DDGun: an untrained predictor of protein stability changes upon amino acid variants. Issue Volume 50:Issue W1(2022) (7th May 2022)

• Record Type: Journal Article
• Authors: Montanucci, Ludovica; Capriotti, Emidio; Birolo, Giovanni; Benevenuta, Silvia; Pancotti, Corrado; Lal, Dennis; Fariselli, Piero
• Is Part Of: Nucleic acids research. Volume 50:Issue W1(2022)
• Page Start: W222
• Page End: W227
• Publication Date: 2022-05-07
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkac325
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔ G ). Here, we present the web-server of the DDGun method, which was previously developed for the ΔΔ G prediction upon amino acid variants. DDGun is an untrained method based on basic features derived from evolutionary information. It is antisymmetric, as it predicts opposite ΔΔ G values for direct (A → B) and reverse (B → A) single and multiple site variants. DDGun is available in two versions, one based on only sequence information and the other one based on sequence and structure information. Despite being untrained, DDGun reaches prediction performances comparable to those of trained methods. Here we make DDGun available as a web server. For the web server version, we updated the protein sequence database used for the computation of the evolutionary features, and we compiled two new data sets of protein variants to do a blind test of its performances. On these blind data sets of single and multiple site variants, DDGun confirms its prediction performance, reaching an average correlation coefficient between experimental and predicted ΔΔ G of 0.45 and 0.49 for the sequence-based and structure-based versions, respectively. Besides being used for the prediction of ΔΔ G, we suggest that DDGun should be adopted as a benchmark method to assess the predictive capabilities of newly developed methods. Releasing DDGun as a web-server, stand-alone program and docker image will facilitate the necessary process of method comparison to improve ΔΔ G prediction. Graphical Abstract: