D-I-TASSER PF17097

D-I-TASSER results for PF17097

Input Sequence in FASTA format

>PF17097 (378 residues)
METQAVKTIRDELRELENTVAQASDMVLSEQDHRNASAIREMTQSVIAMSKENSLISVSN
EIDYNEEIGNLAIDPSLIDEKIKQSNNFVELLKLTHLEQEALDYFLRYTISSTNTLELES
TSDPKFVSLENEVTELENKTLTEHRDKIQEAKKDISDKSKDLANKQDQINELCLGAANSV
DECWKMLNELEDIHSQRDNDVKETLSQDTTTTSDLIEETYKEWSSLQTSLTELNNSKDEL
DQLIAFKNEKHKDNDSTKIRNANIKNKTITENVKMLKLLINFWESNFIVPGSKKSKLSNL
EVYPQTKKFQFKCAEQYTVIIQLNQNGGSIKSIEIFENDGKSVQENKNLSSLILNKYKNP
LSSYPIFQVINDIVEELK

Predicted Contact and Distance Map Used in D-I-TASSER

Contact Map

Distance Map

D-I-TASSER simulation is guided by the consensus contact map (left figure, top 2L) and distance map (right figure) derived based on confidence scores of AttentionPotential. In the contact and distance maps, the axes mark the residue index along the sequence. For the contact map, each dot represents a residue pair with predicted contact, while for the distance map a color scale represents a distance of 1-20+ angstroms.

Top 10 threading templates (target type: very hard)

Rank

PDB
hit

ID1

ID2

Cov

Norm.
Zscore

Threading
program

                  20                  40                  60                  80                 100                 120                 140                 160                 180                 200                 220                 240                 260                 280                 300                 320                 340                 360

                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |                   |

Seq

METQAVKTIRDELRELENTVAQASDMVLSEQDHRNASAIREMTQSVIAMSKENSLISVSNEIDYNEEIGNLAIDPSLIDEKIKQSNNFVELLKLTHLEQEALDYFLRYTISSTNTLELESTSDPKFVSLENEVTELENKTLTEHRDKIQEAKKDISDKSKDLANKQDQINELCLGAANSVDECWKMLNELEDIHSQRDNDVKETLSQDTTTTSDLIEETYKEWSSLQTSLTELNNSKDELDQLIAFKNEKHKDNDSTKIRNANIKNKTITENVKMLKLLINFWESNFIVPGSKKSKLSNLEVYPQTKKFQFKCAEQYTVIIQLNQNGGSIKSIEIFENDGKSVQENKNLSSLILNKYKNPLSSYPIFQVINDIVEELK

2p7nA1

0.08

0.03

0.38

0.68

DisCoVER

---------------PA-LAA-ARKADELGQAAREVRAS-VER--QTAYE--TRLAQRSAA---FSGGEPPARREA-P---GA-ELDEARNAQTVSARLFEGNLKGVAQSKQALQYSANAAGGADSDLW--DI--SD-QIGKIKKAFSL--SQLTQKYSNANSLFDNLVKVLSSTISSCL------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

5w1hA

0.09

0.06

0.68

0.67

DEthreader

-EIKAEEMLQRETAVYVAFAARHLSSQTLTILQYFGGH--------------------------------------------LWTDFPFDKYLDDVDFLTDLKDVIYSM-RND---SFHYA--TH--NNGKWNKEL------------------S-VAGSE-----------RFLSPAQANHMLGFLHSYKQYVWDIYRRASTEINH--IAEDKIAGVDITDVDAVIDLSVKLCGTISSLNRNIILSEDRVSRSDIVEYKLKNKKFQEMKNIFRDLMDYSEIADE-QGQINWIYLE----------------ELRNYIEH--------------FRYY---SSF-D-RSFLGIYSEVFVPTILYNILLQHVVSIIRLM

