J Chem Inf Model - Predicting ligand binding modes from neural networks trained on protein-ligand interaction fingerprints.

Tópicos

{ bind(1733) structur(1185) ligand(1036) }
{ model(2656) set(1616) predict(1553) }
{ network(2748) neural(1063) input(814) }
{ featur(1941) imag(1645) propos(1176) }
{ featur(3375) classif(2383) classifi(1994) }
{ structur(1116) can(940) graph(676) }
{ method(984) reconstruct(947) comput(926) }
{ model(2341) predict(2261) use(1141) }
{ compound(1573) activ(1297) structur(1058) }
{ can(774) often(719) complex(702) }
{ method(1219) similar(1157) match(930) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ case(1353) use(1143) diagnosi(1136) }
{ studi(1410) differ(1259) use(1210) }
{ import(1318) role(1303) understand(862) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ first(2504) two(1366) second(1323) }
{ analysi(2126) use(1163) compon(1037) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ model(3404) distribut(989) bayesian(671) }
{ 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) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ 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) }
{ search(2224) databas(1162) retriev(909) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ 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) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ 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) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ 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(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We herewith present a novel approach to predict protein-ligand binding modes from the single two-dimensional structure of the ligand. Known protein-ligand X-ray structures were converted into binary bit strings encoding protein-ligand interactions. An artificial neural network was then set up to first learn and then predict protein-ligand interaction fingerprints from simple ligand descriptors. Specific models were constructed for three targets (CDK2, p38-a, HSP90-a) and 146 ligands for which protein-ligand X-ray structures are available. These models were able to predict protein-ligand interaction fingerprints and to discriminate important features from minor interactions. Predicted interaction fingerprints were successfully used as descriptors to discriminate true ligands from decoys by virtual screening. In some but not all cases, the predicted interaction fingerprints furthermore enable to efficiently rerank cross-docking poses and prioritize the best possible docking solutions.

Resumo Limpo

herewith present novel approach predict proteinligand bind mode singl twodimension structur ligand known proteinligand xray structur convert binari bit string encod proteinligand interact artifici neural network set first learn predict proteinligand interact fingerprint simpl ligand descriptor specif model construct three target cdk pa hspa ligand proteinligand xray structur avail model abl predict proteinligand interact fingerprint discrimin import featur minor interact predict interact fingerprint success use descriptor discrimin true ligand decoy virtual screen case predict interact fingerprint furthermor enabl effici rerank crossdock pose priorit best possibl dock solut

Resumos Similares

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. ( 0,871515214670089 )
J Chem Inf Model - Ligand Identification Scoring Algorithm (LISA). ( 0,869019917022217 )
J Chem Inf Model - Incorporating backbone flexibility in MedusaDock improves ligand-binding pose prediction in the CSAR2011 docking benchmark. ( 0,864401179731819 )
J Chem Inf Model - PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and calorimetry measurements. ( 0,864035585079307 )
J Chem Inf Model - Modeling, molecular dynamics simulation, and mutation validation for structure of cannabinoid receptor 2 based on known crystal structures of GPCRs. ( 0,863167005925421 )
J Chem Inf Model - Molecular binding sites are located near the interface of intrinsic dynamics domains (IDDs). ( 0,860697693239463 )
J Chem Inf Model - Improving the scoring of protein-ligand binding affinity by including the effects of structural water and electronic polarization. ( 0,855357800146057 )
J Chem Inf Model - Global free energy scoring functions based on distance-dependent atom-type pair descriptors. ( 0,851637444083453 )
