Adding diverse noncanonical backbones to rosetta: enabling peptidomimetic design

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β-catenin and NF-κB essential modulator. Among the twelve β-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β-catenin with an IC50 of 0.010 ± 0.06 μM, which is 15-fold better than the parent peptide. For NF-κB essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design.

A simple method for developing lysine targeted covalent protein reagents

Peptide-based covalent probes can target shallow protein surfaces not typically addressable using small molecules, yet there is a need for versatile approaches to convert native peptide sequences into covalent binders that can target a broad range of residues. Here we report protein-based thio-methacrylate esters-electrophiles that can be installed easily on unprotected peptides and proteins via cysteine side chains, and react efficiently and selectively with cysteine and lysine side chains on the target. Methacrylate phosphopeptides derived from 14-3-3-binding proteins irreversibly label 14-3-3σ via either lysine or cysteine residues, depending on the position of the electrophile. Methacrylate peptides targeting a conserved lysine residue exhibit pan-isoform binding of 14-3-3 proteins both in lysates and in extracellular media. Finally, we apply this approach to develop protein-based covalent binders. A methacrylate-modified variant of the colicin E9 immunity protein irreversibly binds to the E9 DNAse, resulting in significantly higher thermal stability relative to the non-covalent complex. Our approach offers a simple and versatile route to convert peptides and proteins into potent covalent binders.

Development of a Systematic and Extensible Force Field for Peptoids (STEPs)

Peptoids (N-substituted glycines) are a class of biomimetic polymers that have attracted significant attention due to their accessible synthesis and enzymatic and thermal stability relative to their naturally occurring counterparts (polypeptides). While these polymers provide the promise of more robust functional materials via hierarchical approaches, they present a new challenge for computational structure prediction for material design. The reliability of calculations hinges on the accuracy of interactions represented in the force field used to model peptoids. For proteins, structure prediction based on sequence and de novo design has made dramatic progress in recent years; however, these models are not readily transferable for peptoids. Current efforts to develop and implement peptoid-specific force fields are spread out, leading to replicated efforts and a fragmented collection of parameterized sidechains. Here, we developed a peptoid-specific force field containing 70 different side chains, using GAFF2 as starting point. The new model is validated based on the generation of Ramachandran-like plots from DFT optimization compared against force field reproduced potential energy and free energy surfaces as well as the reproduction of equilibrium cis/trans values for some residues experimentally known to form helical structures. Equilibrium cis/trans distributions (Kct) are estimated for all parameterized residues to identify which residues have an intrinsic propensity for cis or trans states in the monomeric state.

Lysine vitcylation is a novel vitamin C-derived protein modification that enhances STAT1-mediated immune response

Vitamin C (vitC) is a vital nutrient for health and also used as a therapeutic agent in diseases such as cancer. However, the mechanisms underlying vitC's effects remain elusive. Here we report that vitC directly modifies lysine without enzymes to form vitcyl-lysine, termed "vitcylation", in a dose-, pH-, and sequence-dependent manner across diverse proteins in cells. We further discover that vitC vitcylates K298 site of STAT1, which impairs its interaction with the phosphatase PTPN2, preventing STAT1 Y701 dephosphorylation and leading to increased STAT1-mediated IFN pathway activation in tumor cells. As a result, these cells have increased MHC/HLA class-I expression and activate immune cells in co-cultures. Tumors collected from vitC-treated tumor-bearing mice have enhanced vitcylation, STAT1 phosphorylation and antigen presentation. The identification of vitcylation as a novel PTM and the characterization of its effect in tumor cells opens a new avenue for understanding vitC in cellular processes, disease mechanisms, and therapeutics.

Protocol for iterative optimization of modified peptides bound to protein targets

Peptides are commonly used as therapeutic agents. However, they suffer from easy degradation and instability. Replacing natural by non-natural amino acids can avoid these problems, and potentially improve the affinity towards the target protein. Here, we present a computational pipeline to optimize peptides based on adding non-natural amino acids while improving their binding affinity. The workflow is an iterative computational evolution algorithm, inspired by the PARCE protocol, that performs single-point mutations on the peptide sequence using modules from the Rosetta framework. The modifications can be guided based on the structural properties or previous knowledge of the biological system. At each mutation step, the affinity to the protein is estimated by sampling the complex conformations and applying a consensus metric using various open protein-ligand scoring functions. The mutations are accepted based on the score differences, allowing for an iterative optimization of the initial peptide. The sampling/scoring scheme was benchmarked with a set of protein-peptide complexes where experimental affinity values have been reported. In addition, a basic application using a known protein-peptide complex is also provided. The structure- and dynamic-based approach allows users to optimize bound peptides, with the option to personalize the code for further applications. The protocol, called mPARCE, is available at: https://github.com/rochoa85/mPARCE/.

Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions

Technologies for discovering peptides as potential therapeutics have rapidly advanced in recent years with significant interest from both academic and pharmaceutical labs. These advancements in turn drive the need for new computational tools to design peptides for purposes of advancing lead molecules into the clinic. Here we report the development and application of a new automated tool, AutoRotLib, for parameterizing a diverse set of non-canonical amino acids (NCAAs), N-methyl, or peptoid residues for use with the computational design program Rosetta. In addition, we developed a protocol for designing thioether-cyclized macrocycles within Rosetta, due to their common application in mRNA display using the RaPID platform. To evaluate the utility of these new computational tools, we screened a library of canonical and NCAAs on both a linear peptide and a thioether macrocycle, allowing us to quickly identify mutations that affect peptide binding and subsequently measure our results against previously published data. We anticipate in silico screening of peptides against a diverse chemical space will be a fundamental component for peptide design and optimization, as more amino acids can be explored in a single in silico screen than an in vitro screen. As such, these tools will enable maturation of peptide affinity for protein targets of interest and optimization of peptide pharmacokinetics for therapeutic applications.

Conformational control via sequence for a heteropeptoid in water: coupled NMR and Rosetta modelling

We report a critical advance in the generation and characterization of peptoid hetero-oligomers. A library of sub-monomers with amine and carboxylate side-chains are combined in different sequences using microwave-assisted synthesis. Their sequence-structure propensity is confirmed by circular dichroism, and conformer subtypes are enumerated by NMR. Biasing the ψ-angle backbone to trans (180°) in Monte Carlo modelling favors i to i + 3 naphthyl-naphthyl stacking, and matches experimental ensemble distributions. Taken together, high-yield synthesis of heterooligomers and NMR with structure prediction enables rapid determination of sequences that induce secondary structural propensities for predictive design of hydrophilic peptidomimetic foldamers and their future libraries.

Covalent flexible peptide docking in Rosetta

Electrophilic peptides that form an irreversible covalent bond with their target have great potential for binding targets that have been previously considered undruggable. However, the discovery of such peptides remains a challenge. Here, we present Rosetta CovPepDock, a computational pipeline for peptide docking that incorporates covalent binding between the peptide and a receptor cysteine. We applied CovPepDock retrospectively to a dataset of 115 disulfide-bound peptides and a dataset of 54 electrophilic peptides. It produced a top-five scoring, near-native model, in 89% and 100% of the cases when docking from the native conformation, and 20% and 90% when docking from an extended peptide conformation, respectively. In addition, we developed a protocol for designing electrophilic peptide binders based on known non-covalent binders or protein-protein interfaces. We identified 7154 peptide candidates in the PDB for application of this protocol. As a proof-of-concept we validated the protocol on the non-covalent complex of 14-3-3σ and YAP1 phosphopeptide. The protocol identified seven highly potent and selective irreversible peptide binders. The predicted binding mode of one of the peptides was validated using X-ray crystallography. This case-study demonstrates the utility and impact of CovPepDock. It suggests that many new electrophilic peptide binders can be rapidly discovered, with significant potential as therapeutic molecules and chemical probes.

Metal Cation-Binding Mechanisms of Q-Proline Peptoid Macrocycles in Solution

The rational design of foldable and functionalizable peptidomimetic scaffolds requires the concerted application of both computational and experimental methods. Recently, a new class of designed peptoid macrocycle incorporating spiroligomer proline mimics (Q-prolines) has been found to preorganize when bound by monovalent metal cations. To determine the solution-state structure of these cation-bound macrocycles, we employ a Bayesian inference method (BICePs) to reconcile enhanced-sampling molecular simulations with sparse ROESY correlations from experimental NMR studies to predict and design conformational and binding properties of macrocycles as functional scaffolds for peptidomimetics. Conformations predicted to be most populated in solution were then simulated in the presence of explicit cations to yield trajectories with observed binding events, revealing a highly preorganized all-trans amide conformation, whose formation is likely limited by the slow rate of cis/trans isomerization. Interestingly, this conformation differs from a racemic crystal structure solved in the absence of cation. Free energies of cation binding computed from distance-dependent potentials of mean force suggest Na⁺ has a higher affinity to the macrocycle than K⁺, with both cations binding much more strongly in acetonitrile than water. The simulated affinities are able to correctly rank the extent to which different macrocycle sequences exhibit preorganization in the presence of different metal cations and solvents, suggesting our approach is suitable for solution-state computational design.

Piptides: New, Easily Accessible Chemotypes For Interactions With Biomolecules

Small molecule probe development is pivotal in biomolecular science. Research described here was undertaken to develop a non-peptidic chemotype, piptides, that is amenable to convenient, iterative solid-phase syntheses, and useful in biomolecular probe discovery. Piptides can be made from readily accessible pip acid building blocks and have good proteolytic and pH stabilities. An illustrative application of piptides against a protein-protein interaction (PPI) target was explored. The Exploring Key Orientations (EKO) strategy was used to evaluate piptide candidates for this. A library of only 14 piptides contained five members that disrupted epidermal growth factor (EGF) and its receptor, EGFR, at low micromolar concentrations. These piptides also caused apoptotic cell death, and antagonized EGF-induced phosphorylation of intracellular tyrosine residues in EGFR.