5j1iA

0.07

0.06

0.88

0.64

CEthreader

IRLQLEACETRTVHRLRLPLDKEPARECAQRIAEQQKAQAEVEGLGKGVARLSAEAEKVLALSPAAPTLRS--ELELTLGKLEQVRSLSAIYLEKLKTISLVIRGTQGAEEVLRAHEEQLKEAQAVPATLPELEATKASLKKLRAQAEAQ-----QPTFDALRDELRGAQEVGERLQQRHGERDVEVERWRERVAQLLERWQAVLAQTDVRQRELEQLGRQLRYYRESADPLGAWLQDARRRQEQIQAM-------------------DSQAVREQLRQEQALLEEIERHGEKVEECQR----------FAKQYINAIKDYEL----QLVTYKAQLEKVQSGSESVIQEYVDLRTHY------SELTTLTSQYIKFIS

4wsbC

0.07

0.06

0.77

0.78

EigenThreader

SMERIKELMEMVKN-----------SRMREILTTTMAVIKKALRMKWMMREMIPE--KDEDGNTLTKDAGSNVSPNAVTWWNRAGPRVYKMYFDRLERLTH-------GNQVKNPGHKDLTSREAQEVIMEVVFAQLTITKEKKEELIMVAYMLERELV-----------RTYVEVLHLTQ-------------------------GTCGGEAENDDLDQTLIIASRNI------VRRSIVALASLLSMCHTTEPLVEILRSNPTDEQAVNICKAALG-----------IRINNGYNFK-RVKGSYEEFNVSGKRASAVLKKGAQ----RIQAIIGGRTLEDILNLMITLMVFS--------QEEKMLKAYQLLRHFQ

6ianA

0.12

0.08

0.66

0.36

HHpred

---PKITELTTEIERLNEQEELIVKSVLTQLQQRNKALTDEAAKLKGTLADIN-LAL---EK------STT-QDPSSVKDQATKLNQKRKQVDQLFLNAKE-EALTKKNLENLRILAENQD-FGLYKATRDEAFNVSD-AVLSHQHQIR-LTAKQELL-TKLST------------DPDKKRAAEVLRGILSKRQLKE----ELT--K-QCALSVEEERQLLIKQVKTARGDIEVLERQVNETRDALSESKNRCASL-DEELKSYSGDNIKAFQELQEKD-RELQSF-DSFP--------------------------------------------------------------------------------------

6h7wB

0.16

0.09

0.55

0.47

MRFsearch

--DDWFHDRRVYLDALENQLKALLKANMVAQRKAMAEAAADFSASLHALSTV--------------------ELSPTLSGPLDALSELQLAIRDVYERQAQQ----------------------DVLTFGIIIEEYIRGSVKQAFSQRQKAFHSWHSAESELMKKKAAQDKLLRQGKTQQDRLNQVNAEVIDAERK-------------------VHQARLLFEDMGRL------LRSELDRFEREKVEDFKS----------GVETFLESAVEAQKELIEKWETFL-------------------------------------------------------------------------------------------

4l3iA

0.10

0.09

0.87

1.15

FFAS-3D

---ESIVCLQKALNHLLQRTEVVKKHIKESLKERLIKSISVCQKELNTLCSE------LHVEPFQEEGETTILQLEKDLRTQVELKQKKERKQELKLLQEQDQELCEILCPHYDIDSASVPSLEELNQFRQHVTTLRE-TKASRREEFVSIKRQIILLSLENIATLQKLLRQLEQKSQNEAVCEGLRTQIRELWDRLQEEEREAVATISGSKAKVRKALQLEVDRLEELKQNKKVIEAIRVELVQYWDQCQEQRQAFAPFCAEDYTESLLQLHDALFEGVQKWEETW-------RLFLEFERKASDPN--------------------------RFTNRGGNLLKEEKQRAKLQK-------LPKLEEELKARIELWE

