J Chem Inf Model - ReverseScreen3D: a structure-based ligand matching method to identify protein targets.

Tópicos

{ bind(1733) structur(1185) ligand(1036) }
{ compound(1573) activ(1297) structur(1058) }
{ method(1219) similar(1157) match(930) }
{ search(2224) databas(1162) retriev(909) }
{ perform(1367) use(1326) method(1137) }
{ perform(999) metric(946) measur(919) }
{ activ(1138) subject(705) human(624) }
{ take(945) account(800) differ(722) }
{ method(1969) cluster(1462) data(1082) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ data(1714) softwar(1251) tool(1186) }
{ howev(809) still(633) remain(590) }
{ implement(1333) system(1263) develop(1122) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2675) segment(2577) method(1081) }
{ chang(1828) time(1643) increas(1301) }
{ clinic(1479) use(1117) guidelin(835) }
{ featur(1941) imag(1645) propos(1176) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ record(1888) medic(1808) patient(1693) }
{ monitor(1329) mobil(1314) devic(1160) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ case(1353) use(1143) diagnosi(1136) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

Ligand promiscuity, which is now recognized as an extremely common phenomenon, is a major underlying cause of drug toxicity. We have developed a new reverse virtual screening (VS) method called ReverseScreen3D, which can be used to predict the potential protein targets of a query compound of interest. The method uses a 2D fingerprint-based method to select a ligand template from each unique binding site of each protein within a target database. The target database contains only the structurally determined bioactive conformations of known ligands. The 2D comparison is followed by a 3D structural comparison to the selected query ligand using a geometric matching method, in order to prioritize each target binding site in the database. We have evaluated the performance of the ReverseScreen2D and 3D methods using a diverse set of small molecule protein inhibitors known to have multiple targets, and have shown that they are able to provide a highly significant enrichment of true targets in the database. Furthermore, we have shown that the 3D structural comparison improves early enrichment when compared with the 2D method alone, and that the 3D method performs well even in the absence of 2D similarity to the template ligands. By carrying out further experimental screening on the prioritized list of targets, it may be possible to determine the potential targets of a new compound or determine the off-targets of an existing drug. The ReverseScreen3D method has been incorporated into a Web server, which is freely available at http://www.modelling.leeds.ac.uk/ReverseScreen3D .

Resumo Limpo

ligand promiscu now recogn extrem common phenomenon major under caus drug toxic develop new revers virtual screen vs method call reversescreend can use predict potenti protein target queri compound interest method use d fingerprintbas method select ligand templat uniqu bind site protein within target databas target databas contain structur determin bioactiv conform known ligand d comparison follow d structur comparison select queri ligand use geometr match method order priorit target bind site databas evalu perform reversescreend d method use divers set small molecul protein inhibitor known multipl target shown abl provid high signific enrich true target databas furthermor shown d structur comparison improv earli enrich compar d method alon d method perform well even absenc d similar templat ligand carri experiment screen priorit list target may possibl determin potenti target new compound determin offtarget exist drug reversescreend method incorpor web server freeli avail httpwwwmodellingleedsacukreversescreend

