02548nas a2200241   4500008004100000245006500041210006300106300000900169490000600178520182800184100001302012700002002025700001502045700001402060700001902074700001602093700001502109700001702124700002202141700001302163700002402176856010602200       2009                        eng d00aA kernel for open source drug discovery in tropical diseases0 akernel for open source drug discovery in tropical diseases  ae4180 v33 aBACKGROUND: Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels". METHODOLOGY/PRINCIPAL FINDINGS: HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. CONCLUSIONS/SIGNIFICANCE: The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.1 aOrti, L.1 aCarbajo, R., J.1 aPieper, U.1 aEswar, N.1 aMaurer, S., M.1 aRai, A., K.1 aTaylor, G.1 aTodd, M., H.1 aPineda-Lucena, A.1 aSali, A.1 aMarti-Renom, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1938128600678nas a2200229   4500008004100000245004900041210004700090300001000137490000700147100001300154700002000167700001500187700001400202700001900216700001600235700001500251700001700266700002200283700001300305700002400318856010600342       2009                        eng d00aA kernel for the Tropical Disease Initiative0 akernel for the Tropical Disease Initiative  a320-10 v271 aOrti, L.1 aCarbajo, R., J.1 aPieper, U.1 aEswar, N.1 aMaurer, S., M.1 aRai, A., K.1 aTaylor, G.1 aTodd, M., H.1 aPineda-Lucena, A.1 aSali, A.1 aMarti-Renom, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1935236202308nas a2200325   4500008004100000245009900041210006900140300001200209490000700221520117300228653001501401653003601416653004401452653003601496653009601532653005201628100001501680700001401695700001701709700001601726700001401742700001901756700001501775700001501790700001601805700002401821700001801845700001301863856010601876       2009                        eng d00aMODBASE, a database of annotated comparative protein structure models and associated resources0 aMODBASE a database of annotated comparative protein structure mo  aD347-540 v373 aMODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/).10a*Databases10aMolecular Mutation Polymorphism10aProtein Genomics Humans Ligands *Models10aProtein User-Computer Interface10aSingle Nucleotide Protein Folding Protein Interaction Domains and Motifs *Protein Structure10aTertiary Proteins/genetics *Structural Homology1 aPieper, U.1 aEswar, N.1 aWebb, B., M.1 aEramian, D.1 aKelly, L.1 aBarkan, D., T.1 aCarter, H.1 aMankoo, P.1 aKarchin, R.1 aMarti-Renom, M., A.1 aDavis, F., P.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1894828202280nas a2200169   4500008004100000245007900041210006900120520169500189653002001884100001501904700001601919700001801935700002401953700001301977700001401990856010602004       2009                        eng d00aModLink+: Improving fold recognition by using protein-protein interactions0 aModLink Improving fold recognition by using proteinprotein inter3 a<p>MOTIVATION: Several strategies have been developed to predict the fold of a target protein sequence, most of which are based on aligning the target sequence to other sequences of known structure. Previously, we demonstrated that the consideration of protein-protein interactions significantly increases the accuracy of fold assignment compared to PSI-BLAST sequence comparisons. A drawback of our method was the low number of proteins to which a fold could be assigned. Here, we present an improved version of the method that addresses this limitation. We also compare our method to other state-of-the-art fold assignment methodologies. RESULTS: Our approach (ModLink+) has been tested on 3,716 proteins with domain folds classified in the Structural Classification Of Proteins (SCOP) as well as known interacting partners in the Database of Interacting Proteins (DIP). For this test set, the ratio of success (PPV) on fold assignment increases from 75% for PSI-BLAST, 83% for HHSearch and 81% for PRC to more than 90% for ModLink+ at the e-value cutoff of 10(-3). Under this e-value, ModLink+ can assign a fold to 30-45% of the proteins in the test set, while our previous method could cover less than 25%. When applied to 6,384 proteins with unknown fold in the yeast proteome, ModLink+ combined with PSI-BLAST assigns a fold for domains in 3,738 proteins, while PSI-BLAST alone only covers 2,122 proteins, HHSearch 2,969 and PRC 2,826 proteins, using a threshold e-value that would represent a PPV higher than 82% for each method in the test set. AVAILABILITY: The ModLink+ server is freely accessible in the World Wide Web at http://sbi.imim.es/modlink/. CONTACT: boliva@imim.es.