Computational Design of Peptides with Improved Recognition of the Focal Adhesion Kinase FAT Domain

We describe a two-stage computational protein design (CPD) methodology for the design of peptides binding to the FAT domain of the protein focal adhesion kinase. The first stage involves high-throughput CPD calculations with the Proteus software. The energies of the folded state are described by a physics-based energy function and of the unfolded peptides by a knowledge-based model that reproduces aminoacid compositions consistent with a helicity scale. The obtained sequences are filtered in terms of the affinity and the stability of the complex. In the second stage, design sequences are further evaluated by all-atom molecular dynamics simulations and binding free energy calculations with a molecular mechanics/implicit solvent free energy function.

Computational Design of PDZ-Peptide Binding

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 medium-throughput 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.

Comparing pairwise-additive and many-body generalized Born models for acid/base calculations and protein design

Generalized Born (GB) solvent models are common in acid/base calculations and protein design. With GB, the interaction between a pair of solute atoms depends on the shape of the protein/solvent boundary and, therefore, the positions of all solute atoms, so that GB is a many-body potential. For compute-intensive applications, the model is often simplified further, by introducing a mean, native-like protein/solvent boundary, which removes the many-body property. We investigate a method for both acid/base calculations and protein design that uses Monte Carlo simulations in which side chains can explore rotamers, bind/release protons, or mutate. The fluctuating protein/solvent dielectric boundary is treated in a way that is numerically exact (within the GB framework), in contrast to a mean boundary. Its originality is that it captures the many-body character while retaining the residue-pairwise complexity given by a fixed boundary. The method is implemented in the Proteus protein design software. It yields a slight but systematic improvement for acid/base constants in nine proteins and a significant improvement for the computational design of three PDZ domains. It eliminates a source of model uncertainty, which will facilitate the analysis of other model limitations. © 2017 Wiley Periodicals, Inc.