Computational Design of Potent and Selective d-Peptide Agonists of the Glucagon-like Peptide-2 Receptor.

Here, we designed three d-GLP-2 agonists that activated the glucagon-like peptide-2 receptor (GLP-2R) cyclic adenosine monophosphate (cAMP) accumulation without stimulating the glucagon-like peptide-1 receptor (GLP-1R). All the d-GLP-2 agonists increased the protein kinase B phosphorylated (p-AKT) expression levels in a time- and concentration-dependent manner in vitro. The most effective d-GLP-2 analogue boosted the AKT phosphorylation 2.28 times more effectively compared to the native l-GLP-2. The enhancement in the p-AKT levels induced by the d-GLP-2 analogues could be explained by GLP-2R's more prolonged activation, given that the d-GLP-2 analogues induce a lower ß-arrestin recruitment. The higher stability to protease degradation of our d-GLP-2 agonists helps us envision their potential applications in enhancing intestinal absorption and treating inflammatory bowel illness while lowering the high dosage required by the current treatments.

Disrupting the α-synuclein-ESCRT interaction with a peptide inhibitor mitigates neurodegeneration in preclinical models of Parkinson's disease.

Accumulation of α-synuclein into toxic oligomers or fibrils is implicated in dopaminergic neurodegeneration in Parkinson's disease. Here we performed a high-throughput, proteome-wide peptide screen to identify protein-protein interaction inhibitors that reduce α-synuclein oligomer levels and their associated cytotoxicity. We find that the most potent peptide inhibitor disrupts the direct interaction between the C-terminal region of α-synuclein and CHarged Multivesicular body Protein 2B (CHMP2B), a component of the Endosomal Sorting Complex Required for Transport-III (ESCRT-III). We show that α-synuclein impedes endolysosomal activity via this interaction, thereby inhibiting its own degradation. Conversely, the peptide inhibitor restores endolysosomal function and thereby decreases α-synuclein levels in multiple models, including female and male human cells harboring disease-causing α-synuclein mutations. Furthermore, the peptide inhibitor protects dopaminergic neurons from α-synuclein-mediated degeneration in hermaphroditic C. elegans and preclinical Parkinson's disease models using female rats. Thus, the α-synuclein-CHMP2B interaction is a potential therapeutic target for neurodegenerative disorders.

A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

Cys2His2 zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Here we describe the screening of 49 billion protein-DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any genomic target. ZFDesign is a modern machine learning method that models global and target-specific differences induced by a range of library environments and specifically takes into account compatibility of neighboring fingers using a novel hierarchical transformer architecture. We demonstrate the versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. These factors could be used to upregulate an allele of haploinsufficiency, downregulate a gain-of-function mutation or test the consequence of regulation of a single gene as opposed to the many genes that a transcription factor would normally influence.

Targeting the Receptor-Binding Motif of SARS-CoV-2 with D-Peptides Mimicking the ACE2 Binding Helix: Lessons for Inhibiting Omicron and Future Variants of Concern.

The COVID-19 pandemic continues to spread around the world, with several new variants emerging, particularly those of concern (VOCs). Omicron (B.1.1.529), a recent VOC with many mutations in the spike protein's receptor-binding domain (RBD), has attracted a great deal of scientific and public interest. We previously developed two D-peptide inhibitors for the infection of the original SARS-CoV-2 and its VOCs, alpha and beta, in vitro. Here, we demonstrated that Covid3 and Covid_extended_1 maintained their high-affinity binding (29.4-31.3 nM) to the omicron RBD. Both D-peptides blocked the omicron variant in vitro infection with IC50s of 3.13 and 5.56 µM, respectively. We predicted that Covid3 shares a larger overlapping binding region with the ACE2 binding motif than different classes of neutralizing monoclonal antibodies. We envisioned the design of D-peptide inhibitors targeting the receptor-binding motif as the most promising approach for inhibiting current and future VOCs of SARS-CoV-2, given that the ACE2 binding interface is more limited to tolerate mutations than most of the RBD's surface.

PepNN: a deep attention model for the identification of peptide binding sites.