</p>10aprotein folding1 aFornes, O.1 aAragues, R.1 aEspadaler, J.1 aMarti-Renom, M., A.1 aSali, A.1 aOliva, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1935710002031nas a2200253   4500008004100000245009200041210006900133300000800202490000600210520096700216653008801183653004501271653013401316653003101450653007301481100001801554700001401572700001501586700001901601700001301620700002401633700001401657856010601671       2008                        eng d00aPrediction of enzyme function by combining sequence similarity and protein interactions0 aPrediction of enzyme function by combining sequence similarity a  a2490 v93 aBACKGROUND: A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners. RESULTS: The method has been tested against the PSI-BLAST program using a set of 3,890 protein sequences from which interaction data was available. For protein sequences that align with at least 40% sequence identity to a known enzyme, the specificity of our method in predicting the first three EC digits increased from 80% to 90% at 80% coverage when compared to PSI-BLAST. CONCLUSION: Our method can also be used in proteins for which homologous sequences with known interacting partners can be detected. Thus, our method could increase 10% the specificity of genome-wide enzyme predictions based on sequence matching by PSI-BLAST alone.10aAmino Acid *Software Structure-Activity Relationship Substrate Specificity/genetics10aAmino Acid Sequence/physiology Databases10aAutomated Predictive Value of Tests Protein Interaction Mapping Proteins/analysis/metabolism Sequence Alignment Sequence Analysis10aProtein *Sequence Homology10aProtein Enzymes/analysis/*metabolism Fuzzy Logic Pattern Recognition1 aEspadaler, J.1 aEswar, N.1 aQuerol, E.1 aAviles, F., X.1 aSali, A.1 aMarti-Renom, M., A.1 aOliva, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1850556202500nas a2200253   4500008004100000245008800041210006900129300000700198490001400205520139900219653007701618653005701695653019901752653003901951653002701990100002402017700001402041700002402055700001802079700001502097700001502112700001302127856010602140       2007                        eng d00aThe AnnoLite and AnnoLyze programs for comparative annotation of protein structures0 aAnnoLite and AnnoLyze programs for comparative annotation of pro  aS40 v8 Suppl 43 aBACKGROUND: Advances in structural biology, including structural genomics, have resulted in a rapid increase in the number of experimentally determined protein structures. However, about half of the structures deposited by the structural genomics consortia have little or no information about their biological function. Therefore, there is a need for tools for automatically and comprehensively annotating the function of protein structures. We aim to provide such tools by applying comparative protein structure annotation that relies on detectable relationships between protein structures to transfer functional annotations. Here we introduce two programs, AnnoLite and AnnoLyze, which use the structural alignments deposited in the DBAli database. DESCRIPTION: AnnoLite predicts the SCOP, CATH, EC, InterPro, PfamA, and GO terms with an average sensitivity of  90% and average precision of  80%. AnnoLyze predicts ligand binding site and domain interaction patches with an average sensitivity of  70% and average precision of  30%, correctly localizing binding sites for small molecules in  95% of its predictions. CONCLUSION: The AnnoLite and AnnoLyze programs for comparative annotation of protein structures can reliably and automatically annotate new protein structures. The programs are fully accessible via the Internet as part of the DBAli suite of tools at http://salilab.org/DBAli/.10a*Algorithms Amino Acid Sequence Confidence Intervals Data Interpretation10aAmino Acid *Software Structure-Activity Relationship10aProtein Information Storage and Retrieval/methods Molecular Sequence Data Proteins/*chemistry/classification/*metabolism Sensitivity and Specificity Sequence Alignment/*methods Sequence Analysis10aProtein/*methods Sequence Homology10aStatistical *Databases1 aMarti-Renom, M., A.1 aRossi, A.1 aAl-Shahrour, Fatima1 aDavis, F., P.1 aPieper, U.1 aDopazo, J.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1757014702608nas a2200217   4500008004100000245009500041210006900136300001200205490000600217520172500223653008401948653002102032653012902053653002102182100001602203700001302219700001402232700002402246700001402270856010602284       2007                        eng d00aCharacterization of protein hubs by inferring interacting motifs from protein interactions0 aCharacterization of protein hubs by inferring interacting motifs  a1761-710 v33 aThe characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs (e.g., a domain, sequence segments, a binding site, or a set of residues). Here, we develop and apply a method for delineating the interacting motifs of hub proteins (i.e., highly connected proteins). The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks.10aAmino Acid Motifs Amino Acid Sequence Binding Sites Computer Simulation *Models10aChemical *Models10aMolecular Molecular Sequence Data Protein Binding Protein Interaction Mapping/*methods Proteins/*chemistry Sequence Analysis10aProtein/*methods1 aAragues, R.1 aSali, A.1 aBonet, J.1 aMarti-Renom, M., A.1 aOliva, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1794170502151nas a2200277   4500008004100000245005200041210005100093300001100144490000700155520104000162653008701202653005701289653019401346653003901540653002701579100002401606700001501630700002401645700001401669700001401683700001801697700002401715700001501739700001301754856010601767       2007                        eng d00aDBAli tools: mining the protein structure space0 aDBAli tools mining the protein structure space  aW393-70 v353 aThe DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions.10a*Algorithms Amino Acid Sequence Computational Biology/*methods Data Interpretation10aAmino Acid *Software Structure-Activity Relationship10aProtein Internet Molecular Sequence Data Protein Conformation Proteins/*chemistry/classification/*metabolism Pseudomonas aeruginosa/*metabolism Sequence Alignment/*methods Sequence Analysis10aProtein/*methods Sequence Homology10aStatistical *Databases1 aMarti-Renom, M., A.1 aPieper, U.1 aMadhusudhan, M., S.1 aRossi, A.1 aEswar, N.1 aDavis, F., P.1 aAl-Shahrour, Fatima1 aDopazo, J.