Spatial organization of hydrophobic and charged residues affects protein thermal stability and binding affinity

Abstract

What are the molecular determinants of protein–protein binding affinity and whether they are similar to those regulating fold stability are two major questions of molecular biology, whose answers bring important implications both from a theoretical and applicative point of view. Here, we analyze chemical and physical features on a large dataset of protein–protein complexes with reliable experimental binding affinity data and compare them with a set of monomeric proteins for which melting temperature data was available. In particular, we probed the spatial organization of protein (1) intramolecular and intermolecular interaction energies among residues, (2) amino acidic composition, and (3) their hydropathy features. Analyzing the interaction energies, we found that strong Coulombic interactions are preferentially associated with a high protein thermal stability, while strong intermolecular van der Waals energies correlate with stronger protein–protein binding affinity. Statistical analysis of amino acids abundances, exposed to the molecular surface and/or in interaction with the molecular partner, confirmed that hydrophobic residues present on the protein surfaces are preferentially located in the binding regions, while charged residues behave oppositely. Leveraging on the important role of van der Waals interface interactions in binding affinity, we focused on the molecular surfaces in the binding regions and evaluated their shape complementarity, decomposing the molecular patches in the 2D Zernike basis. For the first time, we quantified the correlation between local shape complementarity and binding affinity via the Zernike formalism. In addition, considering the solvent interactions via the residue hydropathy, we found that the hydrophobicity of the binding regions dictates their shape complementary as much as the correlation between van der Waals energy and binding affinity. In turn, these relationships pave the way to the fast and accurate prediction and design of optimal binding regions as the 2D Zernike formalism allows a rapid and superposition-free comparison between possible binding surfaces.

Investigating the competition between ACE2 natural molecular interactors and SARS-CoV-2 candidate inhibitors

“…Thus, we were able to identify regions of the ACE2 molecular surface with high shape complementarity with any other region of each of the 10 MP. In this way we can identify, for each ACE2-MP pair, the regions of ACE2 that are more prone to bind, since the higher the shape complementarity, the greater the probability to have a binding mediated by van der Waals forces [ 25 ]. Using the Zernike-based description, we defined the binding propensity , a residue-level descriptor indicating the tendency of each residue to interact with a specific protein.…”

“…The aim of this procedure is both to refine the predicted docking model [ 47 , 48 ] and to statistically study the structural stability of the estimated complex by analyzing the inter-molecular interactions during the simulation. Indeed, the greater the stability of the interaction between the ACE2 receptor and the corresponding molecular partner (each of the 4 selected), the greater the probability that the two macromolecules interact with high-affinity [ 16 , 25 ]. For this purpose, we first calculate two descriptors that provide information on the mobility of each system: the Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF).…”

“…It is known that the balance between hydrophilicity and hydrophobicity in the binding regions plays a primary role in the recognition and interaction between two proteins [ 25 , 49 ]. Therefore, a quantification of both the hydrophilic and hydrophobic contribution, and therefore of the entire hydropathy profile, is useful to better characterize the interaction between two proteins.…”

“…The advantage of combining the molecular docking approach with the analysis based on Zernike polynomials to evaluate the shape complementarity between interacting regions, allows to perform a screening of the poses proposed by the docking algorithm based on the van der Waals contribution which is known to play a crucial role in protein binding [ 25 ], as well as allows exploiting the speed of execution of the Zernike method which can thus be applied to many possible conformations [ 24 ]. The best ACE2-MP complexes obtained from the molecular docking procedure and characterized by high shape complementarity were selected and analyzed through short molecular dynamics simulations, to perform statistical analysis on inter-molecular contacts and refine the docking model.…”

ProtInteract: A deep learning framework for predicting protein–protein interactions

“…For inter-protein bindings to form, amino acids should chemically complement each other, allowing hydrophobic, polar, or charged interactions [99] . The hydropathy properties of amino acids, i.e., hydrophobicity and hydrophilicity, are the dominant properties of protein-protein binding [100] , [101] . The helix and sheet probabilities are two statistically driven parameters [102] .…”