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I-TASSER results for job id S802695

(Click on S802695_results.tar.bz2 to download the tarball file including all modeling results listed on this page. Click on Annotation of I-TASSER Output to read the instructions for how to interpret the results on this page. Model results are kept on the server for 60 days, there is no way to retrieve the modeling data older than 2 months)

  Submitted Sequence in FASTA format

>protein
EVWTQRLHGGSAPLPQDRGFLVVQGDPRELRLWARGDARGASRADEKPLRRKRSAALQPE
PIKVYGQVSLNDSHNQMVVHWAGEKSNVIVALARDSLALARPKSSDVYVSYDYGKSFKKI
SDKLNFGLGNRSEAVIAQFYHSPADNKRYIFADAYAQYLWITFDFCNTLQGFSIPFRAAD
LLLHSKASNLLLGFDRSHPNKQLWKSDDFGQTWIMIQEHVKSFSWGIDPYDKPNTIYIER
HEPSGYSTVFRSTDFFQSRENQEVILEEVRDFQLRDKYMFATKVVHLLGSEQQSSVQLWV
SFGRKPMRAAQFVTRHPINEYYIADASEDQVFVCVSHSNNRTNLYISEAEGLKFSLSLEN
VLYYSPGGAGSDTLVRYFANEPFADFHRVEGLQGVYIATLINGSMNEENMRSVITFDKGG
TWEFLQAPAFTGYGEKINCELSQGCSLHLAQRLSQLLNLQLRRMPILSKESAPGLIIATG
SVGKNLASKTNVYISSSAGARWREALPGPHYYTWGDHGGIITAIAQGMETNELKYSTNEG
ETWKTFIFSEKPVFVYGLLTEPGEKSTVFTIFGSNKENVHSWLILQVNATDALGVPCTEN
DYKLWSPSDERGNECLLGHKTVFKRRTPHATCFNGEDFDRPVVVSNCSCTREDYECDFGF
KMSEDLSLEVCVPDPEFSGKSYSPPVPCPVGSTYRRTRGYRKISGDTCSGGDVEARLEGE
LVPCPLAEENEFILYAVRKSIYRYDLASGATEQLPLTGLRAAVALDFDYEHNCLYWSDLA
LDVIQRLCLNGSTGQEVIINSGLETVEALAFEPLSQLLYWVDAGFKKIEVANPDGDFRLT
IVNSSVLDRPRALVLVPQEGVMFWTDWGDLKPGIYRSNMDGSAAYHLVSEDVKWPNGISV
DDQWIYWTDAYLECIERITFSGQQRSVILDNLPHPYAIAVFKNEIYWDDWSQLSIFRASK
YSGSQMEILANQLTGLMDMKIFYKGKNTGSNACVPRPCSLLCLPKANNSRSCRCPEDVSS
SVLPSGDLMCDCPQGYQLKNNTCVKQENTCLRNQYRCSNGNCINSIWWCDFDNDCGDMSD
ERNCPTTICDLDTQFRCQESGTCIPLSYKCDLEDDCGDNSDESHCEMHQCRSDEYNCSSG
MCIRSSWVCDGDNDCRDWSDEANCTAIYHTCEASNFQCRNGHCIPQRWAC

  Predicted Secondary Structure

Sequence                  20                  40                  60                  80                 100                 120                 140                 160                 180                 200                 220                 240                 260                 280                 300                 320                 340                 360                 380                 400                 420                 440                 460                 480                 500                 520                 540                 560                 580                 600                 620                 640                 660                 680                 700                 720                 740                 760                 780                 800                 820                 840                 860                 880                 900                 920                 940                 960                 980                1000                1020                1040                1060                1080                1100                1120                1140                1160                1180
                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |          
EVWTQRLHGGSAPLPQDRGFLVVQGDPRELRLWARGDARGASRADEKPLRRKRSAALQPEPIKVYGQVSLNDSHNQMVVHWAGEKSNVIVALARDSLALARPKSSDVYVSYDYGKSFKKISDKLNFGLGNRSEAVIAQFYHSPADNKRYIFADAYAQYLWITFDFCNTLQGFSIPFRAADLLLHSKASNLLLGFDRSHPNKQLWKSDDFGQTWIMIQEHVKSFSWGIDPYDKPNTIYIERHEPSGYSTVFRSTDFFQSRENQEVILEEVRDFQLRDKYMFATKVVHLLGSEQQSSVQLWVSFGRKPMRAAQFVTRHPINEYYIADASEDQVFVCVSHSNNRTNLYISEAEGLKFSLSLENVLYYSPGGAGSDTLVRYFANEPFADFHRVEGLQGVYIATLINGSMNEENMRSVITFDKGGTWEFLQAPAFTGYGEKINCELSQGCSLHLAQRLSQLLNLQLRRMPILSKESAPGLIIATGSVGKNLASKTNVYISSSAGARWREALPGPHYYTWGDHGGIITAIAQGMETNELKYSTNEGETWKTFIFSEKPVFVYGLLTEPGEKSTVFTIFGSNKENVHSWLILQVNATDALGVPCTENDYKLWSPSDERGNECLLGHKTVFKRRTPHATCFNGEDFDRPVVVSNCSCTREDYECDFGFKMSEDLSLEVCVPDPEFSGKSYSPPVPCPVGSTYRRTRGYRKISGDTCSGGDVEARLEGELVPCPLAEENEFILYAVRKSIYRYDLASGATEQLPLTGLRAAVALDFDYEHNCLYWSDLALDVIQRLCLNGSTGQEVIINSGLETVEALAFEPLSQLLYWVDAGFKKIEVANPDGDFRLTIVNSSVLDRPRALVLVPQEGVMFWTDWGDLKPGIYRSNMDGSAAYHLVSEDVKWPNGISVDDQWIYWTDAYLECIERITFSGQQRSVILDNLPHPYAIAVFKNEIYWDDWSQLSIFRASKYSGSQMEILANQLTGLMDMKIFYKGKNTGSNACVPRPCSLLCLPKANNSRSCRCPEDVSSSVLPSGDLMCDCPQGYQLKNNTCVKQENTCLRNQYRCSNGNCINSIWWCDFDNDCGDMSDERNCPTTICDLDTQFRCQESGTCIPLSYKCDLEDDCGDNSDESHCEMHQCRSDEYNCSSGMCIRSSWVCDGDNDCRDWSDEANCTAIYHTCEASNFQCRNGHCIPQRWAC
PredictionCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCSSSCCCCCCSSSSSSSCCCCCSSSSSSSCCCCSSSCCCCCSSSSCCCCCCCSSCHHHHCCCCCCCCCCSSSSSSCCCCCCCSSSSSSCCCCSSSSSCCCCCCCSSSSCCCCCCSSSSCCCCCCSSSSSCCCCCCCCSSSSCCCCCCHHHHHHCCSSSSSCCCCCCCCCSSSSSSSSCCCCSSSSSSCCCCCCCCCCCSSCCCCCSSSSSCCSSSSSSSSCCCCCCCCCCSSSSSSCCCCCCSSSSCCCCCCCCSSSSSSCCCCCSSSSSSCCCCCSSSSSSCCCCCSSSSSHHHCCCCCCCCCCCCCCCCCCCCCCSSSSSSSCCCCCSSSSSSCCCCCCCCCSSSSSSSCCCCCSSCCCCCCCCCCCCCCCCCCCCCCSSSSSCCCCCCCCCCCCCCCCCCCCCCCSSSSSSCCCCHHHCCCCCSSSSCCCCCCCSSCCCCCSSSSSSCCCCSSSSSSCCCCCCSSSSCCCCCCCSSSSSCCCCCSSSSSSSSCCCCCCSSSSSSSSCCCCCCCSSSSSSCCCCCCCCCCCCCCCSSSCCCCCCCCCCCCCCSSSSSSSCCCCCCCCHHHCCCCCCCCCCCCCCCCCCCCCCCSSCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCSSSSSCCCCCCCCCCCCCCCCCSSSSCCCCCSSSSSSCCCCCCSSSSSCCCCCCCCSSSSSSCCCSSSSSCCCCCSSSSSSCCCCCSSSSSSCCCCCCCSSSSSCCCCCSSSSSSCCCCCCSSSSSSCCCCCCSSSSSCCCCCCSSSSSCCCSSSSCCCCCCSSSSSCCCCCCSSSSCCCCCCCCCCSSCCCCSSSCCCCCCSSSSSSCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCHHHHCCSSSCCCCCCCCCCCCCCCCCCCCCCCCCCSSSCCCCSSSCHHHCCCCCCCCCCCCCCCCCCCCCCCCCSSSCCCCCSSCHHHCCCCCCCCCCCCHHCCCCCCCCCCCCCCSSCCCCCSSCCCCCC
Conf.Score95422157789998844477444578653335787501277734457654101035788888643047605888738999914898438997503420241257658995379988178903406444577762799983388788469999579875999546883863784899743158688999989998678877767896889977644742625689315788999889999992799649999626556655652322674066774558999987301235788867999978997203666898766532798406899589999437884379998999968788721223467676433332222367623556883586506999960477766733789998799976317798755679883579988858998300101457676667655468877799997535710045788899688995431304873578981687369998578865559981778986022303788669889996799980599999967888874399995255666776488874232365677883324868645204775689987876776445654333677678998188988864520786411255677775657998233677766533553333245544554411000246542100113421000111356420010102242222221024781588515899899863378981389982898687779888779838996689997999967999258999489988961899716656679986687786699995799983899958888861675448577712255781787627998126401146776430304672111247764487520467984122211125655433344432222101356554312246543333122342211100000134554434520146654433222221101436843405335997987889953136787566578875475679989602644899988999866789867887898169999989773764798455949720048888897779991398999887953789
H:Helix; S:Strand; C:Coil