5j1iA

0.08

0.06

0.77

0.85

SPARKS-K

RCISELKDIRLQLEACETRTVHRLARECAQRIAEQQKAQAEVEGLGKGVARLSAEAEVLALSPAAPTLRSELTLLEQVRSLSAIYLEKLKTISLVIRGTQGAEEVLRAHEEKEAQAVPATLPEASLKKLRAQAEAPTFDALRDELRGAQEVGERLQQRHAQLLERWQAVLAQTDVRQRELEQLGRQLRYYRESADPLGARQEQIQAMDSQAVREQLRQEQALLEEIERHGEKVEECQRFAKQYINAIKDYELQLVTYKAQLEKVQSVDLRTHYSELTTLTSQYIKFISET----------------------------------------------------------------------------------------

3r6nA

0.13

0.11

0.84

0.94

CNFpred

VDLASVEQHINSHRGIHNSIGDYRWQLDKIKADLEKSAIYQLEEEYENLLKAS----------------------FERMDHLRQLQNIIQATSREIMWINDCEEEELLYDWSDKNTNIAQK-QEAFSIRMSQLEVKEK-ELNKLKQESDQLVLNQHPASDKIEAYMDTLQTQWSWILQITKCIDVHLKENAAYFQF----------------FEEAQSTEAYLKGLQDSI-PLQHLLEQIKELEKEREKILEY------------KRQVQNLVNKSKKIVQLKPRNPDYRSNKPIILRALCDYKQGDECILKDNERSKWYVTGP-GGVDMLVPSVGLIIPPPNPLAVDLSCKIEQYYE------AILALWNQLYINMK

6sl2A

0.05

0.03

0.60

0.67

DEthreader

------AMADSELV--WENFSESFKDGKVFCALIHRHRP--D-----------------------------------------LDWETVGEDDRANLE--VDDIVNMPRPD---------------------SV-MT-YVAALYKVFSSNDQVEKAGKRAGNFLDLLRATEGMVHDYEQRAQALNIFIINISAERALRSKLNTAMRNCLIALRKA-FADPANATDAKINEYRTFVTDETSEAPLEE---------------------------------------------FNYEQTSMFEIVYIEAQI------------EASSGVTQMFKFDDGNHD--ILDLEFRSCLSSMGLVYMEND----------------

(a)	ID1 is the number of template residues identical to query divided by number of aligned residues.
(b)	ID2 is the number of template residues identical to query divided by query sequence length.
(c)	Cov is equal the number of aligned template residues divided by query sequence length.
(d)	Norm. Zscore is the normalized Z-score of the threading alignments. A Normalized Z-score >1 means a good alignment and is highlighted in bold.
(e)	Threading program lists the threading program used to identify the template.
(f)	Template residues identical to query sequence are highlighted in color.

Top 5 final models from D-I-TASSER

Click to view	Rank^a	Download	Estimated TM-score^b
	1	model1.pdb.gz	0.48
	2	model2.pdb.gz
	3	model3.pdb.gz
	4	model4.pdb.gz
	5	model5.pdb.gz

(a)

D-I-TASSER simulations generate a large ensemble of structural conformations, i.e. decoys. These decoys are clustered by SPICKER based on pairwise structure similarity to report up to five final models from the five largest clusters. Models are ranked in descending order of cluster size. If the simulations converge well, it is possible to have less than 5 models generated, which is usually an indication of good model quality.

(b)

The model confidence is quantitatified by estimated TM-score, calculated based on significance of threading template alignments, contact satisfaction rate, mean absolute error between distnace of model and distance of AttentionPotential, and convergence of D-I-TASSER simulations. higher estimated TM-score signifies higher model confidence.

References:
1.	Wei Zheng, Yang Li, Chengxin Zhang, Robin Pearce, S. M. Mortuza, Yang Zhang. "Deep-learning contact-map guided protein structure prediction in CASP13." Proteins: Structure, Function, and Bioinformatics, 87: 1149-1164 (2019).