J Chem Inf Model - Modeling flexible pharmacophores with distance geometry, scoring, and bound stretching.

Tópicos

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

Resumo

The study of pharmacophores, i.e., of common features between different ligands, is important for the quantitative identification of "compatible" enzymes and binding species. A pharmacophore-based technique is developed that combines multiple conformations with a distance geometry method to create flexible pharmacophore representations. It uses a set of low-energy conformations combined with a new process we call bound stretching to create sets of distance bounds, which contain all or most of the low-energy conformations. The bounds can be obtained using the exact distances between pairs of atoms from the different low-energy conformations. To avoid missing conformations, we can take advantage of the triangle distance inequality between sets of three points to logically expand a set of upper and lower distance bounds (bound stretching). The flexible pharmacophore can be found using a 3-D maximal common subgraph method, which uses the overlap of distance bounds to determine the overlapping structure. A scoring routine is implemented to select the substructures with the largest overlap because there will typically be many overlaps with the maximum number of overlapping bounds. A case study is presented in which 3-D flexible pharmacophores are generated and used to eliminate potential binding species identified by a 2-D pharmacophore method. A second case study creates flexible pharmacophores from a set of thrombin ligands. These are used to compare the new method with existing pharmacophore identification software.

Resumo Limpo

studi pharmacophor ie common featur differ ligand import quantit identif compat enzym bind speci pharmacophorebas techniqu develop combin multipl conform distanc geometri method creat flexibl pharmacophor represent use set lowenergi conform combin new process call bound stretch creat set distanc bound contain lowenergi conform bound can obtain use exact distanc pair atom differ lowenergi conform avoid miss conform can take advantag triangl distanc inequ set three point logic expand set upper lower distanc bound bound stretch flexibl pharmacophor can found use d maxim common subgraph method use overlap distanc bound determin overlap structur score routin implement select substructur largest overlap will typic mani overlap maximum number overlap bound case studi present d flexibl pharmacophor generat use elimin potenti bind speci identifi d pharmacophor method second case studi creat flexibl pharmacophor set thrombin ligand use compar new method exist pharmacophor identif softwar