  Predicted Solvent Accessibility

Sequence                  20                  40                  60                  80                 100                 120                 140                 160                 180                 200                 220                 240                 260                 280                 300                 320                 340                 360                 380                 400                 420                 440                 460                 480                 500                 520                 540                 560                 580                 600                 620                 640                 660                 680                 700                 720                 740                 760                 780                 800                 820                 840                 860                 880                 900                 920                 940                 960                 980                1000                1020                1040                1060                1080                1100                1120                1140                1160                1180
                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |          
EVWTQRLHGGSAPLPQDRGFLVVQGDPRELRLWARGDARGASRADEKPLRRKRSAALQPEPIKVYGQVSLNDSHNQMVVHWAGEKSNVIVALARDSLALARPKSSDVYVSYDYGKSFKKISDKLNFGLGNRSEAVIAQFYHSPADNKRYIFADAYAQYLWITFDFCNTLQGFSIPFRAADLLLHSKASNLLLGFDRSHPNKQLWKSDDFGQTWIMIQEHVKSFSWGIDPYDKPNTIYIERHEPSGYSTVFRSTDFFQSRENQEVILEEVRDFQLRDKYMFATKVVHLLGSEQQSSVQLWVSFGRKPMRAAQFVTRHPINEYYIADASEDQVFVCVSHSNNRTNLYISEAEGLKFSLSLENVLYYSPGGAGSDTLVRYFANEPFADFHRVEGLQGVYIATLINGSMNEENMRSVITFDKGGTWEFLQAPAFTGYGEKINCELSQGCSLHLAQRLSQLLNLQLRRMPILSKESAPGLIIATGSVGKNLASKTNVYISSSAGARWREALPGPHYYTWGDHGGIITAIAQGMETNELKYSTNEGETWKTFIFSEKPVFVYGLLTEPGEKSTVFTIFGSNKENVHSWLILQVNATDALGVPCTENDYKLWSPSDERGNECLLGHKTVFKRRTPHATCFNGEDFDRPVVVSNCSCTREDYECDFGFKMSEDLSLEVCVPDPEFSGKSYSPPVPCPVGSTYRRTRGYRKISGDTCSGGDVEARLEGELVPCPLAEENEFILYAVRKSIYRYDLASGATEQLPLTGLRAAVALDFDYEHNCLYWSDLALDVIQRLCLNGSTGQEVIINSGLETVEALAFEPLSQLLYWVDAGFKKIEVANPDGDFRLTIVNSSVLDRPRALVLVPQEGVMFWTDWGDLKPGIYRSNMDGSAAYHLVSEDVKWPNGISVDDQWIYWTDAYLECIERITFSGQQRSVILDNLPHPYAIAVFKNEIYWDDWSQLSIFRASKYSGSQMEILANQLTGLMDMKIFYKGKNTGSNACVPRPCSLLCLPKANNSRSCRCPEDVSSSVLPSGDLMCDCPQGYQLKNNTCVKQENTCLRNQYRCSNGNCINSIWWCDFDNDCGDMSDERNCPTTICDLDTQFRCQESGTCIPLSYKCDLEDDCGDNSDESHCEMHQCRSDEYNCSSGMCIRSSWVCDGDNDCRDWSDEANCTAIYHTCEASNFQCRNGHCIPQRWAC
Prediction73144404414443264444422443443222243444442544644344443544462544624342333433210001012441300000031310013144220020202032033024414333344332002100111234100000104432000010102103314140403302011433100000034123220000321143033015202201003453444200000022463311001020103345444221430210112320000021133344444330100001234303303004514431000001332000000002333000010113002000002200223433434333243244310010020341300000000336544431202001021140330411442463440615345300000001013234242321211134000000000000043045412000021002003202533100000020000000244341320100000043044140464302022010113440100000012463433100000103302535046420220103424433001133221312344230221430433323430303343222110010363221000000000121221141202321000001010000000000000112133322122233220001110010000012342322201233020000000003410000011113101001013643330013340320000000010200000003221010031424200000346304300000000320000000024430200101010430330044403100000003410000003222000010314302101330310000010310000001113001101111143111012302100000011111010111012100000010121021302010010001001012300133212022243313431024320203222102320200033103231114404334021432134433320023403012441041312244043330464204042230023302001341031200043043444404443040431300244267
Values range from 0 (buried residue) to 9 (highly exposed residue)

