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I-TASSER results for job id S693100

(Click on S693100_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
MFNFRNLPLFDKDNSKDTDDIKNFINSYEGIYFSSSNKFINVFLDKILIIDPSSLNVITI
NTDLYVVDFLFNEKNKNLIVLGKGKNSLICSVYNIRECNFTLLKKIQLSKNINNIKKTLI
AKCNEYIITLENKKITFYFLNKDYSINQSELIEDGKELIENIYLSKNHILLVIKNSYVYI
YQLDIKNSHISYTLIDSFNLNLSYLRESINNKKKHINKINDVSNNDPKKDNNEKNTSSNN
ITHNNYNDISNNNNNNNNINGVKDHINNNTLENNDEPILSIYNEDLNVLYICQNMYNVLF
VLNLNNLSFEFILLENKIINLFSCKFYLILLKEVNKKFFLHIYIIYEDMKLLVSTLLLNE
PISNVIFFNNLFCLLIEEEISKPEIKVDKFYFYEQLKLKLHSKLDGDALKILKRDHVTNN
INICKQEEKKYKLKNEQKDVEIYNKNNTVDSTNNTIYNSEVNHYKYITNDFFLNEDVNTD
IKKELYNLNHTKDNINHDTLKKQTNQNDITMSLSKDEKNNDTNKFFNENMFTLNKFFKNC
RNQIKIILKERNINEIINMFKKKKLYQWLIKYANLNKNYQIININFIHKIYADFLFEKEQ
YENAIYEYIQTINYLETSYVIHKYLNLDLYEYLTIYLEKLHVYHHFNDEHTMMLLSCYKK
QCKKKKMISFIKKNKDKINLNKTYKFLLNAGYYNIVLNLSKKYKDHFTYVSILIEKYENY
EKSLKYIFKLDVENICILLFKYGYKFIKYYPQLTIYLLKKIIKKYNINLTIFIPLFLDNI
DFLFMFIVKFLDKNVNINKINHIQEKQKHHYSNMYHDSDELNSVTKQKNKINSFLLYEKD
NNQNHNGIPSDSHNLSDDNNSQESTTAVNLINKTNLQLDIFNGEYDYILFVTVIQILLQK
YKKSMQEENQTNNQSSKQTNNQSSKQTNKQSINQSNNQSINQTNNQSIKQTNIQTNKQKG
NSTTNKIINKDETNEQNNILTFNIDKLIQNNKDKNINFLSVLLLSIYNYNKGLIYTSTQM
NKYDISLLFSIHKFINNTKGKINKINMDEHKPNEQTMQINSSYKILNRNFQKVIYNICIN
HLKLNGSLSYNYIFYYLSMLNDEKYLIKFIKKIRQDMNLSIFNLIQILKKYNKSYSCIQN
MVVAYMNDMNKNINDKCIEIEKDKKELEKIKKKQLKKKYNFYLIDNAYCSICKEILSVPM
IHFLCKHSYHSYCLKDNNVCILCHNKDKEKKLLKEKAINSIQNFDEFFKYLQGSTDKFSY
ISNYLSYGITPK

  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                1200                1220                1240                1260
                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |            
MFNFRNLPLFDKDNSKDTDDIKNFINSYEGIYFSSSNKFINVFLDKILIIDPSSLNVITINTDLYVVDFLFNEKNKNLIVLGKGKNSLICSVYNIRECNFTLLKKIQLSKNINNIKKTLIAKCNEYIITLENKKITFYFLNKDYSINQSELIEDGKELIENIYLSKNHILLVIKNSYVYIYQLDIKNSHISYTLIDSFNLNLSYLRESINNKKKHINKINDVSNNDPKKDNNEKNTSSNNITHNNYNDISNNNNNNNNINGVKDHINNNTLENNDEPILSIYNEDLNVLYICQNMYNVLFVLNLNNLSFEFILLENKIINLFSCKFYLILLKEVNKKFFLHIYIIYEDMKLLVSTLLLNEPISNVIFFNNLFCLLIEEEISKPEIKVDKFYFYEQLKLKLHSKLDGDALKILKRDHVTNNINICKQEEKKYKLKNEQKDVEIYNKNNTVDSTNNTIYNSEVNHYKYITNDFFLNEDVNTDIKKELYNLNHTKDNINHDTLKKQTNQNDITMSLSKDEKNNDTNKFFNENMFTLNKFFKNCRNQIKIILKERNINEIINMFKKKKLYQWLIKYANLNKNYQIININFIHKIYADFLFEKEQYENAIYEYIQTINYLETSYVIHKYLNLDLYEYLTIYLEKLHVYHHFNDEHTMMLLSCYKKQCKKKKMISFIKKNKDKINLNKTYKFLLNAGYYNIVLNLSKKYKDHFTYVSILIEKYENYEKSLKYIFKLDVENICILLFKYGYKFIKYYPQLTIYLLKKIIKKYNINLTIFIPLFLDNIDFLFMFIVKFLDKNVNINKINHIQEKQKHHYSNMYHDSDELNSVTKQKNKINSFLLYEKDNNQNHNGIPSDSHNLSDDNNSQESTTAVNLINKTNLQLDIFNGEYDYILFVTVIQILLQKYKKSMQEENQTNNQSSKQTNNQSSKQTNKQSINQSNNQSINQTNNQSIKQTNIQTNKQKGNSTTNKIINKDETNEQNNILTFNIDKLIQNNKDKNINFLSVLLLSIYNYNKGLIYTSTQMNKYDISLLFSIHKFINNTKGKINKINMDEHKPNEQTMQINSSYKILNRNFQKVIYNICINHLKLNGSLSYNYIFYYLSMLNDEKYLIKFIKKIRQDMNLSIFNLIQILKKYNKSYSCIQNMVVAYMNDMNKNINDKCIEIEKDKKELEKIKKKQLKKKYNFYLIDNAYCSICKEILSVPMIHFLCKHSYHSYCLKDNNVCILCHNKDKEKKLLKEKAINSIQNFDEFFKYLQGSTDKFSYISNYLSYGITPK