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. ( 0,850079118875881 )
J Chem Inf Model - MM/GBSA binding energy prediction on the PDBbind data set: successes, failures, and directions for further improvement. ( 0,846809139557768 )
J Chem Inf Model - The assembly-inducing laulimalide/peloruside a binding site on tubulin: molecular modeling and biochemical studies with [?H]peloruside A. ( 0,840793920471633 )
J Chem Inf Model - Computational analysis of human OGA structure in complex with PUGNAc and NAG-thiazoline derivatives. ( 0,839171692038208 )
J Chem Inf Model - Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach. ( 0,836735733001068 )
J Chem Inf Model - Investigation on the effect of key water molecules on docking performance in CSARdock exercise. ( 0,833583289662142 )
J Chem Inf Model - Analyzing the topology of active sites: on the prediction of pockets and subpockets. ( 0,83145992338998 )
J Chem Inf Model - Construction and test of ligand decoy sets using MDock: community structure-activity resource benchmarks for binding mode prediction. ( 0,829010233334807 )
J Chem Inf Model - Combined application of cheminformatics- and physical force field-based scoring functions improves binding affinity prediction for CSAR data sets. ( 0,828042080640981 )
Comput Biol Chem - Docking assay of small molecule antivirals to p7 of HCV. ( 0,825968538126015 )
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. ( 0,824112720080132 )
J Chem Inf Model - Ligand binding site identification by higher dimension molecular dynamics. ( 0,824008045886363 )
J Chem Inf Model - Molecular modeling of neurokinin B and tachykinin NK3 receptor complex. ( 0,822826286294868 )
J Chem Inf Model - Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. ( 0,821729656387374 )
J Chem Inf Model - Ensemble-based docking using biased molecular dynamics. ( 0,821620909405289 )
J Chem Inf Model - Significant enhancement of docking sensitivity using implicit ligand sampling. ( 0,821271289477198 )
J Chem Inf Model - Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. ( 0,820876371660875 )
J Chem Inf Model - Including explicit water molecules as part of the protein structure in MM/PBSA calculations. ( 0,820199532264724 )
J. Comput. Biol. - HarmonyDOCK: the structural analysis of poses in protein-ligand docking. ( 0,820028794011267 )
J Chem Inf Model - Hands-off linear interaction energy approach to binding mode and affinity estimation of estrogens. ( 0,819888096799795 )
J Chem Inf Model - AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors. ( 0,818017707742504 )
J Chem Inf Model - Comprehensive classification and diversity assessment of atomic contacts in protein-small ligand interactions. ( 0,817471826238291 )
J Chem Inf Model - Application of binding free energy calculations to prediction of binding modes and affinities of MDM2 and MDMX inhibitors. ( 0,817469230190605 )
J Chem Inf Model - Comparative study on the use of docking and Bayesian categorization to predict ligand binding to nicotinic acetylcholine receptors (nAChRs) subtypes. ( 0,817446829264904 )
J Chem Inf Model - Current assessment of docking into GPCR crystal structures and homology models: successes, challenges, and guidelines. ( 0,817010815161173 )
J Chem Inf Model - Molecular dynamics simulation, free energy calculation and structure-based 3D-QSAR studies of B-RAF kinase inhibitors. ( 0,816799965141429 )
J Chem Inf Model - Flexibility and explicit solvent in molecular-dynamics-based docking of protein-glycosaminoglycan systems. ( 0,816671842967392 )
J Chem Inf Model - Structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) for inhibitor design. ( 0,81655696026834 )
J Chem Inf Model - Molecular docking with ligand attached water molecules. ( 0,814747760145334 )
J Chem Inf Model - Conformational free energy modeling of druglike molecules by metadynamics in the WHIM space. ( 0,81343555920593 )
J Chem Inf Model - New aryl hydrocarbon receptor homology model targeted to improve docking reliability. ( 0,813181698294922 )
J Chem Inf Model - Protein-ligand docking using hamiltonian replica exchange simulations with soft core potentials. ( 0,811133911189916 )
Comput Biol Chem - Computational simulation of ligand docking to L-type pyruvate kinase subunit. ( 0,810612285997622 )