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1747851302005nas a2200253   4500008004100000245005800041210005800099300001300157490001400170520105600184653008801240653002101328653012901349653003101478100001401509700001301523700002401536700002401560700001601584700001701600700001501617700001301632856010601645       2006                        eng d00aComparative protein structure modeling using Modeller0 aComparative protein structure modeling using Modeller  aUnit 5 60 vChapter 53 aFunctional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.10aAlgorithms Amino Acid Sequence Computer Simulation Crystallography/*methods *Models10aChemical *Models10aMolecular Molecular Sequence Data Protein Conformation Protein Folding Proteins/*chemistry/*ultrastructure Sequence Analysis10aProtein/*methods *Software1 aEswar, N.1 aWebb, B.1 aMarti-Renom, M., A.1 aMadhusudhan, M., S.1 aEramian, D.1 aShen, M., Y.1 aPieper, U.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1842876702131nas a2200217   4500008004100000245007200041210006900113300001200182490000700194520144000201653001201641653002101653653003601674100001601710700001701726700001401743700001301757700001301770700002401783856010601807       2006                        eng d00aA composite score for predicting errors in protein structure models0 acomposite score for predicting errors in protein structure model  a1653-660 v153 aReliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning-based scoring functions. Individual scores were also used to construct approximately 85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (DeltaRMSD) from 0.63 A to 0.45 A, while having a higher Pearson correlation coefficient to RMSD (r=0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target-template alignment, and loop modeling.10a*Models10aMolecular Models10aTheoretical Proteins/*chemistry1 aEramian, D.1 aShen, M., Y.1 aDevos, D.1 aMelo, F.1 aSali, A.1 aMarti-Renom, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1675160602048nas a2200157   4500008004100000245010400041210006900145300001200214490000700226520134600233653015401579100001401733700002401747700001301771856010601784       2006                        eng d00aLocalization of binding sites in protein structures by optimization of a composite scoring function0 aLocalization of binding sites in protein structures by optimizat  a2366-800 v153 aThe rise in the number of functionally uncharacterized protein structures is increasing the demand for structure-based methods for functional annotation. Here, we describe a method for predicting the location of a binding site of a given type on a target protein structure. The method begins by constructing a scoring function, followed by a Monte Carlo optimization, to find a good scoring patch on the protein surface. The scoring function is a weighted linear combination of the z-scores of various properties of protein structure and sequence, including amino acid residue conservation, compactness, protrusion, convexity, rigidity, hydrophobicity, and charge density; the weights are calculated from a set of previously identified instances of the binding-site type on known protein structures. The scoring function can easily incorporate different types of information useful in localization, thus increasing the applicability and accuracy of the approach. To test the method, 1008 known protein structures were split into 20 different groups according to the type of the bound ligand. For nonsugar ligands, such as various nucleotides, binding sites were correctly identified in 55%-73% of the cases. The method is completely automated (http://salilab.org/patcher) and can be applied on a large scale in a structural genomics setting.10aAmino Acid Sequence Binding Sites Biomechanics Hydrophobicity Ligands *Monte Carlo Method Protein Conformation Proteins/*chemistry Static Electricity1 aRossi, A.1 aMarti-Renom, M., A.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1696364502671nas a2200349   4500008004100000245009900041210006900140300001100209490000700220520147800227653002901705653002701734653004401761653005601805653004001861653008301901100001501984700001401999700001802013700001602031700002402047700001402071700002402085700001602109700001702125700001602142700001702158700001402175700001302189700001302202856010602215       2006                        eng d00aMODBASE: a database of annotated comparative protein structure models and associated resources0 aMODBASE a database of annotated comparative protein structure mo  aD291-50 v343 aMODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (http://salilab.org/modweb). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, http://salilab.org/dbali), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, http://salilab.org/pibase) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, http://salilab.org/LS-SNP).10aBinding Sites *Databases10aMolecular Polymorphism10aProtein Humans Internet Ligands *Models10aProtein Systems Integration User-Computer Interface10aSingle Nucleotide Protein Structure10aTertiary Proteins/*chemistry/genetics/metabolism Software *Structural Homology1 aPieper, U.1 aEswar, N.1 aDavis, F., P.1 aBraberg, H.1 aMadhusudhan, M., S.1 aRossi, A.1 aMarti-Renom, M., A.1 aKarchin, R.1 aWebb, B., M.1 aEramian, D.1 aShen, M., Y.1 aKelly, L.1 aMelo, F.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1638186902791nas a2200193   4500008004100000245009800041210006900139300001200208490000800220520199800228653005602226653012802282100001302410700001802423700002402441700001302465700001302478856010602491       2006                        eng d00aRefinement of protein structures by iterative comparative modeling and CryoEM density fitting0 aRefinement of protein structures by iterative comparative modeli  a1655-680 v3573 aWe developed a method for structure characterization of assembly components by iterative comparative protein structure modeling and fitting into cryo-electron microscopy (cryoEM) density maps. Specifically, we calculate a comparative model of a given component by considering many alternative alignments between the target sequence and a related template structure while optimizing the fit of a model into the corresponding density map. The method relies on the previously developed Moulder protocol that iterates over alignment, model building, and model assessment. The protocol was benchmarked using 20 varied target-template pairs of known structures with less than 30% sequence identity and corresponding simulated density maps at resolutions from 5A to 25A. Relative to the models based on the best existing sequence profile alignment methods, the percentage of C(alpha) atoms that are within 5A of the corresponding C(alpha) atoms in the superposed native structure increases on average from 52% to 66%, which is half-way between the starting models and the models from the best possible alignments (82%). The test also reveals that despite the improvements in the accuracy of the fitness function, this function is still the bottleneck in reducing the remaining errors. To demonstrate the usefulness of the protocol, we applied it to the upper domain of the P8 capsid protein of rice dwarf virus that has been studied by cryoEM at 6.8A. The C(alpha) root-mean-square deviation of the model based on the remotely related template, bluetongue virus VP7, improved from 8.7A to 6.0A, while the best possible model has a C(alpha) RMSD value of 5.3A. Moreover, the resulting model fits better into the cryoEM density map than the initial template structure. The method is being implemented in our program MODELLER for protein structure modeling by satisfaction of spatial restraints and will be applicable to the rapidly increasing number of cryoEM density maps of macromolecular assemblies.10aAmino Acid Sequence Cryoelectron Microscopy *Models10aMolecular Molecular Sequence Data Plant Viruses/chemistry *Protein Conformation Software Viral Proteins/*chemistry/genetics1 aTopf, M.1 aBaker, M., L.1 aMarti-Renom, M., A.1 aChiu, W.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1649020702070nas a2200205   4500008004100000245006600041210006500107300001100172490000700183520128000190653004201470653002501512653010401537653004001641100002401681700002401705700001601729700001301745856010601758       2006                        eng d00aVariable gap penalty for protein sequence-structure alignment0 aVariable gap penalty for protein sequencestructure alignment  a129-330 v193 aThe penalty for inserting gaps into an alignment between two protein sequences is a major determinant of the alignment accuracy. Here, we present an algorithm for finding a globally optimal alignment by dynamic programming that can use a variable gap penalty (VGP) function of any form. We also describe a specific function that depends on the structural context of an insertion or deletion. It penalizes gaps that are introduced within regions of regular secondary structure, buried regions, straight segments and also between two spatially distant residues. The parameters of the penalty function were optimized on a set of 240 sequence pairs of known structure, spanning the sequence identity range of 20-40%. We then tested the algorithm on another set of 238 sequence pairs of known structures. The use of the VGP function increases the number of correctly aligned residues from 81.0 to 84.5% in comparison with the optimized affine gap penalty function; this difference is statistically significant according to Student’s t-test. We estimate that the new algorithm allows us to produce comparative models with an additional approximately 7 million accurately modeled residues in the approximately 1.1 million proteins that are detectably related to a known structure.10aAlgorithms Amino Acid Sequence Models10aAmino Acid *Software10aMolecular Molecular Sequence Data Proteins/*chemistry Sequence Alignment/*methods Sequence Analysis10aProtein/*methods *Sequence Homology1 aMadhusudhan, M., S.1 aMarti-Renom, M., A.1 aSanchez, R.