Accurate de novo design of hyperstable constrained peptides

Design of a Novel Peptide with Esterolytic Activity toward PET by Mimicking the Catalytic Motif of Serine Hydrolases

Serine hydrolases have become increasingly important for recycling PET plastics. However, their properties are inherently constrained by their 3D structure, which in turn limits the conditions for their application. Considering peptides as catalysts for industrial depolymerization processes can help us to escape some of these limitations. In this article, a 25 amino acid thermostable peptide, HSH-25, was designed to depolymerize PET. The peptide incorporates a His-Ser-His motif, inspired by the catalytic triad found in the serine hydrolase family, into a β-hairpin fold. Stability of the fold was investigated by molecular dynamics simulations. Esterolytic activity of the peptide toward model substrates was detected within a pH range from pH 7 to pH 9.5. Degradation of polymeric PET substrates was confirmed by atomic force microscopy imaging on spin-coated PET thin films.

Protocol for Designing De Novo Noncanonical Peptide Binders in OSPREY

D-peptides, the mirror image of canonical L-peptides, offer numerous biological advantages that make them effective therapeutics. This article details how to use DexDesign, the newest OSPREY-based algorithm, for designing these D-peptides de novo. OSPREY physics-based models precisely mimic energy-equivariant reflection operations, enabling the generation of D-peptide scaffolds from L-peptide templates. Due to the scarcity of D-peptide:L-protein structural data, DexDesign calls a geometric hashing algorithm, Method of Accelerated Search for Tertiary Ensemble Representatives, as a subroutine to produce a synthetic structural dataset. DexDesign enables mixed-chirality designs with a new user interface and also reduces the conformation and sequence search space using three new design techniques: Minimum Flexible Set, Inverse Alanine Scanning, and K*-based Mutational Scanning.

Modulation of the pharmacokinetics of soluble ACE2 decoy receptors through glycosylation

The Spike of SARS-CoV-2 recognizes a transmembrane protease, angiotensin-converting enzyme 2 (ACE2), on host cells to initiate infection. Soluble derivatives of ACE2, in which Spike affinity is enhanced and the protein is fused to Fc of an immunoglobulin, are potent decoy receptors that reduce disease in animal models of COVID-19. Mutations were introduced into an ACE2 decoy receptor, including adding custom N-glycosylation sites and a cavity-filling substitution together with Fc modifications, which increased the decoy's catalytic activity and provided small to moderate enhancements of pharmacokinetics following intravenous and subcutaneous administration in humanized FcRn mice. Most prominently, sialylation of native glycans increases exposures by orders of magnitude, and the optimized decoy is therapeutically efficacious in a mouse COVID-19 model. Ultimately, an engineered and highly sialylated decoy receptor produced using methods suitable for manufacture of representative drug substance has high exposure with a 5- to 9-day half-life. Finally, peptide epitopes at mutated sites in the decoys generally have low binding to common HLA class II alleles and the predicted immunogenicity risk is low. Overall, glycosylation is a critical molecular attribute of ACE2 decoy receptors and modifications that combine tighter blocking of Spike with enhanced pharmacokinetics elevate this class of molecules as viable drug candidates.

Triscysteine disulfide-directing motifs enabling design and discovery of multicyclic peptide binders

Peptides are valuable for therapeutic development, with multicyclic peptides showing promise in mimicking antigen-binding potency of antibodies. However, our capability to engineer multicyclic peptide scaffolds, particularly for the construction of large combinatorial libraries, is still limited. Here, we study the interplay of disulfide pairing between three biscysteine motifs, and designed a range of triscysteine motifs with unique disulfide-directing capability for regulating the oxidative folding of multicyclic peptides. We demonstrate that incorporating these motifs into random sequences allows the design of disulfide-directed multicyclic peptide (DDMP) libraries with up to four disulfide bonds, which have been applied for the successful discovery of peptide binders with nanomolar affinity to several challenging targets. This study encourages the use of more diverse disulfide-directing motifs for creating multicyclic peptide libraries and opens an avenue for discovering functional peptides in sequence and structural space beyond existing peptide scaffolds, potentially advancing the field of peptide drug discovery.

Mixed Stereochemistry Macrocycle Acts as a Helix-Stabilizing Peptide N-Cap

Interactions between proteins and α-helical peptides have been the focus of drug discovery campaigns. However, the large interfaces formed between multiple turns of an α-helix and a binding protein represent a significant challenge to inhibitor discovery. Modified peptides featuring helix-stabilizing macrocycles have shown promise as inhibitors of these interactions. Here, we tested the ability of N-terminal to side-chain thioether-cyclized peptides to inhibit the α-helix binding protein Mcl-1, by screening a trillion-scale library. The enriched peptides were lariats featuring a small, four-amino-acid N-terminal macrocycle followed by a short linear sequence that resembled the natural α-helical Mcl-1 ligands. These "Heliats" (helical lariats) bound Mcl-1 with tens of nM affinity, and inhibited the interaction between Mcl-1 and a natural peptide ligand. Macrocyclization was found to stabilize α-helical structures and significantly contribute to affinity and potency. Yet, the 2nd and 3rd positions within the macrocycle were permissible to sequence variation, so that a minimal macrocyclic motif, of an N-acetylated d-phenylalanine at the 1st position thioether connected to a cysteine at the 4th, could be grafted into a range of peptides and stabilize helical conformations. We found that d-stereochemistry is more helix-stabilizing than l- at the 1st position in the motif, as the d-amino acid can utilize polyproline II torsional angles that allow for more optimal intrachain hydrogen bonding. This mixed stereochemistry macrocyclic N-cap is synthetically accessible, requiring only minor modifications to standard solid-phase peptide synthesis, and its compatibility with peptide screening can provide ready access to helix-focused peptide libraries for de novo inhibitor discovery.