Resumos Similares

J Chem Inf Model - Fast protein binding site comparison via an index-based screening technology. ( 0,891901678830889 )
J Chem Inf Model - Virtual screening of PRK1 inhibitors: ensemble docking, rescoring using binding free energy calculation and QSAR model development. ( 0,823126260435584 )
J Chem Inf Model - SimG: an alignment based method for evaluating the similarity of small molecules and binding sites. ( 0,822409964786199 )
J Chem Inf Model - Identification of ligand templates using local structure alignment for structure-based drug design. ( 0,8206391239593 )
J Chem Inf Model - Ligand and decoy sets for docking to G protein-coupled receptors. ( 0,819191838837036 )
J Chem Inf Model - Exploration of 3D activity cliffs on the basis of compound binding modes and comparison of 2D and 3D cliffs. ( 0,814978634226537 )
J Chem Inf Model - Development and evaluation of an integrated virtual screening strategy by combining molecular docking and pharmacophore searching based on multiple protein structures. ( 0,810267907097822 )
J Chem Inf Model - Discovery of new inhibitors of Mycobacterium tuberculosis InhA enzyme using virtual screening and a 3D-pharmacophore-based approach. ( 0,804233822335952 )
J Chem Inf Model - Discovery of novel tubulin inhibitors via structure-based hierarchical virtual screening. ( 0,803620898813888 )
J Chem Inf Model - Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery. ( 0,796899032858056 )
J Chem Inf Model - Novel inhibitor discovery through virtual screening against multiple protein conformations generated via ligand-directed modeling: a maternal embryonic leucine zipper kinase example. ( 0,796485836667089 )
J Chem Inf Model - Enrichment factor analyses on G-protein coupled receptors with known crystal structure. ( 0,793727670141948 )
J Chem Inf Model - Comprehensive strategy for proton chemical shift prediction: linear prediction with nonlinear corrections. ( 0,793010752688172 )
J Chem Inf Model - Validation of the AmpC ?-lactamase binding site and identification of inhibitors with novel scaffolds. ( 0,791984938852486 )
J Chem Inf Model - Complementarity between in silico and biophysical screening approaches in fragment-based lead discovery against the A(2A) adenosine receptor. ( 0,788316535209374 )
J Chem Inf Model - Structure-based design and screen of novel inhibitors for class II 3-hydroxy-3-methylglutaryl coenzyme A reductase from Streptococcus pneumoniae. ( 0,788188787873347 )
J Chem Inf Model - Identification of sumoylation inhibitors targeting a predicted pocket in Ubc9. ( 0,787375667076774 )
J Chem Inf Model - Multi-objective evolutionary design of adenosine receptor ligands. ( 0,785510710830662 )
J Chem Inf Model - Structure-based virtual screening approach for discovery of covalently bound ligands. ( 0,784805771601695 )
J Chem Inf Model - Design of linear ligands for selective separation using a genetic algorithm applied to molecular architecture. ( 0,783857448724878 )
J Chem Inf Model - Targeting dynamic pockets of HIV-1 protease by structure-based computational screening for allosteric inhibitors. ( 0,783534327573611 )
J Chem Inf Model - Alignment-independent comparison of binding sites based on DrugScore potential fields encoded by 3D Zernike descriptors. ( 0,781790828522063 )
J Chem Inf Model - How to improve docking accuracy of AutoDock4.2: a case study using different electrostatic potentials. ( 0,780100668448979 )
J Chem Inf Model - Modeling androgen receptor flexibility: a binding mode hypothesis of CYP17 inhibitors/antiandrogens for prostate cancer therapy. ( 0,77593977932645 )
J Chem Inf Model - Potential and limitations of ensemble docking. ( 0,775558495361656 )
J Chem Inf Model - Structure-based fragment hopping for lead optimization using predocked fragment database. ( 0,772355664489528 )
Comput. Biol. Med. - In-silico characterization of ECE-1 inhibitors. ( 0,771393983454608 )
J Chem Inf Model - Multiple e-pharmacophore modeling, 3D-QSAR, and high-throughput virtual screening of hepatitis C virus NS5B polymerase inhibitors. ( 0,770153082775312 )
J Chem Inf Model - Extended template-based modeling and evaluation method using consensus of binding mode of GPCRs for virtual screening. ( 0,767702940466808 )
J Chem Inf Model - Ligand-based target prediction with signature fingerprints. ( 0,76644214597164 )
J Chem Inf Model - Molecular topology applied to the discovery of 1-benzyl-2-(3-fluorophenyl)-4-hydroxy-3-(3-phenylpropanoyl)-2H-pyrrole-5-one as a non-ligand-binding-pocket antiandrogen. ( 0,765454155989691 )
J Chem Inf Model - LIBSA--a method for the determination of ligand-binding preference to allosteric sites on receptor ensembles. ( 0,763766242446308 )
J Chem Inf Model - Identification of novel phosphodiesterase-4D inhibitors prescreened by molecular dynamics-augmented modeling and validated by bioassay. ( 0,7635744270909 )
J Chem Inf Model - Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling. ( 0,762501725223512 )
