Project name: 468718088117aa1 [mutate: GS35A, AS49A, AS75A, KR87A] [mutate: AP14A, RY59A, HN84A] [mutate: YR59A] [mutate: MT78A, PA88A, QL120A] [mutate: RY59A] [mutate: AP88A]
Status: done
Started: 2026-03-06 13:53:28
| Chain sequence(s) |
A: EVQLVESGGGLVQPGGSLRLSCAASGRTFSSYAMSWFRQAPGKEREFVSAISRSAGTTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAAATLQSTVVLSTHNYDYWGQGTLVTVSS
input PDB |
| Selected Chain(s) | A |
| Distance of aggregation | 5 Å |
| FoldX usage | Yes |
| pH calculations | No |
| alphaCutter usage | No |
| Dynamic mode | Yes |
| Automated mutations | No |
| Mutated residues | AP88A |
| Energy difference between WT (input) and mutated protein (by FoldX) | 4.27991 kcal/mol
CAUTION: Your mutation/s can destabilize the protein structure |
| Downloads | Download all the data |
| Simulation log |
[INFO] Logger: Verbosity set to: 2 - [INFO] (00:00:02)
[WARNING] runJob: Working directory already exists (possibly overwriting previous results -ow
to prevent this behavior) (00:00:02)
[INFO] runJob: Starting aggrescan3d job on: input.pdb with A chain(s) selected (00:00:02)
[INFO] runJob: Creating pdb object from: input.pdb (00:00:02)
[INFO] FoldX: Starting FoldX energy minimization (00:00:02)
[INFO] FoldX: Building mutant model (00:01:27)
[INFO] CABS: Running CABS flex simulation (00:01:30)
[INFO] Analysis: Starting Aggrescan4D on model_8.pdb (00:26:48)
[INFO] Analysis: Starting Aggrescan4D on model_6.pdb (00:26:49)
[INFO] Analysis: Starting Aggrescan4D on model_11.pdb (00:26:49)
[INFO] Analysis: Starting Aggrescan4D on model_7.pdb (00:26:50)
[INFO] Analysis: Starting Aggrescan4D on model_3.pdb (00:26:51)
[INFO] Analysis: Starting Aggrescan4D on model_5.pdb (00:26:51)
[INFO] Analysis: Starting Aggrescan4D on model_1.pdb (00:26:52)
[INFO] Analysis: Starting Aggrescan4D on model_0.pdb (00:26:53)
[INFO] Analysis: Starting Aggrescan4D on model_2.pdb (00:26:53)
[INFO] Analysis: Starting Aggrescan4D on model_4.pdb (00:26:54)
[INFO] Analysis: Starting Aggrescan4D on model_10.pdb (00:26:55)
[INFO] Analysis: Starting Aggrescan4D on model_9.pdb (00:26:56)
[INFO] Analysis: Starting Aggrescan4D on input.pdb (00:26:56)
[INFO] Analysis: Starting Aggrescan4D on folded.pdb (00:27:01)
[INFO] Main: Simulation completed successfully. (00:27:03)
|
Note Your prediction results will be deleted after 360 days.
Drag cursor over the plot to display residue labels.
Show buried residues
- Minimal score value
- -2.324
- Maximal score value
- 1.9727
- Average score
- -0.1156
- Total score value
- -14.4533
The table below lists A4D score for protein residues. Residues with A4D score > 0.0000 are marked by yellow rows.
| residue index | residue name | chain | Aggrescan4D score | mutation |
|---|---|---|---|---|
| 1 | E | A | -1.8199 | |
| 2 | V | A | 0.0000 | |
| 3 | Q | A | -1.1983 | |
| 4 | L | A | 0.0000 | |
| 5 | V | A | 0.9770 | |
| 6 | E | A | -0.0184 | |
| 7 | S | A | -0.1998 | |
| 8 | G | A | -0.2832 | |
| 9 | G | A | -0.5872 | |
| 10 | G | A | -0.5548 | |
| 11 | L | A | 0.0000 | |
| 12 | V | A | 1.7656 | |
| 13 | Q | A | 0.0000 | |
| 14 | P | A | -0.1512 | |
| 15 | G | A | -0.5667 | |
| 16 | G | A | -0.5950 | |
| 17 | S | A | -0.4097 | |
| 18 | L | A | 0.0000 | |
| 19 | R | A | -1.8961 | |
| 20 | L | A | 0.0000 | |
| 21 | S | A | -0.0044 | |
| 22 | C | A | 0.0000 | |
| 23 | A | A | 0.0244 | |
| 24 | A | A | 0.0000 | |
| 25 | S | A | -0.1377 | |
| 26 | G | A | -0.6950 | |
| 27 | R | A | -1.9066 | |
| 28 | T | A | 0.0000 | |
| 29 | F | A | 0.0000 | |
| 30 | S | A | -0.1674 | |
| 31 | S | A | -0.0620 | |
| 32 | Y | A | 0.0000 | |
| 33 | A | A | 0.0000 | |
| 34 | M | A | 0.0000 | |
| 35 | S | A | 0.0000 | |
| 36 | W | A | 0.0000 | |
| 37 | F | A | 0.5068 | |
| 38 | R | A | 0.0000 | |
| 39 | Q | A | -0.6368 | |
| 40 | A | A | 0.0000 | |
| 41 | P | A | -0.4236 | |
| 42 | G | A | -0.6631 | |
