Andrew M. Watkins

Accurate de novo design of hyperstable constrained peptides.

Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes that have evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small-molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for accurate de novo design of conformationally restricted peptides, and the use of these methods to design 18–47 residue, disulfide-crosslinked peptides, a subset of which are heterochiral and/or N–C backbone-cyclized. Both genetically encodable and non-canonical peptides are exceptionally stable to thermal and chemical denaturation, and 12 experimentally determined X-ray and NMR structures are nearly identical to the computational design models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs.

Anatomy of β-Strands at Protein–Protein Interfaces

The development of inhibitors for protein–protein interactions frequently involves the mimicry of secondary structure motifs. While helical protein–protein interactions have been heavily targeted, a similar level of success for the inhibition of β-strand and β-sheet rich interfaces has been elusive. We describe an assessment of the full range of β-strand interfaces whose high-resolution structures are available in the Protein Data Bank. This analysis identifies complexes where a β-stand or β-sheet contributes significantly to binding. The results highlight the molecular recognition complexity in strand-mediated interactions relative to helical interfaces and offer guidelines for the construction of β-strand and β-sheet mimics as ligands for protein receptors. The online data set will potentially serve as an entry-point to new classes of protein–protein interaction inhibitors.

Adding Diverse Noncanonical Backbones to Rosetta: Enabling Peptidomimetic Design

Peptidomimetics are classes of molecules that mimic structural and functional attributes of polypeptides. Peptidomimetic oligomers can frequently be synthesized using efficient solid phase synthesis procedures similar to peptide synthesis. Conformationally ordered peptidomimetic oligomers are finding broad applications for molecular recognition and for inhibiting protein-protein interactions. One critical limitation is the limited set of design tools for identifying oligomer sequences that can adopt desired conformations. Here, we present expansions to the ROSETTA platform that enable structure prediction and design of five non-peptidic oligomer scaffolds (noncanonical backbones), oligooxopiperazines, oligo-peptoids, -peptides, hydrogen bond surrogate helices and oligosaccharides. This work is complementary to prior additions to model noncanonical protein side chains in ROSETTA. The main purpose of our manuscript is to give a detailed description to current and future developers of how each of these noncanonical backbones was implemented. Furthermore, we provide a general outline for implementation of new backbone types not discussed here. To illustrate the utility of this approach, we describe the first tests of the ROSETTA molecular mechanics energy function in the context of oligooxopiperazines, using quantum mechanical calculations as comparison points, scanning through backbone and side chain torsion angles for a model peptidomimetic. Finally, as an example of a novel design application, we describe the automated design of an oligooxopiperazine that inhibits the p53-MDM2 protein-protein interaction. For the general biological and bioengineering community, several noncanonical backbones have been incorporated into web applications that allow users to freely and rapidly test the presented protocols (http://rosie.rosettacommons.org). This work helps address the peptidomimetic communitys need for an automated and expandable modeling tool for noncanonical backbones.

Protein–Protein Interactions Mediated by Helical Tertiary Structure Motifs

The modulation of protein–protein interactions (PPIs) by means of creating or stabilizing secondary structure conformations is a rapidly growing area of research. Recent success in the inhibition of difficult PPIs by secondary structure mimetics also points to potential limitations, because often, specific cases require tertiary structure mimetics. To streamline protein structure-based inhibitor design, we have previously described the examination of protein complexes in the Protein Data Bank where α-helices or β-strands form critical contacts. Here, we examined coiled coils and helix bundles that mediate complex formation to create a platform for the discovery of potential tertiary structure mimetics. Though there has been extensive analysis of coiled coil motifs, the interactions between pre-formed coiled coils and globular proteins have not been systematically analyzed. This article identifies critical features of these helical interfaces with respect to coiled coil and other helical PPIs. We expect the analysis to prove useful for the rational design of modulators of this fundamental class of protein assemblies.

HippDB: A database of readily targeted helical protein-protein interactions

SUMMARY HippDB catalogs every protein-protein interaction whose structure is available in the Protein Data Bank and which exhibits one or more helices at the interface. The Web site accepts queries on variables such as helix length and sequence, and it provides computational alanine scanning and change in solvent-accessible surface area values for every interfacial residue. HippDB is intended to serve as a starting point for structure-based small molecule and peptidomimetic drug development. AVAILABILITY AND IMPLEMENTATION HippDB is freely available on the web at http://www.nyu.edu/projects/arora/hippdb. The Web site is implemented in PHP, MySQL and Apache. Source code freely available for download at http://code.google.com/p/helidb, implemented in Perl and supported on Linux. CONTACT arora@nyu.edu.

Effects of side chains in helix nucleation differ from helix propagation

Significance Complete description of the kinetics and thermodynamics of α-helix formation is fundamental to the understanding of protein folding because α-helices are the most abundant class of secondary structures and are implicated in the earliest steps of the folding process. Kinetic models of protein folding suggest that helix folding is rate-limited by formation of a nucleus followed by rapid propagation. The influence of individual residues on propagation has been evaluated in numerous model peptides and proteins. Here, we describe a synthetic model that enables experimental assessment of the role of individual residues in helix nucleation. Our results suggest that amino acids contribute differently to nucleation than to propagation. Helix–coil transition theory connects observable properties of the α-helix to an ensemble of microstates and provides a foundation for analyzing secondary structure formation in proteins. Classical models account for cooperative helix formation in terms of an energetically demanding nucleation event (described by the σ constant) followed by a more facile propagation reaction, with corresponding s constants that are sequence dependent. Extensive studies of folding and unfolding in model peptides have led to the determination of the propagation constants for amino acids. However, the role of individual side chains in helix nucleation has not been separately accessible, so the σ constant is treated as independent of sequence. We describe here a synthetic model that allows the assessment of the role of individual amino acids in helix nucleation. Studies with this model lead to the surprising conclusion that widely accepted scales of helical propensity are not predictive of helix nucleation. Residues known to be helix stabilizers or breakers in propagation have only a tenuous relationship to residues that favor or disfavor helix nucleation.

