Robust Prediction of Relative Binding Energies for Protein-Protein Complex Mutations Using Free Energy Perturbation Calculations.

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how Protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.

Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations.

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.

Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design.

Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal structure of the antibody bound to the antigen for structural predictions. Here, we show an example of successful in silico affinity maturation of a hybridoma derived antibody, AB1, using just a homology model of the antibody fragment variable region and a protein-protein docking model of the AB1 antibody bound to the antigen, murine CCL20 (muCCL20). In silico affinity maturation, together with alanine scanning, has allowed us to fine-tune the protein-protein docking model to subsequently enable the identification of two single-point mutations that increase the affinity of AB1 for muCCL20. To our knowledge, this is one of the first examples of the use of homology modelling and protein docking for affinity maturation and represents an approach that can be widely deployed.

Enzymatically activated emulsions stabilised by interfacial nanofibre networks.

We report on-demand formation of emulsions stabilised by interfacial nanoscale networks. These are formed through biocatalytic dephosphorylation and self-assembly of Fmoc(9-fluorenylmethoxycarbonyl)dipeptide amphiphiles in aqueous/organic mixtures. This is achieved by using alkaline phosphatase which transforms surfactant-like phosphorylated precursors into self-assembling aromatic peptide amphiphiles (Fmoc-tyrosine-leucine, Fmoc-YL) that form nanofibrous networks. In biphasic organic/aqueous systems, these networks form preferentially at the interface thus providing a means of emulsion stabilisation. We demonstrate on-demand emulsification by enzyme addition, even after storage of the biphasic mixture for several weeks. Experimental (Fluorescence, FTIR spectroscopy, fluorescence microscopy, electron microscopy, atomic force microscopy) and computational techniques (atomistic molecular dynamics) are used to characterise the interfacial self-assembly process.

Influence of Solvent in Controlling Peptide-Surface Interactions.

Protein binding to surfaces is an important phenomenon in biology and in modern technological applications. Extensive experimental and theoretical research has been focused in recent years on revealing the factors that govern binding affinity to surfaces. Theoretical studies mainly focus on examining the contribution of the individual amino acids or, alternatively, the binding potential energies of the full peptide, which are unable to capture entropic contributions and neglect the dynamic nature of the system. We present here a methodology that involves the combination of nonequilibrium dynamics simulations with strategic mutation of polar residues to reveal the different factors governing the binding free energy of a peptide to a surface. Using a gold-binding peptide as an example, we show that relative binding free energies are a consequence of the balance between strong interactions of the peptide with the surface and the ability for the bulk solvent to stabilize the peptide.