Generative β-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

Overexpression of a Designed Mutant Oxyanion Binding Protein ModA/WtpA in Acidithiobacillus ferrooxidans for the Low pH Recovery of Molybdenum and Rhenium

Molybdenum and rhenium are critically important metals for a number of emerging technologies. We identified and characterized a molybdenum/tungsten transport protein (ModA/WtpA) of Acidithiobacillus ferrooxidans and demonstrated the binding of tungstate, molybdate, and chromate. We used computational design to expand the binding capabilities of the protein to include perrhenate. A disulfide bond was engineered into the binding pocket of ModA/WtpA to introduce a more favorable geometric coordination and surface charge distribution for oxyanion binding. The mutant protein experimentally demonstrated a 2-fold higher binding affinity for molybdate and 6-fold higher affinity for perrhenate. The overexpression of the wild-type and mutant ModA/WtpA proteins in A. ferrooxidans cells enhanced the innate tungstate, molybdate, and chromate binding capacities of the cells to up to 2-fold higher. In addition, the engineered cells expressing the mutant protein exhibited enhanced perrhenate binding, showing 5-fold and 2-fold higher binding capacities compared to the wild-type and ModA/WtpA-overexpressing cells, respectively. Furthermore, the engineered cell lines enhanced biocorrosion of stainless steel as well as the recovered valuable metals from an acidic wastewater generated from molybdenite processing. The improved binding efficiency for the oxyanion metals, along with the high selectivity over nontargeted metals under mixed metal environments, highlights the potential value of the engineered strains for practical microbial metal reclamation under low pH conditions.

Single-residue effects on the behavior of a nascent polypeptide chain inside the ribosome exit tunnel

Nascent polypeptide chains (NCs) are extruded from the ribosome through an exit tunnel (ET) traversing the large ribosomal subunit. The ET's irregular and chemically complex wall allows for various NC-ET interactions. Translational arrest peptides (APs) bind in the ET to induce translational arrest, a property that can be exploited to study NC-ET interactions by Force Profile Analysis (FPA). We employed FPA and molecular dynamics (MD) simulations to investigate how individual residues placed in a glycine-serine repeat segment within an AP-stalled NC interact with the ET to exert a pulling force on the AP and release stalling. Our results indicate that large and hydrophobic residues generate a pulling force on the NC when placed ≳10 residues away from the peptidyl transfer center (PTC). Moreover, an asparagine placed 12 residues from the PTC makes a specific stabilizing interaction with the tip of ribosomal protein uL22 that reduces the pulling force on the NC, while a lysine or leucine residue in the same position increases the pulling force. Finally, the MD simulations suggest how the Mannheimia succiniproducens SecM AP interacts with the ET to promote translational stalling.

Unified and Versatile Multiplex Platform for Expedited Method Development and Comprehensive Characterization of Therapeutic Peptides

Agile analytical approaches are needed for fast and comprehensive characterization of peptide drug candidates. In this study, a unified and versatile multiplex platform was developed to expedite method development and enable the routine determination of multiple quality attributes simultaneously. The platform integrates the automation of size exclusion chromatography (SEC), reversed phase liquid chromatography coupled to reversed phase liquid chromatography (RPLC-RPLC), and hydrophilic interaction liquid chromatography hyphenated to charged aerosol detection (HILIC-CAD). Various therapeutic peptide constructs, including macrocyclic peptides and disulfide constrained peptides, across different lots were studied. The effect of the mobile phase acetonitrile content on the impurity profiles was systematically studied using two SEC columns. A prototype MaxPeak Premier SEC 125 Å column packed with BEH PEO particles achieved the separation of impurities (>2.0% area), whereas no impurities could be observed with an ACQUITY UPLC Protein BEH SEC 125 Å column packed with BEH diol particles. Comprehensive impurity profiling and expedited method development was performed utilizing RPLC-RPLC. Each peptide was analyzed by a combination of 12 conditions in the second dimension, using four columns with octadecyl, phenyl-hexyl, and cyano bonded phases, and three mobile phases with various solvents, modifiers, and pH compositions. Additionally, a HILIC-CAD method was developed for the quantification of TFA, commonly present in peptide products.

SANS investigation of fungal loosenins reveal substrate dependent impacts of protein 1 action on inter-fibril distance and packing order of cellulosic substrates

BACKGROUND

Microbial expansin-related proteins include fungal loosenins, which have been previously shown to disrupt cellulose networks and enhance the enzymatic conversion of cellulosic substrates. Despite showing beneficial impacts to cellulose processing, detailed characterization of cellulosic materials after loosenin treatment is lacking. In this study, small-angle neutron scattering (SANS) was used to investigate the effects of three recombinantly produced loosenins that originate from Phanerochaete carnosa, PcaLOOL7, PcaLOOL9, and PcaLOOL12, on the organization of holocellulose preparations from Eucalyptus and Spruce wood samples.

RESULTS

Whereas the SANS analysis of Spruce holocellulose revealed an increase in interfibril spacing of neighboring cellulose microfibrils following treatment with PcaLOOL12 and to a lesser extent PcaLOOL7, the analysis of Eucalyptus holocellulose revealed a reduction in packing number following treatment with PcaLOOL12 and to a lesser extent PcaLOOL9. Parallel SEC-SAXS characterization of PcaLOOL7, PcaLOOL9, and PcaLOOL12 indicated the proteins likely function as monomers; moreover, all appear to retain a flexible disordered N-terminus and folded C-terminal region. The comparatively high impact of PcaLOOL12 motivated its NMR structural characterization, revealing a double-psi b-barrel (DPBB) domain surrounded by three alpha-helices - the largest nestled against the DPBB core and the other two part of loops extending from the core.

