DrugScorePPI Knowledge-Based Potentials Used as Scoring and Objective Function in Protein-Protein Docking

Abstract

The distance-dependent knowledge-based DrugScorePPI potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking “unbound perturbation” (“unbound docking”) decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScorePPI/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ∼2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. This suggests that DrugScorePPI balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScorePPI/FRODOCK will be successful.

Different combinations of atomic interactions predict protein‐small molecule and protein‐DNA/RNA affinities with similar accuracy

“…67,68 Bayesian model selection strategies-such as the Bayesian Information Criterion (BIC)-provide a more conservative approach, although they can be biased in favor of too-simple models. 69 As expected, we observed a general decrease in the complexity of best-fit models when we used BIC for model selection instead of AIC (t test P < 2.3 3 10 242 , U test P < 1.2 3 10 230 , SI Fig. S8A).…”

“…Although the results of examining residuals and outliers argue against model over fitting, concerns have been raised that AIC can be biased toward selecting overly complex models in some cases . Bayesian model selection strategies—such as the Bayesian Information Criterion (BIC)—provide a more conservative approach, although they can be biased in favor of too‐simple models . As expected, we observed a general decrease in the complexity of best‐fit models when we used BIC for model selection instead of AIC ( t test P < 2.3 × 10 −42 , U test P < 1.2 × 10 −30 , SI Fig.…”

Score_set: A CAPRI benchmark for scoring protein complexes

“…The energy terms may represent physics‐based residue or atom pair potentials, or a statistical simplification thereof . However, in order to account for the complex interplay between interactions at the interface, multi‐body, empirical or knowledge‐based potentials are increasingly used. Novel approaches use multi‐body residue interactions to construct the ligand‐receptor interaction networks and evaluate them .…”

DISPOT: A simple knowledge-based protein domain interaction statistical potential

“…The quantification of the odds for a domain from one domain family to interact with a domain from another family is defined in this work as a knowledge-based statistical potential. Statistical potentials are widely used in biophysical applications, often for characterizing the residue contacts between the protein chains (Krüger, et al, 2014;Lu, et al, 2003). One of the main applications of the residue-level statistical potentials is in protein docking (Kozakov, et al, 2006).…”