J Chem Inf Model - Probabilistic models for capturing more physicochemical properties on protein-protein interface.

Tópicos

{ state(1844) use(1261) util(961) }
{ bind(1733) structur(1185) ligand(1036) }
{ imag(1057) registr(996) error(939) }
{ method(1219) similar(1157) match(930) }
{ model(3404) distribut(989) bayesian(671) }
{ featur(1941) imag(1645) propos(1176) }
{ import(1318) role(1303) understand(862) }
{ activ(1452) weight(1219) physic(1104) }
{ model(3480) simul(1196) paramet(876) }
{ first(2504) two(1366) second(1323) }
{ can(774) often(719) complex(702) }
{ take(945) account(800) differ(722) }
{ framework(1458) process(801) describ(734) }
{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2675) segment(2577) method(1081) }
{ assess(1506) score(1403) qualiti(1306) }
{ method(1557) propos(1049) approach(1037) }
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{ howev(809) still(633) remain(590) }
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{ signal(2180) analysi(812) frequenc(800) }
{ gene(2352) biolog(1181) express(1162) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ process(1125) use(805) approach(778) }
{ method(2212) result(1239) propos(1039) }
{ 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) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
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{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ 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) }
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{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
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{ perform(1367) use(1326) method(1137) }
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{ health(3367) inform(1360) care(1135) }
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{ ehr(2073) health(1662) electron(1139) }
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{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
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{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
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{ structur(1116) can(940) graph(676) }
{ 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) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Protein-protein interactions play a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. It is of great interest to understand how proteins interact with each other. The general approach is to explore all possible poses and identify near-native ones with the energy function. The key issue here is to design an effective energy function, based on various physicochemical properties. In this paper, we first identify two new features, the coupled dihedral angles on the interfaces and the geometrical information on p-p interactions. We study these two features through statistical methods: a mixture of bivariate von Mises distributions is used to model the correlation of the coupled dihedral angles, while a mixture of bivariate normal distributions is used to model the orientation of the aromatic rings on p-p interactions. Using 6438 complexes, we parametrize the joint distribution of each new feature. Then, we propose a novel method to construct the energy function for protein-protein interface prediction, which includes the new features as well as the existing energy items such as dDFIRE energy, side-chain energy, atom contact energy, and amino acid energy. Experiments show that our method outperforms the state-of-the-art methods, ZRANK and ClusPro. We use the CAPRI evaluation criteria, Irmsd value, and Fnat value. On Benchmark v4.0, our method has an average Irmsd value of 3.39 ? and Fnat value of 62%, which improves upon the average Irmsd value of 3.89 ? and Fnat value of 49% for ZRANK, and the average Irmsd value of 3.99 ? and Fnat value of 46% for ClusPro. On the CAPRI targets, our method has an average Irmsd value of 3.56 ? and Fnat value of 42%, which improves upon the average Irmsd value of 4.27 ? and Fnat value of 39% for ZRANK, the average Irmsd value of 5.15 ? and Fnat value of 30% for ClusPro.

Resumo Limpo

proteinprotein interact play key role multitud biolog process signal transduct de novo drug design immun respons enzymat activ great interest understand protein interact general approach explor possibl pose identifi nearnat one energi function key issu design effect energi function base various physicochem properti paper first identifi two new featur coupl dihedr angl interfac geometr inform pp interact studi two featur statist method mixtur bivari von mise distribut use model correl coupl dihedr angl mixtur bivari normal distribut use model orient aromat ring pp interact use complex parametr joint distribut new featur propos novel method construct energi function proteinprotein interfac predict includ new featur well exist energi item ddfire energi sidechain energi atom contact energi amino acid energi experi show method outperform stateoftheart method zrank cluspro use capri evalu criteria irmsd valu fnat valu benchmark v method averag irmsd valu fnat valu improv upon averag irmsd valu fnat valu zrank averag irmsd valu fnat valu cluspro capri target method averag irmsd valu fnat valu improv upon averag irmsd valu fnat valu zrank averag irmsd valu fnat valu cluspro

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