CONCLUSIONS

The SANS analysis of PcaLOOL action on holocellulose samples confirms their ability to disrupt cellulose fiber networks and suggests a progression from reducing microfibril packing to increasing interfibril distance. The most impactful PcaLOOL, PcaLOOL12, was previously observed to be the most highly expressed loosenin in P. carnosa. Its structural characterization herein reveals its stabilization through two disulfide linkages, and an extended N-terminal region distal to a negatively charged and surface accessible polysaccharide binding groove.

Accurate de novo design of heterochiral protein-protein interactions

Abiotic D-proteins that selectively bind to natural L-proteins have gained significant biotechnological interest. However, the underlying structural principles governing such heterochiral protein-protein interactions remain largely unknown. In this study, we present the de novo design of D-proteins consisting of 50-65 residues, aiming to target specific surface regions of L-proteins or L-peptides. Our designer D-protein binders exhibit nanomolar affinity toward an artificial L-peptide, as well as two naturally occurring proteins of therapeutic significance: the D5 domain of human tropomyosin receptor kinase A (TrkA) and human interleukin-6 (IL-6). Notably, these D-protein binders demonstrate high enantiomeric specificity and target specificity. In cell-based experiments, designer D-protein binders effectively inhibited the downstream signaling of TrkA and IL-6 with high potency. Moreover, these binders exhibited remarkable thermal stability and resistance to protease degradation. Crystal structure of the designed heterochiral D-protein-L-peptide complex, obtained at a resolution of 2.0 Å, closely resembled the design model, indicating that the computational method employed is highly accurate. Furthermore, the crystal structure provides valuable information regarding the interactions between helical L-peptides and D-proteins, particularly elucidating a novel mode of heterochiral helix-helix interactions. Leveraging the design of D-proteins specifically targeting L-peptides or L-proteins opens up avenues for systematic exploration of the mirror-image protein universe, paving the way for a diverse range of applications.

Reinvigoration of cytotoxic T lymphocytes in microsatellite instability-high colon adenocarcinoma through lysosomal degradation of PD-L1

Compensation and intracellular storage of PD-L1 may compromise the efficacy of antibody drugs targeting the conformational blockade of PD1/PD-L1 on the cell surface. Alternative therapies aiming to reduce the overall cellular abundance of PD-L1 thus might overcome resistance to conventional immune checkpoint blockade. Here we show by bioinformatics analysis that colon adenocarcinoma (COAD) with high microsatellite instability (MSI-H) presents the most promising potential for this therapeutic intervention, and that overall PD-L1 abundance could be controlled via HSC70-mediated lysosomal degradation. Proteomic and metabolomic analyses of mice COAD with MSI-H in situ unveil a prominent acidic tumor microenvironment. To harness these properties, an artificial protein, IgP β, is engineered using pH-responsive peptidic foldamers. This features customized peptide patterns and designed molecular function to facilitate interaction between neoplastic PD-L1 and HSC70. IgP β effectively reduces neoplastic PD-L1 levels via HSC70-mediated lysosomal degradation, thereby persistently revitalizing the action of tumor-infiltrating CD8 + T cells. Notably, the anti-tumor effect of lysosomal-degradation-based therapy surpasses that of antibody-based immune checkpoint blockade for MSI-H COAD in multiple mouse models. The presented strategy expands the use of peptidic foldamers in discovering artificial protein drugs for targeted cancer immunotherapy.

Preclinical proof of principle for orally delivered Th17 antagonist miniproteins

Interleukin (IL)-23 and IL-17 are well-validated therapeutic targets in autoinflammatory diseases. Antibodies targeting IL-23 and IL-17 have shown clinical efficacy but are limited by high costs, safety risks, lack of sustained efficacy, and poor patient convenience as they require parenteral administration. Here, we present designed miniproteins inhibiting IL-23R and IL-17 with antibody-like, low picomolar affinities at a fraction of the molecular size. The minibinders potently block cell signaling in vitro and are extremely stable, enabling oral administration and low-cost manufacturing. The orally administered IL-23R minibinder shows efficacy better than a clinical anti-IL-23 antibody in mouse colitis and has a favorable pharmacokinetics (PK) and biodistribution profile in rats. This work demonstrates that orally administered de novo-designed minibinders can reach a therapeutic target past the gut epithelial barrier. With high potency, gut stability, and straightforward manufacturability, de novo-designed minibinders are a promising modality for oral biologics.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Accurate Structure Prediction for Cyclic Peptides Containing Proline Residues with High-Temperature Molecular Dynamics

Cyclic peptides (CPs) are emerging as promising drug candidates. Numerous natural CPs and their analogs are effective therapeutics against various diseases. Notably, many of them contain peptidyl cis-prolyl bonds. Due to the high rotational barrier of peptide bonds, conventional molecular dynamics simulations struggle to effectively sample the cis/trans-isomerization of peptide bonds. Previous studies have highlighted the high accuracy of the residue-specific force field (RSFF) and the high sampling efficiency of high-temperature molecular dynamics (high-T MD). Herein, we propose a protocol that combines high-T MD with RSFF2C and a recently developed reweighting method based on probability densities for accurate structure prediction of proline-containing CPs. Our method successfully predicted 19 out of 23 CPs with the backbone rmsd < 1.0 Å compared to X-ray structures. Furthermore, we performed high-T MD and density reweighting on the sunflower trypsin inhibitor (SFTI-1)/trypsin complex to demonstrate its applicability in studying CP-complexes containing cis-prolines. Our results show that the conformation of SFTI-1 in aqueous solution is consistent with its bound conformation, potentially facilitating its binding.