Protein-peptide interactions play a fundamental role in many cellular processes, but remain underexplored experimentally and difficult to model computationally. Here, we present PepNN-Struct and PepNN-Seq, structure and sequence-based approaches for the prediction of peptide binding sites on a protein. A main difficulty for the prediction of peptide-protein interactions is the flexibility of peptides and their tendency to undergo conformational changes upon binding. Motivated by this, we developed reciprocal attention to simultaneously update the encodings of peptide and protein residues while enforcing symmetry, allowing for information flow between the two inputs. PepNN integrates this module with modern graph neural network layers and a series of transfer learning steps are used during training to compensate for the scarcity of peptide-protein complex information. We show that PepNN-Struct achieves consistently high performance across different benchmark datasets. We also show that PepNN makes reasonable peptide-agnostic predictions, allowing for the identification of novel peptide binding proteins.

Computational Design of Potent D-Peptide Inhibitors of SARS-CoV-2.

Blocking the association between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) is an attractive therapeutic approach to prevent the virus from entering human cells. While antibodies and other modalities have been developed to this end, d-amino acid peptides offer unique advantages, including serum stability, low immunogenicity, and low cost of production. Here, we designed potent novel D-peptide inhibitors that mimic the ACE2 α1-binding helix by searching a mirror-image version of the PDB. The two best designs bound the RBD with affinities of 29 and 31 nM and blocked the infection of Vero cells by SARS-CoV-2 with IC50 values of 5.76 and 6.56 µM, respectively. Notably, both D-peptides neutralized with a similar potency the infection of two variants of concern: B.1.1.7 and B.1.351 in vitro. These potent D-peptide inhibitors are promising lead candidates for developing SARS-CoV-2 prophylactic or therapeutic treatments.

Rapid and accurate structure-based therapeutic peptide design using GPU accelerated thermodynamic integration.

Peptide-based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side-chains was found to be less accurate than for non-charged, and use of a more complex 3-step TI protocol was found to boost accuracy in these cases. Using the 3-step protocol for non-charged side-chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non-canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.

Method to generate highly stable D-amino acid analogs of bioactive helical peptides using a mirror image of the entire PDB.

Biologics are a rapidly growing class of therapeutics with many advantages over traditional small molecule drugs. A major obstacle to their development is that proteins and peptides are easily destroyed by proteases and, thus, typically have prohibitively short half-lives in human gut, plasma, and cells. One of the most effective ways to prevent degradation is to engineer analogs from dextrorotary (D)-amino acids, with up to 105-fold improvements in potency reported. We here propose a general peptide-engineering platform that overcomes limitations of previous methods. By creating a mirror image of every structure in the Protein Data Bank (PDB), we generate a database of ∼2.8 million D-peptides. To obtain a D-analog of a given peptide, we search the (D)-PDB for similar configurations of its critical-"hotspot"-residues. As a proof of concept, we apply our method to two peptides that are Food and Drug Administration approved as therapeutics for diabetes and osteoporosis, respectively. We obtain D-analogs that activate the GLP1 and PTH1 receptors with the same efficacy as their natural counterparts and show greatly increased half-life.

Generation and Validation of Intracellular Ubiquitin Variant Inhibitors for USP7 and USP10.

Post-translational modification of the p53 signaling pathway plays an important role in cell cycle progression and stress-induced apoptosis. Indeed, a large body of work has shown that dysregulation of p53 and its E3 ligase MDM2 by the ubiquitin-proteasome system (UPS) promotes carcinogenesis and malignant transformation. Thus, drug discovery efforts have focused on the restoration of wild-type p53 activity or inactivation of oncogenic mutant p53 by targeted inhibition of UPS components, particularly key deubiquitinases (DUBs) of the ubiquitin-specific protease (USP) class. However, development of selective small-molecule USP inhibitors has been challenging, partly due to the highly conserved structural features of the catalytic sites across the class. To tackle this problem, we devised a protein engineering strategy for rational design of inhibitors for DUBs and other UPS proteins. We employed a phage-displayed ubiquitin variant (UbV) library to develop inhibitors targeting the DUBs USP7 and USP10, which are involved in regulating levels of p53 and MDM2. We were able to identify UbVs that bound USP7 or USP10 with high affinity and inhibited deubiquitination activity. We solved the crystal structure of UbV.7.2 and rationalized the molecular basis for enhanced affinity and specificity for USP7. Finally, cell death was increased significantly by UbV.7.2 expression in a colon cancer cell line that was treated with the chemotherapy drug cisplatin, demonstrating the therapeutic potential of inhibiting USP7 by this approach.