Resumos Similares

J Chem Inf Model - Pharmacophore fingerprint-based approach to binding site subpocket similarity and its application to bioisostere replacement. ( 0,799211740308961 )
J Chem Inf Model - Improved docking of polypeptides with Glide. ( 0,798858013732948 )
J Chem Inf Model - A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series. ( 0,793691596918592 )
Comput. Biol. Med. - A scalable and accurate method for classifying protein-ligand binding geometries using a MapReduce approach. ( 0,791843666429881 )
J Chem Inf Model - AcquaAlta: a directional approach to the solvation of ligand-protein complexes. ( 0,782538691743482 )
Comput Biol Chem - Computational simulation of ligand docking to L-type pyruvate kinase subunit. ( 0,778137049810614 )
J Chem Inf Model - Accurate prediction of the bound form of the Akt pleckstrin homology domain using normal mode analysis to explore structural flexibility. ( 0,773973505402004 )
J Chem Inf Model - Are homology models sufficiently good for free-energy simulations? ( 0,773433689897045 )
J Chem Inf Model - DOLINA--docking based on a local induced-fit algorithm: application toward small-molecule binding to nuclear receptors. ( 0,771809749990628 )
J Chem Inf Model - Numerical errors and chaotic behavior in docking simulations. ( 0,765267459667133 )
J Chem Inf Model - Accurate prediction of adsorption energies on graphene, using a dispersion-corrected semiempirical method including solvation. ( 0,758965494960245 )
Comput. Biol. Med. - Cyclin-dependent kinases 5 template: useful for virtual screening. ( 0,758185676158479 )
Comput Math Methods Med - patGPCR: a multitemplate approach for improving 3D structure prediction of transmembrane helices of G-protein-coupled receptors. ( 0,756017185640932 )
J Chem Inf Model - Ligand aligning method for molecular docking: alignment of property-weighted vectors. ( 0,755519227561805 )
J Chem Inf Model - A cooperative mechanism of clotrimazoles in P450 revealed by the dissociation picture of clotrimazole from P450. ( 0,755254513531726 )
J Chem Inf Model - Transplant-insert-constrain-relax-assemble (TICRA): protein-ligand complex structure modeling and application to kinases. ( 0,754966918751537 )
J Chem Inf Model - 3D matched pairs: integrating ligand- and structure-based knowledge for ligand design and receptor annotation. ( 0,75347368751001 )
J Chem Inf Model - Computational rationale for the selective inhibition of the herpes simplex virus type 1 uracil-DNA glycosylase enzyme. ( 0,75187450418592 )
Comput Biol Chem - Theoretical improvement of the specific inhibitor of human carbonic anhydrase VII. ( 0,74959634380669 )
J Chem Inf Model - Statistical potential for modeling and ranking of protein-ligand interactions. ( 0,749482557359522 )
J Chem Inf Model - An extensive and diverse set of molecular overlays for the validation of pharmacophore programs. ( 0,749317820088777 )
J Chem Inf Model - A contribution to the drug resistance mechanism of darunavir, amprenavir, indinavir, and saquinavir complexes with HIV-1 protease due to flap mutation I50V: a systematic MM-PBSA and thermodynamic integration study. ( 0,748514074200477 )
J Chem Inf Model - Virtual screening of PRK1 inhibitors: ensemble docking, rescoring using binding free energy calculation and QSAR model development. ( 0,74438921294924 )
J Chem Inf Model - Current assessment of docking into GPCR crystal structures and homology models: successes, challenges, and guidelines. ( 0,741382786675643 )
J Chem Inf Model - Large-scale mining for similar protein binding pockets: with RAPMAD retrieval on the fly becomes real. ( 0,741371094215843 )
J Chem Inf Model - Elucidation of allosteric inhibition mechanism of 2-Cys human peroxiredoxin by molecular modeling. ( 0,736242154276545 )
J Chem Inf Model - Binding selectivity studies of phosphoinositide 3-kinases using free energy calculations. ( 0,73597867847227 )
J Chem Inf Model - Global free energy scoring functions based on distance-dependent atom-type pair descriptors. ( 0,733839619119252 )
J Chem Inf Model - Molecular dynamics simulation and free energy calculation studies of the binding mechanism of allosteric inhibitors with p38a MAP kinase. ( 0,73327812875041 )
J Chem Inf Model - Rigorous treatment of multispecies multimode ligand-receptor interactions in 3D-QSAR: CoMFA analysis of thyroxine analogs binding to transthyretin. ( 0,731440723553748 )
J Chem Inf Model - Ligand binding mode prediction by docking: mdm2/mdmx inhibitors as a case study. ( 0,726426569071988 )
J Chem Inf Model - A normal mode-based geometric simulation approach for exploring biologically relevant conformational transitions in proteins. ( 0,725997467309921 )