PredictionCCCCCCCCCCCCCCCCCCHHHHHHHHHCCCSSSSCCHHHHHHHHHHHSSSCCCCCCSSSSCCCSSSSSSSSCCCCCCSSSSSCCCCSSSSSSSSSHHHHSSHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCSSSSSSSCCCCSSCHHHHHHHHHHHHHHHHHCCCCSSSSSSCCCSSSSSSSSCCCSSSSSSSSSSCCCHHHHHHHHHCHHHCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCHHHHHHCCCCSSSSSCCCCCSSSSSSCCCCCCSSSSSCCCSSSSSSSSSSSSSHHHHHHHSSSSSSSSSHHHHHHHHCCCHHHHHHHHHHHHCCHHSSSHHHCCCHHHCCCCHHHHHHHCCCCCCCCCCCCCCCCCHHHHHHHHHHHHHHHHHHHCCCCCCCSSSSCCCCCCCCCCCHHHCCCCCSSSSCCCCCCCCCCCCCCHHHHHHHHCCCCCCCCCCHHHCCCCCCCSSHHHHHHHHHCCCHHHHHHHHHCCCCSSSSSCCCSSSSSSSCCHHHHHHHHHHHHHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHHCCCHHHHHHHHHHHHCCCCHHHHHHHHHCCCCHHHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCHHHHHHHHHHCCCHHHHHHHHHHHCCHHHHHHHHHHHHHHHHHHHHHHHHCCHHHHHHHHHHHHHHHHHHCHHHHHHHHHHHHHCCCCCHHHHHHHCCCCHHHHHHHHHHHHHCCCCCCCCCHHHHHHHHHHHCCCCCCHHHHHHHHHHHHHHHHHCCCCCCCCCCCCCCCCHHHHCCCCCCCCCCCCCCCCCCCCCCSSCCCCCCCCHHHHHHHHHHHHHHHHCCCCCCHHHHHHHHHCCCCCCCCCCCCCCCCHHHHHHHHHHHHHHHHHHHCCCCHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHCCCCCCCCCHHHHHHHHHCCCCHHHHHHHHHCCCCHHHHHHHHHHHCCCHHHHHHHHHHHHCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCCHHHHHHHHHHHHHHCCCHHHHHHHHHHHHHCCCCCHHHHHHHHCCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCCSSSSCCCCCCCCCCCCCCCSSSSCCCCHHHHHHCCCCCCCCCHHHHHHHHHHHHHHHHHHHCHHHHHHHHHHCCCCCHHHHHHHCCCCCCC
Conf.Score977234755265445562778887640363577412478998675403644654413886165499997761688717999557753787642201101102357677767888899999999878740104606899954750441135421189998877611134799983570478999852865568988863377023343330100014555556789867788776566676455863212466555678975212222468887654077765310233446425314799997699860379855726677554222322154312005888985056677753462001336788861306112064313402311200378875057365666221323450666767888767776420367532047514766667687211114300378505874345523222066653310234665554222046650010012124441332000344321104389992797799999678899999998761799999999763699979999999999999885358999999999875356779999997440126999999999997488847899999999997533999999981677457989999999978989999999998298799999999875689999999984898889999999999999869299999999998568998898766405986999999999984378743332278999998741565606566653310134442013333432322357632231011254212332124677764000145765520325769999998764025664033344430044010334200011000244530116666544300141455666555530001254556798879987426556774248999987538835699999974570688899886520135889999987531630256777666666400125789999999605787789999988764137689999999999865898889999984438987899999999999999999999999999999999999999998415866735880545377677873564358716467617788587765668889999999997132599999998537998643698862777799
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                1200                1220                1240                1260
                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |            
MFNFRNLPLFDKDNSKDTDDIKNFINSYEGIYFSSSNKFINVFLDKILIIDPSSLNVITINTDLYVVDFLFNEKNKNLIVLGKGKNSLICSVYNIRECNFTLLKKIQLSKNINNIKKTLIAKCNEYIITLENKKITFYFLNKDYSINQSELIEDGKELIENIYLSKNHILLVIKNSYVYIYQLDIKNSHISYTLIDSFNLNLSYLRESINNKKKHINKINDVSNNDPKKDNNEKNTSSNNITHNNYNDISNNNNNNNNINGVKDHINNNTLENNDEPILSIYNEDLNVLYICQNMYNVLFVLNLNNLSFEFILLENKIINLFSCKFYLILLKEVNKKFFLHIYIIYEDMKLLVSTLLLNEPISNVIFFNNLFCLLIEEEISKPEIKVDKFYFYEQLKLKLHSKLDGDALKILKRDHVTNNINICKQEEKKYKLKNEQKDVEIYNKNNTVDSTNNTIYNSEVNHYKYITNDFFLNEDVNTDIKKELYNLNHTKDNINHDTLKKQTNQNDITMSLSKDEKNNDTNKFFNENMFTLNKFFKNCRNQIKIILKERNINEIINMFKKKKLYQWLIKYANLNKNYQIININFIHKIYADFLFEKEQYENAIYEYIQTINYLETSYVIHKYLNLDLYEYLTIYLEKLHVYHHFNDEHTMMLLSCYKKQCKKKKMISFIKKNKDKINLNKTYKFLLNAGYYNIVLNLSKKYKDHFTYVSILIEKYENYEKSLKYIFKLDVENICILLFKYGYKFIKYYPQLTIYLLKKIIKKYNINLTIFIPLFLDNIDFLFMFIVKFLDKNVNINKINHIQEKQKHHYSNMYHDSDELNSVTKQKNKINSFLLYEKDNNQNHNGIPSDSHNLSDDNNSQESTTAVNLINKTNLQLDIFNGEYDYILFVTVIQILLQKYKKSMQEENQTNNQSSKQTNNQSSKQTNKQSINQSNNQSINQTNNQSIKQTNIQTNKQKGNSTTNKIINKDETNEQNNILTFNIDKLIQNNKDKNINFLSVLLLSIYNYNKGLIYTSTQMNKYDISLLFSIHKFINNTKGKINKINMDEHKPNEQTMQINSSYKILNRNFQKVIYNICINHLKLNGSLSYNYIFYYLSMLNDEKYLIKFIKKIRQDMNLSIFNLIQILKKYNKSYSCIQNMVVAYMNDMNKNINDKCIEIEKDKKELEKIKKKQLKKKYNFYLIDNAYCSICKEILSVPMIHFLCKHSYHSYCLKDNNVCILCHNKDKEKKLLKEKAINSIQNFDEFFKYLQGSTDKFSYISNYLSYGITPK
Prediction623133110123444433630332033120001111320010002100001112020110300000000000342310000033320000000203311010033131444144034201330111012034430000101331202444235444401420100340000002320020010004332000000210204342244444444433442443454444444544334444444444443444444443243143314434144234201311344000000013001000000033230210103220000000100000034133301000010134031103223111000300003100000023213424143431101330203132423142233144331143143144444444444432314143432333433321231313102121232124442334344314322444443344434444421202023344423333243123101201100000141200203224143103201334003000300344542444203301320021004343143003100300420300100110133622320030033016454344300100010023243363034004446441204200300362311300020033133132003001422630430040036032630240024003200330143003001310242413342004000332410130033004434444433212321122133453444414522444442243343445344444422433431454443434433244045641312013332320111110120034114434444333132332344444344444234313442344434432342424464444423244234444434444314320420143443342310000000012033000102411421432120112233433331131032243334333232333231344433420140024104443300230023004466436203400430474400001200310343512031014001420453354146345314504541452454045044303304443033044301100000101000023005554300401442440441254145346334403520562622011002101321258
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                1200                1220                1240                1260
                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |            
Sec.Str
Seq
CCCCCCCCCCCCCCCCCCHHHHHHHHHCCCSSSSCCHHHHHHHHHHHSSSCCCCCCSSSSCCCSSSSSSSSCCCCCCSSSSSCCCCSSSSSSSSSHHHHSSHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCSSSSSSSCCCCSSCHHHHHHHHHHHHHHHHHCCCCSSSSSSCCCSSSSSSSSCCCSSSSSSSSSSCCCHHHHHHHHHCHHHCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCHHHHHHCCCCSSSSSCCCCCSSSSSSCCCCCCSSSSSCCCSSSSSSSSSSSSSHHHHHHHSSSSSSSSSHHHHHHHHCCCHHHHHHHHHHHHCCHHSSSHHHCCCHHHCCCCHHHHHHHCCCCCCCCCCCCCCCCCHHHHHHHHHHHHHHHHHHHCCCCCCCSSSSCCCCCCCCCCCHHHCCCCCSSSSCCCCCCCCCCCCCCHHHHHHHHCCCCCCCCCCHHHCCCCCCCSSHHHHHHHHHCCCHHHHHHHHHCCCCSSSSSCCCSSSSSSSCCHHHHHHHHHHHHHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHHCCCHHHHHHHHHHHHCCCCHHHHHHHHHCCCCHHHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCHHHHHHHHHHCCCHHHHHHHHHHHCCHHHHHHHHHHHHHHHHHHHHHHHHCCHHHHHHHHHHHHHHHHHHCHHHHHHHHHHHHHCCCCCHHHHHHHCCCCHHHHHHHHHHHHHCCCCCCCCCHHHHHHHHHHHCCCCCCHHHHHHHHHHHHHHHHHCCCCCCCCCCCCCCCCHHHHCCCCCCCCCCCCCCCCCCCCCCSSCCCCCCCCHHHHHHHHHHHHHHHHCCCCCCHHHHHHHHHCCCCCCCCCCCCCCCCHHHHHHHHHHHHHHHHHHHCCCCHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHCCCCCCCCCHHHHHHHHHCCCCHHHHHHHHHCCCCHHHHHHHHHHHCCCHHHHHHHHHHHHCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCCHHHHHHHHHHHHHHCCCHHHHHHHHHHHHHCCCCCHHHHHHHHCCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCCSSSSCCCCCCCCCCCCCCCSSSSCCCCHHHHHHCCCCCCCCCHHHHHHHHHHHHHHHHHHHCHHHHHHHHHHCCCCCHHHHHHHCCCCCCC
