Electrostatic complementarity at the interface drives transient protein-protein interactions

Abstract

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes’ stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of $$\sim$$ ∼ 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Influence of N-protonation on electronic properties of acridine derivatives by quantum crystallography

“…Since electrostatic potential (ESP) often plays a vital role in molecular recognition in biological systems 90,91 we also compute ESP for two macromolecular complexes: previously mentioned human acetylcholinesterase and DNA complexed by 9-amino- N -(2-dimethylaminoethyl)acridine-4-carboxamide (9AD, PDB entry ). 92 The results indicate significant change in electrostatic potential upon protonation of the ligands (Fig.…”

Computational Approaches to Predict Protein–Protein Interactions in Crowded Cellular Environments

“…The protocol called Zepyros (ZErnike Polynomials analYsis of pROtein Shapes) has been developed and distributed freely as a web application by Miotto et al . and applied to characterize different biological systems − and to optimize biomolecule interfaces. − The work by Grassmann et al extended the procedure to account for electrostatic similarity.…”

Topology-Driven Negative Sampling Enhances Generalizability in Protein-Protein Interaction Prediction

“…Understanding the evolutionary reasoning for protein interactions and non-interactions is a complex task. However, recent research has unveiled the complementarity-driven mechanisms [42] driving protein-protein interactions, which include an electrostatic charge or Coulombian complementarity [45], hydrophobic mismatch [46], conformational complementarity [47], hydrogen bond complementarity [48], etc. (see Figure 1C).…”