Using collections of structural models to predict changes of binding affinity caused by mutations in protein–protein interactions

Abstract

Protein-protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state-of-the-art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state-of-art methods. K E Y W O R D S prediction of binding affinity, protein interaction comparative modeling, protein-protein binding affinity, protein-protein interactions

Probing conformational landscapes of binding and allostery in the SARS-CoV-2 omicron variant complexes using microsecond atomistic simulations and perturbation-based profiling approaches: hidden role of omicron mutations as modulators of allosteric signaling and epistatic relationships

“…In silico mutational scanning of the interfacial RBD residues was done using BeAtMuSiC approach 98 that demonstrated high accuracy in several independent benchmark studies of both protein stability and binding affinity. 118,119 Structure-based comparisons of BeAtMuSiC approach with more rigorous, physics-based FoldX 120 and Rosetta approaches 121 showed similar differentiation of stabilizing and destabilizing mutations as well as robust agreement with the experimental stability and binding affinity energies. To provide a systematic comparison, we constructed mutational heatmaps for the RBD interface residues in each of the studied Omicron RBD-hACE2 complexes (Fig.…”

Structure-based learning to model complex protein-DNA interactions and transcription-factor co-operativity incis-regulatory elements

“…There are many methods to score the quality of protein folding [23][24][25] and proteinprotein interactions 26,27 , such as knowledge-based potentials, also known as statistical potentials [28][29][30][31][32] . In previous works we developed a set of statistical potentials 33 to analyse protein structures and their interactions [34][35][36] . Ours is a structure-based learning approach that considers the frequency of contacts between pairs of residues and includes their structural environment, such as solvent accessibility and type of secondary structure, to evaluate the interaction between transcription factors (TFs) and nucleic-acids.…”

“…Interactions between TFs and transcription co-factors (TcoFs) are retrieved from the TcoF-DB database 51 . After selecting a set of protein-protein and protein-DNA interactions, these can be modeled using a homology modeling pipeline 25, 34 . Then, the server models the structure of DNA in a specific conformation (which by default is in B conformation), and for very long DNA sequences the server splits the sequence in fragments of 250 base-pairs (with an overlap of 50bp to be able to assemble them later).…”

Modelling SARS-CoV-2 spike-protein mutation effects on ACE2 binding

“…We used the BeatMusic method (default setting) [53], which has shown very promising accuracy in independent benchmarks [54,55], to compute the binding affinity (ΔΔG bind , in units of kcal/mol) for the 21 selected structures. For each input structure, we define the two interacting partners – one being the three chains of S-protein and the other being ACE2.…”