Design of amyloidogenic peptide traps

Segments of proteins with high β-strand propensity can self-associate to form amyloid fibrils implicated in many diseases. We describe a general approach to bind such segments in β-strand and β-hairpin conformations using de novo designed scaffolds that contain deep peptide-binding clefts. The designs bind their cognate peptides in vitro with nanomolar affinities. The crystal structure of a designed protein-peptide complex is close to the design model, and NMR characterization reveals how the peptide-binding cleft is protected in the apo state. We use the approach to design binders to the amyloid-forming proteins transthyretin, tau, serum amyloid A1 and amyloid β1-42 (Aβ42). The Aβ binders block the assembly of Aβ fibrils as effectively as the most potent of the clinically tested antibodies to date and protect cells from toxic Aβ42 species.

De novo design of buttressed loops for sculpting protein functions

In natural proteins, structured loops have central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that designs tandem repeat proteins with structured loops (9-14 residues) buttressed by extensive hydrogen bonding interactions. Experimental characterization shows that the designs are monodisperse, highly soluble, folded and thermally stable. Crystal structures are in close agreement with the design models, with the loops structured and buttressed as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute generally to efforts to design new protein functions.

Systematic computer-aided disulfide design as a general strategy to stabilize prefusion class I fusion proteins

Numerous enveloped viruses, such as coronaviruses, influenza, and respiratory syncytial virus (RSV), utilize class I fusion proteins for cell entry. During this process, the proteins transition from a prefusion to a postfusion state, undergoing substantial and irreversible conformational changes. The prefusion conformation has repeatedly shown significant potential in vaccine development. However, the instability of this state poses challenges for its practical application in vaccines. While non-native disulfides have been effective in maintaining the prefusion structure, identifying stabilizing disulfide bonds remains an intricate task. Here, we present a general computational approach to systematically identify prefusion-stabilizing disulfides. Our method assesses the geometric constraints of disulfide bonds and introduces a ranking system to estimate their potential in stabilizing the prefusion conformation. We hypothesized that disulfides restricting the initial stages of the conformational switch could offer higher stability to the prefusion state than those preventing unfolding at a later stage. The implementation of our algorithm on the RSV F protein led to the discovery of prefusion-stabilizing disulfides that supported our hypothesis. Furthermore, the evaluation of our top design as a vaccine candidate in a cotton rat model demonstrated robust protection against RSV infection, highlighting the potential of our approach for vaccine development.

Protein semisynthesis reveals plasticity in HECT E3 ubiquitin ligase mechanisms

Lys ubiquitination is catalysed by E3 ubiquitin ligases and is central to the regulation of protein stability and cell signalling in normal and disease states. There are gaps in our understanding of E3 mechanisms, and here we use protein semisynthesis, chemical rescue, microscale thermophoresis and other biochemical approaches to dissect the role of catalytic base/acid function and conformational interconversion in HECT-domain E3 catalysis. We demonstrate that there is plasticity in the use of the terminal side chain or backbone carboxylate for proton transfer in HECT E3 ubiquitin ligase reactions, with yeast Rsp5 orthologues appearing to be possible evolutionary intermediates. We also show that the HECT-domain ubiquitin covalent intermediate appears to eject the E2 conjugating enzyme, promoting catalytic turnover. These findings provide key mechanistic insights into how protein ubiquitination occurs and provide a framework for understanding E3 functions and regulation.

Biospecific Chemistry for Covalent Linking of Biomacromolecules

Interactions among biomacromolecules, predominantly noncovalent, underpin biological processes. However, recent advancements in biospecific chemistry have enabled the creation of specific covalent bonds between biomolecules, both in vitro and in vivo. This Review traces the evolution of biospecific chemistry in proteins, emphasizing the role of genetically encoded latent bioreactive amino acids. These amino acids react selectively with adjacent natural groups through proximity-enabled bioreactivity, enabling targeted covalent linkages. We explore various latent bioreactive amino acids designed to target different protein residues, ribonucleic acids, and carbohydrates. We then discuss how these novel covalent linkages can drive challenging protein properties and capture transient protein-protein and protein-RNA interactions in vivo. Additionally, we examine the application of covalent peptides as potential therapeutic agents and site-specific conjugates for native antibodies, highlighting their capacity to form stable linkages with target molecules. A significant focus is placed on proximity-enabled reactive therapeutics (PERx), a pioneering technology in covalent protein therapeutics. We detail its wide-ranging applications in immunotherapy, viral neutralization, and targeted radionuclide therapy. Finally, we present a perspective on the existing challenges within biospecific chemistry and discuss the potential avenues for future exploration and advancement in this rapidly evolving field.

Heuristic energy-based cyclic peptide design

Rational computational design is crucial to the pursuit of novel drugs and therapeutic agents. Meso-scale cyclic peptides, which consist of 7-40 amino acid residues, are of particular interest due to their conformational rigidity, binding specificity, degradation resistance, and potential cell permeability. Because there are few natural cyclic peptides, de novo design involving non-canonical amino acids is a potentially useful goal. Here, we develop an efficient pipeline (CyclicChamp) for cyclic peptide design. After converting the cyclic constraint into an error function, we employ a variant of simulated annealing to search for low-energy peptide backbones while maintaining peptide closure. Compared to the previous random sampling approach, which was capable of sampling conformations of cyclic peptides of up to 14 residues, our method both greatly accelerates the computation speed for sampling conformations of small macrocycles (ca. 7 residues), and addresses the high-dimensionality challenge that large macrocycle designs often encounter. As a result, CyclicChamp makes conformational sampling tractable for 15- to 24-residue cyclic peptides, thus permitting the design of macrocycles in this size range. Microsecond-length molecular dynamics simulations on the resulting 15, 20, and 24 amino acid cyclic designs identify trajectories with kinetic stability. To test their thermodynamic stability, we perform additional replica exchange molecular dynamics simulations and generate free energy surfaces. Two 15-residue designs and one 20-residue design emerge as promising candidates, along with one viable 24-residue candidate.

