Stability Prediction
Collection
This group contains all relative training datasets and model checkpoints trained on these datasets for Stability Prediction.
•
2 items
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Updated
seq
stringlengths 86
100
| fitness
float64 -1.97
3.4
| stage
stringclasses 1
value |
|---|---|---|
G#S#Q#E#V#N#S#G#T#Q#T#Y#K#N#A#S#P#E#E#A#E#R#I#A#R#K#A#G#A#T#T#W#T#E#K#G#N#K#W#E#I#R#I# | 0.17 | train |
G#S#T#T#I#E#E#A#Q#N#K#K#Y#Q#A#E#P#R#S#W#T#K#A#G#R#T#I#G#G#K#N#W#E#T#E#V#N#R#A#E#A#S#I# | -0.18 | train |
G#S#R#E#T#K#K#I#T#T#V#G#A#R#G#E#A#T#A#E#Q#A#A#T#E#E#G#P#K#N#N#S#R#I#S#N#Y#K#E#Q#W#W#I# | 1.36 | train |
G#S#Y#E#L#E#V#G#N#Y#R#Y#R#A#D#D#P#E#Q#L#K#E#E#A#K#K#V#G#A#R#D#V#Q#T#D#G#N#N#F#K#V#R#G# | 0.65 | train |
G#S#Y#K#G#D#L#L#Y#E#N#R#E#A#D#N#V#Y#K#A#T#K#R#G#R#D#P#F#G#E#R#G#K#E#D#V#Q#N#V#E#V#Q#A# | -0.47 | train |
G#S#Y#A#K#D#K#E#G#L#Y#D#A#G#Y#G#T#R#R#P#E#V#A#D#R#G#N#E#E#K#V#K#L#N#V#N#E#Q#Q#V#D#R#F# | -0.15 | train |
G#S#W#R#V#H#F#R#G#E#T#Y#T#A#D#T#E#D#D#A#K#Q#L#A#K#D#A#G#A#R#R#I#E#S#S#N#G#E#V#R#V#E#L# | 0.06 | train |
G#S#I#R#L#E#V#R#G#H#T#Q#S#A#S#N#R#K#D#A#T#R#A#V#T#D#G#W#G#K#D#V#E#E#Y#D#L#E#A#R#A#E#F# | -0.9 | train |
G#S#E#E#L#T#H#N#W#D#E#E#F#A#G#V#R#Q#T#G#D#S#A#T#R#Y#G#V#A#K#A#V#K#E#R#D#L#A#S#R#D#R#I# | -0.77 | train |
G#S#Y#T#I#E#E#N#G#E#K#Y#T#F#R#T#R#D#E#A#E#E#W#A#R#R#Q#G#A#Q#T#V#E#T#R#G#T#E#L#R#S#R#A# | -0.32 | train |
G#S#K#E#A#Y#E#T#I#T#Q#R#T#A#R#S#E#D#N#G#E#E#W#F#R#E#R#A#A#R#Q#L#E#T#R#G#Y#T#V#T#R#E#G# | 0.4 | train |
G#S#V#E#E#E#T#E#E#W#I#A#Q#A#R#S#N#T#D#R#Q#E#R#L#E#K#A#Y#R#G#T#G#Y#R#T#F#E#R#R#T#A#T#G# | 0.03 | train |
G#S#V#E#L#E#D#N#G#R#R#V#E#A#T#S#T#Q#E#A#R#D#R#A#K#K#E#G#A#T#T#W#T#E#S#G#T#R#I#E#V#R#G# | -1.18 | train |
G#S#A#E#V#K#T#T#V#R#D#G#T#G#K#D#E#E#R#G#T#N#R#I#E#T#R#W#A#R#T#G#S#E#R#A#E#Q#V#S#A#E#L# | -0.65 | train |
G#S#Q#E#E#R#T#E#S#S#V#T#E#K#R#T#R#T#G#T#A#I#D#N#E#A#E#K#W#L#V#R#T#V#G#G#A#E#R#D#A#R#G# | -0.42 | train |
G#S#W#E#V#E#V#Q#G#K#R#Y#E#A#S#T#E#D#E#A#E#E#W#A#R#R#E#G#A#T#E#I#R#T#D#G#N#R#I#E#V#R#K# | 0.26 | train |
G#S#I#E#A#Y#I#R#V#R#E#R#S#V#R#Q#E#R#E#G#E#E#A#G#T#K#D#G#A#R#N#W#E#E#T#A#E#K#W#D#V#E#T# | -0.24 | train |
G#S#E#A#T#T#K#R#E#E#N#R#A#A#W#W#V#G#E#R#V#V#Y#E#E#I#G#D#R#E#T#E#I#A#K#D#Q#R#S#E#E#G#R# | -0.39 | train |
G#S#Q#T#V#K#D#G#T#K#T#I#D#T#D#N#A#D#E#T#A#E#K#L#A#R#K#Y#N#G#T#Y#E#K#R#G#D#E#V#E#V#R#W# | 0.2 | train |
G#S#D#I#Y#V#K#E#R#N#G#V#A#T#R#L#T#E#K#D#A#T#E#T#D#R#D#D#T#A#E#K#Q#E#G#K#Y#V#W#T#N#K#G# | -0.66 | train |
G#S#V#R#V#E#I#N#G#Q#E#Y#D#A#D#T#T#E#E#A#K#R#W#A#K#E#Q#G#A#R#K#I#Q#T#K#G#T#K#L#E#V#H#K# | 0.17 | train |
G#S#G#E#I#T#V#Q#A#R#Q#E#D#I#K#K#D#K#T#A#R#T#V#A#H#K#N#V#G#K#E#G#E#Q#E#A#E#R#W#K#L#Y#T# | -0.32 | train |
G#S#R#K#G#Q#E#R#T#L#K#V#G#V#D#A#E#N#Q#E#Q#T#D#W#E#T#A#R#I#E#K#T#V#A#I#K#E#G#A#K#H#K#Y# | -0.65 | train |
G#S#W#T#V#R#L#G#T#Y#E#Y#T#A#S#S#S#E#E#A#E#R#W#A#K#K#L#G#A#T#T#W#T#T#D#G#N#E#I#E#F#R#K# | -0.12 | train |
G#S#F#S#I#K#L#A#K#T#D#Y#S#W#N#T#R#Y#K#W#T#R#A#L#S#T#A#G#W#E#E#A#E#T#T#V#E#E#G#E#G#R#T# | 0.01 | train |
G#S#R#Y#F#D#T#W#K#S#T#E#L#A#W#W#E#T#S#E#G#E#L#T#T#S#K#N#R#T#A#K#I#Y#E#G#R#A#G#T#A#E#V# | -0.5 | train |
G#S#Y#T#L#T#N#G#T#E#K#Y#D#A#R#S#A#E#E#A#K#K#W#A#K#K#R#G#A#Q#S#W#T#V#D#G#N#H#V#E#V#R#E# | -0.13 | train |
G#S#Q#K#A#Y#K#G#E#H#K#D#N#G#K#E#A#S#N#L#E#T#A#V#Y#R#K#V#A#D#R#W#T#V#R#G#T#T#W#S#A#E#E# | -0.29 | train |
G#S#K#T#F#D#V#N#G#E#T#Y#Q#G#P#N#A#E#K#E#A#R#R#Q#A#E#E#Q#N#R#P#I#Q#R#D#G#N#R#F#E#V#H#T# | -0.15 | train |
G#S#E#Q#F#T#G#N#P#N#K#E#N#A#A#R#G#T#E#K#A#R#D#D#P#N#E#R#Y#Q#V#V#E#Q#E#G#R#Q#I#T#F#H#R# | -0.31 | train |
G#S#D#E#Q#T#G#A#R#R#Q#G#A#T#E#E#V#F#T#N#N#Q#K#D#K#E#H#F#P#P#Q#G#Y#I#N#E#V#R#N#E#A#R#R# | 0.06 | train |
G#S#A#R#Y#T#V#N#G#E#T#Y#T#A#R#S#E#E#E#A#E#K#E#A#K#K#R#G#A#K#T#W#R#R#E#N#G#E#L#H#F#T#V# | 0.21 | train |
G#S#V#E#K#T#W#E#V#R#N#E#T#L#R#K#R#R#Y#F#E#K#T#G#T#R#E#G#A#E#E#A#N#E#H#K#A#T#G#Y#A#S#A# | -0.07 | train |
G#S#Y#E#T#A#K#G#R#A#V#G#A#A#E#W#A#Y#R#K#E#E#T#T#H#K#F#N#R#K#E#E#T#N#L#E#G#R#S#E#R#V#T# | 0.01 | train |
G#S#P#K#V#E#V#G#N#K#T#F#T#A#P#T#E#E#A#A#R#R#A#A#K#K#Y#G#A#N#E#V#R#K#H#G#D#R#F#E#V#H#K# | -0.02 | train |
G#S#G#N#A#K#A#P#R#K#T#V#T#V#A#K#N#T#V#A#E#R#G#F#K#K#R#A#G#R#Y#F#D#E#E#A#H#E#P#H#V#E#K# | 0.04 | train |
G#S#V#F#R#H#A#G#K#E#A#E#E#Y#R#H#A#V#R#G#K#G#K#N#T#A#T#V#V#K#F#R#A#K#A#E#E#P#N#K#D#P#T# | -0.04 | train |