J Chem Inf Model - Pharmacophore and 3D-QSAR characterization of 6-arylquinazolin-4-amines as Cdc2-like kinase 4 (Clk4) and dual specificity tyrosine-phosphorylation-regulated kinase 1A (Dyrk1A) inhibitors. ( 0,810363897686608 )
Comput Biol Chem - Computer modeling of the dynamic properties of the cAMP-dependent protein kinase catalytic subunit. ( 0,809927921862457 )
J Chem Inf Model - Correlating protein hot spot surface analysis using ProBiS with simulated free energies of protein-protein interfacial residues. ( 0,809840439745167 )
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. ( 0,809691265699951 )
J Chem Inf Model - Predicting the sites and energies of noncovalent intermolecular interactions using local properties. ( 0,808943542868561 )
J Chem Inf Model - Ligand aligning method for molecular docking: alignment of property-weighted vectors. ( 0,808830596473177 )
J Chem Inf Model - Molecular dynamics simulations of CXCL-8 and its interactions with a receptor peptide, heparin fragments, and sulfated linked cyclitols. ( 0,808565814936964 )
J Chem Inf Model - Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched? ( 0,808466412000044 )
J Chem Inf Model - Cytochrome P450 3A4 inhibition by ketoconazole: tackling the problem of ligand cooperativity using molecular dynamics simulations and free-energy calculations. ( 0,808005266812046 )
J Chem Inf Model - Computational comparison of imidazoline association with the I2 binding site in human monoamine oxidases. ( 0,807740117399233 )
J Chem Inf Model - Postprocessing of docked protein-ligand complexes using implicit solvation models. ( 0,80614252804172 )
J Chem Inf Model - Combining solvent thermodynamic profiles with functionality maps of the Hsp90 binding site to predict the displacement of water molecules. ( 0,805199244456263 )
J Chem Inf Model - Using free energy of binding calculations to improve the accuracy of virtual screening predictions. ( 0,804966177421963 )
J Chem Inf Model - Ligand binding mode prediction by docking: mdm2/mdmx inhibitors as a case study. ( 0,804775760250892 )
J Chem Inf Model - Protein-protein binding sites prediction by 3D structural similarities. ( 0,804388688849813 )
J Chem Inf Model - Binding conformation of 2-oxoamide inhibitors to group IVA cytosolic phospholipase A2 determined by molecular docking combined with molecular dynamics. ( 0,804189795986553 )
J Chem Inf Model - Toward an optimal docking and free energy calculation scheme in ligand design with application to COX-1 inhibitors. ( 0,80348774459246 )
J Chem Inf Model - Molecular recognition in a diverse set of protein-ligand interactions studied with molecular dynamics simulations and end-point free energy calculations. ( 0,803435810057788 )
J Chem Inf Model - Structural insight into the unique binding properties of pyridylethanol(phenylethyl)amine inhibitor in human CYP51. ( 0,803334922784619 )
Comput Biol Chem - Theoretical improvement of the specific inhibitor of human carbonic anhydrase VII. ( 0,802969619563146 )
J Chem Inf Model - Comparative binding effects of aspirin and anti-inflammatory Cu complex in the active site of LOX-1. ( 0,802130743163491 )
Comput. Biol. Med. - Analysis of the structure of calpain-10 and its interaction with the protease inhibitor SNJ-1715. ( 0,800889354041551 )
J Chem Inf Model - Molecular modeling of p38a mitogen-activated protein kinase inhibitors through 3D-QSAR and molecular dynamics simulations. ( 0,800603174551303 )
J Chem Inf Model - Perturbation of fluid dynamics properties of water molecules during G protein-coupled receptor-ligand recognition: the human A2A adenosine receptor as a key study. ( 0,800074277802191 )
J Chem Inf Model - Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging. ( 0,800023074223693 )
J Chem Inf Model - Computational modeling of the catalytic mechanism of human placental alkaline phosphatase (PLAP). ( 0,799387326379476 )
J Chem Inf Model - Accurate prediction of the bound form of the Akt pleckstrin homology domain using normal mode analysis to explore structural flexibility. ( 0,799311547305506 )
J Chem Inf Model - Binding selectivity studies of phosphoinositide 3-kinases using free energy calculations. ( 0,799142569278991 )
J Chem Inf Model - 3D structure prediction of TAS2R38 bitter receptors bound to agonists phenylthiocarbamide (PTC) and 6-n-propylthiouracil (PROP). ( 0,798766313242165 )
J Chem Inf Model - Rationalizing tight ligand binding through cooperative interaction networks. ( 0,798717522645547 )