   Predicted normalized B-factor

(B-factor is a value to indicate the extent of the inherent thermal mobility of residues/atoms in proteins. In I-TASSER, this value is deduced from threading template proteins from the PDB in combination with the sequence profiles derived from sequence databases. The reported B-factor profile in the figure below corresponds to the normalized B-factor of the target protein, defined by B=(B'-u)/s, where B' is the raw B-factor value, u and s are respectively the mean and standard deviation of the raw B-factors along the sequence. Click here to read more about predicted normalized B-factor)


  Top 10 threading templates used by I-TASSER

(I-TASSER modeling starts from the structure templates identified by LOMETS from the PDB library. LOMETS is a meta-server threading approach containing multiple threading programs, where each threading program can generate tens of thousands of template alignments. I-TASSER only uses the templates of the highest significance in the threading alignments, the significance of which are measured by the Z-score, i.e. the difference between the raw and average scores in the unit of standard deviation. The templates in this section are the 10 best templates selected from the LOMETS threading programs. Usually, one template of the highest Z-score is selected from each threading program, where the threading programs are sorted by the average performance in the large-scale benchmark test experiments.)

Rank PDB
Hit		Iden1Iden2CovNorm.
Z-score		Download
Align.		                   20                  40                  60                  80                 100                 120                 140                 160                 180                 200                 220                 240                 260                 280                 300                 320                 340                 360                 380                 400                 420                 440                 460                 480                 500                 520                 540                 560                 580                 600                 620                 640                 660                 680                 700                 720                 740                 760                 780                 800                 820                 840                 860                 880                 900                 920                 940                 960                 980                1000                1020                1040                1060                1080                1100                1120                1140                1160                1180
                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |          
Sec.Str
Seq		CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCSSSCCCCCCSSSSSSSCCCCCSSSSSSSCCCCSSSCCCCCSSSSCCCCCCCSSCHHHHCCCCCCCCCCSSSSSSCCCCCCCSSSSSSCCCCSSSSSCCCCCCCSSSSCCCCCCSSSSCCCCCCSSSSSCCCCCCCCSSSSCCCCCCHHHHHHCCSSSSSCCCCCCCCCSSSSSSSSCCCCSSSSSSCCCCCCCCCCCSSCCCCCSSSSSCCSSSSSSSSCCCCCCCCCCSSSSSSCCCCCCSSSSCCCCCCCCSSSSSSCCCCCSSSSSSCCCCCSSSSSSCCCCCSSSSSHHHCCCCCCCCCCCCCCCCCCCCCCSSSSSSSCCCCCSSSSSSCCCCCCCCCSSSSSSSCCCCCSSCCCCCCCCCCCCCCCCCCCCCCSSSSSCCCCCCCCCCCCCCCCCCCCCCCSSSSSSCCCCHHHCCCCCSSSSCCCCCCCSSCCCCCSSSSSSCCCCSSSSSSCCCCCCSSSSCCCCCCCSSSSSCCCCCSSSSSSSSCCCCCCSSSSSSSSCCCCCCCSSSSSSCCCCCCCCCCCCCCCSSSCCCCCCCCCCCCCCSSSSSSSCCCCCCCCHHHCCCCCCCCCCCCCCCCCCCCCCCSSCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCSSSSSCCCCCCCCCCCCCCCCCSSSSCCCCCSSSSSSCCCCCCSSSSSCCCCCCCCSSSSSSCCCSSSSSCCCCCSSSSSSCCCCCSSSSSSCCCCCCCSSSSSCCCCCSSSSSSCCCCCCSSSSSSCCCCCCSSSSSCCCCCCSSSSSCCCSSSSCCCCCCSSSSSCCCCCCSSSSCCCCCCCCCCSSCCCCSSSCCCCCCSSSSSSCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCHHHHCCSSSCCCCCCCCCCCCCCCCCCCCCCCCCCSSSCCCCSSSCHHHCCCCCCCCCCCCCCCCCCCCCCCCCSSSCCCCCSSCHHHCCCCCCCCCCCCHHCCCCCCCCCCCCCCSSCCCCCSSCCCCCC