G#S#Y#T#V#E#S#G#T#K#R#Y#N#A#S#S#E#D#E#A#K#E#W#A#K#K#E#G#A#T#T#W#T#T#K#G#T#R#V#E#V#Q#E# | 0.14 | train |
G#S#E#N#G#T#E#A#T#E#Q#T#Y#V#T#K#K#R#S#A#K#T#G#G#S#S#K#A#W#K#R#V#E#D#E#A#T#E#W#Y#V#E#T# | 0.19 | train |
G#S#N#A#V#E#A#T#S#A#E#G#V#S#A#W#Y#K#Q#T#K#T#E#T#E#K#T#W#R#Y#K#V#E#E#D#G#S#K#T#E#R#G#T# | 0.15 | train |
G#S#I#T#V#T#I#G#T#T#T#Y#T#F#R#S#T#D#E#A#Q#E#Y#A#K#R#Q#G#A#T#T#E#H#T#H#G#N#T#Y#E#F#R#F# | 0.23 | train |
G#S#I#R#G#Y#F#I#Y#T#S#T#H#G#T#E#T#Q#K#G#N#E#Y#A#D#R#T#A#F#T#T#T#T#E#R#F#T#Q#T#H#A#E#V# | 0.11 | train |
G#S#P#E#V#R#D#N#G#K#T#Y#K#A#S#T#D#E#E#A#E#R#I#A#R#N#L#G#A#S#E#I#N#V#S#N#G#E#V#E#V#R#R# | -0.05 | train |
G#S#G#S#P#R#S#K#G#E#E#N#N#V#K#N#E#N#Y#A#R#E#L#A#D#E#V#A#A#R#T#V#T#I#S#R#I#E#G#E#V#R#D# | 0.39 | train |
G#S#A#Y#T#G#V#I#E#N#I#K#R#R#D#V#G#E#N#V#S#D#E#E#A#A#R#A#V#P#K#E#L#E#G#N#S#N#S#T#R#E#R# | -1.09 | train |
G#S#T#S#V#E#V#G#N#L#Q#Y#D#G#D#D#D#E#A#K#K#R#A#K#D#D#A#G#A#R#T#E#R#R#S#N#G#R#V#E#W#E#V# | 0.3 | train |
G#S#R#D#G#E#L#W#D#G#K#D#R#G#R#S#D#K#A#K#E#D#V#E#E#Y#V#A#V#R#E#N#Q#N#T#T#A#S#G#R#A#D#V# | 0.28 | train |
G#S#A#D#E#K#D#R#V#S#G#D#V#E#D#E#W#D#N#N#K#A#R#D#R#T#Q#S#T#A#R#L#V#E#G#E#R#G#K#Y#V#A#G# | 0.15 | train |
G#S#P#R#Y#K#S#G#T#E#E#Y#R#G#G#T#E#D#Q#A#E#K#T#R#E#K#D#G#A#Q#T#S#E#K#R#G#N#E#I#R#I#Q#A# | 0.06 | train |
G#S#G#E#R#S#E#I#N#E#K#K#K#P#G#D#R#Y#R#A#T#R#E#T#T#R#T#G#I#E#Q#D#K#E#Y#A#Q#Q#G#S#A#E#G# | 0.31 | train |
G#S#K#I#Q#R#K#G#N#P#E#Q#G#G#R#G#I#T#D#Y#G#R#Q#K#E#E#A#E#A#R#S#S#K#E#A#T#Y#T#T#E#D#R#E# | -0.1 | train |
G#S#Y#K#V#D#I#N#G#E#T#Y#T#A#S#T#R#D#E#A#E#E#R#A#R#R#A#G#A#K#T#F#R#E#D#N#G#E#I#E#V#H#K# | 0.33 | train |
G#S#N#D#A#S#I#D#V#R#E#Y#E#G#R#T#E#H#T#A#R#E#R#G#R#E#V#A#A#K#T#F#K#D#E#E#I#Y#A#K#G#N#T# | 0.3 | train |
G#S#T#N#G#E#H#R#T#D#V#K#Y#E#A#A#R#D#K#F#A#D#T#G#E#K#E#E#R#E#N#R#V#S#G#I#A#I#R#T#Y#A#E# | 0.01 | train |
G#S#Y#K#V#D#I#G#P#Q#T#Y#D#V#D#S#T#E#E#A#K#E#L#A#K#K#A#G#A#T#T#V#R#V#D#G#N#T#I#E#V#R#K# | 0.18 | train |
G#S#Y#K#G#E#I#P#I#S#T#D#K#V#E#E#N#T#R#V#T#T#A#A#K#D#L#G#V#K#Q#V#E#A#K#G#Y#D#V#T#A#D#R# | 0.24 | train |