Discovery of short linear motif-mediated interactions through phage display of intrinsically disordered regions of the human proteome.

The intrinsically disordered regions of eukaryotic proteomes are enriched in short linear motifs (SLiMs), which are of crucial relevance for cellular signaling and protein regulation; many mediate interactions by providing binding sites for peptide-binding domains. The vast majority of SLiMs remain to be discovered highlighting the need for experimental methods for their large-scale identification. We present a novel proteomic peptide phage display (ProP-PD) library that displays peptides representing the disordered regions of the human proteome, allowing direct large-scale interrogation of most potential binding SLiMs in the proteome. The performance of the ProP-PD library was validated through selections against SLiM-binding bait domains with distinct folds and binding preferences. The vast majority of identified binding peptides contained sequences that matched the known SLiM-binding specificities of the bait proteins. For SHANK1 PDZ, we establish a novel consensus TxF motif for its non-C-terminal ligands. The binding peptides mostly represented novel target proteins, however, several previously validated protein-protein interactions (PPIs) were also discovered. We determined the affinities between the VHS domain of GGA1 and three identified ligands to 40-130 µm through isothermal titration calorimetry, and confirmed interactions through coimmunoprecipitation using full-length proteins. Taken together, we outline a general pipeline for the design and construction of ProP-PD libraries and the analysis of ProP-PD-derived, SLiM-based PPIs. We demonstrated the methods potential to identify low affinity motif-mediated interactions for modular domains with distinct binding preferences. The approach is a highly useful complement to the current toolbox of methods for PPI discovery.

Strategies to Develop Inhibitors of Motif-Mediated Protein-Protein Interactions as Drug Leads.

Protein-protein interactions are fundamental for virtually all functions of the cell. A large fraction of these interactions involve short peptide motifs, and there has been increased interest in targeting them using peptide-based therapeutics. Peptides benefit from being specific, relatively safe, and easy to produce. They are also easy to modify using chemical synthesis and molecular biology techniques. However, significant challenges remain regarding the use of peptides as therapeutic agents. Identification of peptide motifs is difficult, and peptides typically display low cell permeability and sensitivity to enzymatic degradation. In this review, we outline the principal high-throughput methodologies for motif discovery and describe current methods for overcoming pharmacokinetic and bioavailability limitations.

Large-Scale Interaction Profiling of Protein Domains Through Proteomic Peptide-Phage Display Using Custom Peptidomes.

Protein-protein interactions are essential to cellular functions and signaling pathways. We recently combined bioinformatics and custom oligonucleotide arrays to construct custom-made peptide-phage libraries for screening peptide-protein interactions, an approach we call proteomic peptide-phage display (ProP-PD). In this chapter, we describe protocols for phage display for the identification of natural peptide binders for a given protein. We finally describe deep sequencing for the analysis of the proteomic peptide-phage display.

Protein engineering by highly parallel screening of computationally designed variants.

Current combinatorial selection strategies for protein engineering have been successful at generating binders against a range of targets; however, the combinatorial nature of the libraries and their vast undersampling of sequence space inherently limit these methods due to the difficulty in finely controlling protein properties of the engineered region. Meanwhile, great advances in computational protein design that can address these issues have largely been underutilized. We describe an integrated approach that computationally designs thousands of individual protein binders for high-throughput synthesis and selection to engineer high-affinity binders. We show that a computationally designed library enriches for tight-binding variants by many orders of magnitude as compared to conventional randomization strategies. We thus demonstrate the feasibility of our approach in a proof-of-concept study and successfully obtain low-nanomolar binders using in vitro and in vivo selection systems.

Pooled screening for antiproliferative inhibitors of protein-protein interactions.

