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ELASPIC ΔΔG
The final Gibbs free energy change (ΔΔG) in kcal/mol is predicted using more than 70 sequential, molecular and energetic features with the Stochastic Gradient Boosting of Decision Trees algorithm.
For more information see the ELASPIC publication.
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ELASPIC2 (EL2) score
The ELASPIC2 (EL2) score is obtained using features generated by pretrained ProteinSolver and ProtBert neural networks with the Stochastic Gradient Boosting of Decision Trees algorithm and a ranking objective function.
For more information see the ELASPIC2 (EL2) publication.

O60674.K539L

Detailed results for job #oncokb produced by ELASPIC

O60674, Tyrosine-protein kinase JAK2:
K539L
1
1132
ERBB2, Receptor tyrosine-protein kinase erbB-2:
1
1255
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Model: Wild-type | Mutant | Both
Protein: O60674 | ERBB2 | Cofactors | All
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O60674.K539L

Completed successfully

Protein, domain: O60674 Pkinase_Tyr

Interacting with: ERBB2 Pkinase_Tyr

PDB templates used: 3pp0_A, 3pp0_B

Seq identity: 0.223, 0.986 Model score: -0.779

ELASPIC ΔΔG: 3.013

ELASPIC2 (EL2) score: -1.470

Mutation in databases: ClinVar, UniProt, COSMIC, COSMIC.

Download:  PDBwt   | PDBmut

Citation


If you use these results, please cite the papers on this page.