Hydrogen bond surrogate helices as minimal mimics of protein α-helices

Structural information in therapeutic peptides: Emerging applications in biomedicine

Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.

Rational design of small-sized peptidomimetic inhibitors disrupting protein-protein interaction

Protein-protein interactions are fundamental to nearly all biological processes. Due to their structural flexibility, peptides have emerged as promising candidates for developing inhibitors targeting large and planar PPI interfaces. However, their limited drug-like properties pose challenges. Hence, rational modifications based on peptide structures are anticipated to expedite the innovation of peptide-based therapeutics. This review comprehensively examines the design strategies for developing small-sized peptidomimetic inhibitors targeting PPI interfaces, which predominantly encompass two primary categories: peptidomimetics with abbreviated sequences and low molecular weights and peptidomimetics mimicking secondary structural conformations. We have also meticulously detailed several instances of designing and optimizing small-sized peptidomimetics targeting PPIs, including MLL1-WDR5, PD-1/PD-L1, and Bak/Bcl-xL, among others, to elucidate the potential application prospects of these design strategies. Hopefully, this review will provide valuable insights and inspiration for the future development of PPI small-sized peptidomimetic inhibitors in pharmaceutical research endeavors.

Bioinformatics leading to conveniently accessible, helix enforcing, bicyclic ASX motif mimics (BAMMs)

Helix mimicry provides probes to perturb protein-protein interactions (PPIs). Helical conformations can be stabilized by joining side chains of non-terminal residues (stapling) or via capping fragments. Nature exclusively uses capping, but synthetic helical mimics are heavily biased towards stapling. This study comprises: (i) creation of a searchable database of unique helical N-caps (ASX motifs, a protein structural motif with two intramolecular hydrogen-bonds between aspartic acid/asparagine and following residues); (ii) testing trends observed in this database using linear peptides comprising only canonical L-amino acids; and, (iii) novel synthetic N-caps for helical interface mimicry. Here we show many natural ASX motifs comprise hydrophobic triangles, validate their effect in linear peptides, and further develop a biomimetic of them, Bicyclic ASX Motif Mimics (BAMMs). BAMMs are powerful helix inducing motifs. They are synthetically accessible, and potentially useful to a broad section of the community studying disruption of PPIs using secondary structure mimics.

Oligo-benzamide-based peptide mimicking tools for modulating biology

The oligo-benzamide scaffold is a rigid organic framework that can hold 2-3 functional groups as O-alkyl substituents on its benzamide units, mirroring their natural arrangement in an α-helix. Oligo-benzamides demonstrated outstanding α-helix mimicry and can be readily synthesized by following high yielding and iterative reaction steps in both solution-phase and solid-phase. A number of oligo-benzamides have been designed to emulate α-helical peptide segments in biologically active proteins and showed strong protein binding, in turn effectively disrupting protein-protein interactions in vitro and in vivo. In this chapter, the design of oligo-benzamides for mimicking α-helices, efficient synthetic routes for producing them, and their biomedical studies showing remarkable potency in inhibiting protein functions are discussed.

Design-rules for stapled peptides with in vivo activity and their application to Mdm2/X antagonists

Although stapled α-helical peptides can address challenging targets, their advancement is impeded by poor understandings for making them cell permeable while avoiding off-target toxicities. By synthesizing >350 molecules, we present workflows for identifying stapled peptides against Mdm2(X) with in vivo activity and no off-target effects. Key insights include a clear correlation between lipophilicity and permeability, removal of positive charge to avoid off-target toxicities, judicious anionic residue placement to enhance solubility/behavior, optimization of C-terminal length/helicity to enhance potency, and optimization of staple type/number to avoid polypharmacology. Workflow application gives peptides with >292x improved cell proliferation potencies and no off-target cell proliferation effects ( > 3800x on-target index). Application of these 'design rules' to a distinct Mdm2(X) peptide series improves ( > 150x) cellular potencies and removes off-target toxicities. The outlined workflow should facilitate therapeutic impacts, especially for those targets such as Mdm2(X) that have hydrophobic interfaces and are targetable with a helical motif.

AcrNET: predicting anti-CRISPR with deep learning

MOTIVATION

As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance.

RESULTS

On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly.

AVAILABILITY AND IMPLEMENTATION

Web server: https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/. Training code and pre-trained model are available at.

Modulating Angiogenesis by Proteomimetics of Vascular Endothelial Growth Factor

Angiogenesis, formation of new blood vessels from the existing vascular network, is a hallmark of cancer cells that leads to tumor vascular proliferation and metastasis. This process is mediated through the binding interaction of VEGF-A with VEGF receptors. However, the balance between pro-angiogenic and anti-angiogenic effect after ligand binding yet remains elusive and is therefore challenging to manipulate. To interrogate this interaction, herein we designed a few sulfono-γ-AA peptide based helical peptidomimetics that could effectively mimic a key binding interface on VEGF (helix-α1) for VEGFR recognition. Intriguingly, although both sulfono-γ-AA peptide sequences V2 and V3 bound to VEGF receptors tightly, in vitro angiogenesis assays demonstrated that V3 potently inhibited angiogenesis, whereas V2 activated angiogenesis effectively instead. Our findings suggested that this distinct modulation of angiogenesis might be due to the result of selective binding of V2 to VEGFR-1 and V3 to VEGFR-2, respectively. These molecules thus provide us a key to switch the angiogenic signaling, a biological process that balances the effects of pro-angiogenic and anti-angiogenic factors, where imbalances lead to several diseases including cancer. In addition, both V2 and V3 exhibited remarkable stability toward proteolytic hydrolysis, suggesting that V2 and V3 are promising therapeutic agents for the intervention of disease conditions arising due to angiogenic imbalances and could also be used as novel molecular switching probes to interrogate the mechanism of VEGFR signaling. The findings also further demonstrated the potential of sulfono-γ-AA peptides to mimic the α-helical domain for protein recognition and modulation of protein-protein interactions.