J Chem Inf Model - Prediction of active site cleft using support vector machines.

Tópicos

{ perform(999) metric(946) measur(919) }
{ sequenc(1873) structur(1644) protein(1328) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(2212) result(1239) propos(1039) }
{ model(2656) set(1616) predict(1553) }
{ method(1969) cluster(1462) data(1082) }
{ can(981) present(881) function(850) }
{ use(1733) differ(960) four(931) }
{ studi(2440) review(1878) systemat(933) }
{ clinic(1479) use(1117) guidelin(835) }
{ model(3480) simul(1196) paramet(876) }
{ first(2504) two(1366) second(1323) }
{ data(1737) use(1416) pattern(1282) }
{ network(2748) neural(1063) input(814) }
{ method(984) reconstruct(947) comput(926) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ signal(2180) analysi(812) frequenc(800) }
{ imag(1057) registr(996) error(939) }
{ case(1353) use(1143) diagnosi(1136) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ sampl(1606) size(1419) use(1276) }
{ patient(1821) servic(1111) care(1106) }
{ use(976) code(926) identifi(902) }
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{ estim(2440) model(1874) function(577) }
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{ activ(1452) weight(1219) physic(1104) }
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{ health(3367) inform(1360) care(1135) }
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{ 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) }
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{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
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{ data(3008) multipl(1320) sourc(1022) }
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{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ 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) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Computational tools are available today for the detection and delineation of the clefts and cavities in protein 3D structure and ranking them on the basis of probable binding site clefts. There is a need to improve the ranking of clefts and accuracy of predicting catalytic site clefts. Our results show that the distance of the clefts from protein centroid and sequence entropy of the lining residues, when used in conjunction with the volume, are valuable descriptors for predicting the catalytic site. We have applied the SVM approach for recognizing and ranking the active site clefts and tested its performance using different combinations of attributes. In both the ligand-bound and the unbound forms of structures, our method correctly predicts the active site clefts in 73% of cases at rank one. If we consider the results at rank 3 (i.e., the correct solution is among one of the top three solutions), the correctly predicted cases are 94% and 90% for the bound and the unbound forms of structures, respectively. Our approach improves the ranking of binding site clefts in comparison with CASTp and is comparable to other existing methods like Fpocket. Although the data set for training the SVM approach is rather small in size, the results are encouraging for the method to be used as complementary to other existing tools.

Resumo Limpo

comput tool avail today detect delin cleft caviti protein d structur rank basi probabl bind site cleft need improv rank cleft accuraci predict catalyt site cleft result show distanc cleft protein centroid sequenc entropi line residu use conjunct volum valuabl descriptor predict catalyt site appli svm approach recogn rank activ site cleft test perform use differ combin attribut ligandbound unbound form structur method correct predict activ site cleft case rank one consid result rank ie correct solut among one top three solut correct predict case bound unbound form structur respect approach improv rank bind site cleft comparison castp compar exist method like fpocket although data set train svm approach rather small size result encourag method use complementari exist tool

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