MFNFRNLPLFDKDNSKDTDDIKNFINSYEGIYFSSSNKFINVFLDKILIIDPSSLNVITINTDLYVVDFLFNEKNKNLIVLGKGKNSLICSVYNIRECNFTLLKKIQLSKNINNIKKTLIAKCNEYIITLENKKITFYFLNKDYSINQSELIEDGKELIENIYLSKNHILLVIKNSYVYIYQLDIKNSHISYTLIDSFNLNLSYLRESINNKKKHINKINDVSNNDPKKDNNEKNTSSNNITHNNYNDISNNNNNNNNINGVKDHINNNTLENNDEPILSIYNEDLNVLYICQNMYNVLFVLNLNNLSFEFILLENKIINLFSCKFYLILLKEVNKKFFLHIYIIYEDMKLLVSTLLLNEPISNVIFFNNLFCLLIEEEISKPEIKVDKFYFYEQLKLKLHSKLDGDALKILKRDHVTNNINICKQEEKKYKLKNEQKDVEIYNKNNTVDSTNNTIYNSEVNHYKYITNDFFLNEDVNTDIKKELYNLNHTKDNINHDTLKKQTNQNDITMSLSKDEKNNDTNKFFNENMFTLNKFFKNCRNQIKIILKERNINEIINMFKKKKLYQWLIKYANLNKNYQIININFIHKIYADFLFEKEQYENAIYEYIQTINYLETSYVIHKYLNLDLYEYLTIYLEKLHVYHHFNDEHTMMLLSCYKKQCKKKKMISFIKKNKDKINLNKTYKFLLNAGYYNIVLNLSKKYKDHFTYVSILIEKYENYEKSLKYIFKLDVENICILLFKYGYKFIKYYPQLTIYLLKKIIKKYNINLTIFIPLFLDNIDFLFMFIVKFLDKNVNINKINHIQEKQKHHYSNMYHDSDELNSVTKQKNKINSFLLYEKDNNQNHNGIPSDSHNLSDDNNSQESTTAVNLINKTNLQLDIFNGEYDYILFVTVIQILLQKYKKSMQEENQTNNQSSKQTNNQSSKQTNKQSINQSNNQSINQTNNQSIKQTNIQTNKQKGNSTTNKIINKDETNEQNNILTFNIDKLIQNNKDKNINFLSVLLLSIYNYNKGLIYTSTQMNKYDISLLFSIHKFINNTKGKINKINMDEHKPNEQTMQINSSYKILNRNFQKVIYNICINHLKLNGSLSYNYIFYYLSMLNDEKYLIKFIKKIRQDMNLSIFNLIQILKKYNKSYSCIQNMVVAYMNDMNKNINDKCIEIEKDKKELEKIKKKQLKKKYNFYLIDNAYCSICKEILSVPMIHFLCKHSYHSYCLKDNNVCILCHNKDKEKKLLKEKAINSIQNFDEFFKYLQGSTDKFSYISNYLSYGITPK
11xi4A 0.13 0.19 0.91 1.83Download M--AQILPI----RFQEHLQLQNLINP-ANIGFSTSDKFICIREAQVVIIDMNDPNPIRISADSAIMN-----PASKVIALKAGKT---LQIFNIEM--KSKMKAHTMTDDVTFISLNTVA-----LVT----DNAVYHWSMEGESQPVKMFDRHSSLIINYRTDAKQKWLLLQQNRVVAMQLYSVDRKVSQPIFAQFKMEFCFAVRGQAGGKLHIIEVGTPPTG------------------------------NQPFPKKAVDVFFPPEAQNDFPVAMQISEKHDVVFLITK-YGYIHLYDLETGT---CIYMNRISTIFVTAPHEATAGIVNRKGQLSVCVEEENIIPYITNVLQNPDLALRMAVRNNLAGAEELFARKFNALFAQGNYSEAAKVAANAPKG-----ILRTPDTIRRFQLLQYFGILLDQGQLNKYESLELCRPVLQQGRKQLLEKWLKEDKLECSEELGDLVKSVDPTLALSVYLRANVPNKVIQCFAETGQVQKIVLYAKKVGYTPDWIFLLRNVMRISPDQGQQFAQMLVQDEEADITQIVDVFMEYNLIQQCTAFLLDALKNNPQVADAILAHIAQLCEKAGLLQRALEHFTDL---YDIKRAVVH-THLLNPEWLVNYFGSLSVEDSLECLRAMLSAN---IRQNLQICVQVASKYHEQLSTQSLIELFESFKSFEGLFYFLGSIPDVHFKYIQAACKTGQIKEVERICREYDPERVKNFLKEAKPLIIVCDRFDFVHDLVLYLYRNNLQKYIEIYVQKVNPSRLPVVIGGLLD-----VDCSEDVIKNLILVVRGQFSTDELVAEVEKRNRLKLLLPWLEARIHEGCEEPATHNAL---------AKIYIDSNNNPERFLRENPYYDSRVVGKYCEKRDPHLACVAYERGQCDLELINVCNENSLFKSLSRYLVRRKDPELWGSVLLESNPYRRPLIDQVVQTALSETQDPEEVSVTVKAFMTLLEKIVLDNSVFSEHQNLLILTAIKADRTRVMEYINRLDNYDAPDIANIAISNELFEEAFAIFRKFDVNTSAVQVLIE------HIGNLDRAYEFAER--CNEPAVWSQLAKAQLQKGMVKEAIDEVVQAANTSGNWELVKYLQMARKKARESYVETELIFALAKNRLAELEEFINQVGDRCYDEKMYDAAKLLYNNVSNFGRLASTLVHLGEYQAAV-------DGARKANS--------TRTWKEVCFACVDGKEFRLAQMCGLHIVVHADELEELINYYQDRGYFEE
27qpgR 0.09 0.26 0.93 1.09Download ---WNDIELLTNSGYLSVG-------SRK----EHGTALYQ--VDLLVKISSLNPKIQACSLDGFIIVILLDSICRSLQLHLVFDTEVDVVGLCQ-EGKF-------------------------LLVGERSGNLHLIHVTSKQTLLTNAFVQKANDTYQNLVIEKDG-----SNEGTYYMLLLTYS---GFFCITNLQL-LKIQQAIENVDFSTAKKLQGQIKSSFISTENYHTLGCLSLVAGDLAEVTGNPDSSKKGMTVKNLIDAEIIKGAKKQLIDNL-------LFVLDTDNVLSLWDIYTLVWNWPSLHVEEFLLTTEALKLIALTASAKMKNLMVYSLPTMLVQTGISTDTIYLLEGVCKDSVSVLVLRCLTEALPE------NRLSRLLHKHR---FAEAESFAIQFGL-DVELVYKVKSNHILEKLALSS-----VDASEQTEWQQLVDQDDEFVVNYCLKQWITYETTQEMLNYAKTRLLKTALIYSDGLKEVLRAHAKLTTFYGAFGPKFSGSSWIEFLNN------------------EDDLKDIFLQLKEGNLVCAQYLWLRESRFDVKMLESLLNSVIPFVRRTEGQIILAKWLEQAARNLEGLQLAEIFFTAEKTRTLVNNLRELITLHRKYNNTTTIVFRMFDPSILEKFIRVYMREHDLQEELLYIEDLLNRCSWEAKAMAVIACLSDTDLIFDAVLKIMYALEDALKVAQAFMLSD-DEIYSLRIIDLIDRTAERVIIWARLALQEEPDHSKEGKAWRMSVAKTSVDILKILCDIQKDNLEMLKLFKEVASLQENFEVSNSSLVADLREQHIKAHEVAQAKHKPGSTPEEAELTLRALKDGNIKTALKKCSDLFKYKLCQMLADGLNLPSMIHDLASQAATLDALELCKHTLMAVELSRDCGILMKASFGTHKDIVLESQMVLPVIYELISSLVPLAESKRYPLESTSLPYCSLNDGLVLPVINSISALLQNLQSSQWELALRFVVGSFGTCLQHSVTLVKSRHVVMELKEKAVIFIREATTLLHKVFNCRLVDLDLALGYCNLWKLIDKDKILAISLVGSELASLYQETDAQWGIRLGKLRQHFLTKKDLIKALVENIDMDTSLILEYCSTFQLCDAVLQLFIETLLHNTNARHPKLLAKALEMVPLTKDLVISLSGILHKLDPYERADEKITNININQALSIYRRISPLEYQYMLEHVIHLIFFGTAQNFWKILSTEL-SEESFPTLLLISKLMKFSLDTLYVSTAKHVFEK
36wcj 0.12 0.09 0.30 3.68Download ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------NSTRTWKEVCFACVDGKEFRLA-QMCGLHIHADELEELINYYQDRGYFEELITMLEAALGLERAHMGMFTELAILYSKFKP-QKMREHLELFWSRVNIPKVLRAAEQAHLWAELVFLYDKYEEYDNAIITMMNHPTDEGQFKDIITKVAN---VELYYRAIQFYLEFKPLLLNDLLMVLS--PRLDHTRAVNYFSKVKPLVKPYLRSVQ--NHN-----------------------------------------------------------------------------------------NKSVNESLNNLFITEED------------------------------------------------------------------------------YQALRTSIDA--YDNFDISLAQRLEKHELIEFRAYLFKGNNRWKQSVELCKK-D-SLYKD---------------------------------AMQYASESK--DTELAEELLQWFLQEEKR----ECFGACLFTCDLLRPDVVLETAWR-HNIMDFAMPYFIQVMKEYLTKVDKLDASESLRKEEEQATETQPIVYGQPQLML----------------------------------------------------------------------------------------
46wcj 0.13 0.09 0.30 2.20Download ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------NSTRTWKEVCFACVDGKEFRLA-QMCGLHVHADELEELINYYQDRGYFEELITMLEAALGLERAMGMFTELAI-LYSKFKP-QKMREHLELFWSRVNIPKVLRAAEQAHLWAELVFLYDKYEEYDNAIITMMNHPTDEGQFKDIITKVANV---ELYYRAIQFYLEFKPLLLNDLLMVLSP--RLDHTRAVNYFSKVKQ---------------------------------------------------------------------------------------------------------LPLVKPYLRSVQ-N------------------------------------------HNNKSVNESLNNLFITEED---------------YQAL----RTSIDA-YDNFDNISLAQRLEKHELIEFRAYLFKGNNRWKQSVELCKK--DSLYKDAMQYA---------------------------------SESK--DTELAEELLQWFLQEE-KRECFGACLFT--CYDLLRPDVVLETAWR-HNIMDFAMPYFIQVMKEYLTKVDKLDASESLRKEEEQATETQPIVYGQ--------------PQLML--------------------------------------------------------------------------
57wkkB 0.09 0.18 0.99 2.60Download --TSRELILLGRSSMKDGYQIKAELDRYGLAYYKPPSTRSADKVKANKNLSPALQELGLRLSKFLALDEEQSVELLQTYLQYDYRGTQESVKVLPQDERQSQALMLKMADYYYEERISLLRCVLYILNYFQDDKHPYSAEFSKCVMEQKELFGKYLKQFESLCREEAPTWETHGNTERQVSRWFVQRLREQAMLLEIIFLYFACFAASPSDLLALTKLFKEQGFGCRLQNHHLVEPSMDPLVERLDMDTLMTCSLSDKIEQHPFSSEEQVCKEMDSILVTLGDVPHHGPVLLAWAL-LRFTLNPDKVTSAVRKMGSTAIQLHLFQYLTRMLQSLRSGTSTACLCVYTFLAYVLSTLEEQVSQSQQDLVETACQVFAAPNLPDLFWNMEPTAGLGILLDSVVGMFPFRISPLLKLFTALVSKSSAKKVYSFLDRMSSYTEHYRHKPHDILSHDDETLWKRQTPKLLYALGLGQTNSCWTLFTCEIEMLLHVVSTADVIHQCQRVKPIIDLVHKVISTLSIADCLLPITSRIYMLLQRLTTVMNDCLTALATRLPAKVWTDLRHTGGYGSLFGIEQSQGEYSTLSFLRLITTLVKGQLGSTQSQGLVPCILFVLREMLPNYHRWRYNSGVREQLGFQILSLIHAILNLLRSLCIFSLTNTEAGQAVINIMGIG-----VDTLNTVMLTQAGSSGTEGQGQMLMQTIKLAFSITNNVIRLKPLEHALTQHGAHGNNLIAVLAKYIYHRYDPSLPRLAIQLLKRLAMVAPMS---VYACLGSDAAAIRDAFLSRLRDNIDWGAPLLHRSAIAFLHALWQDRRDSAMTVLRTKPNFWENLTSPLFGTLACPSESSELSILETCAFIMKIICLEIYYASLKKILKKFSEEERFTYWSNYVHSLVCQVAETEGNSLTEYQQLLSAWRMFLIVATHNADVMHLTNPEVRQKLFKDILGGTQALLLCTVMIILLRRWKNDLPEDILNSLTQILEGVLQRDQQLVEKTKAQIFSALISVLEMKPMKASEIPQYSQLLLNVCETLQEEVVSLVDHTRHEVQKDQRDGVCVLGLHLAKELCEADEEGDQWQQVLRKLLPILFSALEVSLRIKQNLHFCEAILHFLFTLAKITQTVCLPLLSVYQSWPGVYRLTVSLMERLLKTLRYNFLSEALDFVGVHQERILQCLSAVRTSLSCLEEADHTVGFLLQLSNFTKEWHFHLPQLIKDVQVNLCYLCQACTSLLHSLELRQLRTVQHSLLKILGKTLAATVNVTLSMLGEMDKKK