Protein-protein interactions (PPIs) are emerging as a promising new class of drug targets. Here, we present a novel high-throughput approach to screen inhibitors of PPIs in cells. We designed a library of 50,000 human peptide-binding motifs and used a pooled lentiviral system to express them intracellularly and screen for their effects on cell proliferation. We thereby identified inhibitors that drastically reduced the viability of a pancreatic cancer line (RWP1) while leaving a control line virtually unaffected. We identified their target interactions computationally, and validated a subset in experiments. We also discovered their potential mechanisms of action, including apoptosis and cell cycle arrest. Finally, we confirmed that synthetic lipopeptide versions of our inhibitors have similarly specific and dosage-dependent effects on cancer cell growth. Our screen reveals new drug targets and peptide drug leads, and it provides a rich data set covering phenotypes for the inhibition of thousands of interactions.

Subcellular localization of coagulation factor II receptor-like 1 in neurons governs angiogenesis.

Neurons have an important role in retinal vascular development. Here we show that the G protein-coupled receptor (GPCR) coagulation factor II receptor-like 1 (F2rl1, previously known as Par2) is abundant in retinal ganglion cells and is associated with new blood vessel formation during retinal development and in ischemic retinopathy. After stimulation, F2rl1 in retinal ganglion cells translocates from the plasma membrane to the cell nucleus using a microtubule-dependent shuttle that requires sorting nexin 11 (Snx11). At the nucleus, F2rl1 facilitates recruitment of the transcription factor Sp1 to trigger Vegfa expression and, in turn, neovascularization. In contrast, classical plasma membrane activation of F2rl1 leads to the expression of distinct genes, including Ang1, that are involved in vessel maturation. Mutant versions of F2rl1 that prevent nuclear relocalization but not plasma membrane activation interfere with Vegfa but not Ang1 expression. Complementary angiogenic factors are therefore regulated by the subcellular localization of a receptor (F2rl1) that governs angiogenesis. These findings may have implications for the selectivity of drug actions based on the subcellular distribution of their targets.

A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening.

We present an integrated approach that predicts and validates novel anti-cancer drug targets. We first built a classifier that integrates a variety of genomic and systematic datasets to prioritize drug targets specific for breast, pancreatic and ovarian cancer. We then devised strategies to inhibit these anti-cancer drug targets and selected a set of targets that are amenable to inhibition by small molecules, antibodies and synthetic peptides. We validated the predicted drug targets by showing strong anti-proliferative effects of both synthetic peptide and small molecule inhibitors against our predicted targets.

Large-scale interaction profiling of PDZ domains through proteomic peptide-phage display using human and viral phage peptidomes.

The human proteome contains a plethora of short linear motifs (SLiMs) that serve as binding interfaces for modular protein domains. Such interactions are crucial for signaling and other cellular processes, but are difficult to detect because of their low to moderate affinities. Here we developed a dedicated approach, proteomic peptide-phage display (ProP-PD), to identify domain-SLiM interactions. Specifically, we generated phage libraries containing all human and viral C-terminal peptides using custom oligonucleotide microarrays. With these libraries we screened the nine PSD-95/Dlg/ZO-1 (PDZ) domains of human Densin-180, Erbin, Scribble, and Disks large homolog 1 for peptide ligands. We identified several known and putative interactions potentially relevant to cellular signaling pathways and confirmed interactions between full-length Scribble and the target proteins ß-PIX, plakophilin-4, and guanylate cyclase soluble subunit α-2 using colocalization and coimmunoprecipitation experiments. The affinities of recombinant Scribble PDZ domains and the synthetic peptides representing the C termini of these proteins were in the 1- to 40-µM range. Furthermore, we identified several well-established host-virus protein-protein interactions, and confirmed that PDZ domains of Scribble interact with the C terminus of Tax-1 of human T-cell leukemia virus with micromolar affinity. Previously unknown putative viral protein ligands for the PDZ domains of Scribble and Erbin were also identified. Thus, we demonstrate that our ProP-PD libraries are useful tools for probing PDZ domain interactions. The method can be extended to interrogate all potential eukaryotic, bacterial, and viral SLiMs and we suggest it will be a highly valuable approach for studying cellular and pathogen-host protein-protein interactions.

Caveolin-1 regulation of dynamin-dependent, raft-mediated endocytosis of cholera toxin-B sub-unit occurs independently of caveolae.

