Web tools support predicting protein–nucleic acid complexes stability with affinity changes

Abstract

Numerous biological processes, such as transcription, replication, and translation, rely on protein–nucleic acid interactions (PNIs). Demonstrating the binding stability of protein–nucleic acid complexes is vital to deciphering the code for PNIs. Numerous web‐based tools have been developed to attach importance to protein–nucleic acid stability, facilitating the prediction of PNIs characteristics rapidly. However, the data and tools are dispersed and lack comprehensive integration to understand the stability of PNIs better. In this review, we first summarize existing databases for evaluating the stability of protein–nucleic acid binding. Then, we compare and evaluate the pros and cons of web tools for forecasting the interaction energies of protein–nucleic acid complexes. Finally, we discuss the application of combining models and capabilities of PNIs. We may hope these web‐based tools will facilitate the discovery of recognition mechanisms for protein–nucleic acid binding stability.This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein‐RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA‐Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein‐RNA Interactions: Functional Implications

Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations

“…In terms of the performance of the leading predictors of free energy change, ∆G folding, and ∆G binding , we would like to reiterate again that our goal is not to compare their absolute performance but rather to see the difference in the performance of SNVs vs. non-SNVs cases. A comparison of their performance has been carried out in numerous papers of the developers [26,28,30,32,34,36,[39][40][41][42]44,45,47,[53][54][55][56], as well as in third-party manuscripts [57]. The common observation is that almost all algorithms tested on the corresponding datasets perform worse on SNVs as compared with non-SNVs.…”

Predicting the Effect of Mutations on Protein Stability and Binding: Assessment of Leading Algorithms Performance and Databases Content with Respect to Types of Mutations

“…In terms of the performance of the leading predictors of the free energy change, ΔGfolding, and ΔGbinding, we would like to reiterate again that our goal is not to compare their absolute performance but rather to see the difference of the performance on SNVs vs non-SNVs cases. Comparison of their performance has been done in numerous papers of the developers [26,28,30,32,34,36,[39][40][41][42]44,45,47,[53][54][55][56] as well as third-party manuscripts [57]. The common observation is that almost all algorithms as tested on the corresponding datasets perform worse on SNVs as compared with non-SNVs.…”