G#S#V#V#G#Q#G#D#T#K#V#K#A#S#E#T#G#D#T#D#A#R#D#P#E#T#I#L#E#T#K#A#A#Y#Y#V#N#R#E#K#K#I#V# | -0.29 | train |
G#S#Y#E#I#R#V#G#N#L#R#Y#E#G#P#D#P#D#K#L#Q#K#L#A#K#K#A#G#A#T#T#W#T#Q#D#G#N#T#W#R#V#Q#V# | -0.16 | train |
G#S#D#R#W#T#G#P#D#A#D#E#T#L#V#K#A#Y#Q#G#N#K#A#V#R#K#G#L#P#T#N#W#R#Q#Q#G#E#T#L#K#I#Y#V# | -0.13 | train |
G#S#Q#V#Q#G#L#R#N#E#D#A#N#V#T#A#T#K#G#D#T#K#K#T#W#P#L#W#G#E#K#Y#V#L#A#I#G#R#D#Q#P#R#Y# | -0.34 | train |
G#S#I#E#L#E#S#G#N#E#H#Y#T#F#K#T#E#E#E#A#R#R#W#A#K#K#Q#G#A#K#T#W#T#R#D#G#D#R#I#R#V#Q#Q# | -0.09 | train |
G#S#G#E#I#Q#S#V#T#D#R#R#R#G#T#T#E#Q#Y#G#E#Q#I#W#D#K#E#W#L#T#K#A#E#R#N#A#H#E#A#R#F#K#K# | 0.1 | train |
G#S#K#E#Y#G#S#E#A#T#R#G#R#K#L#K#T#N#T#V#T#E#K#W#E#R#D#D#F#R#I#G#Q#W#I#A#R#H#E#A#Q#E#Q# | 0.31 | train |
G#S#K#R#V#E#V#G#T#K#E#F#T#A#T#S#T#K#E#A#E#D#I#A#R#K#E#G#A#R#T#V#Q#T#D#G#D#R#Y#E#V#R#K# | 0.44 | train |
G#S#R#E#V#T#A#A#E#T#R#I#D#G#E#E#T#K#D#V#R#K#A#G#S#D#T#V#G#Y#K#A#R#K#K#F#T#Q#T#E#V#E#R# | -0.17 | train |
G#S#E#D#V#G#A#S#G#V#R#A#E#E#R#K#T#K#R#D#G#E#K#T#K#R#V#F#V#T#E#A#T#E#R#D#T#T#I#Y#K#Q#A# | -0.54 | train |
G#S#T#R#V#R#D#G#N#T#E#Y#E#A#G#N#P#E#E#A#R#R#W#A#E#E#A#G#A#R#S#V#E#I#R#G#E#T#V#R#S#Q#R# | 0.11 | train |
G#S#D#R#A#R#E#V#E#T#R#Y#N#A#W#E#G#R#R#P#E#E#I#A#R#E#V#A#G#R#S#V#E#G#S#G#E#R#A#N#Q#T#T# | 0.06 | train |
G#S#D#G#E#A#S#G#R#R#R#T#G#I#Y#E#R#V#E#W#T#Q#S#N#N#A#E#G#R#V#E#V#E#A#E#R#R#A#A#P#R#E#T# | 0.19 | train |
G#S#G#E#V#T#F#N#G#E#T#Y#R#F#S#S#D#E#E#A#R#E#Y#A#E#R#Q#G#A#Q#Q#V#K#E#T#N#G#R#F#H#W#H#G# | -0 | train |
G#S#F#N#G#S#G#E#G#E#N#Y#R#W#Q#D#H#T#R#G#E#E#E#V#E#Y#E#F#A#S#R#V#H#Q#R#T#G#T#A#Q#A#K#F# | 0.15 | train |
G#S#S#A#H#E#F#G#T#G#G#R#Y#D#T#R#R#T#W#E#F#Q#E#K#S#N#Q#A#E#A#V#F#G#V#H#Q#Y#E#G#E#R#E#N# | -0.55 | train |
G#S#I#R#F#R#V#G#T#L#E#F#E#S#S#D#P#E#E#A#R#R#R#A#R#K#A#G#A#T#R#E#E#T#H#G#D#T#Y#R#L#Q#A# | -0.14 | train |
G#S#V#R#G#E#F#A#R#P#K#G#S#E#D#R#L#R#T#L#R#S#T#A#T#E#F#A#A#R#H#Y#E#Q#R#I#D#E#R#T#A#E#G# | -0.18 | train |
G#S#D#E#R#R#R#R#G#S#R#R#K#I#E#A#A#T#A#S#E#H#R#G#G#T#Y#F#P#R#L#F#Q#E#V#E#L#T#E#T#A#A#D# | 0.13 | train |
G#S#Y#T#V#E#V#N#G#Q#R#Y#E#F#S#S#S#E#E#A#E#R#W#A#R#K#Q#G#A#T#E#V#E#K#D#G#T#R#I#R#V#R#T# | 0.72 | train |