EVWTQRLHGGSAPLPQDRGFLVVQGDPRELRLWARGDARGASRADEKPLRRKRSAALQPEPIKVYGQVSLNDSHNQMVVHWAGEKSNVIVALARDSLALARPKSSDVYVSYDYGKSFKKISDKLNFGLGNRSEAVIAQFYHSPADNKRYIFADAYAQYLWITFDFCNTLQGFSIPFRAADLLLHSKASNLLLGFDRSHPNKQLWKSDDFGQTWIMIQEHVKSFSWGIDPYDKPNTIYIERHEPSGYSTVFRSTDFFQSRENQEVILEEVRDFQLRDKYMFATKVVHLLGSEQQSSVQLWVSFGRKPMRAAQFVTRHPINEYYIADASEDQVFVCVSHSNNRTNLYISEAEGLKFSLSLENVLYYSPGGAGSDTLVRYFANEPFADFHRVEGLQGVYIATLINGSMNEENMRSVITFDKGGTWEFLQAPAFTGYGEKINCELSQGCSLHLAQRLSQLLNLQLRRMPILSKESAPGLIIATGSVGKNLASKTNVYISSSAGARWREALPGPHYYTWGDHGGIITAIAQGMETNELKYSTNEGETWKTFIFSEKPVFVYGLLTEPGEKSTVFTIFGSNKENVHSWLILQVNATDALGVPCTENDYKLWSPSDERGNECLLGHKTVFKRRTPHATCFNGEDFDRPVVVSNCSCTREDYECDFGFKMSEDLSLEVCVPDPEFSGKSYSPPVPCPVGSTYRRTRGYRKISGDTCSGGDVEARLEGELVPCPLAEENEFILYAVRKSIYRYDLASGATEQLPLTGLRAAVALDFDYEHNCLYWSDLALDVIQRLCLNGSTGQEVIINSGLETVEALAFEPLSQLLYWVDAGFKKIEVANPDGDFRLTIVNSSVLDRPRALVLVPQEGVMFWTDWGDLKPGIYRSNMDGSAAYHLVSEDVKWPNGISVDDQWIYWTDAYLECIERITFSGQQRSVILDNLPHPYAIAVFKNEIYWDDWSQLSIFRASKYSGSQMEILANQLTGLMDMKIFYKGKNTGSNACVPRPCSLLCLPKANNSRSCRCPEDVSSSVLPSGDLMCDCPQGYQLKNNTCVKQENTCLRNQYRCSNGNCINSIWWCDFDNDCGDMSDERNCPTTICDLDTQFRCQESGTCIPLSYKCDLEDDCGDNSDESHCEMHQCRSDEYNCSSGMCIRSSWVCDGDNDCRDWSDEANCTAIYHTCEASNFQCRNGHCIPQRWAC
16ffyA 0.24 0.23 0.76 2.89Download ---------------------------------------------------------QVSLISTSFVLKGDATHNQAMVHWTGENSSVILILTKYYHAMGKVLESSLWRSSDFGTTYTKL------TLQPGVTTVIDNFYICPAKIILVSSSLGREQSLFLSTDEGATFQKYPVPFLVETLLFHPKEEDKVLAYT---KDSKLYVSSDLGKKWTLLQERVDHVFWAVSGVDDPNLVHVEAQDLSGGYRYYTCLIYNCHIAPFSGPIDRG-SLTVQDEYIFLKAT-------STNRTKYYVSYRRSDFVLMKLPKYALPKDLQIISTDEQQVFVAVQEWNDTYNLYQSDLRGVRYSLVLENVRSSRQA-----------EENVVIDILEVRGVKGVFLANQKVD----GKVTTVITYNKGRDWDYLRPPSTDMNGKPTNCQPPD-CYLHLHLRWADNPYVS---GTVHTKDTAPGLIMGAGNLGSQLVEKEEMYITSDCGHTWRQVFEEEHHVLYLDHGGVIAAIKDSIPLKILKFSVDEGHTWSTHNFTSTSVFVDGLLSEPGDETLVMTVFGHISF-RSDWELVKVDFRPSFPRQCGEDDYSSWDLTDLQGDHCIMGQQRSYRKRKSTSWCVKGRSFTSALTSRVCKCRDSDFLCDYGFERSSSSESTANKCSANFWFNPLSPPEDCVLGQTYTSSLGYRKVVSNVCEGGVDLQQSPVQLQCPLQAPRG--LQVSIRGEAVAVRPREDVLFVVRQEQGDV---------LTTKYQVDLGDGFKAMYVNLTLTGE--PIRHHYESPGIYRVSVRAENMAGHDEAVLFVQVNSPLQALYLEVIGVNQEVNLTAVLLNPNLTVFYWWIGHSLQP---LLSLDNSVTTKFTDAQAACGNSVLQDSRLVRVLDQFQVVPLRFSRELDTFNPNTPEWREDVGLVVTRLLSKETSIPEELLVTVVKPGLPTIADLYVLLPLTSDKRLAAVQQALNSHRISFILRGGLRILVELRD-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
27okqA 0.09 0.20 0.86 1.50Download MSYNYVVTAQKPTAVNGCVTGFTSAEDLNLLIAKNTVTAEGLRPVKEVGMYGKIAVMRPKGESKDLLFILTAKYNACILEYKQSGESITRAHGNVQIIGIIDPECRGLRLYDGLFKVIPLDRDNKELKAEELHVIDVKFLYGCQAPTICFVYQDPQGRHVKTYEVKGPWKQENVEAEASMVIAVPEPFGGAIIIG--QESITYHNGDKYLAIAPPIIKQSTIVCHNRVDPNGSRYLLGDKEEQMDTLKDLRVELLGETSIAECLTYLDNGVVFVGSRLGDSQLVKLNDSNEQGSYVVAMETFTNLGPIVDMCLERQGQGQLVTCSGAFKEGSLRIIRNPGIKGLWPLRSDPDTLVLSFVRVLMLNGEEVEETELMGFVDDQQTFFCGNVAHQ---QLIQITSAS------VRLVSQEPKALVSEWKE----PQAKNIS-----------VASCNSSQ-----------------VVVAVGR--------ALYYLQIHPQELRHTEMEHEVACLDITLSPLCAIGLWT-DISARILKLPSFELLHKEMLGGEI-IPRSILMTTFESSHYLLCALG----DGALFYFGLNIETGLKVTLGTQPTVLRTFRSLSTTNVFACSDRPTVIYSSN----------HKLVFSNVNLKEVNYMCPLNSDGYPDSLALANNSTLTIDEIQKLHIRTVPLYESPRKICYQEVSQCFGVLSSRIEVQDTSALRPSASTQAL--------SSSVSSSKLTAPHETSFGEEVEVHNLLIIDQHTEVLHAHQFLQNSLVSCKLGKDPNTYFIVGTAMVYPEE------------AEPKQGRIVVFQYSDGKLQTVAEKEVKGAVYSMVEFNG---KLLASINS---TVRLYEWTTEKELRTECNNNIMALYLKTKGDFILVGDLM-RSVLLLAYKPMEEEIARDFNPNWMSAVEIDDDNFLGAENAFNLFVCQKDSAATTDEERQHLQEVGLFHEFVNVFCHGSLVMQNL--------PTQGSVLFGTVNGMIGLVTSLSESWYNLLLDMQNRLNKVIKSVGKIEHSFWRSTERKTEPATGFIDGDLIESFLDISRPKMQEVVANLQYDDGSGMKREATADDLIKVVEELTRIH----------------------------------------------------------------------
33wsx 0.99 0.51 0.51 2.81Download ------------------------------------------------------------PIKVYGQVSLNDSHNQMVVHWAGEKSNVIVALARDS-----PKSSDVYVSYDYGKSFKKISDKLNFGLGNRSEAVIAQFYHSPADNKRYIFADAYAQYLWITFDFCNTLQGFSIPFRAADLLLHSKASNLLLGFDRSHPNKQLWKSDDFGQTWIMIQEHVKSFSWGIDPYDKPNTIYIERHEPSGYSTVFRSTDFFQSRENQEVILEEVRDFQLRDKYMFATKVVH-----QQSSVQLWVSFGRKPMRAAQFVTRHPINEYYIADASEDQVFVCVSHSNNRTNLYISEAEGLKFSLSLENVLYYSPGGAGSDTLVRYFANEPFADFHRVEGLQGVYIATLIN-------MRSVITFDKGGTWEFLQAP---------------GCSLHLAQ------------MPILSKESAPGLIIATGSVGK----KTNVYISSSAGARWREALPGPHYYTWGDHGGIITAIAQGMETNELKYSTNEGETWKTFIFSEKPVFVYGLLTEPGEKSTVFTIFGSN----HSWLILQVNATDALGVPCTENDYKLWSPSDERGNECLLGHKTVFKRRTPHATCFNGEDFDRPVVVSNCSCTREDYECDFGFKMSEDLSLEVCVPDPEFS------GKSYPVGSTYRRTRGYRKISGDTCSGGDVEARLEGELVP-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