66wcj 0.11 0.09 0.29 4.09Download ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------CVDGKEFRLA--Q-MCGLIVVELEELINYYQDRGYFEELITMLEAALGLERAMGMFTELAI-LYSKFKPQ-KMREHLELFWSRVNIPKVLRAAEQAHLWAELVFLYDKYEEYDNAIITMMNHPTDEGQFKDIITKVANV---ELYYRAIQFYLEFKPLLLNDLLMVLSPR-----------------------------------------------------------------------------------------------------------LDHTRAVNYFSKVKQLPLVKPYLRSVQ------NHNNKSVNESLNNLFITEED--------------------------------------------------------YQALRTSIDA-YDNFDNISLAQRLEKHELIEFRAYLFKGNNRWKQSVELCKK--DSLYKDAMQYASESK---------------------DTELAEELLQWFLQEE---------------KRECFGACLFT--CYDLLRPDVVLETAWRH-NIMDFAMPYFIQVMKEYLTKVDKLDASESLRKEEEQATETQPIVY-----------------------------------------------------------------------------------------------
71b89A 0.07 0.09 0.22 1.97Download ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------RSNFGRLASTLVHLGEYQAAVDGARKAN------STRTWKEVCFACVDGKEFRLAQMCGLHIVVADELEELINYYQDRGYFEELITMLEAALGLERAHMGMFTELAILYSK-FKPQKMREHLELFWSRVNIPKVLRAAEQAHLWAELVFLYDKYEEYDNAIITMMNHPTDAWKEGQFKDIITKVANVELYYRAIQFYLEFKPLLLNDLLMVLSPRLDHTRAVNYFSKVKQLPLVKPYLRSVQNHNNKSVNESLNNLFITEIDAYDNFDNISLAQRLEKHRRIAAYLF---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
86aayA 0.14 0.19 0.85 0.88Download -----NIY-YNPFKPQDKSYFAGYFNAAE-----NTDSVFRELGKRLKGKEYTSENFFDFKENISLVEY---ERYVKLLLLDKKEVPIKERKENFKK-NFGIIKAVRDLRNFYT-IFGVLDELKSTVLTVKKKK-----VKTD-QKKLEYLRDTARKIEEKRRNQRE------RGEKELVAPFKYSDKR------DLIAIYNDAFDVYIDKKKDSLKESSKAKYNTKSDPQQEEGDLKIPISKNGVVFLLSLFLTKSKIAGFKATVIDEAVSHGKNSICFATHE----IF-SHLAYKKL-VRTAAEQLSVYAKETLQLDELSKVPVYQNLSEDVQKTF-----IEDWNEYLKENNTEEEQVIHPVIKFNYFAIRFLDEFAQFPTLRVHL-YLHDSRP-----KENLISDRRIKEKTVFGRLSELEHKKALFIKNTETNE----DREHYWEIFPNPNYDFPKENISVNDKDFPIAGSILD---------REKQPVAGKIGIKVKLLNQQYVSEVDKAVKAHQLKQRIQNIIEEIVPINESNPKEAIVFGGQPTAY--LSNDIHSILYEFFDKWEKKKEKLEKKGEKELRKEIGKELEKK--IVGKIQAQIQQIIDKDTNAKILKPYQIDKEKLIKDLKQEQNILQKLKDEQTVREKEYNDFIAYQDKNREINKVNHKQYLKDNLKRKYPEAPARKEVLYYREKGKVAVWLANDIKRFPTDFKNEWKGEQHSLLQKSLAYYEQCKEELKNLLPEKVFQHFKLGGYFQQKYLYQFYTCYLDKRLEYISGLVQQAENFKSENK--VFKKVENECFKFLKKQNYTHKELDARVQSILGYPIFEKPTIIKGKTFKGNEALFAD-RYYKEYQNFQTFYDTENYPLV----ELEKKQADRKRKTKIYQQKKNDVFTLLAKHIFKSVFKQDSIDQFSLEDLYQS------REERLGNQERARQTGERNTNYIWNKTVDCDGKITVENVKLKNVGDFIKYEYDQ---RVQAFLKYEENIEWQAFLIEEENYPYVVEREIEQYEKVR-REELLKEVHLIEEYILEKVKDKEILKKGDNQNFKYYILNGLLKQLKNE---DVESYKVFNLNTEPEDVNINQLKQEATDLEQKAFVLTYIANKHNQL----KKEFWDYCQEKYGKIEKEKTYAEYFAEVFKKEKEALIKL-----------------------------------------------------------------------------------------------