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

Modulating Protein-Protein Interactions by Cyclic and Macrocyclic Peptides. Prominent Strategies and Examples

Cyclic and macrocyclic peptides constitute advanced molecules for modulating protein-protein interactions (PPIs). Although still peptide derivatives, they are metabolically more stable than linear counterparts, and should have a lower degree of flexibility, with more defined secondary structure conformations that can be adapted to imitate protein interfaces. In this review, we analyze recent progress on the main methods to access cyclic/macrocyclic peptide derivatives, with emphasis in a few selected examples designed to interfere within PPIs. These types of peptides can be from natural origin, or prepared by biochemical or synthetic methodologies, and their design could be aided by computational approaches. Some advances to facilitate the permeability of these quite big molecules by conjugation with cell penetrating peptides, and the incorporation of β-amino acid and peptoid structures to improve metabolic stability, are also commented. It is predicted that this field of research could have an important future mission, running in parallel to the discovery of new, relevant PPIs involved in pathological processes.

Computational methods-guided design of modulators targeting protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.

A twist in the road less traveled: The AMBER ff15ipq-m force field for protein mimetics

We present a new force field, AMBER ff15ipq-m, for simulations of protein mimetics in applications from therapeutics to biomaterials. This force field is an expansion of the AMBER ff15ipq force field that was developed for canonical proteins and enables the modeling of four classes of artificial backbone units that are commonly used alongside natural α residues in blended or "heterogeneous" backbones: chirality-reversed D-α-residues, the Cα-methylated α-residue Aib, homologated β-residues (β3) bearing proteinogenic side chains, and two cyclic β residues (βcyc; APC and ACPC). The ff15ipq-m force field includes 472 unique atomic charges and 148 unique torsion terms. Consistent with the AMBER IPolQ lineage of force fields, the charges were derived using the Implicitly Polarized Charge (IPolQ) scheme in the presence of explicit solvent. To our knowledge, no general force field reported to date models the combination of artificial building blocks examined here. In addition, we have derived Karplus coefficients for the calculation of backbone amide J-coupling constants for β3Ala and ACPC β residues. The AMBER ff15ipq-m force field reproduces experimentally observed J-coupling constants in simple tetrapeptides and maintains the expected conformational propensities in reported structures of proteins/peptides containing the artificial building blocks of interest-all on the μs timescale. These encouraging results demonstrate the power and robustness of the IPolQ lineage of force fields in modeling the structure and dynamics of natural proteins as well as mimetics with protein-inspired artificial backbones in atomic detail.

The emerging role of computational design in peptide macrocycle drug discovery

Drug discovery is a laborious process with rising cost per new drug. Peptide macrocycles are promising therapeutics, though conformational flexibility can reduce target affinity and specificity. Recent computational advancements address this problem by enabling rational design of rigidly folded peptide macrocycles.

AREAS COVERED

This review summarizes currently approved peptide macrocycle therapeutics and discusses advantages of mesoscale drugs over small molecules or protein therapeutics. It describes the history, rationale, and state of the art of computational tools, such as Rosetta, that allow the design of rigidly structured peptide macrocycles. The emerging pipeline for designing peptide macrocycle drugs is described, including current challenges in designing permeable molecules that can emulate the chameleonic behavior of natural macrocycles. Prospects for reducing computational cost and improving accuracy with emerging computational technologies are also discussed.

EXPERT OPINION

To embrace computational design of peptide macrocycle drugs, we must shift current attitudes regarding the role of computation in drug discovery, and move beyond Lipinski's rules. This technology has the potential to shift failures to earlier in silico stages of the drug discovery process, improving success rates in costly clinical trials. Given the available tools, now is the time for drug developers to incorporate peptide macrocycle design into drug discovery pipelines.