J Chem Inf Model - Identification of inhibitors against p90 ribosomal S6 kinase 2 (RSK2) through structure-based virtual screening with the inhibitor-constrained refined homology model. ( 0,761607471259073 )
J Chem Inf Model - Inhibitor design strategy based on an enzyme structural flexibility: a case of bacterial MurD ligase. ( 0,7602185967016 )
J Chem Inf Model - Structure-based virtual screening of MT2 melatonin receptor: influence of template choice and structural refinement. ( 0,760068814374178 )
Comput Biol Chem - Targeting the Akt1 allosteric site to identify novel scaffolds through virtual screening. ( 0,759414623493753 )
J Chem Inf Model - Structure-based virtual screening of the nociceptin receptor: hybrid docking and shape-based approaches for improved hit identification. ( 0,758825096347367 )
J Chem Inf Model - Effective virtual screening strategy toward covalent ligands: identification of novel NEDD8-activating enzyme inhibitors. ( 0,755597530751083 )
J Chem Inf Model - Identification and validation of novel PERK inhibitors. ( 0,752107185258857 )
J Chem Inf Model - Collecting and assessing human lactate dehydrogenase-A conformations for structure-based virtual screening. ( 0,752017415976792 )
J Chem Inf Model - Cyclophilin A inhibition: targeting transition-state-bound enzyme conformations for structure-based drug design. ( 0,751049562339784 )
J Chem Inf Model - Investigation of the differences in activity between hydroxycycloalkyl N1 substituted pyrazole derivatives as inhibitors of B-Raf kinase by using docking, molecular dynamics, QM/MM, and fragment-based de novo design: study of binding mode of diastereomer compounds. ( 0,746902093783363 )
J Chem Inf Model - Structure-based design technology contour and its application to the design of renin inhibitors. ( 0,74674630757281 )
J Chem Inf Model - Virtual target screening: validation using kinase inhibitors. ( 0,746546486006922 )
J Chem Inf Model - Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. ( 0,745644685038997 )
J Chem Inf Model - Molecular modeling on pyrimidine-urea inhibitors of TNF-a production: an integrated approach using a combination of molecular docking, classification techniques, and 3D-QSAR CoMSIA. ( 0,74535989569453 )
J Chem Inf Model - Molecular docking and pharmacophore filtering in the discovery of dual-inhibitors for human leukotriene A4 hydrolase and leukotriene C4 synthase. ( 0,745123256705285 )
J Chem Inf Model - The 5-HT(1A) agonism potential of substituted piperazine-ethyl-amide derivatives is conserved in the hexyl homologues: molecular modeling and pharmacological evaluation. ( 0,744719853488214 )
J Chem Inf Model - Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures. ( 0,744639664877694 )
J Chem Inf Model - Importance of receptor flexibility in binding of cyclam compounds to the chemokine receptor CXCR4. ( 0,744310812997036 )
J Chem Inf Model - Prediction of substrates for glutathione transferases by covalent docking. ( 0,743791507380672 )
Comput Biol Chem - Potential drug-like inhibitors of Group 1 influenza neuraminidase identified through computer-aided drug design. ( 0,743751781856327 )
J Chem Inf Model - CSBB-ConeExclusion, adapting structure based solution virtual screening to libraries on solid support. ( 0,743606430078493 )
J Chem Inf Model - Virtual screening for ligands of the insect molting hormone receptor. ( 0,742384889208117 )
J Chem Inf Model - Ligand-optimized homology models of D1 and D2 dopamine receptors: application for virtual screening. ( 0,739412985799097 )
J Chem Inf Model - Bridging molecular docking to membrane molecular dynamics to investigate GPCR-ligand recognition: the human A2A adenosine receptor as a key study. ( 0,73909100022117 )
J Chem Inf Model - G protein- and agonist-bound serotonin 5-HT2A receptor model activated by steered molecular dynamics simulations. ( 0,73741651508914 )
J Chem Inf Model - Combining ligand- and structure-based approaches for the discovery of new inhibitors of the EPHA2-ephrin-A1 interaction. ( 0,73535744092989 )
J Chem Inf Model - Consensus scoring approach to identify the inhibitors of AMP-activated protein kinase a2 with virtual screening. ( 0,73457108540565 )
J Chem Inf Model - Large-scale mining for similar protein binding pockets: with RAPMAD retrieval on the fly becomes real. ( 0,734149018714079 )
J Chem Inf Model - Beware of machine learning-based scoring functions-on the danger of developing black boxes. ( 0,73399872100458 )
J Chem Inf Model - Combined approach using ligand efficiency, cross-docking, and antitarget hits for wild-type and drug-resistant Y181C HIV-1 reverse transcriptase. ( 0,732897299271519 )
J Chem Inf Model - Extracting sets of chemical substructures and protein domains governing drug-target interactions. ( 0,732258966067529 )
J Chem Inf Model - Identification of alternative binding sites for inhibitors of HIV-1 ribonuclease H through comparative analysis of virtual enrichment studies. ( 0,731889426276197 )