97t3uA 0.08 0.17 0.77 2.25Download AEGSVNGFISTLGLVDDR-----------------------------CVVEPAAGDLDNPPKKFRDCLFKVCPMNRY------------------SAQKQYWKAKQTDVVLLQKLQHAAQMEQKQNDTENKKVHGDVVKYGSVIQLL----------------HMKSNKYLTVN------KRLPALLEKNAMRVTLDATGNEGSWLFIQPFWKLRSNGDNVVVGDKVILNPVNAGQPLHASNYELSDNAGCKEVNSVNCNTSWKINLFMQFVLKGGDVVRLFHAEQEKFLTCDEYKGKLQVFLRTTLRQSATSATSSNALWEVEVVHLYRFKHLATGNYLAAEE---------------------------------------------------------------------------------------------NKYCLVAVPHGNDIASLFELDPTYVRLRHLCTNTWIQST-----------------NVPIDIEEERPIRLMLGTCPTK-------------------------EDKEAFAIVSVPVSEIRDLDFANDASSMLASAVEKEGFISQNDRRFVIQLLEDLVFFVSDVPNNGQNVLDIMVTKPNRERQKLMREQNILKQVFGILKAPFRPLVRLEELSD---QKNAPYQHMFRLCYRVLRHSQEDYRKNQEHIAKQFG------MMQSQIGYDILAEDTITALLHNNRKLLEKHIT------KTEVETFVSLVRKNREPRFLDYLSDLCVSNHIA----------IPVTQELICKCVLDPKNSDILIRTELRPEVWLTWTDKNNEHHEKSVRQLAQEARAGNAHDENVLSYYRYQLKLFARMCLDRQYLAIDEISQQLGVDLIFLCMADEMLPFDLRASFCHLMLHVHVDRDPQELVTPVKFARLWTEIPTAITIKDYDSNLNNKFANTMEFVEDY--------LNNVVSEAVPFANEEKNKLTFEVVSLAHNLIYFGFYSFSELLRLTRTLLGIIDCDIVVMETKLKILEILQFILNVRLDYRISYLLSVFKKEFLDRIGEQAEAMFGSMLEVDDEGGRMFLRVLIHLHDYAPLVSGALQLLFKHFSQRQEAMHTFKQVQLLISAQDVENYKVI-------KSELDRLRTMVEKNYQIVKGILERLNKMCQMRKKQQRLLKNMDA---------------------------HKVMLDLLQIPYD---------------KGDAKMMEILRYTHQFLQKFCAGNPGNQALLHK----H
106aayA 0.10 0.19 0.87 1.22Download EYTSENFFIFKENIS--LVEYERYVKLLSD-YFPARENFKKNFKGIIKAVRDLEHGEVEITDEI----FGVLDELKSTVLTVKKKK--------VKTDKTKEILKKSIEKQLDILCQKKLEYLRDTARKIEEKRRNQRERGEKELVAPFKYSDKRDDLIAAIYNDAFDVYIDKKKDSLSKAKYNTKSDPQQEEGDLKIPISKNGVVFLLSTKQEIHAFKSKIAGFKATIDEATVSEATVSHGKNSICFATHEIFSHLAYKKLKRKVR---TAAEQLSVYAKETLQLDEL---SKVPDVVY----QNLSED------------VQKTFIEDWNEYLKENNTEEEQVIHPVIRKRYEDKFNYFAIRFLDEFA--------QFPTLRFQVHLGNYLHDSRPK---ENLISDRRIKEKITVFGRLSELEHKKALFIKNTETN----EDREHYWEIFPNPNYDFPKENISVNDKDFPIAGSILDREKQPVAGKIGIKVKLLNQQYVSEVDKAVKAHQLKQRKASKPSIQNIIEEIVPINE-----SNPKEAIVFGGQPTAYLSNDIHSILYEFFDKWEKKKEKLEKKGEKELRKEIGKELEKK--IVGKIQAQIQQIIDKDTNAKILKPYQDGDKEKLIKDLKQEQNILQKLKDEQTVREKEYNDFIAYQDKNREINKVRDRNHKQYLKDNLKRKYPEAPARKEVLYYREKGKVAVWLANDIKRFP---TDFKNEWKGEQHSLLQKSLAY-YEQCKEELKNLLPEKVFQHLPFKLGGYFQQKYLYQFYTCYLDKRLEYISGENFKSENKVFKKVENECFKFLKKQNYTHKELDARVQSILGYPIFLERGFDEKPTIIKGKTFKGNEALFADWFRYYKEYQNFQTFYDTENYPLVELEKKQADRKRKTKIYQQKKNDVFTLLAKHIFKSVFKQDSIDQFSLEDLYQSREERLGNQERARQTGERNTNYIWNKTVDLKLCDGKITVENVKLKNVGDFIKYEYDQRVQAFLKYEENIEWQAFLIEEENYPYREIEQYEKVRREELLKEVHLIEEYILEKVKDKEILKKGDNQNFKYYILNGLLKQLKNEDVESYKV---FNLNTEPEDVNINQLKQEATDLEQKAFV-LTYIANKFAHNQLP----KKEFWDYCQEKYGKIEKEKTYAEYFAEVFKKEKEALIKL-----------------------------------------------------------------------------------------------
(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: FFAS-3D   2: SPARKS-X   3: HHSEARCH2   4: HHSEARCH I   5: Neff-PPAS   6: HHSEARCH   7: pGenTHREADER   8: wdPPAS   9: PROSPECT2   10: SP3   

   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=-0.16 (Read more about C-score)
  • Estimated TM-score = 0.69±0.12
  • Estimated RMSD = 9.8±4.6Å

  • Download Model 2
  • C-score = -1.96

  • Download Model 3
  • C-score = -1.46

  • Download Model 4
  • C-score = -3.74

  • Download Model 5
  • C-score = -3.85


  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
17wkkB0.983 1.300.0820.993Download
27mvxA0.806 5.340.0620.950Download
37wo9A0.777 5.270.0790.917Download
45ijoJ0.667 5.700.0730.792Download
57wooF0.596 7.660.0670.808Download
66lk8A0.596 7.780.0550.811Download
77mvtA0.581 6.910.0770.745Download
84kf7A0.555 4.830.0620.630Download
97n84X0.500 7.130.0740.656Download

(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
10.14 9 2puyA ZN N/A 1189,1192,1210,1213
20.06 4 3wmoK CRT Rep, Mult 1028,1031,1034,1042,1045
30.03 2 4gmxC K85 Rep, Mult 582,584,585,588,606,607,610,611,619
40.03 2 2ot8A PEPTIDE Rep, Mult 550,582,590,631,634,635,638