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1642384602167nas a2200301   4500008004100000245008600041210006900127300001100196490000700207520100200214653012701216653002601343653013701369653003901506653001801545100002001563700001901583700001901602700001901621700001401640700002401654700001701678700001801695700001301713700001901726700001401745856010601759       2005                        eng d00aThe C-type lectin fold as an evolutionary solution for massive sequence variation0 aCtype lectin fold as an evolutionary solution for massive sequen  a886-920 v123 aOnly few instances are known of protein folds that tolerate massive sequence variation for the sake of binding diversity. The most extensively characterized is the immunoglobulin fold. We now add to this the C-type lectin (CLec) fold, as found in the major tropism determinant (Mtd), a retroelement-encoded receptor-binding protein of Bordetella bacteriophage. Variation in Mtd, with its approximately 10(13) possible sequences, enables phage adaptation to Bordetella spp. Mtd is an intertwined, pyramid-shaped trimer, with variable residues organized by its CLec fold into discrete receptor-binding sites. The CLec fold provides a highly static scaffold for combinatorial display of variable residues, probably reflecting a different evolutionary solution for balancing diversity against stability from that in the immunoglobulin fold. Mtd variants are biased toward the receptor pertactin, and there is evidence that the CLec fold is used broadly for sequence variation by related retroelements.10aAmino Acid Sequence Bacterial Outer Membrane Proteins/*chemistry Bacteriophages/*metabolism Bordetella/*virology Evolution10aBordetella/*chemistry10aC-Type/*chemistry Molecular Sequence Data Protein Conformation Protein Folding Viral Proteins/*chemistry/*genetics Virulence Factors10aMolecular Genetic Variation Genome10aViral Lectins1 aMcMahon, S., A.1 aMiller, J., L.1 aLawton, J., A.1 aKerkow, D., E.1 aHodes, A.1 aMarti-Renom, M., A.1 aDoulatov, S.1 aNarayanan, E.1 aSali, A.1 aMiller, J., F.1 aGhosh, P.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1617032402245nas a2200253   4500008004100000245008600041210006900127300001100196490000800207520134200215653001501557653003601572653012101608653002301729100001801752700001601770700001401786700002401800700001501824700001901839700001301858700001401871856010601885       2005                        eng d00aDetecting remotely related proteins by their interactions and sequence similarity0 aDetecting remotely related proteins by their interactions and se  a7151-60 v1023 aThe function of an uncharacterized protein is usually inferred either from its homology to, or its interactions with, characterized proteins. Here, we use both sequence similarity and protein interactions to identify relationships between remotely related protein sequences. We rely on the fact that homologous sequences share similar interactions, and, therefore, the set of interacting partners of the partners of a given protein is enriched by its homologs. The approach was bench-marked by assigning the fold and functional family to test sequences of known structure. Specifically, we relied on 1,434 proteins with known folds, as defined in the Structural Classification of Proteins (SCOP) database, and with known interacting partners, as defined in the Database of Interacting Proteins (DIP). For this subset, the specificity of fold assignment was increased from 54% for position-specific iterative BLAST to 75% for our approach, with a concomitant increase in sensitivity for a few percentage points. Similarly, the specificity of family assignment at the e-value threshold of 10(-8) was increased from 70% to 87%. The proposed method would be a useful tool for large-scale automated discovery of remote relationships between protein sequences, given its unique reliance on sequence similarity and protein-protein interactions.10aAmino Acid10aComputational Biology Databases10aMolecular Protein Conformation Protein Folding Proteins/*genetics/*metabolism Proteomics/*methods *Sequence Homology10aProtein *Evolution1 aEspadaler, J.1 aAragues, R.1 aEswar, N.1 aMarti-Renom, M., A.1 aQuerol, E.1 aAviles, F., X.1 aSali, A.1 aOliva, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1588337202319nas a2200181   4500008004100000245005300041210005300094300001200147490000700159520157000166653006801736653008501804653008101889100002401970700002401994700001302018856010602031       2004                        eng d00aAlignment of protein sequences by their profiles0 aAlignment of protein sequences by their profiles  a1071-870 v133 aThe accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences.10a*Algorithms Amino Acid Sequence Computational Biology Databases10aProtein Markov Chains Molecular Sequence Data *Protein Folding Protein Structure10aTertiary Proteins/*chemistry *Sequence Alignment Sequence Homology *Software1 aMarti-Renom, M., A.1 aMadhusudhan, M., S.