C-I-TASSER PF15104

C-I-TASSER results for PF15104

Input Sequence in FASTA format

>PF15104 (82 residues)
LKKMVDRWRNSHTHCLWQMTLSQRRNPYAILRMQDTMVQELALANKQLLMVRQAALHQLF
EKEHQQYQQELNQMGKAFYVER

Predicted contact map

C-I-TASSER simulation is guided by consensus contact map derived based on confidence scores of TripletRes ResTriplet, ResPRE, and NeBcon. In the contact map, the axes mark the residue indexes along the sequence, while each dot represents a residue pair with predicted contact. The upper left triangle is for top L contact map, while the lower right triangle is for top L/5 contact map. No contact is close to the diagonal, because C-I-TASSER does not consider contacts for residue pairs separated by <6 residues.

Top 10 threading templates (target type: hard)

Rank

PDB
hit

ID1

ID2

Cov

Norm.
Zscore

Threading
program

                  20                  40                  60                  80

                   |                   |                   |                   |

Seq

LKKMVDRWRNSHTHCLWQMTLSQRRNPYAILRMQDTMVQELALANKQLLMVRQAALHQLFEKEHQQYQQELNQMGKAFYVER

2kubA

0.09

3.21

0.91

SPARKS-K

LDKMISEAEVLNDMAARKLITLDAEQQLELMKSLVATQSQLEATKNLIGVADLQIAYTTLGNNTQALGNELIKL--------

3nrxA

0.11

0.10

3.50

0.79

FFAS-3D

--EGVQKWEETWRLFLEFERKASRFNLLKEEKQRAKLQKMLPKLEEELKARIELKFMEYVAEQWEMHRLEKER---------

3jct5

0.13

0.10

3.37

0.23

HHpred

--------------TSWELKKQKRLEDKQFKERLKALKDEKEEARQAKITMLKERREKKEENER--YERLAAKMHA------

2kubA

0.09

3.21

1.05

MUSTER

LDKMISEAEVLNDMAARKLITLDAEQQLELMKSLVATQSQLEATKNLIGVADLQIAYTTLGNNTQALGNELIKL--------

2j68A

0.04

1.89

0.49

CNFpred

ARLACNHTREAVARRIPLLEQ-DVNELKKRIDSVEPEFNKLTGIRDEFQKEIINTRDTQARTISESFRSYVLNLG-------

3fxdC

0.09

0.05

1.85

0.40

HHsearch-2

KETILKALNDAIEKGPWDKSNFLRVIGKKLIAIRDRFLKRIGAA--------------------------------------

2kubA

0.08

0.07

2.94

0.92

Neff-PPAS

LDKMISEAEVLNDMAARKLITLDAEQQLELMKSLVATQSQLEATKNLIGD--PNATVADLQIAYTTLGNNTQALGNELIKL-

2e3cA1

0.16

0.09

2.83

0.49

HHsearch

APALT---------------------KSQTDRLEVLLGKPFRELESELLSRRKKDLQQIYAEER------------------

3ke3A

0.07

2.98

0.64

SP3

SAAAIKKVESTESLKQWLTIRAYENGGHAYHATPTDSLRQFRDAILEAKEIGFDILRDAQWELGNRVRKVLTDKGIESVAAE

2kubA

0.09

3.21

0.53

PROSPECTOR2

LDKMISEAEVLNDMAARKLITLDAEQQLELMKSLVATQSQLEATKNLIGVADLQIAYTTLGNNTQALGNELIKL--------

(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 C-I-TASSER

Click to view	Rank^a	Download	C-score^b	Estimated TM-score^c	Estimated RMSD^c
	1	model1.pdb.gz	-3.28	0.45±0.08	9.6±2.5Å
	2	model2.pdb.gz	-4.64
	3	model3.pdb.gz	-4.64
	4	model4.pdb.gz	-4.64
	5	model5.pdb.gz	-4.90

(a)	C-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 C-score, calculated based on significance of threading template alignments and convergence of C-I-TASSER simulations. higher C-score signifies higher model confidence.
(c)	Model TM-score and RMSD are estimated based on C-score and protein length for the first model.

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).