Ganglioside GM1-bound cholera toxin-B sub-unit (CT-b) enters the cell via clathrin-coated pits and dynamin-independent non-caveolar raft-dependent endocytosis. Caveolin-1 (Cav1), associated with caveolae formation, is a negative regulator of non-caveolar raft-dependent endocytosis. In mammary epithelial tumour cells deficient for Mgat5, Cav1 is stably expressed at levels below the threshold for caveolae formation, forming stable oligomerized Cav1 microdomains or scaffolds that were shown to suppress EGFR signalling and reduce the plasma membrane diffusion rate of both EGFR and CT-b. Below threshold levels of Cav1 also inhibit the dynamin-dependent raft-mediated endocytosis of CT-b to the Golgi indicating that Cav1-negative regulation of raft-dependent endocytosis is caveolae independent. Inhibition of CT-b internalization does not require Cav1 phosphorylation but does require an intact Cav1 scaffolding domain. By flow cytometry, both over-expression of Cav1 and the dynamin K44A mutant block CT-b internalization from the plasma membrane defining a dynamin-dependent raft pathway for CT-b endocytosis in these cells. However, only minimal co-localization between CT-b and Cav1 is observed. These results suggest that Cav1 regulates raft-dependent internalization of CT-b indirectly via a mechanism that requires the Cav1 scaffolding domain and the formation of oligomerized Cav1 microdomains but not caveolae.

Early effects of spinal cord transection on skeletal muscle properties.

Modulation of beta-adrenergic receptor (beta-AR) activity affects muscle mass and could have a role in the reduction of muscle mass observed following spinal cord transection (Tx). The aims of this study were to examine the early acute effects of Tx on muscle mass, total and myofibrillar protein concentrations, cytochrome c oxidase activity, and beta-AR density of skeletal muscle, to ascertain if any change in muscle properties could be related to beta-AR signalling events. Female Sprague-Dawley rats (n = 33; approximately 255 g) were randomly assigned to 4 experimental groups: control 4 d, control 8 d, Tx 4 d, and Tx 8 d. A complete Tx was performed surgically at the T10 cord level. Compared with controls, muscle mass and muscle - body mass ratios decreased significantly following Tx, with no significant change observed in total and myofibrillar protein concentrations. Spinal cord Tx also resulted in a significant decrease in plantaris cytochrome c oxidase activity by 24% at Tx 4 d and 28% at Tx 8 d (p < 0.05). Beta-AR density of the lateral gastrocnemius was unchanged; however, the beta-AR density of the forelimb triceps brachii m. was found to increase after Tx. Our results suggest that changes in muscle mass and cytochrome c oxidase activity rapidly occur after Tx and do not appear to be related to changes in beta-AR density.

Proangiogenic effects of protease-activated receptor 2 are tumor necrosis factor-alpha and consecutively Tie2 dependent.

OBJECTIVE: Angiogenesis is essential physiologically in growth and pathologically in tumor development, chronic inflammatory disorders, and proliferative retinopathies. Activation of protease-activated receptor 2 (PAR2) leads to a proangiogenic response, but its mechanisms have yet to be specifically described. Here, we investigated the mode of action of PAR2 in retinal angiogenesis. METHODS AND RESULTS: PAR2-activating peptide, SLIGRL, increased retinal angiogenesis associated with an induction of vascular endothelial growth factor and angiopoetin-2 and most notably tie2 in the retina in vivo as well as in cultured neuroretinal endothelial cells. SLIGRL also induced release of the proinflammatory and angiogenic mediator tumor necrosis factor-alpha (TNF-alpha) via the MEK/extracellular signal-regulated kinase (ERK) (MEK/ERK) pathway in these endothelial cells. TNF-alpha, in turn, elicited tie2 expression by activating the MEK/ERK pathway. PAR2-evoked tie2 expression, endothelium proliferation (in vitro), and retinal neovascularization (in vivo) were abrogated by selective TNF-alpha blockers (neutralizing antibody infliximab and soluble TNF-alpha receptor-Fc fusion protein etanercept) as well as the MEK inhibitor PD98059. CONCLUSIONS: The proangiogenic properties of PAR2 are intertwined with its proinflammatory effects, such that in retinal vasculature, they depend on TNF-alpha and subsequent induction of tie2 via the MEK/ERK pathway.