50.02 1 4haxC RJA Rep, Mult 565,584,596,600,603,606,614


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
10.1272uv8G0.288 9.870.0220.457 2.3.1.86  NA
20.1252vkzG0.290 9.790.0270.455 2.3.1.38 3.1.2.14  NA
30.1143ecqB0.255 9.950.0500.403 3.2.1.97  NA
40.1132j5wA0.252 9.510.0430.389 1.16.3.1  NA
50.1112zxqA0.252 9.660.0480.389 3.2.1.97  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 0.340.3297 7.02 0.04 0.433gb8A GO:0005737 GO:0015031 GO:0000776 GO:0000278 GO:0005654 GO:0003723 GO:0016070 GO:0006810 GO:0044419 GO:0005829 GO:0006886 GO:0030529 GO:0046796 GO:0000087 GO:0051028 GO:0005730 GO:0016032 GO:0005635 GO:0016071 GO:0005488 GO:0005634 GO:0005642 GO:0015030 GO:0000236 GO:0005515 GO:0008565 GO:0019058
2 0.290.3375 7.59 0.06 0.453gjxA GO:0005654 GO:0034504 GO:0051028 GO:0042176 GO:0000776 GO:0005730 GO:0046825 GO:0005737 GO:0015031 GO:0005515 GO:0005634 GO:0030529 GO:0005642 GO:0003723 GO:0010824 GO:0009615 GO:0006810 GO:0008565 GO:0015030 GO:0006611 GO:0005488 GO:0006886
3 0.250.3154 8.11 0.07 0.443a6pA GO:0005515 GO:0015031 GO:0005730 GO:0005829 GO:0003723 GO:0000049 GO:0031047 GO:0005634 GO:0006810 GO:0006886 GO:0005654 GO:0008565 GO:0005488 GO:0005737 GO:0006611
4 0.160.3805 7.25 0.05 0.501z3hB GO:0005737 GO:0007049 GO:0005488 GO:0008262 GO:0006611 GO:0008283 GO:0015031 GO:0007067 GO:0051301 GO:0005634 GO:0008565 GO:0005635 GO:0006886 GO:0006810
5 0.150.3582 7.03 0.06 0.461u6gC GO:0043086 GO:0030154 GO:0006351 GO:0000151 GO:0005488 GO:0005515 GO:0016567 GO:0005634 GO:0006355
6 0.150.3380 7.37 0.06 0.443m1iC GO:0006810 GO:0005634 GO:0005816 GO:0000776 GO:0048471 GO:0005515 GO:0015031 GO:0000055 GO:0005737 GO:0008565 GO:0006406 GO:0006611 GO:0034501 GO:0005488 GO:0006886
7 0.140.3420 6.83 0.07 0.443opbA GO:0005515 GO:0017022 GO:0008298 GO:0030036 GO:0005737 GO:0007533 GO:0005488
8 0.140.3189 7.78 0.05 0.433nd2A GO:0000176 GO:0006656 GO:0005515 GO:0005635 GO:0006612 GO:0005087 GO:0005737 GO:0051292 GO:0005643 GO:0005634 GO:0006810 GO:0008565 GO:0006606 GO:0060188 GO:0015031 GO:0005488 GO:0006886
9 0.140.3229 7.46 0.05 0.431wa5C GO:0008565 GO:0051301 GO:0005634 GO:0005635 GO:0006810 GO:0006886 GO:0005737 GO:0007049 GO:0005488 GO:0008262 GO:0006611 GO:0008283 GO:0007067 GO:0015031
10 0.130.3153 7.39 0.04 0.421qgkA GO:0006606 GO:0046796 GO:0006610 GO:0005634 GO:0005488 GO:0005515 GO:0016032 GO:0005654 GO:0019904 GO:0005643 GO:0019058 GO:0005635 GO:0015031 GO:0006886 GO:0000060 GO:0006309 GO:0008139 GO:0005737 GO:0006607 GO:0044419 GO:0005829 GO:0008565 GO:0006915 GO:0008270 GO:0006810 GO:0006921


Consensus prediction of GO terms
 
Molecular Function GO:0008565 GO:0003723 GO:0005049
GO-Score 0.70 0.65 0.32
Biological Process GO:0009894 GO:0033365 GO:0046605 GO:0046822 GO:0051246 GO:0051298 GO:0033157 GO:0051707 GO:0030163 GO:0006611
GO-Score 0.58 0.58 0.58 0.58 0.58 0.58 0.58 0.58 0.56 0.56
Cellular Component GO:0005730 GO:0000776 GO:0015030 GO:0005642 GO:0030529 GO:0005829 GO:0005635
GO-Score 0.65 0.53 0.53 0.53 0.53 0.51 0.45

(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 S693100_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).
  • Jianyi Yang, Yang Zhang. I-TASSER server: new development for protein structure and function predictions, Nucleic Acids Research, 43: W174-W181, 2015.