Secondary Structure Stabilization of Macrocyclic Antimicrobial Peptides via Cross-Link Swapping

Lactam cross-links have been employed to stabilize the helical secondary structure and enhance the activity and physiological stability of antimicrobial peptides; however, stabilization of β-sheets via lactamization has not been observed. In the present study, lactams between the side chains of C- and N-terminal residues have been used to stabilize the β-sheet conformation in a short ten-residue analogue of chicken angiogenin-4. Designed using a combination of molecular dynamics simulations and Markov state models, the lactam cross-linked peptides are shown to adopt stabilized β-sheet conformations consistent with simulated structures. Replacement of the peptide side-chain Cys-Cys disulfide by a lactam cross-link enhanced the broad-spectrum antibacterial activity compared to the parent peptide and exhibited greater propensity to induce proinflammatory activity in macrophages. The combination of molecular simulations and conformational and biological analyses of the synthetic peptides provides a useful paradigm for the rational design of therapeutically active peptides with constrained β-sheet structures.

Toward high-resolution modeling of small molecule-ion channel interactions

Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two RosettaLigand docking methods, RosettaLigand and GALigandDock, on the structures of known ligand-cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1-2 Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand-ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state-specific interactions of the ion channel, and inherent receptor site flexibility that could influence ligand binding.

Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

Expansive discovery of chemically diverse structured macrocyclic oligoamides

Small macrocycles with four or fewer amino acids are among the most potent natural products known, but there is currently no way to systematically generate such compounds. We describe a computational method for identifying ordered macrocycles composed of alpha, beta, gamma, and 17 other amino acid backbone chemistries, which we used to predict 14.9 million closed cycles composed of >42,000 monomer combinations. We chemically synthesized 18 macrocycles predicted to adopt single low-energy states and determined their x-ray or nuclear magnetic resonance structures; 15 of these were very close to the design models. We illustrate the therapeutic potential of these macrocycle designs by developing selective inhibitors of three protein targets of current interest. By opening up a vast space of readily synthesizable drug-like macrocycles, our results should considerably enhance structure-based drug design.

Design of Functional Globular β-Sheet Miniproteins

The design of discrete β-sheet peptides is far less advanced than e. g. the design of α-helical peptides. The reputation of β-sheet peptides as being poorly soluble and aggregation-prone often hinders active design efforts. Here, we show that this reputation is unfounded. We demonstrate this by looking at the β-hairpin and WW domain. Their structure and folding have been extensively studied and they have long served as model systems to investigate protein folding and folding kinetics. The resulting fundamental understanding has led to the development of hyperstable β-sheet scaffolds that fold at temperatures of 100 °C or high concentrations of denaturants. These have been used to design functional miniproteins with protein or nucleic acid binding properties, in some cases with such success that medical applications are conceivable. The β-sheet scaffolds are not always completely rigid, but can be specifically designed to respond to changes in pH, redox potential or presence of metal ions. Some engineered β-sheet peptides also exhibit catalytic properties, although not comparable to those of natural proteins. Previous reviews have focused on the design of stably folded and non-aggregating β-sheet sequences. In our review, we now also address design strategies to obtain functional miniproteins from β-sheet folding motifs.

The structural landscape of the immunoglobulin fold by large-scale de novo design

De novo designing immunoglobulin-like frameworks that allow for functional loop diversification shows great potential for crafting antibody-like scaffolds with fully customizable structures and functions. In this work, we combined de novo parametric design with deep-learning methods for protein structure prediction and design to explore the structural landscape of 7-stranded immunoglobulin domains. After screening folding of nearly 4 million designs, we have assembled a structurally diverse library of ~50,000 immunoglobulin domains with high-confidence AlphaFold2 predictions and structures diverging from naturally occurring ones. The designed dataset enabled us to identify structural requirements for the correct folding of immunoglobulin domains, shed light on β-sheet-β-sheet rotational preferences and how these are linked to functional properties. Our approach eliminates the need for preset loop conformations and opens the route to large-scale de novo design of immunoglobulin-like frameworks.

HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.

Cyclization Scaffolding for Improved Vaccine Immunogen Stability: Application to Tau Protein in Alzheimer's Disease

Effective scaffolding of immunogens is crucial for generating conformationally selective antibodies through active immunization, particularly in the treatment of protein misfolding diseases such as Alzheimer's and Parkinson's disease. Previous computational work has revealed that a disorder-prone region of the tau protein, when in a stacked form, is predicted to structurally resemble a small, soluble protofibril, having conformational properties similar to those of experimental in vitro tau oligomers. Such an oligomeric structural mimic has the potential to serve as a vaccine immunogen design for Alzheimer's disease. In this study, we developed a cyclization scaffolding method in Rosetta, in which multiple cyclic peptides are stacked into a protofibril. Cyclization results in significant stabilization of protofibril-like structures by constraining the conformational space. Applying this method to the disorder-prone region of the tau fibril, we evaluated the metastability of the cyclized tau immunogen using molecular dynamics simulations, and we identified sequences of two cyclic constructs having high metastability in the protofibril. We then assessed their thermodynamic stability by computing the free energy required to separate a distal chain from the rest of the stacked structure. Our computational results, based on molecular dynamics simulations and free energy calculations, demonstrate that two cyclized constructs, cyclo-(VKSEKLDFKDRVQSKIFyN) and cyclo-(VKSEKLDFKDRVQSKIYvG) (lowercase letters indicate d-form amino acids), possess significantly increased thermodynamic stability in the protofibril over an uncyclized linear construct VKSEKLDFKDRVQSKI. The cyclization scaffolding approach proposed here holds promise as a means to effectively design immunogens for protein misfolding diseases, particularly those involving liposome-conjugated peptide constructs.