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1504473603269nas a2200337   4500008004100000245010000041210006900141300001200210490000700222520201600229653008002245653005102325653005202376653014002428100001502568700001402583700001602597700002402613700001802637700001902655700001702674700001402691700002402705700001402729700001302743700001902756700001802775700001902793700001302812856010602825       2004                        eng d00aMODBASE, a database of annotated comparative protein structure models, and associated resources0 aMODBASE a database of annotated comparative protein structure mo  aD217-220 v323 aMODBASE (http://salilab.org/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on the MODELLER package for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE uses the MySQL relational database management system for flexible querying and CHIMERA for viewing the sequences and structures (http://www.cgl.ucsf.edu/chimera/). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different data sets. The largest data set contains 1,26,629 models for domains in 659,495 out of 1,182,126 unique protein sequences in the complete Swiss-Prot/TrEMBL database (August 25, 2003); only models based on alignments with significant similarity scores and models assessed to have the correct fold despite insignificant alignments are included. Another model data set supports target selection and structure-based annotation by the New York Structural Genomics Research Consortium; e.g. the 53 new structures produced by the consortium allowed us to characterize structurally 24,113 sequences. MODBASE also contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Our other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI, http://salilab.org/dbali) as well as web servers for automated comparative modeling with MODPIPE (MODWEB, http://salilab. org/modweb), modeling of loops in protein structures (MODLOOP, http://salilab.org/modloop) and predicting functional consequences of single nucleotide polymorphisms (SNPWEB, http://salilab. org/snpweb).10aAmino Acid Sequence Animals Binding Sites *Computational Biology *Databases10aMolecular Molecular Sequence Data Polymorphism10aProtein Genomics Humans Internet Ligands Models10aSingle Nucleotide Protein Binding Protein Conformation Proteins/*chemistry/genetics Sequence Alignment Software User-Computer Interface1 aPieper, U.1 aEswar, N.1 aBraberg, H.1 aMadhusudhan, M., S.1 aDavis, F., P.1 aStuart, A., C.1 aMirkovic, N.1 aRossi, A.1 aMarti-Renom, M., A.1 aFiser, A.1 aWebb, B.1 aGreenblatt, D.1 aHuang, C., C.1 aFerrin, T., E.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1468139802302nas a2200205   4500008004100000245012700041210006900168300001100237490000700248520137200255653006101627653007901688653013001767100001701897700002401914700001801938700001301956700002101969856010601990       2004                        eng d00aStructure-based assessment of missense mutations in human BRCA1: implications for breast and ovarian cancer predisposition0 aStructurebased assessment of missense mutations in human BRCA1 i  a3790-70 v643 aThe BRCA1 gene from individuals at risk of breast and ovarian cancers can be screened for the presence of mutations. However, the cancer association of most alleles carrying missense mutations is unknown, thus creating significant problems for genetic counseling. To increase our ability to identify cancer-associated mutations in BRCA1, we set out to use the principles of protein three-dimensional structure as well as the correlation between the cancer-associated mutations and those that abolish transcriptional activation. Thirty-one of 37 missense mutations of known impact on the transcriptional activation function of BRCA1 are readily rationalized in structural terms. Loss-of-function mutations involve nonconservative changes in the core of the BRCA1 C-terminus (BRCT) fold or are localized in a groove that presumably forms a binding site involved in the transcriptional activation by BRCA1; mutations that do not abolish transcriptional activation are either conservative changes in the core or are on the surface outside of the putative binding site. Next, structure-based rules for predicting functional consequences of a given missense mutation were applied to 57 germ-line BRCA1 variants of unknown cancer association. Such a structure-based approach may be helpful in an integrated effort to identify mutations that predispose individuals to cancer.10aBRCA1 Genetic Predisposition to Disease Humans *Mutation10aBRCA1 Protein/*chemistry/genetics Breast Neoplasms/*genetics Female *Genes10aMissense Ovarian Neoplasms/*genetics Pedigree Protein Conformation Structure-Activity Relationship Transcriptional Activation1 aMirkovic, N.1 aMarti-Renom, M., A.1 aWeber, B., L.1 aSali, A.1 aMonteiro, A., N.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1517298502057nas a2200301   4500008004100000245006000041210005900101300001100160490000700171520105900178653002501237653001201262653007701274653003201351653007901383100001601462700001901478700002401497700001901521700002401540700001401564700001401578700001401592700001701606700001301623700001301636856010601649       2003                        eng d00aEVA: Evaluation of protein structure prediction servers0 aEVA Evaluation of protein structure prediction servers  a3311-50 v313 aEVA (http://cubic.bioc.