46ffyA 0.24 0.23 0.75 5.43Download ----------------------------------------------------------VSLISTSFVLKGDATHNQAMVHWTGENSSVILILTKYYHAMGKVLESSLWRSSDFGTTYTKLTLQPGVT------TVIDNFYICPAKIILVSSSLGREQSLFLSTDEGATFQKYPVPFLVETLLFHPKEEDKVLAYTKDS---KLYVSSDLGKKWTLLQERVDHVFWAVSGVDDPNLVHVEAQDLSGGYRYYTCLIYNCHIAPFSGPIDRG-SLTVQDEYIFLKAT-------STNRTKYYVSYRRSDFVLMKLPKYALPKDLQIISTDEQQVFVAVQEWNQVYNLYQSDLRGVRYSLVLENVRSSRQA-----------EENVVIDILEVRGVKGVFLANQKV----DGKVTTVITYNKGRDWDYLRPPSTDMNGKPTNCQPPD-CYLHLHLRWA---DNPYVSGTVHTKDTAPGLIMGAGNLGSQLVEKEEMYITSDCGHTWRQVFEEEHHVLYLDHGGVIAAIKDTSILKILKFSVDEGHTWSTHNFTSTSVFVDGLLSEPGDETLVMTVFGHI-SFRSDWELVKVDFRPSFPRQCGEDDYSSWDLTDLQGDHCIMGQQRSYRKRKSTSWCVKGRSFTSALTSRVCKCRDSDFLCDYGFERSSSSESTANKCSANFWFNPLSPPEDCVLGQTYTSSLGYRKVVSNVCEGGVDLQQSPVQLQCPLQAPRGLQVSIRGEAVAVRPREDVLFVVRQ----------EQGDVLTTKYQVDLGDGFKAMYVNLTLTGE---PIRHHYESPGIYRVSVRAENMAGHDEAVLFVQVNSPLQALYLEVVPVIGVNQLTAVLLNPNLTVFYWWIGSLDNSVTTKFTDAGDVRVTVQAAC---GNSVLQDSRLVRVLDQFQVVPLRFSRELDTFNPNTPEWREDVGLVVTRLLSKETSIPEELLVTVVKPGLPTIADLYVLLP-----LTSDKRLAAVQQALNSHRISFILRGGLRILVELRD--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
53wsx 1.00 0.51 0.51 2.71Download ------------------------------------------------------------PIKVYGQVSLNDSHNQMVVHWAGEKSNVIVALARDS-----PKSSDVYVSYDYGKSFKKISDKLNFGLGNRSEAVIAQFYHSPADNKRYIFADAYAQYLWITFDFCNTLQGFSIPFRAADLLLHSKASNLLLGFDRSHPNKQLWKSDDFGQTWIMIQEHVKSFSWGIDPYDKPNTIYIERHEPSGYSTVFRSTDFFQSRENQEVILEEVRDFQLRDKYMFATKVVH-----QQSSVQLWVSFGRKPMRAAQFVTRHPINEYYIADASEDQVFVCVSHSNNRTNLYISEAEGLKFSLSLENVLYYSPGGAGSDTLVRYFANEPFADFHRVEGLQGVYIATLIN-------MRSVITFDKGGTWEFLQAP---------------GCSLHLAQ------------MPILSKESAPGLIIATGSVGK----KTNVYISSSAGARWREALPGPHYYTWGDHGGIITAIAQGMETNELKYSTNEGETWKTFIFSEKPVFVYGLLTEPGEKSTVFTIFGSN----HSWLILQVNATDALGVPCTENDYKLWSPSDERGNECLLGHKTVFKRRTPHATCFNGEDFDRPVVVSNCSCTREDYECDFGFKMSEDLSLEVCVPDPEFSGKS--YS---PVGSTYRRTRGYRKISGDTCSGGDVEARLEGELVP-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
66ffy 0.24 0.23 0.74 2.59Download -----------------------------------------------------------QVSLITSFVLKGDAHNQAMVHWTGENSSVILILTKYYHDMGKVLESSLWRSSDFGTTYTKLTLQPG------VTTVIDNFYICPA--K-IILVSSREQSLFLSTDEGATFQKYPVPFLVETLLFHPKEEDKVLAYTK---DSKLYVSSDLGKKWTLLQERVDHVFWAVSGVDDPNLVHVEAQDLS--GGYRYYTCLIYNCHIAPFSPIDRGSLTVQDEYIFLKATS-------TNRTKYYVSYRRSDFVLMKLPKYALPKDLQIISTDEQQVFVAVQEWNDTYNLYQSDLRGVRYSLVLENVRSSR-Q----------AEENVVIDILEVRGVKGVFLANQKVD----GKVTTVITYNKGRDWDYLRPPSTDMNGKPTNCQP-PDCYLHLHLRWADNP---YVSGTVHTKDTAPGLIMGAGNLGSQLVEKEEMYITSDCGHTWRQVFEEEHHVLYLDHGGVIAAIKDTIPLKILKFSVDEGHTWSTHNFTSTSVFVDGLLSEPGDETLVMTVFGHI-SFRSDWELVKVDFRPSFPRQCGEDDYSSWDLTDLQGDHCIMGQQRSYRKRKSTSWCVKGRSFTSALTSRVCKCRDSDFLCDYGFERSSSSTANKCSANFWF--NPLSPPEDCVLGQTYTSSLGYRKVVSNVCEGGV-DLQQSPVQLQCPLQAPRGLQVSIRGEAVA---VRPREDVLFVVRGDVLTTKYQVDLGDGF---------KAMYVNLLTGEPIRHHYES--PGIYRVS-------VRAENMA-GHDEA------VLFVQV--N--SPLQALYLEVVNQEVNLTAVL----------LNPN------------L-------TVFYWW-IGHSLQPLL-SLDNSVTTKF-TDAGDVRVT-----VQAACGNSVLQ--------DSRLVRVL--DQFQVVLRFSRELDT-FNPNTLVVTRLLSKETS-------IPEELLVTVVKPGL-----PT------------IA-----D-----LYVLLPL---TS-----DKRLAAVQQALNSHR-ISFILR-GGLRILVEL-----RD---------------------------------------------------------------------------
76ffy 0.30 0.23 0.53 3.43Download -------------------------------------------------------------------VLKGDAHNQAMVHWTGENSSVILILTKYYADMGKVLESSLWRSSDFGTTYTKLTLQPG------VTTVIDNFYICPA--KIILVSSDREQSLFLSTDEGATFQKYPVPFLVETLLFHPKEEDKVLAYTKD---SKLYVSSDLGKKWTLLQERVDHVFWAVSGVDDPNLVHVEAQDLSG--GYRYYTCLIYNCHIAPFSPIDRGSLTVQDEYIFLKATST-------NRTKYYVSYRRSDFVLMKLPKYALPKDLQIISTDEQQVFVAVQEWNDTYNLYQSDLRGVRYSLVLENVRSSR-----------QAEENVVIDILEVRGVKGVFLANQKV----DGKVTTVITYNKGRDWDYLRPPSTDMNGKPTNCQP-PDCYLHLHLRWADN---PYVSGTVHTKDTAPGLIMGAGNLGSQLVEKEEMYITSDCGHTWRQVFEEEHHVLYLDHGGVIAAIKDTIPLKILKFSVDEGHTWSTHNFTSTSVFVDGLLSEPGDETLVMTVFGHISF-RSDWELVKVDFRPSFPRQCGEDDYSSWDLTDLQGDHCIMGQQRSYRKRKSTSWCVKGRSFTSALTSRVCKCRDSDFLCDYGFERSSSSTANKCSANFWF--NPLSPPEDCVLGQTYTSSLGYRKVVSNVCEGGV-DLQQSPVQLQCPLQAPRGLQVSIRGEA------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