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.

Modeling membrane geometries implicitly in Rosetta

Interactions between membrane proteins (MPs) and lipid bilayers are critical for many cellular functions. In the Rosetta molecular modeling suite, the implicit membrane energy function is based on a "slab" model, which represent the membrane as a flat bilayer. However, in nature membranes often have a curvature that is important for function and/or stability. Even more prevalent, in structural biology research MPs are reconstituted in model membrane systems such as micelles, bicelles, nanodiscs, or liposomes. Thus, we have modified the existing membrane energy potentials within the RosettaMP framework to allow users to model MPs in different membrane geometries. We show that these modifications can be utilized in core applications within Rosetta such as structure refinement, protein-protein docking, and protein design. For MP structures found in curved membranes, refining these structures in curved, implicit membranes produces higher quality models with structures closer to experimentally determined structures. For MP systems embedded in multiple membranes, representing both membranes results in more favorable scores compared to only representing one of the membranes. Modeling MPs in geometries mimicking the membrane model system used in structure determination can improve model quality and model discrimination.

DexDesign: A new OSPREY-based algorithm for designing de novo D-peptide inhibitors

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan, which enable exponential reductions in the size of the peptide sequence search space. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a new framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead therapeutic candidates. We provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

A general computational design strategy for stabilizing viral class I fusion proteins

Many pathogenic viruses rely on class I fusion proteins to fuse their viral membrane with the host cell membrane. To drive the fusion process, class I fusion proteins undergo an irreversible conformational change from a metastable prefusion state to an energetically more stable postfusion state. Mounting evidence underscores that antibodies targeting the prefusion conformation are the most potent, making it a compelling vaccine candidate. Here, we establish a computational design protocol that stabilizes the prefusion state while destabilizing the postfusion conformation. With this protocol, we stabilize the fusion proteins of the RSV, hMPV, and SARS-CoV-2 viruses, testing fewer than a handful of designs. The solved structures of these designed proteins from all three viruses evidence the atomic accuracy of our approach. Furthermore, the humoral response of the redesigned RSV F protein compares to that of the recently approved vaccine in a mouse model. While the parallel design of two conformations allows the identification of energetically sub-optimal positions for one conformation, our protocol also reveals diverse molecular strategies for stabilization. Given the clinical significance of viruses using class I fusion proteins, our algorithm can substantially contribute to vaccine development by reducing the time and resources needed to optimize these immunogens.

Design of complicated all-α protein structures

A wide range of de novo protein structure designs have been achieved, but the complexity of naturally occurring protein structures is still far beyond these designs. Here, to expand the diversity and complexity of de novo designed protein structures, we sought to develop a method for designing 'difficult-to-describe' α-helical protein structures composed of irregularly aligned α-helices like globins. Backbone structure libraries consisting of a myriad of α-helical structures with five or six helices were generated by combining 18 helix-loop-helix motifs and canonical α-helices, and five distinct topologies were selected for de novo design. The designs were found to be monomeric with high thermal stability in solution and fold into the target topologies with atomic accuracy. This study demonstrated that complicated α-helical proteins are created using typical building blocks. The method we developed will enable us to explore the universe of protein structures for designing novel functional proteins.

DexDesign: an OSPREY-based algorithm for designing de novo D-peptide inhibitors

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead inhibitors. We also provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

Computational Design of Peptide Assemblies

With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.

Tpgen: a language model for stable protein design with a specific topology structure

BACKGROUND

Natural proteins occupy a small portion of the protein sequence space, whereas artificial proteins can explore a wider range of possibilities within the sequence space. However, specific requirements may not be met when generating sequences blindly. Research indicates that small proteins have notable advantages, including high stability, accurate resolution prediction, and facile specificity modification.

RESULTS

This study involves the construction of a neural network model named TopoProGenerator(TPGen) using a transformer decoder. The model is trained with sequences consisting of a maximum of 65 amino acids. The training process of TopoProGenerator incorporates reinforcement learning and adversarial learning, for fine-tuning. Additionally, it encompasses a stability predictive model trained with a dataset comprising over 200,000 sequences. The results demonstrate that TopoProGenerator is capable of designing stable small protein sequences with specified topology structures.

CONCLUSION

TPGen has the ability to generate protein sequences that fold into the specified topology, and the pretraining and fine-tuning methods proposed in this study can serve as a framework for designing various types of proteins.

Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.

Peptiligase, an enzyme for efficient chemo-enzymatic synthesis of aviptadil

Increasing attention to peptides as prospective therapeutics has created a renaissance in searching for new alternatives to the current peptide synthetic approaches as well as their modification. In this context, it is necessary to develop different approaches for peptide ligation. Using enzymes as a novel strategy and powerful tool for the peptide and protein ligation has recently received a lot of attention. We here designed a fully convergent chemo-enzymatic peptide synthesis (CEPS) process for the synthesis of aviptadil a 28-mer therapeutic peptide with potential therapeutic effects in various medical contexts specially in the treatment of acute respiratory distress syndrome (ARDS) by coupling two peptide segments with four different peptiligase variants in aqueous environments. Our study reveals that peptiligase variants are capable of ligation reaction in 15 min. The overall time of ligation is shorter than those peptides with similar lengths and hinderance to aviptadil which reported for conventional synthesis by full solid-phase peptide synthesis. Yields ranged from 54 % to 76 %.