G#S#E#T#F#K#V#R#V#Q#S#D#R#I#Y#E#S#K#E#G#T#T#A#V#R#T#R#V#A#Y#R#G#N#Q#E#G#S#E#A#E#W#E#R# | 0.04 | train |
G#S#E#F#G#E#A#E#R#V#R#A#E#Q#S#D#W#R#G#S#Y#T#T#T#N#K#T#R#Q#S#R#E#V#Y#V#I#R#V#E#E#G#K#A# | -0.27 | train |
G#S#Y#E#V#N#L#N#G#K#T#F#T#F#D#S#P#Q#K#A#D#E#Q#A#K#K#E#G#A#T#T#R#E#K#H#G#N#E#F#R#Y#R#G# | 1.28 | train |
G#S#H#E#G#N#G#T#F#R#D#G#T#G#T#Y#V#K#K#A#N#E#R#A#S#T#Q#P#F#E#E#K#D#K#Q#L#R#E#A#Y#N#K#F# | -0.02 | train |
G#S#L#D#E#R#F#E#R#H#T#G#A#S#K#P#E#N#Q#N#N#K#T#A#F#D#Y#Y#A#T#K#F#K#E#Q#V#G#T#G#G#E#K#R# | 0.46 | train |
G#S#T#T#I#T#F#K#G#Q#E#F#T#A#T#T#D#D#E#R#E#E#A#R#K#K#A#G#A#K#T#Y#K#E#E#N#G#Q#V#K#L#Q#E# | 0.25 | train |
G#S#K#E#F#T#L#D#A#T#R#V#Y#A#E#E#K#T#R#T#D#E#G#N#T#E#A#I#G#E#T#K#K#K#Q#K#A#Q#F#E#G#Q#T# | 0.12 | train |
G#S#E#A#T#E#K#A#G#K#L#T#I#Q#T#T#E#V#K#D#R#E#G#T#K#K#F#E#T#F#N#Y#Q#T#G#A#A#E#R#E#Q#K#D# | 0.03 | train |
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G#S#V#T#V#E#V#G#G#K#T#F#T#A#T#S#E#S#E#A#E#E#I#A#K#R#Y#G#A#T#K#K#T#K#K#G#T#K#F#E#Y#R#P# | -0.4 | train |
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G#S#G#E#V#R#S#H#G#Q#R#Y#T#A#S#S#K#D#E#A#R#E#W#A#K#K#W#G#A#T#E#V#K#E#D#G#N#R#W#E#V#R#K# | -0.03 | train |
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Description
Stability Landscape Prediction is a regression task where each input protein x is mapped to a label y ∈ R measuring the most extreme circumstances in which protein x maintains its fold above a concentration threshold (a proxy for intrinsic stability).
Splits
Structure type: None
The dataset is from Evaluating Protein Transfer Learning with TAPE. We follow the original data splits, with the number of training, validation and test set shown below:
- Train: 53614
- Valid: 2512
- Test: 12851
Data format
We organize all data in LMDB format. The architecture of the databse is like:
length: The number of samples
0:
- seq: The structure-aware sequence
- fitness: fitness label of the sequence
1:
···
- Downloads last month
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Size of downloaded dataset files:
8.07 MB
Size of the auto-converted Parquet files:
2.59 MB
Number of rows:
68,977