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods.10aAutomation Databases10aProtein10aProtein Internet *Protein Conformation Protein Folding Protein Structure10aProtein Structural Homology10aSecondary Proteins/chemistry Reproducibility of Results *Sequence Analysis1 aKoh, I., Y.1 aEyrich, V., A.1 aMarti-Renom, M., A.1 aPrzybylski, D.1 aMadhusudhan, M., S.1 aEswar, N.1 aGrana, O.1 aPazos, F.1 aValencia, A.1 aSali, A.1 aRost, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1282431501742nas a2200217   4500008004100000245007200041210006900113300001000182490000700192520087900199653006301078653004601141653012601187100001801313700001501331700001901346700002401365700001601389700001301405856010601418       2003                        eng d00aModView, visualization of multiple protein sequences and structures0 aModView visualization of multiple protein sequences and structur  a165-60 v193 aSUMMARY: We describe ModView, a web application for visualization of multiple protein sequences and structures. ModView integrates a multiple structure viewer, a multiple sequence alignment editor, and a database querying engine. It is possible to interactively manipulate hundreds of proteins, to visualize conservative and variable residues, active and binding sites, fragments, and domains in protein families, as well as to display large macromolecular complexes such as ribosomes or viruses. As a Netscape plug-in, ModView can be included in HTML pages along with text and figures, which makes it useful for teaching and presentations. ModView is also suitable as a graphical interface to various databases because it can be controlled through JavaScript commands and called from CGI scripts. AVAILABILITY: ModView is available at http://guitar.rockefeller.edu/modview.10a*Database Management Systems Documentation/methods Imaging10aProtein/*methods *User-Computer Interface10aThree-Dimensional/methods Protein Conformation Proteins/*chemistry/genetics Sequence Alignment/*methods Sequence Analysis1 aIlyin, V., A.1 aPieper, U.1 aStuart, A., C.1 aMarti-Renom, M., A.1 aMcMahan, L.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1249931301823nas a2200289   4500008004100000245006600041210006600107300001200173490000700185520083500192653004601027653002001073653011301093653003201206100001401238700001301252700001701265700001401282700001801296700001501314700001901329700002401348700002401372700001801396700001301414856010601427       2003                        eng d00aTools for comparative protein structure modeling and analysis0 aTools for comparative protein structure modeling and analysis  a3375-800 v313 aThe following resources for comparative protein structure modeling and analysis are described (http://salilab.org): MODELLER, a program for comparative modeling by satisfaction of spatial restraints; MODWEB, a web server for automated comparative modeling that relies on PSI-BLAST, IMPALA and MODELLER; MODLOOP, a web server for automated loop modeling that relies on MODELLER; MOULDER, a CPU intensive protocol of MODWEB for building comparative models based on distant known structures; MODBASE, a comprehensive database of annotated comparative models for all sequences detectably related to a known structure; MODVIEW, a Netscape plugin for Linux that integrates viewing of multiple sequences and structures; and SNPWEB, a web server for structure-based prediction of the functional impact of a single amino acid substitution.10aAmino Acid *Software *Structural Homology10aInternet Models10aMolecular Protein Folding Proteins/chemistry Reproducibility of Results Sequence Alignment Sequence Homology10aProtein Systems Integration1 aEswar, N.1 aJohn, B.1 aMirkovic, N.1 aFiser, A.1 aIlyin, V., A.1 aPieper, U.1 aStuart, A., C.1 aMarti-Renom, M., A.1 aMadhusudhan, M., S.1 aYerkovich, B.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1282433101632nas a2200193   4500008004100000245007000041210006900111300001100180490000700191520092300198653004001121653008301161100002401244700002401268700001401292700001301306700001301319856010601332       2002                        eng d00aReliability of assessment of protein structure prediction methods0 aReliability of assessment of protein structure prediction method  a435-400 v103 a<p>The reliability of ranking of protein structure modeling methods is assessed. The assessment is based on the parametric Student’s t test and the nonparametric Wilcox signed rank test of statistical significance of the difference between paired samples. The approach is applied to the ranking of the comparative modeling methods tested at the fourth meeting on Critical Assessment of Techniques for Protein Structure Prediction (CASP). It is shown that the 14 CASP4 test sequences may not be sufficient to reliably distinguish between the top eight methods, given the model quality differences and their standard deviations. We suggest that CASP needs to be supplemented by an assessment of protein structure prediction methods that is automated, continuous in time, based on several criteria applied to a large number of models, and with quantitative statistical reliability assigned to each characterization.