Design and evaluation of α-helix-based peptide inhibitors for blocking PD-1/PD-L1 interaction

The current research in tumor immunotherapy indicates that blocking the protein-protein interaction (PPI) between PD-1 and its ligand, PD-L1, may be one of the most effective treatments for cancer patients. The α-helix is a common elements of protein secondary structure and is often involved in protein interaction. Thus, α-helix-based peptides could mimic proteins involved in such interactions and are also capable of modulating PPI in vivo. In this study, starting from a potential α-helix-rich protein, we designed a series of α-helix-based peptide candidates to block PD-1/PD-L1 interaction. These candidates were first screened using molecular docking and molecular dynamics simulations, and then their capacities to inhibit PD-1/PD-L1 interactions and to restore antitumor immune activities were investigated using the HTRF assay, SPR assay, cellular co-culture experiments and animal model experiments. Two peptides exhibited the best anti-tumor effects and the strong ability to restore the immunity of tumor-infiltrating T-cells. Further D-amino acid substitution was employed to improve the serum stability of peptide candidate, making the intravenous administration easier while maintaining the therapeutic efficacy. The resultant peptides showed promise as checkpoint inhibitors for application in tumor immunotherapy. These findings suggested that our strategy for developing peptides starting from an α-helical structure could be used in the design of bioactive inhibitors to potential block protein-protein interactions.

De Novo Discovery of Cysteine Frameworks for Developing Multicyclic Peptide Libraries for Ligand Discovery

Conserved cysteine frameworks are essential components of disulfide-rich peptides (DRPs), which dominantly define the structural diversity of both naturally occurring and de novo-designed DRPs. However, there are only very limited numbers of conserved cysteine frameworks, and general methods enabling de novo discovery of cysteine frameworks with robust foldability are still not available. Here, we devised a "touchstone"-based strategy that relies on chasing oxidative foldability between two individual disulfide-rich folds on the phage surface to discover new cysteine frameworks from random sequences. Unique cysteine frameworks with a high degree of compatibility with phage display systems and broad sequence tolerance were successfully identified, which were subsequently exploited for the development of multicyclic DRP libraries, enabling the rapid discovery of new peptide ligands with low-nanomolar and picomolar binding affinity. This study provides an unprecedented method for exploring and exploiting the sequence and structure space of DRPs that is not readily accessible by existing strategies, holding the potential to revolutionize the study of DRPs and significantly advance the design and discovery of multicyclic peptide ligands and drugs.

Design and structural validation of peptide-drug conjugate ligands of the kappa-opioid receptor

Despite the increasing number of GPCR structures and recent advances in peptide design, the development of efficient technologies allowing rational design of high-affinity peptide ligands for single GPCRs remains an unmet challenge. Here, we develop a computational approach for designing conjugates of lariat-shaped macrocyclized peptides and a small molecule opioid ligand. We demonstrate its feasibility by discovering chemical scaffolds for the kappa-opioid receptor (KOR) with desired pharmacological activities. The designed De Novo Cyclic Peptide (DNCP)-β-naloxamine (NalA) exhibit in vitro potent mixed KOR agonism/mu-opioid receptor (MOR) antagonism, nanomolar binding affinity, selectivity, and efficacy bias at KOR. Proof-of-concept in vivo efficacy studies demonstrate that DNCP-β-NalA(1) induces a potent KOR-mediated antinociception in male mice. The high-resolution cryo-EM structure (2.6 Å) of the DNCP-β-NalA-KOR-Gi1 complex and molecular dynamics simulations are harnessed to validate the computational design model. This reveals a network of residues in ECL2/3 and TM6/7 controlling the intrinsic efficacy of KOR. In general, our computational de novo platform overcomes extensive lead optimization encountered in ultra-large library docking and virtual small molecule screening campaigns and offers innovation for GPCR ligand discovery. This may drive the development of next-generation therapeutics for medical applications such as pain conditions.

Cyclization of Peptides Enhances the Inhibitory Activity against Ganglioside-Induced Aβ Fibril Formation

Alzheimer's disease is a progressive neurodegenerative disease and is the most common cause of dementia. It has been reported that the assembly of amyloid β-protein (Aβ) on the cell membrane is induced by the interaction of the Aβ monomer with gangliosides such as GM1. The ganglioside-bound Aβ (GAβ) complex acts as a seed to promote the toxic assembly of the Aβ fibrils. In a previous study, we found that a GM1 cluster-binding peptide (GCBP) specifically recognizes Aβ-sensitive ganglioside nanoclusters and inhibits the assembly of Aβ on a GM1-containing lipid membrane. In this study, cysteine-substituted double mutants of GCBP were designed and cyclized by intramolecular disulfide bond formation. Affinity assays indicated that one of the cyclic peptides had a higher affinity to a GM1-containing membrane compared to that of GCBP. Furthermore, surface topography analysis indicated that this peptide recognizes GM1 nanoclusters on the lipid membrane. An evaluation of the inhibitory kinetics indicated that the cyclic peptide could inhibit the formation of Aβ fibrils with an IC50 value of 1.2 fM, which is 10,000-fold higher than that of GCBP. The cyclic peptide was also shown to have a clearance effect on Aβ fibrils deposited on the lipid membrane and suppressed the formation of toxic Aβ assemblies. Our results indicate that the cyclic peptide that binds to the Aβ-sensitive ganglioside nanocluster is a potential novel inhibitor of ganglioside-induced Aβ assembly.