Peptide science: A "rule model" for new generations of peptidomimetics

Peptides have been heavily investigated for their biocompatible and bioactive properties. Though a wide array of functionalities can be introduced by varying the amino acid sequence or by structural constraints, properties such as proteolytic stability, catalytic activity, and phase behavior in solution are difficult or impossible to impart upon naturally occurring α-L-peptides. To this end, sequence-controlled peptidomimetics exhibit new folds, morphologies, and chemical modifications that create new structures and functions. The study of these new classes of polymers, especially α-peptoids, has been highly influenced by the analysis, computational, and design techniques developed for peptides. This review examines techniques to determine primary, secondary, and tertiary structure of peptides, and how they have been adapted to investigate peptoid structure. Computational models developed for peptides have been modified to predict the morphologies of peptoids and have increased in accuracy in recent years. The combination of in vitro and in silico techniques have led to secondary and tertiary structure design principles that mirror those for peptides. We then examine several important developments in peptoid applications inspired by peptides such as pharmaceuticals, catalysis, and protein-binding. A brief survey of alternative backbone structures and research investigating these peptidomimetics shows how the advancement of peptide and peptoid science has influenced the growth of numerous fields of study. As peptide, peptoid, and other peptidomimetic studies continue to advance, we will expect to see higher throughput structural analyses, greater computational accuracy and functionality, and wider application space that can improve human health, solve environmental challenges, and meet industrial needs. STATEMENT OF SIGNIFICANCE: Many historical, chemical, and functional relations draw a thread connecting peptides to their recent cognates, the "peptidomimetics". This review presents a comprehensive survey of this field by highlighting the width and relevance of these familial connections. In the first section, we examine the experimental and computational techniques originally developed for peptides and their morphing into a broader analytical and predictive toolbox. The second section presents an excursus of the structures and properties of prominent peptidomimetics, and how the expansion of the chemical and structural diversity has returned new exciting properties. The third section presents an overview of technological applications and new families of peptidomimetics. As the field grows, new compounds emerge with clear potential in medicine and advanced manufacturing.

In-solution enrichment identifies peptide inhibitors of protein-protein interactions

The use of competitive inhibitors to disrupt protein-protein interactions (PPIs) holds great promise for the treatment of disease. However, the discovery of high-affinity inhibitors can be a challenge. Here we report a platform for improving the affinity of peptide-based PPI inhibitors using non-canonical amino acids. The platform utilizes size exclusion-based enrichment from pools of synthetic peptides (1.5-4 kDa) and liquid chromatography-tandem mass spectrometry-based peptide sequencing to identify high-affinity binders to protein targets, without the need for 'reporter' or 'encoding' tags. Using this approach-which is inherently selective for high-affinity binders-we realized gains in affinity of up to ~100- or ~30-fold for binders to the oncogenic ubiquitin ligase MDM2 or HIV capsid protein C-terminal domain, which inhibit MDM2-p53 interaction or HIV capsid protein C-terminal domain dimerization, respectively. Subsequent macrocyclization of select MDM2 inhibitors rendered them cell permeable and cytotoxic toward cancer cells, demonstrating the utility of the identified compounds as functional PPI inhibitors.

Design of Peptoid-peptide Macrocycles to Inhibit the β-catenin TCF Interaction in Prostate Cancer

New chemical inhibitors of protein-protein interactions are needed to propel advances in molecular pharmacology. Peptoids are peptidomimetic oligomers with the capability to inhibit protein-protein interactions by mimicking protein secondary structure motifs. Here we report the in silico design of a macrocycle primarily composed of peptoid subunits that targets the β-catenin:TCF interaction. The β-catenin:TCF interaction plays a critical role in the Wnt signaling pathway which is over-activated in multiple cancers, including prostate cancer. Using the Rosetta suite of protein design algorithms, we evaluate how different macrocycle structures can bind a pocket on β-catenin that associates with TCF. The in silico designed macrocycles are screened in vitro using luciferase reporters to identify promising compounds. The most active macrocycle inhibits both Wnt and AR-signaling in prostate cancer cell lines, and markedly diminishes their proliferation. In vivo potential is demonstrated through a zebrafish model, in which Wnt signaling is potently inhibited.

An adaptive geometric search algorithm for macromolecular scaffold selection

A wide variety of protein and peptidomimetic design tasks require matching functional 3D motifs to potential oligomeric scaffolds. For example, during enzyme design, one aims to graft active-site patterns-typically consisting of 3-15 residues-onto new protein surfaces. Identifying protein scaffolds suitable for such active-site engraftment requires costly searches for protein folds that provide the correct side chain positioning to host the desired active site. Other examples of biodesign tasks that require similar fast exact geometric searches of potential side chain positioning include mimicking binding hotspots, design of metal binding clusters and the design of modular hydrogen binding networks for specificity. In these applications, the speed and scaling of geometric searches limits the scope of downstream design to small patterns. Here, we present an adaptive algorithm capable of searching for side chain take-off angles, which is compatible with an arbitrarily specified functional pattern and which enjoys substantive performance improvements over previous methods. We demonstrate this method in both genetically encoded (protein) and synthetic (peptidomimetic) design scenarios. Examples of using this method with the Rosetta framework for protein design are provided. Our implementation is compatible with multiple protein design frameworks and is freely available as a set of python scripts (https://github.com/JiangTian/adaptive-geometric-search-for-protein-design).

Web-accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE)

The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web-accessible Rosetta protocols. Here we describe a simplification of the ROSIE protocol specification format, one that permits easier implementation of Rosetta protocols. Whereas the previous format required creating multiple separate files in different locations, the new format allows specification of the protocol in a single file. This new, simplified protocol specification has more than doubled the number of Rosetta protocols available under ROSIE. These new applications include pK_a determination, lipid accessibility calculation, ribonucleic acid redesign, protein-protein docking, protein-small molecule docking, symmetric docking, antibody docking, cyclic toxin docking, critical binding peptide determination, and mapping small molecule binding sites. ROSIE is freely available to academic users at http://rosie.rosettacommons.org.

