RS12329760 ISOFORM 2 | NP_001128571.1:38-529

Section I. Input Sequence

1
MPPAPPGGES
11
GCEERGAAGH
21
IEHSRYLSLL
31
DAVDNSKMAL
41
NSGSPPAIGP
51
YYENHGYQPE
61
NPYPAQPTVV
71
PTVYEVHPAQ
81
YYPSPVPQYA
91
PRVLTQASNP
101
VVCTQPKSPS
111
GTVCTSKTKK
121
ALCITLTLGT
131
FLVGAALAAG
141
LLWKFMGSKC
151
SNSGIECDSS
161
GTCINPSNWC
171
DGVSHCPGGE
181
DENRCVRLYG
191
PNFILQMYSS
201
QRKSWHPVCQ
211
DDWNENYGRA
221
ACRDMGYKNN
231
FYSSQGIVDD
241
SGSTSFMKLN
251
TSAGNVDIYK
261
KLYHSDACSS
271
KAVVSLRCIA
281
CGVNLNSSRQ
291
SRIVGGESAL
301
PGAWPWQVSL
311
HVQNVHVCGG
321
SIITPEWIVT
331
AAHCVEKPLN
341
NPWHWTAFAG
351
ILRQSFMFYG
361
AGYQVEKVIS
371
HPNYDSKTKN
381
NDIALMKLQK
391
PLTFNDLVKP
401
VCLPNPGMML
411
QPEQLCWISG
421
WGATEEKGKT
431
SEVLNAAKVL
441
LIETQRCNSR
451
YVYDNLITPA
461
MICAGFLQGN
471
VDSCQGDSGG
481
PLVTSKNNIW
491
WLIGDTSWGS
501
GCAKAYRPGV
511
YGNVMVFTDW
521
IYRQMRADG

 

Section II. Predicted Contact and Distance Matrices

[-] Click to view the predicted contact map of the input sequence
Status
Current status:
Complete
Submitted on:
2020-11-10 12:47:05
Scheduled on:
2020-11-12 05:25:16
Finished on:
2020-11-12 07:53:55
all prediction results.
To open .zip files, you may use 7-zip for Windows or unzip for Linux/Unix/MacOS.
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Hover mouse over a position to display prediction for a specific residue pair.
indices: (0,0)prob:0.0000
Download Predicted Contact Map
the contact image.
To view the contact image.
the contact result file.
Open the result file using a text editor such as WordPad (Windows) or vi (Linux/Unix/MacOS).

[+] Click to view the predicted inter-residue distance matrix of the input sequence

Section III. Predicted 3D Models and Local Structures (see the result by clicking on it)

[+] Click to view the predicted local structure property of the sequence
[+] Click to view the predicted 3D model(s) of the input sequence


Section IV. Multiple Sequence Alignment (See MSAViewer for help)

[+] Click to view the multiple sequence alignment (display up to 5001 sequences)


Please cite the following method papers:
1. Jinbo Xu, Matthew Mcpartlon and Jin Li. Improved protein structure prediction by deep learning irrespective of co-evolution information. Nature Machine Intelligence, 2021
2. Jinbo Xu. Distance-based Protein Folding Powered by Deep Learning. PNAS, 2019
3. Jinbo Xu and Sheng Wang. Analysis of distance-based protein structure prediction by deep learning in CASP13. PROTEINS, 2019
4. Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang and Jinbo Xu. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model. PLoS Computational Biology, 2017.
5. Sheng Wang, Siqi Sun and Jinbo Xu. Analysis of deep learning methods for blind protein contact prediction in CASP12. PROTEINS, 2017
6. Sheng Wang, Zhen Li, Yizhou Yu and Jinbo Xu. Folding Membrane Proteins by Deep Transfer Learning. Cell Systems, 2017