A novel strategy for molecular interfaces optimization: The case of Ferritin-Transferrin receptor interaction

Abstract

Protein-protein interactions regulate almost all cellular functions and rely on a fine tune of surface amino acids properties involved on both molecular partners. The disruption of a molecular association can be caused even by a single residue mutation, often leading to a pathological modification of a biochemical pathway. Therefore the evaluation of the effects of amino acid substitutions on binding, and the ad hoc design of protein-protein interfaces, is one of the biggest challenges in computational biology. Here, we present a novel strategy for computational mutation and optimization of protein-protein interfaces. Modeling the interaction surface properties using the Zernike polynomials, we describe the shape and electrostatics of binding sites with an ordered set of descriptors, making possible the evaluation of complementarity between interacting surfaces. With a Monte Carlo approach, we obtain protein mutants with controlled molecular complementarities. Applying this strategy to the relevant case of the interaction between Ferritin and Transferrin Receptor, we obtain a set of Ferritin mutants with increased or decreased complementarity. The extensive molecular dynamics validation of the method results confirms its efficacy, showing that this strategy represents a very promising approach in designing correct molecular interfaces.

Shape Complementarity Optimization of Antibody–Antigen Interfaces: The Application to SARS-CoV-2 Spike Protein

“…Indeed, in this section, we present our algorithm that exploiting these advantages aims to optimize the shape complementarity of an antibody toward a given molecular target region. A similar procedure has already been presented and tested in our previous work ( Di Rienzo et al, 2020b ), and here for the first time, it is applied to antibody–antigen interaction systems.…”

“…In the last decade, the Zernike approach, in its 3D version, has been widely applied for the analysis of biomolecules ( Venkatraman et al, 2009a ; Venkatraman et al, 2009b ; Kihara et al, 2011 ; Di Rienzo et al, 2017 ; Daberdaku and Ferrari, 2018 ; Daberdaku and Ferrari, 2019 ; Han et al, 2019 ; Di Rienzo et al, 2020a ; Alba et al, 2020 ; and Di Rienzo et al, 2020b ), proving its efficacy in characterizing both global and local protein properties.…”

“…Selecting as the starting template the antibody that has the most complementary patch, we perform a Monte Carlo (MC) simulation for the optimization of the paratope’s structural conformation. Through extensive computational mutagenesis, substituting in each step an interacting antibody residue with a different random one, we accept or reject each mutation according to the gain in shape complementarity, as evaluated by the Zernike method ( Di Rienzo et al, 2020b ; Di Rienzo et al, 2021b ). In this work, the combined application of both the 2D Zernike formalism and a Monte Carlo simulation allows a computationally fast and effective exploration of the space of the possible mutants.…”

Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors

“…Therefore, the distance observed between interacting surfaces (ligand-binding site) is significantly lower than the one observed between non-interacting surfaces (ligand -randomly selected exposed region of the same size of the binding site). In the last years, the Zernike formalism has been widely applied for the study of molecular complementarity [32][33][34][35][36][37][38].…”

Shape Complementarity Optimization of Antibody-Antigen Interfaces: the Application to SARS-CoV-2 Spike Protein

“…In the last decade, the Zernike approach, in its 3D version, has been widely applied for the analysis of biomolecules [22,23,[27][28][29][30][31][32][33][34], proving its efficacy in characterizing both global and local proteins properties.…”