The Sulfur-Linked Analogue of O-GlcNAc (S-GlcNAc) Is an Enzymatically Stable and Reasonable Structural Surrogate for O-GlcNAc at the Peptide and Protein Levels

Synthetic proteins bearing site-specific posttranslational modifications have revolutionized our understanding of their biological functions in vitro and in vivo. One such modification, O-GlcNAcylation, is the dynamic addition of β-N-acetyl glucosamine to the side chains of serine and threonine residues of proteins, yet our understanding of the site-specific impact of O-GlcNAcylation remains difficult to evaluate in vivo because of the potential for enzymatic removal by endogenous O-GlcNAcase (OGA). Thioglycosides are generally perceived to be enzymatically stable structural mimics of O-GlcNAc; however, in vitro experiments with small-molecule GlcNAc thioglycosides have demonstrated that OGA can hydrolyze these linkages, indicating that S-linked β-N-acetyl glucosamine (S-GlcNAc) on peptides or proteins may not be completely stable. Here, we first develop a robust synthetic route to access an S-GlcNAcylated cysteine building block for peptide and protein synthesis. Using this modified amino acid, we establish that S-GlcNAc is an enzymatically stable surrogate for O-GlcNAcylation in its native protein setting. We also applied nuclear magnetic resonance spectroscopy and computational modeling to find that S-GlcNAc is an good structural mimic of O-GlcNAc. Finally, we demonstrate that site-specific S-GlcNAcylation results in biophysical characteristics that are the same as those of O-GlcNAc within the context of the protein α-synuclein. While this study is limited in focus to two model systems, these data suggest that S-GlcNAc broadly resembles O-GlcNAc and that it is indeed a stable analogue in the context of peptides and proteins.

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta's success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.

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.

Residue-centric modeling and design of saccharide and glycoconjugate structures

The RosettaCarbohydrate framework is a new tool for modeling a wide variety of saccharide and glycoconjugate structures. This report describes the development of the framework and highlights its applications. The framework integrates with established protocols within the Rosetta modeling and design suite, and it handles the vast complexity and variety of carbohydrate molecules, including branching and sugar modifications. To address challenges of sampling and scoring, RosettaCarbohydrate can sample glycosidic bonds, side-chain conformations, and ring forms, and it utilizes a glycan-specific term within its scoring function. Rosetta can work with standard PDB, GLYCAM, and GlycoWorkbench (.gws) file formats. Saccharide residue-specific chemical information is stored internally, permitting glycoengineering and design. Carbohydrate-specific applications described herein include virtual glycosylation, loop-modeling of carbohydrates, and docking of glyco-ligands to antibodies. Benchmarking data are presented and compared to other studies, demonstrating Rosetta's ability to predict glyco-ligand binding. The framework expands the tools available to glycoscientists and engineers. © 2016 Wiley Periodicals, Inc.

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.

Structural Diversity in the Type IV Pili of Multidrug-resistant Acinetobacter

Acinetobacter baumannii is a Gram-negative coccobacillus found primarily in hospital settings that has recently emerged as a source of hospital-acquired infections. A. baumannii expresses a variety of virulence factors, including type IV pili, bacterial extracellular appendages often essential for attachment to host cells. Here, we report the high resolution structures of the major pilin subunit, PilA, from three Acinetobacter strains, demonstrating that A. baumannii subsets produce morphologically distinct type IV pilin glycoproteins. We examine the consequences of this heterogeneity for protein folding and assembly as well as host-cell adhesion by Acinetobacter Comparisons of genomic and structural data with pilin proteins from other species of soil gammaproteobacteria suggest that these structural differences stem from evolutionary pressure that has resulted in three distinct classes of type IVa pilins, each found in multiple species.

PPII Helical Peptidomimetics Templated by Cation-π Interactions

Poly-proline type II (PPII) helical PXXP motifs are the recognition elements for a variety of protein-protein interactions that are critical for cellular signaling. Despite development of protocols for locking peptides into α-helical and β-strand conformations, there remains a lack of analogous methods for generating mimics of PPII helical structures. We describe herein a strategy to enforce PPII helical secondary structure in the 19-residue TrpPlexus miniature protein. Through sequence variation, we showed that a network of cation-π interactions could drive the formation of PPII helical conformations for both peptide and N-substituted glycine peptoid residues. The achievement of chemically diverse PPII helical scaffolds provides a new route towards discovering peptidomimetic inhibitors of protein-protein interactions mediated by PXXP motifs.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

Therapeutic Potential of Foldamers: From Chemical Biology Tools To Drug Candidates?