J Chem Inf Model - Molecular docking and competitive binding study discovered different binding modes of microsomal prostaglandin E synthase-1 inhibitors. ( 0,798330067491251 )
J Chem Inf Model - Elucidation of allosteric inhibition mechanism of 2-Cys human peroxiredoxin by molecular modeling. ( 0,79787044846728 )
J Chem Inf Model - Exploring inhibitor release pathways in histone deacetylases using random acceleration molecular dynamics simulations. ( 0,797643000677849 )
J Chem Inf Model - Discovery of FDA-approved drugs as inhibitors of fatty acid binding protein 4 using molecular docking screening. ( 0,7975969896443 )
J Chem Inf Model - DOLINA--docking based on a local induced-fit algorithm: application toward small-molecule binding to nuclear receptors. ( 0,797304067431604 )
J Chem Inf Model - A machine learning-based method to improve docking scoring functions and its application to drug repurposing. ( 0,797231443336068 )
J Chem Inf Model - Unraveling the allosteric inhibition mechanism of PTP1B by free energy calculation based on umbrella sampling. ( 0,796980443651707 )
J Chem Inf Model - Water network perturbation in ligand binding: adenosine A(2A) antagonists as a case study. ( 0,795995432355254 )
J Chem Inf Model - A normal mode-based geometric simulation approach for exploring biologically relevant conformational transitions in proteins. ( 0,7959252568234 )
J Chem Inf Model - Computational insight into small molecule inhibition of cyclophilins. ( 0,795754401040566 )
Comput Biol Chem - The human olfactory receptor 17-40: requisites for fitting into the binding pocket. ( 0,795562730605575 )
J Chem Inf Model - DrugPred: a structure-based approach to predict protein druggability developed using an extensive nonredundant data set. ( 0,795288877527006 )
Comput Biol Chem - Molecular simulation investigation on the interaction between barrier-to-autointegration factor dimer or its Gly25Glu mutant and LEM domain of emerin. ( 0,795283217680213 )
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. ( 0,794747397096155 )
Comput. Biol. Med. - A scalable and accurate method for classifying protein-ligand binding geometries using a MapReduce approach. ( 0,794548229130509 )
J Chem Inf Model - Approximating protein flexibility through dynamic pharmacophore models: application to fatty acid amide hydrolase (FAAH). ( 0,794456717136394 )
J Chem Inf Model - Insight into the fundamental interactions between LEDGF binding site inhibitors and integrase combining docking and molecular dynamics simulations. ( 0,794302999806732 )
J Chem Inf Model - Structure-based multiscale approach for identification of interaction partners of PDZ domains. ( 0,794265380649412 )
J Chem Inf Model - MMGBSA as a tool to understand the binding affinities of filamin-peptide interactions. ( 0,793737769110642 )
J Chem Inf Model - Statistical potential for modeling and ranking of protein-ligand interactions. ( 0,793437694590784 )
J Chem Inf Model - AcquaAlta: a directional approach to the solvation of ligand-protein complexes. ( 0,793089455793926 )
J Chem Inf Model - Strategies for improved modeling of GPCR-drug complexes: blind predictions of serotonin receptors bound to ergotamine. ( 0,792901506157143 )
J Chem Inf Model - Molecular dynamics simulation and free energy calculation studies of the binding mechanism of allosteric inhibitors with p38a MAP kinase. ( 0,791554248273746 )
J Chem Inf Model - Pharmacophore fingerprint-based approach to binding site subpocket similarity and its application to bioisostere replacement. ( 0,791236777678288 )
J Chem Inf Model - Computation of binding energies including their enthalpy and entropy components for protein-ligand complexes using support vector machines. ( 0,789665296251793 )
J Chem Inf Model - Structural basis of specific binding between Aurora A and TPX2 by molecular dynamics simulations. ( 0,789551071698004 )
J Chem Inf Model - Interference of boswellic acids with the ligand binding domain of the glucocorticoid receptor. ( 0,789542905548972 )
J Chem Inf Model - Molecular dynamic behavior and binding affinity of flavonoid analogues to the cyclin dependent kinase 6/cyclin D complex. ( 0,7895038442689 )
J Chem Inf Model - Conformational analysis of 6a- and 6?-naltrexol and derivatives and relationship to opioid receptor affinity. ( 0,789477059089748 )