J Chem Inf Model - Interference of boswellic acids with the ligand binding domain of the glucocorticoid receptor. ( 0,725791463974698 )
Comput Biol Chem - Computer modeling of the dynamic properties of the cAMP-dependent protein kinase catalytic subunit. ( 0,725420193941797 )
J Chem Inf Model - Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched? ( 0,72432547522302 )
J Chem Inf Model - Protein-ligand docking using hamiltonian replica exchange simulations with soft core potentials. ( 0,723687374464969 )
J Chem Inf Model - Ligand Identification Scoring Algorithm (LISA). ( 0,723611162499425 )
J Chem Inf Model - Protein-protein binding sites prediction by 3D structural similarities. ( 0,723352688889109 )
J Chem Inf Model - Correlating protein hot spot surface analysis using ProBiS with simulated free energies of protein-protein interfacial residues. ( 0,721916882713909 )
J Chem Inf Model - Dynamics of noncovalent interactions in all-a and all-? class proteins: implications for the stability of amyloid aggregates. ( 0,721646701766687 )
J Chem Inf Model - SiteBinder: an improved approach for comparing multiple protein structural motifs. ( 0,721286873033961 )
Comput. Biol. Med. - Theoretical study of 3-D molecular similarity and ligand binding modes of orthologous human and rat D2 dopamine receptors. ( 0,720957848823508 )
J Chem Inf Model - Conformational free energy modeling of druglike molecules by metadynamics in the WHIM space. ( 0,717306878238628 )
J Chem Inf Model - GalaxyDock: protein-ligand docking with flexible protein side-chains. ( 0,716874431266295 )
J. Comput. Biol. - HarmonyDOCK: the structural analysis of poses in protein-ligand docking. ( 0,7166650351379 )
J Chem Inf Model - AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors. ( 0,714909450594884 )
J Chem Inf Model - Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. ( 0,714742232594405 )
J Chem Inf Model - Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy. ( 0,71458830750607 )
J Chem Inf Model - Potential and limitations of ensemble docking. ( 0,714577999536212 )
J Chem Inf Model - S4MPLE--sampler for multiple protein-ligand entities: simultaneous docking of several entities. ( 0,714332594549599 )
J Chem Inf Model - ReverseScreen3D: a structure-based ligand matching method to identify protein targets. ( 0,714064204584028 )
J Chem Inf Model - Comparative binding effects of aspirin and anti-inflammatory Cu complex in the active site of LOX-1. ( 0,713118408986572 )
J Chem Inf Model - Do homologous thermophilic-mesophilic proteins exhibit similar structures and dynamics at optimal growth temperatures? A molecular dynamics simulation study. ( 0,711250060730428 )
J Chem Inf Model - Numerical errors in minimization based binding energy calculations. ( 0,710796903432433 )
J Chem Inf Model - Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set. ( 0,710348233020996 )
J Chem Inf Model - Investigation on the effect of key water molecules on docking performance in CSARdock exercise. ( 0,70933094363414 )
J Chem Inf Model - Hydration properties of ligands and drugs in protein binding sites: tightly-bound, bridging water molecules and their effects and consequences on molecular design strategies. ( 0,708934820051846 )
J Chem Inf Model - Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging. ( 0,708904549630292 )
J. Comput. Biol. - A new role for the nonpathogenic nonsynonymous single-nucleotide polymorphisms of acetylcholinesterase in the treatment of Alzheimer's disease: a computational study. ( 0,708638070428107 )
J Chem Inf Model - Structural and energetic analysis of 2-aminobenzimidazole inhibitors in complex with the hepatitis C virus IRES RNA using molecular dynamics simulations. ( 0,708446375003614 )
J Chem Inf Model - Docking challenge: protein sampling and molecular docking performance. ( 0,708055988678876 )
J Chem Inf Model - Molecular docking using the molecular lipophilicity potential as hydrophobic descriptor: impact on GOLD docking performance. ( 0,707819116689637 )
J Chem Inf Model - Molecular dynamics simulation, free energy calculation and structure-based 3D-QSAR studies of B-RAF kinase inhibitors. ( 0,706939155506718 )
Comput Biol Chem - Halogen bonding in complexes of proteins and non-natural amino acids. ( 0,706434127792908 )
J Chem Inf Model - Molecular determinants of binding to the Plasmodium subtilisin-like protease 1. ( 0,706344105192461 )
J Chem Inf Model - Approximating protein flexibility through dynamic pharmacophore models: application to fatty acid amide hydrolase (FAAH). ( 0,706023500102676 )