Over the past decade, foldamers have progressively emerged as useful architectures to mimic secondary structures of proteins. Peptidic foldamers, consisting of various amino acid based backbones, have been the most studied from a therapeutic perspective, while polyaromatic foldamers have barely evolved from their nascency and remain perplexing for medicinal chemists due to their poor drug-like nature. Despite these limitations, this compound class may still offer opportunities to study challenging targets or provide chemical biology tools. The potential of foldamer drug candidates reaching the clinic is still a stretch. Nevertheless, advances in the field have demonstrated their potential for the discovery of next generation therapeutics. In this perspective, the current knowledge of foldamers is reviewed in a drug discovery context. Recent advances in the early phases of drug discovery including hit finding, target validation, and optimization and molecular modeling are discussed. In addition, challenges and focus areas are debated and gaps highlighted.

Conformational preferences of peptide-peptoid hybrid oligomers

Peptomers are oligomeric molecules composed of both α-amino acids and N-substituted glycine monomers, thus creating a hybrid of peptide and peptoid units. Peptomers have been used in several applications such as antimicrobials, protease inhibitors, and antibody mimics. Despite the considerable promise of peptomers as chemically diverse molecular scaffolds, we know little about their conformational tendencies. This lack of knowledge limits the ability to implement computational approaches for peptomer design. Here we computationally evaluate the local structural propensities of the peptide-peptoid linkage. We find some general similarities between the peptide residue conformational preferences and the Ramachandran distribution of residues that precede proline in folded protein structures. However, there are notable differences. For example, several β-turn motifs are disallowed when the i+2 residue is also a peptoid monomer. Significantly, the lowest energy geometry, when dispersion forces are accounted for, corresponds to a "cis-Pro touch-turn" conformation, an unusual turn motif that has been observed at protein catalytic centers and binding sites. The peptomer touch-turn thus represents a useful design element for the construction of folded oligomers capable of molecular recognition and as modules in the assembly of structurally complex peptoid-protein hybrid macromolecules.

β-Peptide bundles: Design. Build. Analyze. Biosynthesize

Peptides containing β-amino acids are unique non-natural polymers known to assemble into protein-like tertiary and quaternary structures. When composed solely of β-amino acids, the structures formed, defined assemblies of 14-helices called β-peptide bundles, fold cooperatively in water solvent into unique and discrete quaternary assemblies that are highly thermostable, bind complex substrates and metal ion cofactors, and, in certain cases, catalyze chemical reactions. In this Perspective, we recount the design and elaboration of β-peptide bundles and provide an outlook on recent, unexpected discoveries that could influence research on β-peptides and β-peptide bundles (and β-amino acid-containing proteins) for decades to come.

Introduction to the Rosetta Special Collection

The Rosetta macromolecular modeling software is a versatile, rapidly developing set of tools that are now being routinely utilized to address state-of-the-art research challenges in academia and industrial research settings. A Rosetta Conference (RosettaCon) describing updates to the Rosetta source code is held annually. Every two years, a Rosetta Conference (RosettaCon) special collection describing the results presented at the annual conference by participating RosettaCommons labs is published by the Public Library of Science (PLOS). This is the introduction to the third RosettaCon 2014 Special Collection published by PLOS.

Insights into Peptoid Helix Folding Cooperativity from an Improved Backbone Potential

Peptoids (N-substituted oligoglycines) are biomimetic polymers that can fold into a variety of unique structural scaffolds. Peptoid helices, which result from the incorporation of bulky chiral side chains, are a key peptoid structural motif whose formation has not yet been accurately modeled in molecular simulations. Here, we report that a simple modification of the backbone φ-angle potential in GAFF is able to produce well-folded cis-amide helices of (S)-N-(1-phenylethyl)glycine (Nspe), consistent with experiment. We validate our results against both QM calculations and NMR experiments. For this latter task, we make quantitative comparisons to sparse NOE data using the Bayesian Inference of Conformational Populations (BICePs) algorithm, a method we have recently developed for this purpose. We then performed extensive REMD simulations of Nspe oligomers as a function of chain length and temperature to probe the molecular forces driving cooperative helix formation. Analysis of simulation data by Lifson-Roig helix-coil theory show that the modified potential predicts much more cooperative folding for Nspe helices. Unlike peptides, per-residue entropy changes for helix nucleation and extension are mostly positive, suggesting that steric bulk provides the main driving force for folding. We expect these results to inform future work aimed at predicting and designing peptoid peptidomimetics and tertiary assemblies of peptoid helices.

Enhancements to the Rosetta Energy Function Enable Improved Identification of Small Molecules that Inhibit Protein-Protein Interactions

Protein-protein interactions are among today’s most exciting and promising targets for therapeutic intervention. To date, identifying small-molecules that selectively disrupt these interactions has proven particularly challenging for virtual screening tools, since these have typically been optimized to perform well on more “traditional” drug discovery targets. Here, we test the performance of the Rosetta energy function for identifying compounds that inhibit protein interactions, when these active compounds have been hidden amongst pools of “decoys.” Through this virtual screening benchmark, we gauge the effect of two recent enhancements to the functional form of the Rosetta energy function: the new “Talaris” update and the “pwSHO” solvation model. Finally, we conclude by developing and validating a new weight set that maximizes Rosetta’s ability to pick out the active compounds in this test set. Looking collectively over the course of these enhancements, we find a marked improvement in Rosetta’s ability to identify small-molecule inhibitors of protein-protein interactions.