Cyclic Peptide Linker Design and Optimization by Molecular Dynamics Simulations

Cyclic peptides are an emerging therapeutic modality that can target protein-protein interaction sites with high affinity and selectivity. A common medicinal chemistry strategy for the optimization of peptide hits is conformational stabilization through macrocyclization. We present a method based on explicit solvent enhanced sampling molecular dynamics simulations for estimating the impact of varying linker lengths and chemistry on the conformational stability of a peptide. The method is demonstrated on three cyclic peptide series that bind to proteins PCSK9, trypsin, and MDM2 adopting loop, β-sheet, and helical secondary structures. In general, the simulations show greater solution stability of the receptor-bound conformation for the higher-affinity peptides, consistent with the idea that preorganizing a ligand for binding can enhance binding affinity. The impact of the force field and sampling is discussed for one series that does not follow this trend. We have successfully applied this method to internal discovery programs to design peptides with increased potency and chemical stability.

De novo design of monomeric helical bundles for pH-controlled membrane lysis

Targeted intracellular delivery via receptor-mediated endocytosis requires the delivered cargo to escape the endosome to prevent lysosomal degradation. This can in principle be achieved by membrane lysis tightly restricted to endosomal membranes upon internalization to avoid general membrane insertion and lysis. Here, we describe the design of small monomeric proteins with buried histidine containing pH-responsive hydrogen bond networks and membrane permeating amphipathic helices. Of the 30 designs that were experimentally tested, all expressed in Escherichia coli, 13 were monomeric with the expected secondary structure, and 4 designs disrupted artificial liposomes in a pH-dependent manner. Mutational analysis showed that the buried histidine hydrogen bond networks mediate pH-responsiveness and control lysis of model membranes within a very narrow range of pH (6.0-5.5) with almost no lysis occurring at neutral pH. These tightly controlled lytic monomers could help mediate endosomal escape in designed targeted delivery platforms.

Exploring a Structural Data Mining Approach to Design Linkers for Head-to-Tail Peptide Cyclization

Peptides have recently regained interest as therapeutic candidates, but their development remains confronted with several limitations including low bioavailability. Backbone head-to-tail cyclization, i.e., setting a covalent peptide bond linking the last amino acid with the first one, is one effective strategy of peptide-based drug design to stabilize the conformation of bioactive peptides while preserving peptide properties in terms of low toxicity, binding affinity, target selectivity, and preventing enzymatic degradation. Starting from an active peptide, it usually requires the design of a linker of a few amino acids to make it possible to cyclize the peptide, possibly preserving the conformation of the initial peptide and not affecting its activity. However, very little is known about the sequence-structure relationship requirements of designing linkers for peptide cyclization in a rational manner. Recently, we have shown that large-scale data-mining of available protein structures can lead to the precise identification of protein loop conformations, even from remote structural classes. Here, we transpose this approach to linkers, allowing head-to-tail peptide cyclization. First we show that given a linker sequence and the conformation of the linear peptide, it is possible to accurately predict the cyclized peptide conformation. Second, and more importantly, we show that it seems possible to elaborate on the information inferred from protein structures to propose effective candidate linker sequences constrained by length and amino acid composition, providing the first framework for the rational design of head-to-tail cyclization linkers. Finally, we illustrate this for two peptides using a limited set of amino-acids likely not to interfere with peptide function. For a linear peptide derived from Nrf2, the peptide cyclized starting from the experimental structure showed a 26-fold increase in the binding affinity. For urotensin II, a peptide already cyclized by a disulfide bond that exerts a broad array of biological activities, we were able, starting from models of the structure, to design a head-to-tail cyclized peptide, the first synthesized bicyclic 14-residue long urotensin II analogue, showing a retention of in vitro activity. Although preliminary, our results strongly suggest that such an approach has strong potential for cyclic peptide-based drug design.

Single-chain dimers from de novo immunoglobulins as robust scaffolds for multiple binding loops

Antibody derivatives have sought to recapitulate the antigen binding properties of antibodies, but with improved biophysical attributes convenient for therapeutic, diagnostic and research applications. However, their success has been limited by the naturally occurring structure of the immunoglobulin dimer displaying hypervariable binding loops, which is hard to modify by traditional engineering approaches. Here, we devise geometrical principles for de novo designing single-chain immunoglobulin dimers, as a tunable two-domain architecture that optimizes biophysical properties through more favorable dimer interfaces. Guided by these principles, we computationally designed protein scaffolds that were hyperstable, structurally accurate and robust for accommodating multiple functional loops, both individually and in combination, as confirmed through biochemical assays and X-ray crystallography. We showcase the modularity of this architecture by deep-learning-based diversification, opening up the possibility for tailoring the number, positioning, and relative orientation of ligand-binding loops targeting one or two distal epitopes. Our results provide a route to custom-design robust protein scaffolds for harboring multiple functional loops.

Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models

Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.

De novo design of buttressed loops for sculpting protein functions

In natural proteins, structured loops play central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that generates structured loops buttressed by extensive hydrogen bonding interactions with two neighboring loops and with secondary structure elements. We use this approach to design tandem repeat proteins with buttressed loops ranging from 9 to 14 residues in length. Experimental characterization shows the designs are folded and monodisperse, highly soluble, and thermally stable. Crystal structures are in close agreement with the computational design models, with the loops structured and buttressed by their neighbors as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute quite generally to efforts to design new protein functions.

Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models

Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.