activities of APE1 at low micromolar concentrations, and is a specific inhibitor of the exonuclease III family of enzymes to which APE1 belongs. At non-cytotoxic concentrations, CRT0044876 potentiates the cytotoxicity of several DNA base-targeting compounds. This enhancement of cytotoxicity is associated with an accumulation of unrepaired AP sites. In silico modeling studies suggest that CRT0044876 binds to the active site of APE1. These studies provide both a novel reagent for probing APE1 function in human cells, and a rational basis for the development of APE1-targeting drugs for antitumor therapy.
PMCID: PMC1188083  PMID: 16113242
19.  3D-GENOMICS: a database to compare structural and functional annotations of proteins between sequenced genomes 
Nucleic Acids Research  2004;32(Database issue):D245-D250.
The 3D-GENOMICS database ( provides structural annotations for proteins from sequenced genomes. In August 2003 the database included data for 93 proteomes. The annotations stored in the database include homologous sequences from various sequence databases, domains from SCOP and Pfam, patterns from Prosite and other predicted sequence features such as transmembrane regions and coiled coils. In addition to annotations at the sequence level, several precomputed cross- proteome comparative analyses are available based on SCOP domain superfamily composition. Annotations are available to the user via a web interface to the database. Multiple points of entry are available so that a user is able to: (i) directly access annotations for a single protein sequence via keywords or accession codes, (ii) examine a sequence of interest chosen from a summary of annotations for a particular proteome, or (iii) access precomputed frequency-based cross-proteome comparative analyses.
PMCID: PMC308798  PMID: 14681404
20.  ArchDB: automated protein loop classification as a tool for structural genomics 
Nucleic Acids Research  2004;32(Database issue):D185-D188.
The annotation of protein function has become a crucial problem with the advent of sequence and structural genomics initiatives. A large body of evidence suggests that protein structural information is frequently encoded in local sequences, and that folds are mainly made up of a number of simple local units of super-secondary structural motifs, consisting of a few secondary structures and their connecting loops. Moreover, protein loops play an important role in protein function. Here we present ArchDB, a classification database of structural motifs, consisting of one loop plus its bracing secondary structures. ArchDB currently contains 12 665 super-secondary elements classified into 1496 motif subclasses. The database provides an easy way to retrieve functional information from protein structures sharing a common motif, to search motifs found in a given SCOP family, superfamily or fold, or to search by keywords on proteins with classified loops. The ArchDB database of loops is located at
PMCID: PMC308737  PMID: 14681390

Results 1-20 (20)