Shannon Alicia Marshall

Energy functions for protein design

Recent successes in protein design have illustrated the promise of computational approaches. These methods rely on energy expressions to evaluate the quality of different amino acid sequences for target protein structures. The force fields optimized for design differ from those typically used in molecular mechanics and molecular dynamics calculations.

Rational design and engineering of therapeutic proteins

An increasing number of engineered protein therapeutics are currently being developed, tested in clinical trials and marketed for use. Many of these proteins arose out of hit-and-miss efforts to discover specific mutations, fusion partners or chemical modifications that confer desired properties. Through these efforts, several useful strategies have emerged for rational optimization of therapeutic candidates. The controlled manipulation of the physical, chemical and biological properties of proteins enabled by structure-based simulation is now being used to refine established rational engineering approaches and to advance new strategies. These methods provide clear, hypothesis-driven routes to solve problems that plague many proteins and to create novel mechanisms of action. We anticipate that rational protein engineering will shape the field of protein therapeutics dramatically by improving existing products and enabling the development of novel therapeutic agents.

One‐ and two‐body decomposable Poisson‐Boltzmann methods for protein design calculations

Successfully modeling electrostatic interactions is one of the key factors required for the computational design of proteins with desired physical, chemical, and biological properties. In this paper, we present formulations of the finite difference Poisson‐Boltzmann (FDPB) model that are pairwise decomposable by side chain. These methods use reduced representations of the protein structure based on the backbone and one or two side chains in order to approximate the dielectric environment in and around the protein. For the desolvation of polar side chains, the two‐body model has a 0.64 kcal/mol RMSD compared to FDPB calculations performed using the full representation of the protein structure. Screened Coulombic interaction energies between side chains are approximated with an RMSD of 0.13 kcal/mol. The methods presented here are compatible with the computational demands of protein design calculations and produce energies that are very similar to the results of traditional FDPB calculations.

Designing proteins for therapeutic applications.

Protein design is becoming an increasingly useful tool for optimizing protein drugs and creating novel biotherapeutics. Recent progress includes the engineering of monoclonal antibodies, cytokines, enzymes and viral fusion inhibitors.

NMR and Temperature-jump Measurements of de Novo Designed Proteins Demonstrate Rapid Folding in the Absence of Explicit Selection for Kinetics

We address the importance of natural selection in the origin and maintenance of rapid protein folding by experimentally characterizing the folding kinetics of two de novo designed proteins, NC3-NCAP and ENH-FSM1. These 51 residue proteins, which adopt the helix-turn-helix homeodomain fold, share as few as 12 residues in common with their most closely related natural analog. Despite the replacement of up to 3/4 of their residues by a computer algorithm optimizing only thermodynamic properties, the designed proteins fold as fast or faster than the 35,000 s(-1) observed for the closest natural analog. Thus these de novo designed proteins, which were produced in the complete absence of selective pressures or design constraints explicitly aimed at ensuring rapid folding, are among the most rapidly folding proteins reported to date.

An improved pairwise decomposable finite-difference Poisson–Boltzmann method for computational protein design

Our goal is to develop accurate electrostatic models that can be implemented in current computational protein design protocols. To this end, we improve upon a previously reported pairwise decomposable, finite difference Poisson–Boltzmann (FDPB) model for protein design (Marshall et al., Protein Sci 2005, 14, 1293). The improvement involves placing generic sidechains at positions with unknown amino acid identity and explicitly capturing two‐body perturbations to the dielectric environment. We compare the original and improved FDPB methods to standard FDPB calculations in which the dielectric environment is completely determined by protein atoms. The generic sidechain approach yields a two to threefold increase in accuracy per residue or residue pair over the original pairwise FDPB implementation, with no additional computational cost. Distance dependent dielectric and solvent‐exclusion models were also compared with standard FDPB energies. The accuracy of the new pairwise FDPB method is shown to be superior to these models, even after reparameterization of the solvent‐exclusion model.

Simple electrostatic model improves designed protein sequences

Electrostatic interactions are important for both protein stability and function, including binding and catalysis. As protein design moves into these areas, an accurate description of electrostatic energy becomes necessary. Here, we show that a simple distance‐dependent Coulombic function parameterized by a comparison to Poisson‐Boltzmann calculations is able to capture some of these electrostatic interactions. Specifically, all three helix N‐capping interactions in the engrailed homeodomain fold are recovered using the newly parameterized model. The stability of this designed protein is similar to a protein forced by sequence restriction to have beneficial electrostatic interactions.