J Chem Inf Model - Application of binding free energy calculations to prediction of binding modes and affinities of MDM2 and MDMX inhibitors. ( 0,705652927941182 )
J Chem Inf Model - Computational comparison of imidazoline association with the I2 binding site in human monoamine oxidases. ( 0,705426396250705 )
J Chem Inf Model - Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. ( 0,704275230285778 )
J Chem Inf Model - Matching cavities in g protein-coupled receptors to infer ligand-binding sites. ( 0,704115207845053 )
J Chem Inf Model - Construction and test of ligand decoy sets using MDock: community structure-activity resource benchmarks for binding mode prediction. ( 0,70386288669252 )
J Chem Inf Model - Molecular dynamics simulations of CXCL-8 and its interactions with a receptor peptide, heparin fragments, and sulfated linked cyclitols. ( 0,70333608218189 )
J Chem Inf Model - Elucidation of conformational states, dynamics, and mechanism of binding in human -opioid receptor complexes. ( 0,703288908916095 )
J Chem Inf Model - Combining solvent thermodynamic profiles with functionality maps of the Hsp90 binding site to predict the displacement of water molecules. ( 0,703226620901249 )
J Chem Inf Model - Computational analysis of human OGA structure in complex with PUGNAc and NAG-thiazoline derivatives. ( 0,703118586829386 )
J Chem Inf Model - Theoretical studies on the interactions and interferences of HIV-1 glycoprotein gp120 and its coreceptor CCR5. ( 0,703095427655231 )
J Chem Inf Model - Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach. ( 0,702971546025627 )
J Chem Inf Model - Fast protein binding site comparison via an index-based screening technology. ( 0,702835060921241 )
J Chem Inf Model - Computational method to identify druggable binding sites that target protein-protein interactions. ( 0,7028325153275 )
J Chem Inf Model - Molecular dynamic behavior and binding affinity of flavonoid analogues to the cyclin dependent kinase 6/cyclin D complex. ( 0,702338394720726 )
J Chem Inf Model - Structural insight into the unique binding properties of pyridylethanol(phenylethyl)amine inhibitor in human CYP51. ( 0,702167494564014 )
J Chem Inf Model - Ligand binding site identification by higher dimension molecular dynamics. ( 0,701957514459068 )
J Chem Inf Model - Significant enhancement of docking sensitivity using implicit ligand sampling. ( 0,701697352555517 )
J Chem Inf Model - Scoring by intermolecular pairwise propensities of exposed residues (SIPPER): a new efficient potential for protein-protein docking. ( 0,701191546532268 )
J Chem Inf Model - Binding conformation of 2-oxoamide inhibitors to group IVA cytosolic phospholipase A2 determined by molecular docking combined with molecular dynamics. ( 0,701157709281869 )
J Chem Inf Model - Profiling the structural determinants for the selectivity of representative factor-Xa and thrombin inhibitors using combined ligand-based and structure-based approaches. ( 0,701096979718468 )
J Chem Inf Model - Strategies to calculate water binding free energies in protein-ligand complexes. ( 0,701012840538376 )
J Chem Inf Model - Incorporating backbone flexibility in MedusaDock improves ligand-binding pose prediction in the CSAR2011 docking benchmark. ( 0,699728050292642 )
J Chem Inf Model - Molecular modeling of neurokinin B and tachykinin NK3 receptor complex. ( 0,69948697286952 )
J Chem Inf Model - Docking simulation study and kinase selectivity of f152A1 and its analogs. ( 0,699187819051742 )
J Chem Inf Model - Conformer generation with OMEGA: learning from the data set and the analysis of failures. ( 0,698810838470458 )
J Chem Inf Model - Elucidating a key component of cancer metastasis: CXCL12 (SDF-1a) binding to CXCR4. ( 0,698338495817471 )
J Chem Inf Model - Unbinding pathways of VEGFR2 inhibitors revealed by steered molecular dynamics. ( 0,698188252587556 )
J Chem Inf Model - The molecular basis for the selectivity of tadalafil toward phosphodiesterase 5 and 6: a modeling study. ( 0,697646577999245 )
J Chem Inf Model - Key binding and susceptibility of NS3/4A serine protease inhibitors against hepatitis C virus. ( 0,697557654703497 )
J Chem Inf Model - Elements of nucleotide specificity in the Trypanosoma brucei mitochondrial RNA editing enzyme RET2. ( 0,697292912431266 )
J Chem Inf Model - Using free energy of binding calculations to improve the accuracy of virtual screening predictions. ( 0,697240935065527 )
J Chem Inf Model - Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. ( 0,696795109371825 )
J Chem Inf Model - Water network perturbation in ligand binding: adenosine A(2A) antagonists as a case study. ( 0,695860611737838 )
J Chem Inf Model - Thermodynamics of fragment binding. ( 0,69537866356318 )