86ffy 0.25 0.23 0.74 2.68Download -----------------------------------------------------------QVSLISTFVLKGDAHNQAMVHWTGENSSVILILTKYYADMGKVLESSLWRSSDFGTTYTKLTLQPG-----VTT-VIDNFYICPA--KIILVSSDREQSLFLSTDEGATFQKYPVPFLVETLLFHPKEEDKVLAYTKD---SKLYVSSDLGKKWTLLQERVDHVFWAVSGVDDPNLVHVEAQDLSG--GYRYYTCLIYNCHIAPFSPIDRGSLTVQDEYIFLKATS-------TNRTKYYVSYRRSDFVLMKLPKYALPKDLQIISTDEQQVFVAVQEWNDTYNLYQSDLRGVRYSLVLENVRSSRQ-----------AEENVVIDILEVRGVKGVFLANQKV----DGKVTTVITYNKGRDWDYLRPPSTDMNGKPTNCQP-PDCYLHLHLRWADN---PYVSGTVHTKDTAPGLIMGAGNLGSQLVEKEEMYITSDCGHTWRQVFEEEHHVLYLDHGGVIAAIKDTIPLKILKFSVDEGHTWSTHNFTSTSVFVDGLLSEPGDETLVMTVFGHIS-FRSDWELVKVDFRPSFPRQCGEDDYSSWDLTDLQGDHCIMGQQRSYRKRKSTSWCVKGRSFTSALTSRVCKCRDSDFLCDYGFERSSANKCS---ANFWF--NPLSPPEDCVLGQTYTSSLGYRKVVSNVCEGGVDLQQSPVQLQCPLQAP-RGLQVSIR---GEAVAVRPREDVLFVVRQEQG------DVLTTKYQVDLGDGFKAMYVNLTGEPIRHHYESPG---------------IYRV-----SVRAENMAGHDEFVQVN-SPLQA-LYLEVVPVNQEVNLTAVPNLTVFYWW-IGHSLQPLL--SLD-N-------SVTTKFTDA-GDVRVQAACGNSVLRLVRVLDQFQVVPL-RFSRELD--TFNPNTPEWREDGLVVTRLLS-KETSIP-----------------------EELLVTVVKPGL---PTIADLYVLLPL----TS-----DKRLAAVQQALNSHRISFILRGGLRILVELRD-------------------------------------------------------------------------------------------------------------------------
93wsyA 0.99 0.56 0.5611.35Download ---------------------------------------------------------QPEPIKVYGQVSLNDSHNQMVVHWAGEKSNVIVALARDSLALARPKSSDVYVSYDYGKSFKKISDKLNFGLGNRSEAVIAQFYHSPADNKRYIFADAYAQYLWITFDFCNTLQGFSIPFRAADLLLHSKASNLLLGFDRSHPNKQLWKSDDFGQTWIMIQEHVKSFSWGIDPYDKPNTIYIERHEPSGYSTVFRSTDFFQSRENQEVILEEVRDFQLRDKYMFATKVVHLLGSEQQSSVQLWVSFGRKPMRAAQFVTRHPINEYYIADASEDQVFVCVSHSNNRTNLYISEAEGLKFSLSLENVLYYSPGGAGSDTLVRYFANEPFADFHRVEGLQGVYIATLINGSMNEENMRSVITFDKGGTWEFLQAPAFTGYGEKINCELSQGCSLHLAQRLSQLLNLQLRRMPILSKESAPGLIIATGSVGKNLASKTNVYISSSAGARWREALPGPHYYTWGDHGGIITAIAQGMETNELKYSTNEGETWKTFIFSEKPVFVYGLLTEPGEKSTVFTIFGSNKENVHSWLILQVNATDALGVPCTENDYKLWSPSDERGNECLLGHKTVFKRRTPHATCFNGEDFDRPVVVSNCSCTREDYECDFGFKMSEDLSLEVCVPDPEFSGP------PVPCPSTYRRTRGYRKISGDTCSGGDVEARLEGELVPCP---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
107oo3D 0.08 0.14 0.77 2.50Download MSYNYVVTAQKPTASAEDLNLLIAKNTRLEIYVVTAEGLRPVKEVGMYGKIAVMELFRPKGESKDLLFILTAKYNACILEYKQSGESIDIIGNVQDRIGRPSETGIIGIIDPECPLDRDNKELKAFNIRLEELHVIDVKFLYGCQAPTICFVYQDPQGKTYEVSLREKWKQENVEAEASMVIAVPEPFGGAIIIGQESI---TYHNGDKYLAIAPPIIKQSTIVCHNRVDPNGSRYMLLLEKEEVTLKDLRVELLGETSIAECL-------TYLDNGVVFVG--------SRLGDSQLVKLNVDSNVAMETFTNLGPIVDMCVVDQGQGQLVTCSGAFKEGSLRIIRNGIGIHEHASI-----------------------------DLPGIKGLWP---LRSDPNRETDDTLVLSFVGQTR-VLMLNGEEVEETELMGFVDDQQTFFCGNV------------------AHQQLIQITSALVSQEPKALVSEWKEPQAKNISVASCNSSQVVVAVGRALYYLQIHPQELRQISHTEMEHEDITPLGDSNGLSPLCAIGLWTDIPSFELLHKEMLGGEIIPRSILMTTFESSHYLLC-----------------ALGDGALFYFGLNIETGLLSDRKKVTLGTQPTVLRTFRSLSTTNVFACSDRPTVIYSSNHKLVFSNVNLKEVNYMCPLNSDGYPDSLALANNSTLTIGTIDEIQKLHIRTVPLYESPRKICYQEVSQCFGV---------------------------LSSRIEVQGTTALRPSASTQALSSSVSSSKLFSEEVEVHNL----LIIDQHTFEVLHAHQFLQNEYALSLVSCKLGKDPGTAMVYPEE------AEPKQGRIVVFQYSDGKLQTVAEKEVKGAYSMVEFNGKLLASINSTVRLYEWTTEKELRTECNHYNNIMLYLKTKGDFILVGDLMRSVLLLAYKPMEGNFEEI--------------------------------------------------------------------------------------------------------------------------------------------------ARDFNPNWMSAVEILDDDNFLGAENAFNLFVCQKDS------DEERQHLGEFVNVFCHGSLVMPTQGSV---LFG
(a)All the residues are colored in black; however, those residues in template which are identical to the residue in the query sequence are highlighted in color. Coloring scheme is based on the property of amino acids, where polar are brightly coloured while non-polar residues are colored in dark shade. (more about the colors used)
(b)Rank of templates represents the top ten threading templates used by I-TASSER.
(c)Ident1 is the percentage sequence identity of the templates in the threading aligned region with the query sequence.
(d)Ident2 is the percentage sequence identity of the whole template chains with query sequence.
(e)Cov represents the coverage of the threading alignment and is equal to the number of aligned residues divided by the length of query protein.
(f)Norm. Z-score is the normalized Z-score of the threading alignments. Alignment with a Normalized Z-score >1 mean a good alignment and vice versa.
(g)Download Align. provides the 3D structure of the aligned regions of the threading templates.
(h)The top 10 alignments reported above (in order of their ranking) are from the following threading programs:
       1: MUSTER   2: SPARKS-X   3: HHSEARCH2   4: Neff-PPAS   5: HHSEARCH I   6: HHSEARCH2   7: HHSEARCH   8: HHSEARCH I   9: pGenTHREADER   10: PROSPECT2   