</p>10a*Computer Simulation Humans *Models10aMolecular *Protein Conformation Proteins/*chemistry Reproducibility of Results1 aMarti-Renom, M., A.1 aMadhusudhan, M., S.1 aFiser, A.1 aRost, B.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1200544102774nas a2200313   4500008004100000245014600041210006900187300001300256490000800269520115600277653004801433653003001481653005001511653023501561653012201796653010201918653014402020100002002164700001502184700002102199700002202220700001702242700002402259700001302283700002002296700001902316700001902335856010602354       2002                        eng d00aUse of single point mutations in domain I of beta 2-glycoprotein I to determine fine antigenic specificity of antiphospholipid autoantibodies0 aUse of single point mutations in domain I of beta 2glycoprotein   a7097-1030 v1693 aAutoantibodies against beta(2)-glycoprotein I (beta(2)GPI) appear to be a critical feature of the antiphospholipid syndrome (APS). As determined using domain deletion mutants, human autoantibodies bind to the first of five domains present in beta(2)GPI. In this study the fine detail of the domain I epitope has been examined using 10 selected mutants of whole beta(2)GPI containing single point mutations in the first domain. The binding to beta(2)GPI was significantly affected by a number of single point mutations in domain I, particularly by mutations in the region of aa 40-43. Molecular modeling predicted these mutations to affect the surface shape and electrostatic charge of a facet of domain I. Mutation K19E also had an effect, albeit one less severe and involving fewer patients. Similar results were obtained in two different laboratories using affinity-purified anti-beta(2)GPI in a competitive inhibition ELISA and with whole serum in a direct binding ELISA. This study confirms that anti-beta(2)GPI autoantibodies bind to domain I, and that the charged surface patch defined by residues 40-43 contributes to a dominant target epitope.10aAmino Acid Substitution/genetics Antibodies10aAntibody/genetics Binding10aAntiphospholipid/blood/*metabolism Antibodies10aCompetitive/genetics/immunology Enzyme-Linked Immunosorbent Assay/methods Epitopes/analysis/*immunology/metabolism Glycine/genetics Glycoproteins/biosynthesis/*genetics/*immunology/isolation & purification/metabolism Humans Models10aMolecular Peptide Fragments/genetics/immunology/isolation & purification/metabolism *Point Mutation Protein Structure10aMonoclonal/blood/metabolism Antiphospholipid Syndrome/immunology Arginine/genetics *Binding Sites10aTertiary/genetics Recombinant Proteins/biosynthesis/immunology/isolation & purification/metabolism Static Electricity beta 2-Glycoprotein I1 aIverson, G., M.1 aReddel, S.1 aVictoria, E., J.1 aCockerill, K., A.1 aWang, Y., X.1 aMarti-Renom, M., A.1 aSali, A.1 aMarquis, D., M.1 aKrilis, S., A.1 aLinnik, M., D.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1247114601470nas a2200169   4500008004100000245005400041210005300095300001000148490000700158520082200165653003700987653011501024100002401139700001801163700001301181856010601194       2001                        eng d00aDBAli: a database of protein structure alignments0 aDBAli a database of protein structure alignments  a746-70 v173 aSUMMARY: The DBAli database includes approximately 35000 alignments of pairs of protein structures from SCOP (Lo Conte et al., Nucleic Acids Res., 28, 257-259, 2000) and CE (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998). DBAli is linked to several resources, including Compare3D (Shindyalov and Bourne, http://www.sdsc.edu/pb/software.htm, 1999) and ModView (Ilyin and Sali, http://guitar.rockefeller.edu/ModView/, 2001) for visualizing sequence alignments and structure superpositions. A flexible search of DBAli by protein sequence and structure properties allows construction of subsets of alignments suitable for a number of applications, such as benchmarking of sequence-sequence and sequence-structure alignment methods under a variety of conditions. AVAILABILITY: http://guitar.rockefeller.edu/DBAli/10aComputational Biology *Databases10aProtein Proteins/*chemistry/*genetics Sequence Alignment/*statistics & numerical data Software Software Design1 aMarti-Renom, M., A.1 aIlyin, V., A.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1152437901566nas a2200229   4500008004100000245008100041210006900122300001100191490000700202520078900209653007500998100001901073700002401092700001901116700002401135700001401159700001401173700001701187700001301204700001301217856010601230       2001                        eng d00aEVA: continuous automatic evaluation of protein structure prediction servers0 aEVA continuous automatic evaluation of protein structure predict  a1242-30 v173 aEvaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. AVAILABILITY: http://cubic.bioc.columbia.edu/eva. CONTACT: eva@cubic.bioc.columbia.edu10aAutomation Internet *Protein Conformation Proteins/*analysis *Software1 aEyrich, V., A.1 aMarti-Renom, M., A.1 aPrzybylski, D.1 aMadhusudhan, M., S.1 aFiser, A.1 aPazos, F.1 aValencia, A.1 aSali, A.1 aRost, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11751240