MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory

Abstract

Multispecificity–the ability of a single receptor protein molecule to interact with multiple substrates–is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificity would aid in the identification of novel enzyme substrates, protein interaction partners, and enable design of novel enzymes targeted towards alternative substrates. The relatively slow speed of current biophysical, structure-based methods limits their use for prediction and, especially, design of multispecificity. Here, we develop a rapid, flexible-backbone self-consistent mean field theory-based technique, MFPred, for multispecificity modeling at protein-peptide interfaces. We benchmark our method by predicting experimentally determined peptide specificity profiles for a range of receptors: protease and kinase enzymes, and protein recognition modules including SH2, SH3, MHC Class I and PDZ domains. We observe robust recapitulation of known specificities for all receptor-peptide complexes, and comparison with other methods shows that MFPred results in equivalent or better prediction accuracy with a ~10-1000-fold decrease in computational expense. We find that modeling bound peptide backbone flexibility is key to the observed accuracy of the method. We used MFPred for predicting with high accuracy the impact of receptor-side mutations on experimentally determined multispecificity of a protease enzyme. Our approach should enable the design of a wide range of altered receptor proteins with programmed multispecificities.

The Limited Information Capacity of Cross-Reactive Sensors Drives the Evolutionary Expansion of Signaling

“…Individual signaling components are also subject to noise due to the presence of non-cognate ligands that promiscuously bind and activate sensors. The ligand binding specificity of sensors is limited by various physicochemical and evolutionary factors Tawfik (2014), and cross-reactivity is commonly observed in many sensors, e.g., G protein-coupled receptors (GPCRs) (Munk et al, 2016;Venkatakrishnan et al, 2013), and in downstream components such as kinases (Rubenstein et al, 2017) or phosphatases (Rowland et al, 2015). As expected, we found that cross-reactivity can severely compromise signaling capacity.…”

Comparison of Rosetta flexible‐backbone computational protein design methods on binding interactions

“…In addition to limitations in sampling methods (as well as the energy function used to rank designs), there are potential limitations related to the benchmark datasets. While this work considers protein/protein and protein/small molecule interactions, the selection of benchmarks could be expanded to include protein/peptide interactions, such as those described in References 40, 54, and 55. There are also potential limitations inherent to some datasets included in this study.…”

Structural basis for peptide substrate specificities of glycosyltransferase GalNAc-T2

“…We characterized the low-energy conformations by a selected set of relevant features. To compare our predictions with experiments, we employed logistic regression and, unless otherwise indicated, we labeled all sequons with experimental glycosylation efficiencies greater than 10% (efficiency threshold) as glycosylatable and those with efficiencies less than 10% as un-glycosylatable, in line with previous work 32 . In SI Table 1, Figure 4C) showed that the dHB based-criterion had an AUC value of 0.922, meaning that this metric had a 92.2% chance of correctly distinguishing the glycosylatable sequons from the nonglycosylatable sequons.…”

“…For comparison with other energy based approaches, we applied the MFPred method by Rubenstein et al 32 to obtain the specificity profile for GalNAc-T2 (SI Figure 17). We obtained an AUC score of 0.76 with this approach, which was significantly lower than the AUC scores obtained in this work ( Table 1, SI Table 3).…”