J Chem Inf Model - Application of docking and QM/MM-GBSA rescoring to screen for novel Myt1 kinase inhibitors. ( 0,731699830695266 )
J Chem Inf Model - Definition of drug-likeness for compound affinity. ( 0,731426995903506 )
J Chem Inf Model - Novel insights of structure-based modeling for RNA-targeted drug discovery. ( 0,731050095871459 )
J Chem Inf Model - EMBM - a new enzyme mechanism-based method for rational design of chemical sites of covalent inhibitors. ( 0,729706748143633 )
J Chem Inf Model - Integrating ligand-based and protein-centric virtual screening of kinase inhibitors using ensembles of multiple protein kinase genes and conformations. ( 0,729438623184582 )
J Chem Inf Model - Application of quantitative structure-activity relationship models of 5-HT1A receptor binding to virtual screening identifies novel and potent 5-HT1A ligands. ( 0,727159236826069 )
J Chem Inf Model - Facing the challenges of structure-based target prediction by inverse virtual screening. ( 0,72624902121474 )
J Chem Inf Model - Protein flexibility in virtual screening: the BACE-1 case study. ( 0,725600503922798 )
J Chem Inf Model - Using free energy of binding calculations to improve the accuracy of virtual screening predictions. ( 0,725487705844993 )
J Chem Inf Model - Improving VEGFR-2 docking-based screening by pharmacophore postfiltering and similarity search postprocessing. ( 0,724241016115348 )
J Chem Inf Model - FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach. ( 0,72363695426092 )
J Chem Inf Model - Fragment-based drug discovery using a multidomain, parallel MD-MM/PBSA screening protocol. ( 0,723080573521573 )
J Chem Inf Model - The hydrogen bond environments of 1H-tetrazole and tetrazolate rings: the structural basis for tetrazole-carboxylic acid bioisosterism. ( 0,722835537142957 )
J Chem Inf Model - An extensive and diverse set of molecular overlays for the validation of pharmacophore programs. ( 0,721261970552123 )
J Chem Inf Model - Docking ligands into flexible and solvated macromolecules. 7. Impact of protein flexibility and water molecules on docking-based virtual screening accuracy. ( 0,720451119437089 )
J Chem Inf Model - Importance of the pharmacological profile of the bound ligand in enrichment on nuclear receptors: toward the use of experimentally validated decoy ligands. ( 0,719390071557279 )
J Chem Inf Model - Virtual fragment screening: discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints. ( 0,719169563532211 )
J Chem Inf Model - Toward fully automated high performance computing drug discovery: a massively parallel virtual screening pipeline for docking and molecular mechanics/generalized Born surface area rescoring to improve enrichment. ( 0,717822336678521 )
J Chem Inf Model - Selecting an optimal number of binding site waters to improve virtual screening enrichments against the adenosine A2A receptor. ( 0,716804412744764 )
J Chem Inf Model - Approximating protein flexibility through dynamic pharmacophore models: application to fatty acid amide hydrolase (FAAH). ( 0,715398349816376 )
J Chem Inf Model - Modeling flexible pharmacophores with distance geometry, scoring, and bound stretching. ( 0,714064204584028 )
J Chem Inf Model - Molecular modeling of potential anticancer agents from African medicinal plants. ( 0,713970578164281 )
J Chem Inf Model - COSMOsim3D: 3D-similarity and alignment based on COSMO polarization charge densities. ( 0,713890889863667 )
J Chem Inf Model - Fighting obesity with a sugar-based library: discovery of novel MCH-1R antagonists by a new computational-VAST approach for exploration of GPCR binding sites. ( 0,713691139601373 )
J Chem Inf Model - In silico fragment-based drug discovery: setup and validation of a fragment-to-lead computational protocol using S4MPLE. ( 0,712782108067139 )
J. Comput. Biol. - Protein-specific scoring method for ligand discovery. ( 0,711751056559386 )
J Chem Inf Model - Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. ( 0,711738336173216 )
J Chem Inf Model - SABRE: ligand/structure-based virtual screening approach using consensus molecular-shape pattern recognition. ( 0,711738105779281 )
J Chem Inf Model - Numerical errors and chaotic behavior in docking simulations. ( 0,711414187369113 )
J Chem Inf Model - A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series. ( 0,711413018520482 )
Comput. Biol. Med. - Computer-aided identification of EGFR tyrosine kinase inhibitors using ginsenosides from Panax ginseng. ( 0,711088197062514 )
J Chem Inf Model - Computer-aided structure-based design of multitarget leads for Alzheimer's disease. ( 0,710213428720693 )
J Chem Inf Model - Three descriptor model sets a high standard for the CSAR-NRC HiQ benchmark. ( 0,710018331811142 )
J Chem Inf Model - Three-dimensional pharmacophore modeling of liver-X receptor agonists. ( 0,708192962897387 )