   Top 5 final models predicted by I-TASSER

(For each target, I-TASSER simulations generate a large ensemble of structural conformations, called decoys. To select the final models, I-TASSER uses the SPICKER program to cluster all the decoys based on the pair-wise structure similarity, and reports up to five models which corresponds to the five largest structure clusters. The confidence of each model is quantitatively measured by C-score that is calculated based on the significance of threading template alignments and the convergence parameters of the structure assembly simulations. C-score is typically in the range of [-5, 2], where a C-score of a higher value signifies a model with a higher confidence and vice-versa. TM-score and RMSD are estimated based on C-score and protein length following the correlation observed between these qualities. Since the top 5 models are ranked by the cluster size, it is possible that the lower-rank models have a higher C-score in rare cases. Although the first model has a better quality in most cases, it is also possible that the lower-rank models have a better quality than the higher-rank models as seen in our benchmark tests. If the I-TASSER simulations converge, it is possible to have less than 5 clusters generated; this is usually an indication that the models have a good quality because of the converged simulations.)
    (By right-click on the images, you can export image file or change the configurations, e.g. modifying the background color or stopping the spin of your models)
  • Download Model 1
  • C-score=-1.60 (Read more about C-score)
  • Estimated TM-score = 0.52±0.15
  • Estimated RMSD = 13.3±4.1Å

  • Download Model 2
  • C-score = -2.52

  • Download Model 3
  • C-score = -2.66

  • Download Model 4
  • C-score = -1.58

  • Download Model 5
  • C-score = -1.41


  Proteins structurally close to the target in the PDB (as identified by TM-align)

(After the structure assembly simulation, I-TASSER uses the TM-align structural alignment program to match the first I-TASSER model to all structures in the PDB library. This section reports the top 10 proteins from the PDB that have the closest structural similarity, i.e. the highest TM-score, to the predicted I-TASSER model. Due to the structural similarity, these proteins often have similar function to the target. However, users are encouraged to use the data in the next section 'Predicted function using COACH' to infer the function of the target protein, since COACH has been extensively trained to derive biological functions from multi-source of sequence and structure features which has on average a higher accuracy than the function annotations derived only from the global structure comparison.)


Top 10 Identified stuctural analogs in PDB

Click
to view		RankPDB HitTM-scoreRMSDaIDENaCovAlignment
16ffyA0.752 0.870.2420.755Download
23wsyA0.488 3.970.8390.535Download
33f6kA0.487 3.650.2780.526Download
43fcsA0.316 7.580.0780.427Download
54g1eA0.307 8.440.0660.440Download
67mexA0.300 9.160.0370.452Download
76uebA0.299 8.990.0400.448Download
85a22A0.298 9.320.0330.456Download
96fb3A0.295 9.490.0400.460Download
107banA0.291 9.510.0270.450Download

(a)Query structure is shown in cartoon, while the structural analog is displayed using backbone trace.
(b)Ranking of proteins is based on TM-score of the structural alignment between the query structure and known structures in the PDB library.
(c)RMSDa is the RMSD between residues that are structurally aligned by TM-align.
(d)IDENa is the percentage sequence identity in the structurally aligned region.
(e)Cov represents the coverage of the alignment by TM-align and is equal to the number of structurally aligned residues divided by length of the query protein.


  Predicted function using COFACTOR and COACH

(This section reports biological annotations of the target protein by COFACTOR and COACH based on the I-TASSER structure prediction. While COFACTOR deduces protein functions (ligand-binding sites, EC and GO) using structure comparison and protein-protein networks, COACH is a meta-server approach that combines multiple function annotation results (on ligand-binding sites) from the COFACTOR, TM-SITE and S-SITE programs.)

  Ligand binding sites


Click
to view		RankC-scoreCluster
size		 PDB
Hit		 Lig
Name		 Download
Complex		 Ligand Binding Site Residues
10.08 3 1x70A k-mer Rep, Mult 396,414,420,421,423
20.06 2 3f6kA k-mer Rep, Mult 112,113,115,566,567,568
30.05 2 3f6kA PEPTIDE Rep, Mult 224,272,273,274,282,296,298,320,321,322,323,363
40.03 1 3F6KA 3F6KA01 Rep, Mult 325,327,328,388,389,390,393
50.03 1 1tyeC k-mer Rep, Mult 259,265


Download the residue-specific ligand binding probability, which is estimated by SVM.
Download the all possible binding ligands and detailed prediction summary.
Download the templates clustering results.
(a)C-score is the confidence score of the prediction. C-score ranges [0-1], where a higher score indicates a more reliable prediction.
(b)Cluster size is the total number of templates in a cluster.
(c)Lig Name is name of possible binding ligand. Click the name to view its information in the BioLiP database.
(d)Rep is a single complex structure with the most representative ligand in the cluster, i.e., the one listed in the Lig Name column.
Mult is the complex structures with all potential binding ligands in the cluster.

