Chapter 14
Computational Design of PDZ-Peptide Binding
Nicolas Panel, Francesco Villa, Vaitea Opuu, David Mignon,
and Thomas Simonson
Abstract
This chapter describes two computational methods for PDZ–peptide binding: high-throughput computational protein design (CPD) and a medium-throughput approach combining molecular dynamics for
conformational sampling with a Poisson–Boltzmann (PB) Linear Interaction Energy for scoring. A new
CPD method is outlined, which uses adaptive Monte Carlo simulations to efficiently sample peptide
variants that tightly bind a PDZ domain, and provides at the same time precise estimates of their relative
binding free energies. A detailed protocol is described based on the Proteus CPD software. The mediumthroughput approach can be performed with standard MD and PB software, such as NAMD and Charmm.
For 40 complexes between Tiam1 and peptide ligands, it gave high a2ccuracy, with mean errors of around
0.5 kcal/mol for relative binding free energies and no large errors. It requires a moderate amount of
parameter fitting before it can be applied, and its transferability to other protein families is still untested.
Key words Protein design, Ligand binding, MC simulation, Proteus program, Molecular mechanics,
Implicit solvent
1 Introduction
We focus here on the design of PDZ–peptide binding with computational approaches. One goal is to discover peptide ligands that
could inhibit or modulate the activity of a given PDZ protein. For
this, one should explore a space of peptide variants, perhaps allowing noncanonical amino acids (ncAAs) at selected positions, for
binding or stability. Another goal is to redesign the PDZ domain
itself, to alter its target binding and manipulate protein interaction
networks in vitro or in cells. For this, one would explore a space of
protein variants, where a few positions close to the peptide are
allowed to mutate. Both applications should potentially consider a
large space of sequences, so that medium or high-throughput
approaches are desirable. Both applications involve designing a
polypeptide, and so both are amenable to high-throughput, structure-based, computational protein design (CPD). CPD aims to
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_14, © Springer Science+Business Media, LLC, part of Springer Nature 2021
237
Computational Design of PDZ-Peptide Binding
Nicolas Panel, Francesco Villa, Vaitea Opuu, David Mignon,
and Thomas Simonson
Abstract
This chapter describes two computational methods for PDZ–peptide binding: high-throughput computational protein design (CPD) and a medium-throughput approach combining molecular dynamics for
conformational sampling with a Poisson–Boltzmann (PB) Linear Interaction Energy for scoring. A new
CPD method is outlined, which uses adaptive Monte Carlo simulations to efficiently sample peptide
variants that tightly bind a PDZ domain, and provides at the same time precise estimates of their relative
binding free energies. A detailed protocol is described based on the Proteus CPD software. The mediumthroughput approach can be performed with standard MD and PB software, such as NAMD and Charmm.
For 40 complexes between Tiam1 and peptide ligands, it gave high a2ccuracy, with mean errors of around
0.5 kcal/mol for relative binding free energies and no large errors. It requires a moderate amount of
parameter fitting before it can be applied, and its transferability to other protein families is still untested.
Key words Protein design, Ligand binding, MC simulation, Proteus program, Molecular mechanics,
Implicit solvent
1 Introduction
We focus here on the design of PDZ–peptide binding with computational approaches. One goal is to discover peptide ligands that
could inhibit or modulate the activity of a given PDZ protein. For
this, one should explore a space of peptide variants, perhaps allowing noncanonical amino acids (ncAAs) at selected positions, for
binding or stability. Another goal is to redesign the PDZ domain
itself, to alter its target binding and manipulate protein interaction
networks in vitro or in cells. For this, one would explore a space of
protein variants, where a few positions close to the peptide are
allowed to mutate. Both applications should potentially consider a
large space of sequences, so that medium or high-throughput
approaches are desirable. Both applications involve designing a
polypeptide, and so both are amenable to high-throughput, structure-based, computational protein design (CPD). CPD aims to
Jean-Paul Borg (ed.), PDZ Mediated Interactions: Methods and Protocols, Methods in Molecular Biology, vol. 2256,
https://doi.org/10.1007/978-1-0716-1166-1_14, © Springer Science+Business Media, LLC, part of Springer Nature 2021
237