SAAMBE-SEQ

About SAAMBE-SEQ

SAAMBE-SEQ is a sequence-based machine learning algorithm to predict the binding energy changes upon single mutation in protein-protein complexes. Unlike other existing methods, SAAMBE-SEQ does not require a 3D complex structure as input. This method can be applied to protein complexes without known structure. The accuracy of SAAMBE-SEQ is comparable to existing structure-based methods. This method can be used to provide understanding the effect of mutation in protein complexes using sequence alone.

Please see the publication for greater details and cite to acknowledge the use of SAAMBE-SEQ in your work:

Li, G.; Pahari, S.; Murthy, A.K.; Liang, S.; Fragoza, R.; Yu, H.; Alexov, E. SAAMBE-SEQ: A Sequence-based Method for Predicting Mutation Effect on Protein-protein Binding Affinity.. Bioinformatics 2021 https://doi.org/10.1093/bioinformatics/btaa761

Single Mutation
Multiple Mutations

Binding Protein Sequence (A)

Paste sequence (takes precedence over file)

Or upload FASTA file

Mutating Protein Sequence (B)

Paste sequence (takes precedence over file)

Or upload FASTA file

Mutation Detail *

Position *

Wildtype residue *

Mutant residue *

Binding Protein Sequence (A)

Paste sequence (takes precedence over file)

Or upload FASTA file

Mutating Protein Sequence (B)

Paste sequence (takes precedence over file)

Or upload FASTA file

Mutation List File *

Upload a plain text file with one mutation per line. View example format

SAAMBE-SEQ: A Sequence-based Method for Predicting Mutation Effect on Protein-protein Binding Affinity

Professor Emil Alexov Group