Sequence Programmable Peptoid Polymers for Diverse Materials Applications

Polymer sequence programmability is required for the diverse structures and complex properties that are achieved by native biological polymers, but efforts towards controlling the sequence of synthetic polymers are, by comparison, still in their infancy. Traditional polymers provide robust and chemically diverse materials, but synthetic control over their monomer sequences is limited. The modular and step-wise synthesis of peptoid polymers, on the other hand, allows for precise control over the monomer sequences, affording opportunities for these chains to fold into well-defined nanostructures. Hundreds of different side chains have been incorporated into peptoid polymers using efficient reaction chemistry, allowing for a seemingly infinite variety of possible synthetically accessible polymer sequences. Combinatorial discovery techniques have allowed the identification of functional polymers within large libraries of peptoids, and newly developed theoretical modeling tools specifically adapted for peptoids enable the future design of polymers with desired functions. Work towards controlling the three-dimensional structure of peptoids, from the conformation of the amide bond to the formation of protein-like tertiary structure, has and will continue to enable the construction of tunable and innovative nanomaterials that bridge the gap between natural and synthetic polymers.

New compstatin peptides containing N-terminal extensions and non-natural amino acids exhibit potent complement inhibition and improved solubility characteristics

Compstatin peptides are complement inhibitors that bind and inhibit cleavage of complement C3. Peptide binding is enhanced by hydrophobic interactions; however, poor solubility promotes aggregation in aqueous environments. We have designed new compstatin peptides derived from the W4A9 sequence (Ac-ICVWQDWGAHRCT-NH2, cyclized between C2 and C12), based on structural, computational, and experimental studies. Furthermore, we developed and utilized a computational framework for the design of peptides containing non-natural amino acids. These new compstatin peptides contain polar N-terminal extensions and non-natural amino acid substitutions at positions 4 and 9. Peptides with α-modified non-natural alanine analogs at position 9, as well as peptides containing only N-terminal polar extensions, exhibited similar activity compared to W4A9, as quantified via ELISA, hemolytic, and cell-based assays, and showed improved solubility, as measured by UV absorbance and reverse-phase HPLC experiments. Because of their potency and solubility, these peptides are promising candidates for therapeutic development in numerous complement-mediated diseases.

Structure-based inhibition of protein-protein interactions

Protein-protein interactions (PPIs) are emerging as attractive targets for drug design because of their central role in directing normal and aberrant cellular functions. These interactions were once considered "undruggable" because their large and dynamic interfaces make small molecule inhibitor design challenging. However, landmark advances in computational analysis, fragment screening and molecular design have enabled development of a host of promising strategies to address the fundamental molecular recognition challenge. An attractive approach for targeting PPIs involves mimicry of protein domains that are critical for complex formation. This approach recognizes that protein subdomains or protein secondary structures are often present at interfaces and serve as organized scaffolds for the presentation of side chain groups that engage the partner protein(s). Design of protein domain mimetics is in principle rather straightforward but is enabled by a host of computational strategies that provide predictions of important residues that should be mimicked. Herein we describe a workflow proceeding from interaction network analysis, to modeling a complex structure, to identifying a high-affinity sub-structure, to developing interaction inhibitors. We apply the design procedure to peptidomimetic inhibitors of Ras-mediated signaling.

Chemically modified peptide scaffolds target the CFTR-associated ligand PDZ domain

PDZ domains are protein-protein interaction modules that coordinate multiple signaling and trafficking pathways in the cell and that include active therapeutic targets for diseases such as cancer, cystic fibrosis, and addiction. Our previous work characterized a PDZ interaction that restricts the apical membrane half-life of the cystic fibrosis transmembrane conductance regulator (CFTR). Using iterative cycles of peptide-array and solution-binding analysis, we targeted the PDZ domain of the CFTR-Associated Ligand (CAL), and showed that an engineered peptide inhibitor rescues cell-surface expression of the most common CFTR disease mutation ΔF508. Here, we present a series of scaffolds containing chemically modifiable side chains at all non-motif positions along the CAL PDZ domain binding cleft. Concordant equilibrium dissociation constants were determined in parallel by fluorescence polarization, isothermal titration calorimetry, and surface plasmon resonance techniques, confirming robust affinity for each scaffold and revealing an enthalpically driven mode of inhibitor binding. Structural studies demonstrate a conserved binding mode for each peptide, opening the possibility of combinatorial modification. Finally, we diversified one of our peptide scaffolds with halogenated substituents that yielded modest increases in binding affinity. Overall, this work validates our approach and provides a stereochemical foundation for further CAL inhibitor design and screening.

