Computer simulations to engineer PDZ-peptide recognition

Abstract : Protein-protein interactions (PPIs) regulate complex signaling networks in eukaryotic cells. Many binding events between several protein domains transfer information through communication pathways. Disrupting or altering the equilibrium between PPIs plays an important role inseveral diseases and the inibition of targeted PPIs is a recognized strategy for computational drug design. In the present thesis we focused on PDZ domains, which are among the most widespread signaling domains. PDZs recognize the 4-10 C-terminal amino acids of their target proteins as well as the corresponding peptides in isolation. We studied PDZ:peptide binding for the Tiam1 protein, which is a Rac GTP exchange factor involved in neuronal protrusion and axon guidance. Tiam1 activity modulates signaling for cell proliferation and migration, whose dysregulation increases growth of metastatic cancers. Its natural binder peptide is Syndecan1 (Sdc1), composed of 8 amino acids. Its last 5 Cter residues drive interactions in the binding pocket. Experimental affinities for several mutants of Sdc1 and in the protein domain constitute a complete dataset to study many ionic interactions with molecular simulations. These calculations are still challenging, despite the dramatic improvement of biomolecular modelling in the 1990's and 2000's. Upon binding, residues are transferred from a solvent-exposed environment to a solvent-poor one. This is expected to change the electron distribution within residues and nearby solvent molecules. Comparing ligands that differ by one or more ionic side-chain mutations, more sophisticated force fields where electronic polarizability is treated explicitly may be required. We developed and tested both Computational Protein Design (CPD) models and more precise free energy calculation methods based on polarizable molecular dynamics. We developed a general, high-througtput CPD protocol to optimize protein:peptide binding. The model has been implemented in on our in-house CPD package Proteus ( Simonson et al, 2014) and has been tested computing relative binding affinities for many variants of the Tiam1:Sdc1 complex. Monte Carlo sampling of equilibrium distributions of protein sequences is performed using an adaptive bias potential which flattens the energy landscape in sequence space and allows to estimate binding affinities for thousands of protein variants in limited CPU time (~1hour). We also improved our CPD implicit solvent model, implementing a more realistic description of the solute-solvent dielectric boundary. The new method, called Fluctuating Dielectric Boundary (FDB) showed a systematic improvement in the prediction of acid:base constants of several proteins. Promising results were also obtained for the complete sequence redesign of three PDZ domains. In the second part of this work we studied Tiam1:peptide affinities with more sophisticated models, based on free energy simulations with the Drude Polarizable Force field (DrudeFF). We first computed relative binding free energies for charge mutations in the Tiam1:Sdc1 complex, obtaining a clear improvement respect to equivalent calculations performed using two additive force fields. We applied the well-enstablished Dual Topology Approach: to our knowledge, this was the first example of such a calculation for a protein:peptide complex with uses the DrudeFF. Then we went on, developing the Drude polarizable models for methyl phosphate (MP) and phospho tyrosine (pTyr). We were interested in the change in binding affinity associated with phosphorylation of a Tyrosine residue of Sdc1, but Drude pTyr parameters were not yet developed. We tested our new phosphate parameters studying standard binding free energies between MP and magnesium (Mg2+) in water solution. Results showed a good agreement with experiment, improving previous calculations performed using additive force field
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Francesco Villa. Computer simulations to engineer PDZ-peptide recognition. Bioinformatics [q-bio.QM]. Université Paris-Saclay, 2018. English. ⟨NNT : 2018SACLX076⟩. ⟨tel-02064175⟩

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