D-I-TASSER PF11011

D-I-TASSER results for PF11011

Input Sequence in FASTA format

>PF11011 (85 residues)
KVVTANDLLDGDVIYLTADDRWTRDLSEAELITDEAHAQLRLLEAERQANKVVGAYLADA
VAGENGPEPTHFREDFRRTGPSNYA

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: hard)

Rank

PDB
hit

ID1

ID2

Cov

Norm.
Zscore

Threading
program

                  20                  40                  60                  80

                   |                   |                   |                   |

Seq

KVVTANDLLDGDVIYLTADDRWTRDLSEAELITDEAHAQLRLLEAERQANKVVGAYLADAVAGENGPEPTHFREDFRRTGPSNYA

6i05A

0.05

0.04

0.67

0.80

DisCoVER

LYLQVGAFANAELLKLSTAAPVFIHRVRLGPIGSADEVSRTQDSIRVA--NLGQPTLV-R-------------------------

2e3zA

0.08

0.07

0.86

1.00

DEthreader

-------VWKAYRFSLSWRIGYVT-H--WDLGWAIQDFTNYAKLCFESFGVQNWITFNPISGGIFAP--TLDSLNTTENGLGYFL

3votA

0.06

1.00

0.70

CEthreader

FAIETLSIQGNVHVLSIGYKFFEEGVYIAPAQLKEETRLAIVKEVTGAVSIHQGPAHTELRLDKDGTPYVIRIGGSGVSHYIVKE

4w2pA

0.05

0.92

0.68

EigenThreader

MGWYRRAPGKQREVASLTTGTAYGDFVTISRDNAENTVDLHMNSLK---PEDTAVYYCHEDPYGMELRYWGQAAAHHHHHH----

5ze4A

0.18

0.15

0.86

0.33

HHpred

QILRGDLAPDGSVAKITGKEGL-YFSGPALVFEGEESMLAAISADPMSFKTVV------VIRG-EGPKGPGMPEMLMGAGL----

6akdA

0.17

0.14

0.81

0.68

MRFsearch

---RVRRAPGGDLVYVGR-------LDSQLQVVEPAEVEHAVRACGADDAVVVGVTELVAFYTGIPVEPRELVRRLREV------

4parB2

0.17

0.16

0.96

0.31

FFAS-3D

RGGALHPYKKDTLIWLYENKDWINTISPTEKSTDETITLKKLEEWKNGPQRFMGLYEFQKADLKDGAVWKRVKSEVQTYSPK---

2kpoA

0.14

0.13

0.91

0.82

SPARKS-K

--------MLLYVLIISNDKKLIEEARKMRTVKTEDELKKYLEEFRKENIKVLILVIVRTRKVTSPDEAKRWIKEFSEEGGSLEH

3zgrA

0.15

0.11

0.73

0.60

CNFpred

SASAGIELEHNVVIAIGMSEQATSELVIAHGVMDAASVRRTIESLGIEMDRIVNVFAKAEAS-----------------------

6hasA

0.08

0.06

0.69

1.00

DEthreader

ITFEHVDARRFSWQSCGT-----------LHIDAESDAISTLEACLVDYD-GEYVRMVGIDPKG--KRRVVET------------

(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	0.79
	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).