| 43 | K | A | -1.2318 | |
| 44 | E | A | -2.3240 | |
| 45 | R | A | -2.2478 | |
| 46 | E | A | 0.0000 | |
| 47 | F | A | 1.9727 | |
| 48 | V | A | 0.0000 | |
| 49 | S | A | 0.0000 | |
| 50 | A | A | 0.0144 | |
| 51 | I | A | 0.0000 | |
| 52 | S | A | 0.0000 | |
| 53 | R | A | -0.2491 | |
| 54 | S | A | -0.1555 | |
| 55 | A | A | -0.0651 | |
| 56 | G | A | -0.4616 | |
| 57 | T | A | -0.1145 | |
| 58 | T | A | 0.1533 | |
| 59 | Y | A | 1.1406 | |
| 60 | Y | A | 1.4952 | |
| 61 | A | A | 0.1679 | |
| 62 | D | A | -0.7415 | |
| 63 | S | A | 0.1325 | |
| 64 | V | A | 1.7624 | |
| 65 | K | A | 0.0000 | |
| 66 | G | A | -0.6350 | |
| 67 | R | A | -1.8976 | |
| 68 | F | A | 0.0000 | |
| 69 | T | A | -0.0599 | |
| 70 | I | A | 0.0000 | |
| 71 | S | A | -0.0776 | |
| 72 | R | A | -0.2271 | |
| 73 | D | A | 0.0000 | |
| 74 | N | A | -0.8200 | |
| 75 | S | A | -0.6834 | |
| 76 | K | A | -1.8189 | |
| 77 | N | A | -0.7377 | |
| 78 | T | A | -0.0863 | |
| 79 | L | A | 0.0000 | |
| 80 | Y | A | 0.4912 | |
| 81 | L | A | 0.0000 | |
| 82 | Q | A | -0.6369 | |
| 83 | M | A | 0.0000 | |
| 84 | N | A | -0.6792 | |
| 85 | S | A | 0.0000 | |
| 86 | L | A | 1.5477 | |
| 87 | R | A | 0.0000 | |
| 88 | P | A | -0.2549 | mutated: AP88A |
| 89 | E | A | 0.0000 | |
| 90 | D | A | -1.7932 | |
| 91 | T | A | -0.3542 | |
| 92 | A | A | 0.0000 | |
| 93 | V | A | 0.3714 | |
| 94 | Y | A | 0.0000 | |
| 95 | Y | A | 0.0720 | |
| 96 | C | A | 0.0000 | |
| 97 | A | A | 0.0000 | |
| 98 | A | A | 0.0000 | |
| 99 | A | A | 0.0000 | |
| 100 | T | A | 0.0312 | |
| 101 | L | A | 0.0416 | |
| 102 | Q | A | -1.1606 | |
| 103 | S | A | -0.2937 | |
| 104 | T | A | 0.2667 | |
| 105 | V | A | 1.8586 | |
| 106 | V | A | 1.4772 | |
| 107 | L | A | 1.1768 | |
| 108 | S | A | 0.1295 | |
| 109 | T | A | -0.2291 | |
| 110 | H | A | -1.1293 | |
| 111 | N | A | -0.6599 | |
| 112 | Y | A | 1.0685 | |
| 113 | D | A | -0.3280 | |
| 114 | Y | A | 0.0000 | |
| 115 | W | A | 1.1644 | |
| 116 | G | A | 0.1250 | |
| 117 | Q | A | -0.5868 | |
| 118 | G | A | -0.3267 | |
| 119 | T | A | 0.2424 | |
| 120 | L | A | 1.5462 | |
| 121 | V | A | 0.0000 | |
| 122 | T | A | 0.1505 | |
| 123 | V | A | 0.1655 | |
| 124 | S | A | -0.1979 | |
| 125 | S | A | -0.2508 |
3Dmol can be expensive computationally for big molecules. If you experience lag or delay click on the "Play the video" button instead.
CABS-flex predictions of flexibility of input structure
In dynamic mode, A4D analysis is performed on the set of models reflecting fluctuations of the input structure (predicted
by CABS-flex method, models are numbered from 0 to 11) and the input model.
Their A4D scores are provided below in the table.
The right panel presents comparison of the most aggregation prone model (with the highest A4D score, -0.1156 in this case) with the input model
(the most aggregation prone model in blue, input in red) and RMSF plot which shows the extent of residue fluctuations in Angstroms (predicted by CABS-flex).
Model |
Average A4D Score |
|||
| input | -0.1156 | View | CSV | PDB |
| model_8 | -0.1428 | View | CSV | PDB |
| model_10 | -0.156 | View | CSV | PDB |
| model_0 | -0.1729 | View | CSV | PDB |
| model_1 | -0.1747 | View | CSV | PDB |
| model_3 | -0.1893 | View | CSV | PDB |
| CABS_average | -0.1931 | View | CSV | PDB |
| model_9 | -0.1984 | View | CSV | PDB |
| model_6 | -0.2048 | View | CSV | PDB |
| model_5 | -0.2063 | View | CSV | PDB |
| model_2 | -0.2068 | View | CSV | PDB |
| model_7 | -0.2132 | View | CSV | PDB |
| model_4 | -0.2258 | View | CSV | PDB |
| model_11 | -0.226 | View | CSV | PDB |
ALA alanine
ARG arginine
ASN asparagine
ASP aspartic acid
CYS cysteine
GLN glutamine
GLU glutamic acid
GLY glycine
HIS histidine
ILE isoleucine
LEU leucine
LYS lysine
MET methionine
PHE phenylalanine
PRO proline
SER serine
THR threonine
TRP tryptophan
TYR tyrosine
VAL valine