D-I-TASSER PF01672

D-I-TASSER results for PF01672

Input Sequence in FASTA format

>PF01672 (85 residues)
AYDYLKIANAIEEGIIEEEFLVQNGFRQTLFVLRNQESATIKKSKQNPIKPLRFQLKKQE
SYDFYKSNAKFTSFLMDELFENQKD

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

                   |                   |                   |                   |

Seq

AYDYLKIANAIEEGIIEEEFLVQNGFRQTLFVLRNQESATIKKSKQNPIKPLRFQLKKQESYDFYKSNAKFTSFLMDELFENQKD

3ip4B3

0.13

0.05

0.36

0.70

DisCoVER

-ENLAGMIKLIEDGTMSSKIA-KKVFPELAAKG----------------------------------------------------

6s3kB

0.04

0.88

1.00

DEthreader

VSISAGTDAITS-AFPLHDYH-VPIAIFLVLVIMILRG---IYPVYLVVALL----SGCS-ALTGVMGILILFSGITVLAYGYGT

1ouvA

0.09

1.00

0.54

CEthreader

AEGCASLGGIYHDGKVVFTKACDLNDGDGCTILGSLYDAGRGTDLKDSPGCFNAGNNFKEALARYSKANGGGCFNLGAMQYNGEG

3uq3A

0.08

1.00

0.57

EigenThreader

DKEKAEGNKFYKARQFDEAHYNKAWELHYLNNRAAAEYEKGESDWPNAVKAYTEMIDARGYSNRAAALAKLEAIADCNKAIEKDP

3wdkA

0.16

0.09

0.59

0.28

HHpred

--YREKIIEGMEKGMTAKAGLIAHGRGEAFDYLIGERTIEPALLAEHPVISV---------------------------------

5xwpA1

0.20

0.15

0.78

0.57

MRFsearch

--KNFAVLKKIYLNNSEELEVFRNDIKKKLNKIN----------------SLKYSFERNIIYDYYRESAKRDAYVFDKLYKEED-

7byjA3

0.12

0.09

0.76

0.46

FFAS-3D

-YLYVQSCNDVVQEELKYDIALRLAALQMYIATVTTKQ------------------TQKISLKYIEKEWGLETFLPSAVLQSMK-

6s8bI1

0.13

0.96

0.71

SPARKS-K

IDFLVNILELIKEKQCNINL---FSAISLTSIVYNNLSNNQSYSTNNPLLKYHIILNDKNKTKDVEEKRNIFKREVAELISRNFK

3q0aA

0.12

0.08

0.66

0.47

CNFpred

--QLKALQENMLHF---FVEPLRNGITQTVGESL------------------------VYSTEQLQKATQIQSVVLEDMFKQRVQ

2h7fX

0.08

0.07

0.91

0.83

DEthreader

ST-QL-VFLMETMFFIR--FG--KML-TLKNKHIIKFVSHEFVG-TFLYNFTILMVKDKNFLDVVSKTFDEFLSIVVDHVKSSTD

(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	0.41
	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).