  Enzyme Commission (EC) numbers and active sites


Click
to view		RankCscoreECPDB
Hit		TM-scoreRMSDaIDENaCovEC NumberActive Site Residues
10.1332uv8G0.278 9.730.0340.439 2.3.1.86  NA
20.0892vkzG0.275 9.750.0290.434 2.3.1.38 3.1.2.14  NA
30.0831r9mB0.255 5.820.0390.309 3.4.14.5  NA
40.0812vz8B0.243 9.530.0220.378 2.3.1.85  NA
50.0811z68A0.254 6.000.0450.314 3.4.21.-  NA

 Click on the radio buttons to visualize predicted active site residues.
(a)CscoreEC is the confidence score for the EC number prediction. CscoreEC values range in between [0-1];
where a higher score indicates a more reliable EC number prediction.
(b)TM-score is a measure of global structural similarity between query and template protein.
(c)RMSDa is the RMSD between residues that are structurally aligned by TM-align.
(d)IDENa is the percentage sequence identity in the structurally aligned region.
(e)Cov represents the coverage of global structural alignment and is equal to the number of structurally aligned residues divided
by length of the query protein.

  Gene Ontology (GO) terms
Top 10 homologous GO templates in PDB 
RankCscoreGOTM-scoreRMSDaIDENaCovPDB HitAssociated GO Terms
1 0.140.4867 3.65 0.28 0.533f6kA GO:0016021
2 0.100.3160 7.58 0.08 0.433fcsA GO:0007229 GO:0005887 GO:0016020 GO:0030168 GO:0002576 GO:0007596 GO:0050840 GO:0007411 GO:0004872 GO:0005925 GO:0016021 GO:0031092 GO:0005886 GO:0007155 GO:0009897 GO:0007160 GO:0042802 GO:0008305
3 0.090.3030 8.37 0.06 0.433ijeA GO:0009897 GO:0044419 GO:0007160 GO:0007596 GO:0008305 GO:0001846 GO:0004872 GO:0005887 GO:0016020 GO:0016021 GO:0043277 GO:0001568 GO:0005515 GO:0045715 GO:0070371 GO:0050748 GO:0052066 GO:0005886 GO:0050900 GO:0043066 GO:0045785 GO:0007155 GO:0001525 GO:0010745 GO:0050764 GO:2000425 GO:0097024 GO:0008284 GO:0010888 GO:0050431 GO:0009986 GO:0031994 GO:0035635 GO:0007411 GO:0032369 GO:0046718 GO:0007229
4 0.090.2780 9.73 0.03 0.442uv8G GO:0016829 GO:0004318 GO:0016409 GO:0008152 GO:0016491 GO:0004317 GO:0005737 GO:0004314 GO:0005811 GO:0004313 GO:0004320 GO:0003824 GO:0008610 GO:0004312 GO:0016296 GO:0055114 GO:0005739 GO:0005829 GO:0004319 GO:0004321 GO:0016297 GO:0019171 GO:0006633 GO:0005835 GO:0005515 GO:0016295 GO:0016740 GO:0016787
5 0.090.2752 9.85 0.03 0.443pvmA GO:0005576 GO:0006954 GO:0006956 GO:0004866 GO:0005615 GO:0005515
6 0.090.2643 9.51 0.02 0.412uvaG GO:0003824 GO:0004312 GO:0005835 GO:0006633 GO:0008152 GO:0016491 GO:0016740 GO:0055114
7 0.090.2700 9.78 0.03 0.432b39A GO:0004866 GO:0005515 GO:0005615 GO:0006954 GO:0006956 GO:0005576
8 0.090.2711 9.16 0.05 0.412pn5A GO:0010951 GO:0004866 GO:0005576 GO:0005615
9 0.080.2690 9.66 0.03 0.433cmuA GO:0003697 GO:0048870 GO:0006310 GO:0017111 GO:0009432 GO:0006281 GO:0006950 GO:0003677 GO:0008094 GO:0006974 GO:0000166 GO:0006259 GO:0005737 GO:0005524
10 0.080.2554 5.82 0.04 0.311r9mB GO:0005576 GO:0042803 GO:0004177 GO:0009986 GO:0031295 GO:0005794 GO:0016324 GO:0008233 GO:0016787 GO:0005102 GO:0030139 GO:0030054 GO:0043542 GO:0030027 GO:0005625 GO:0051234 GO:0046581 GO:0042995 GO:0005886 GO:0016020 GO:0016021 GO:0008284 GO:0005624 GO:0005783 GO:0042110 GO:0071438 GO:0002020 GO:0008239 GO:0045121 GO:0007155 GO:0010716 GO:0002709 GO:0042277 GO:0008236 GO:0005518 GO:0033632 GO:0004872 GO:0005515 GO:0001666 GO:0031258 GO:0004252 GO:0006508


Consensus prediction of GO terms		 
Molecular Function GO:0004872 GO:0050840 GO:0042802 GO:0050431 GO:0097024 GO:0031994 GO:0001846 GO:0004321 GO:0004313 GO:0004318
GO-Score 0.18 0.10 0.10 0.09 0.09 0.09 0.09 0.09 0.09 0.09
Biological Process GO:0042060 GO:0050817 GO:0031589 GO:0007599 GO:0007409 GO:0042221 GO:0042330 GO:0007166
GO-Score 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37
Cellular Component GO:0043235 GO:0009986 GO:0005887
GO-Score 0.37 0.34 0.33

(a)CscoreGO is a combined measure for evaluating global and local similarity between query and template protein. It's range is [0-1] and higher values indicate more confident predictions.
(b)TM-score is a measure of global structural similarity between query and template protein.
(c)RMSDa is the RMSD between residues that are structurally aligned by TM-align.
(d)IDENa is the percentage sequence identity in the structurally aligned region.
(e)Cov represents the coverage of global structural alignment and is equal to the number of structurally aligned residues divided by length of the query protein.
(f)The second table shows a consensus GO terms amongst the top scoring templates. The GO-Score associated with each prediction is defined as the average weight of the GO term, where the weights are assigned based on CscoreGO of the template.


[Click on S802695_results.tar.bz2 to download the tarball file including all modeling results listed on this page]


Please cite the following articles when you use the I-TASSER server:
  • Wei Zheng, Chengxin Zhang, Yang Li, Robin Pearce, Eric W. Bell, Yang Zhang. Folding non-homology proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Reports Methods, 1: 100014 (2021).
  • Chengxin Zhang, Peter L. Freddolino, and Yang Zhang. COFACTOR: improved protein function prediction by combining structure, sequence and protein-protein interaction information. Nucleic Acids Research, 45: W291-299 (2017).
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