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Supplementary Data: Quantification of biases in predictions of protein-protein binding affinity changes upon mutations

Description

Here are all the Supplementary Data related to the paper [1] (2023) https://academic.oup.com/bib/article/25/1/bbad491/7513597.

Authors: Matsvei Tsishyn, Fabrizio Pucci and Marianne Rooman.

Conventions and units

  • All energy values are in kcal/mol and all temperature values are in K.
  • We use the convention that destabilizing mutations have positive ΔΔGb values.
  • Separator in all .csv files is ';' and missing values are marked as 'XXX'. When a .csv cell's value represent a list, the elements are separated by ','.

Content

(1) Datasets

All mutations datasets referenced in the paper and all relative subsets can be found in ./1_datasets/ as well as additional mutations' properties (such as RSA, structural region, secondary structure and predicted ΔΔGb values).

(2) Performances

All evaluations of the performances of the eight predictors on S2536 and C380 as well as on subsets of S2536 (such as to alanine mutations vs. to non-alanine mutations) can be found in ./performances/.

(3) Structures (wild-type and mutated)

Due to GitHub memory limitations, the PDB structures can not be stored here. All wild-type and mutated PDB structures from datasets of mutations SKEMPI 2.0 [2] and CoV [3] are available at http://babylone.3bio.ulb.ac.be/DDGb_bias_structures/. Mutated PDB structures are all modeled using comparative modeling software MODELLER [4] starting from its corresponding wild-type structure.

References

[1] Tsishyn, M., Pucci, F., & Rooman, M. (2023). Quantification of biases in predictions of protein-protein binding affinity changes upon mutations. Briefings in bioinformatics, 25(1), bbad491.

[2] Jankauskaitė, J., Jiménez-García, B., Dapkūnas, J., Fernández-Recio, J., & Moal, I. H. (2019). SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics, 35(3), 462-469.

[3] Starr, T. N., Greaney, A. J., Hilton, S. K., Ellis, D., Crawford, K. H., Dingens, A. S., ... & Bloom, J. D. (2020). Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding. cell, 182(5), 1295-1310.

[4] Webb, B., & Sali, A. (2016). Comparative protein structure modeling using MODELLER. Current protocols in bioinformatics, 54(1), 5-6.