Protein Domain Mimics as Modulators of Protein–Protein Interactions

Protein-protein interactions (PPIs) are ubiquitous in biological systems and often misregulated in disease. As such, specific PPI modulators are desirable to unravel complex PPI pathways and expand the number of druggable targets available for therapeutic intervention. However, the large size and relative flatness of PPI interfaces make them challenging molecular targets. This Account describes our systematic approach using secondary and tertiary protein domain mimics (PDMs) to specifically modulate PPIs. Our strategy focuses on mimicry of regular secondary and tertiary structure elements from one of the PPI partners to inspire rational PDM design. We have compiled three databases (HIPPDB, SIPPDB, and DIPPDB) of secondary and tertiary structures at PPI interfaces to guide our designs and better understand the energetics of PPI secondary and tertiary structures. Our efforts have focused on three of the most common secondary and tertiary structures: α-helices, β-strands, and helix dimers (e.g., coiled coils). To mimic α-helices, we designed the hydrogen bond surrogate (HBS) as an isosteric PDM and the oligooxopiperazine helix mimetic (OHM) as a topographical PDM. The nucleus of the HBS approach is a peptide macrocycle in which the N-terminal i, i + 4 main-chain hydrogen bond is replaced with a covalent carbon-carbon bond. In mimicking a main-chain hydrogen bond, the HBS approach stabilizes the α-helical conformation while leaving all helical faces available for functionalization to tune binding affinity and specificity. The OHM approach, in contrast, envisions a tetrapeptide to mimic one face of a two-turn helix. We anticipated that placement of ethylene bridges between adjacent amides constrains the tetrapeptide backbone to mimic the i, i + 4, and i + 7 side chains on one face of an α-helix. For β-strands, we developed triazolamers, a topographical PDM where the peptide bonds are replaced by triazoles. The triazoles simultaneously stabilize the extended, zigzag conformation of β-strands and transform an otherwise ideal protease substrate into a stable molecule by replacement of the peptide bonds. We turned to a salt bridge surrogate (SBS) approach as a means for stabilizing very short helix dimers. As with the HBS approach, the SBS strategy replaces a noncovalent interaction with a covalent bond. Specifically, we used a bis-triazole linkage that mimics a salt bridge interaction to drive helix association and folding. Using this approach, we were able to stabilize helix dimers that are less than half of the length required to form a coiled coil from two independent strands. In addition to demonstrating the stabilization of desired structures, we have also shown that our designed PDMs specifically modulate target PPIs in vitro and in vivo. Examples of PPIs successfully targeted include HIF1α/p300, p53/MDM2, Bcl-xL/Bak, Ras/Sos, and HIV gp41. The PPI databases and designed PDMs created in these studies will aid development of a versatile set of molecules to probe complex PPI functions and, potentially, PPI-based therapeutics.

Side-Chain Conformational Preferences Govern Protein–Protein Interactions

Protein secondary structures serve as geometrically constrained scaffolds for the display of key interacting residues at protein interfaces. Given the critical role of secondary structures in protein folding and the dependence of folding propensities on backbone dihedrals, secondary structure is expected to influence the identity of residues that are important for complex formation. Counter to this expectation, we find that a narrow set of residues dominates the binding energy in protein-protein complexes independent of backbone conformation. This finding suggests that the binding epitope may instead be substantially influenced by the side-chain conformations adopted. We analyzed side-chain conformational preferences in residues that contribute significantly to binding. This analysis suggests that preferred rotamers contribute directly to specificity in protein complex formation and provides guidelines for peptidomimetic inhibitor design.

Blind prediction of noncanonical RNA structure at atomic accuracy

We report a new algorithm and a battery of blind challenges for the prediction of complex RNA structures at atomic accuracy. Prediction of RNA structure from nucleotide sequence remains an unsolved grand challenge of biochemistry and requires distinct concepts from protein structure prediction. Despite extensive algorithmic development in recent years, modeling of noncanonical base pairs of new RNA structural motifs has not been achieved in blind challenges. We report a stepwise Monte Carlo (SWM) method with a unique add-and-delete move set that enables predictions of noncanonical base pairs of complex RNA structures. A benchmark of 82 diverse motifs establishes the method’s general ability to recover noncanonical pairs ab initio, including multistrand motifs that have been refractory to prior approaches. In a blind challenge, SWM models predicted nucleotide-resolution chemical mapping and compensatory mutagenesis experiments for three in vitro selected tetraloop/receptors with previously unsolved structures (C7.2, C7.10, and R1). As a final test, SWM blindly and correctly predicted all noncanonical pairs of a Zika virus double pseudoknot during a recent community-wide RNA-Puzzle. Stepwise structure formation, as encoded in the SWM method, enables modeling of noncanonical RNA structure in a variety of previously intractable problems.

Modeling and design of peptidomimetics to modulate protein–protein interactions

We describe a modular approach to identify and inhibit protein-protein interactions (PPIs) that are mediated by protein secondary and tertiary structures with rationally designed peptidomimetics. Our analysis begins with entries of high-resolution complexes in the Protein Data Bank and utilizes conformational sampling, scoring, and design capabilities of advanced biomolecular modeling software to develop peptidomimetics.