A rotamer library to enable modeling and design of peptoid foldamers

Peptoids are a family of synthetic oligomers composed of N-substituted glycine units. Along with other "foldamer" systems, peptoid oligomer sequences can be predictably designed to form a variety of stable secondary structures. It is not yet evident if foldamer design can be extended to reliably create tertiary structure features that mimic more complex biomolecular folds and functions. Computational modeling and prediction of peptoid conformations will likely play a critical role in enabling complex biomimetic designs. We introduce a computational approach to provide accurate conformational and energetic parameters for peptoid side chains needed for successful modeling and design. We find that peptoids can be described by a "rotamer" treatment, similar to that established for proteins, in which the peptoid side chains display rotational isomerism to populate discrete regions of the conformational landscape. Because of the insufficient number of solved peptoid structures, we have calculated the relative energies of side-chain conformational states to provide a backbone-dependent (BBD) rotamer library for a set of 54 different peptoid side chains. We evaluated two rotamer library development methods that employ quantum mechanics (QM) and/or molecular mechanics (MM) energy calculations to identify side-chain rotamers. We show by comparison to experimental peptoid structures that both methods provide an accurate prediction of peptoid side chain placements in folded peptoid oligomers and at protein interfaces. We have incorporated our peptoid rotamer libraries into ROSETTA, a molecular design package previously validated in the context of protein design and structure prediction.

Rational design of topographical helix mimics as potent inhibitors of protein-protein interactions

Protein–protein interactions encompass large surface areas, but often a handful of key residues dominate the binding energy landscape. Rationally designed small molecule scaffolds that reproduce the relative positioning and disposition of important binding residues, termed “hotspot residues”, have been shown to successfully inhibit specific protein complexes. Although this strategy has led to development of novel synthetic inhibitors of protein complexes, often direct mimicry of natural amino acid residues does not lead to potent inhibitors. Experimental screening of focused compound libraries is used to further optimize inhibitors but the number of possible designs that can be efficiently synthesized and experimentally tested in academic settings is limited. We have applied the principles of computational protein design to optimization of nonpeptidic helix mimics as ligands for protein complexes. We describe the development of computational tools to design helix mimetics from canonical and noncanonical residue libraries and their application to two therapeutically important protein–protein interactions: p53-MDM2 and p300-HIF1α. The overall study provides a streamlined approach for discovering potent peptidomimetic inhibitors of protein–protein interactions.

Computational screening and selection of cyclic peptide hairpin mimetics by molecular simulation and kinetic network models

Designing peptidomimetic compounds to have a preorganized structure in solution is highly nontrivial. To show how simulation-based approaches can help speed this process, we performed an extensive simulation study of designed cyclic peptide mimics of a β-hairpin from bacterial protein LapD involved in a protein-protein interaction (PPI) pertinent to bacterial biofilm formation. We used replica exchange molecular dynamics (REMD) simulation to screen 20 covalently cross-linked designs with varying stereochemistry and selected the most favorable of these for massively parallel simulation on Folding@home in explicit solvent. Markov state models (MSMs) built from the trajectory data reveal how subtle chemical modifications can have a significant effect on conformational populations, leading to the overall stabilization of the target structure. In particular, we identify a key steric interaction between a methyl substituent and a valine side chain that acts to allosterically shift population between native and near-native states, which could be exploited in future designs. Visualization of this mechanism is aided considerably by the tICA method, which identifies degrees of freedom most important in slow conformational transitions. The combination of quantitative detail and human comprehension provided by MSMs suggests such approaches will be increasingly useful for design.

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.

Serverification of molecular modeling applications: the Rosetta Online Server that Includes Everyone (ROSIE)

The Rosetta molecular modeling software package provides experimentally tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of proteins, nucleic acids, and a growing number of non-natural polymers. Despite its free availability to academic users and improving documentation, use of Rosetta has largely remained confined to developers and their immediate collaborators due to the code's difficulty of use, the requirement for large computational resources, and the unavailability of servers for most of the Rosetta applications. Here, we present a unified web framework for Rosetta applications called ROSIE (Rosetta Online Server that Includes Everyone). ROSIE provides (a) a common user interface for Rosetta protocols, (b) a stable application programming interface for developers to add additional protocols, (c) a flexible back-end to allow leveraging of computer cluster resources shared by RosettaCommons member institutions, and (d) centralized administration by the RosettaCommons to ensure continuous maintenance. This paper describes the ROSIE server infrastructure, a step-by-step 'serverification' protocol for use by Rosetta developers, and the deployment of the first nine ROSIE applications by six separate developer teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated by the number and diversity of these applications, ROSIE offers a general and speedy paradigm for serverification of Rosetta applications that incurs negligible cost to developers and lowers barriers to Rosetta use for the broader biological community. ROSIE is available at http://rosie.rosettacommons.org.