A Target-Based Method for Designing Heterochiral Cyclic Peptide Binders: De Novo Inhibitors of the PD-1/PD-L1 Interaction

NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above. ------------------------------------------------------------------------ Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-i3 and PD-i6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our de novo design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.

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A Target-Based Method for Designing Heterochiral Cyclic Peptide Binders: De Novo Inhibitors of the PD-1/PD-L1 Interaction

10.26434/chemrxiv.11663337.v2 ◽

2020 ◽

Author(s):

Salvador Guardiola ◽

Monica Varese ◽

Xavier Roig ◽

Jesús Garcia ◽

Ernest Giralt

Keyword(s):

Protein Interactions ◽

Cyclic Peptides ◽

General Framework ◽

Large Scale ◽

Cyclic Peptide ◽

De Novo ◽

Inhibitory Effect ◽

Original Text ◽

Retraction Notice ◽

Pharmaceutical Properties

NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above. ------------------------------------------------------------------------ Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-i3 and PD-i6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our de novo design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.

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Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

10.26434/chemrxiv.11663337 ◽

2020 ◽

Author(s):

Salvador Guardiola ◽

Monica Varese ◽

Xavier Roig ◽

Jesús Garcia ◽

Ernest Giralt

Keyword(s):

Protein Interactions ◽

Cyclic Peptides ◽

General Framework ◽

Large Scale ◽

De Novo ◽

Inhibitory Effect ◽

Original Text ◽

Protein Protein Interactions ◽

Retraction Notice ◽

Pharmaceutical Properties

NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above. ------------------------------------------------------------------------ Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-i3 and PD-i6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our de novo design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.

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Methods for the Creation of Cyclic Peptide Libraries for Use in Lead Discovery

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057114566803 ◽

2015 ◽

Vol 20 (5) ◽

pp. 563-576 ◽

Cited By ~ 19

Author(s):

Andrew D. Foster ◽

James D. Ingram ◽

Eilidh K. Leitch ◽

Katherine R. Lennard ◽

Eliot L. Osher ◽

...

Keyword(s):

Protein Interactions ◽

High Throughput Screening ◽

Cyclic Peptides ◽

Cyclic Peptide ◽

Peptide Libraries ◽

Successful Implementation ◽

Protein Protein Interactions ◽

Lead Discovery ◽

Small Molecule Libraries ◽

The Creation

The identification of initial hits is a crucial stage in the drug discovery process. Although many projects adopt high-throughput screening of small-molecule libraries at this stage, there is significant potential for screening libraries of macromolecules created using chemical biology approaches. Not only can the production of the library be directly interfaced with a cell-based assay, but these libraries also require significantly fewer resources to generate and maintain. In this context, cyclic peptides are increasingly viewed as ideal scaffolds and have proven capability against challenging targets such as protein-protein interactions. Here we discuss a range of methods used for the creation of cyclic peptide libraries and detail examples of their successful implementation.

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Target-templated de novo design of macrocyclic d-/l-peptides: discovery of drug-like inhibitors of PD-1

Chemical Science ◽

10.1039/d1sc01031j ◽

2021 ◽

Vol 12 (14) ◽

pp. 5164-5170

Author(s):

Salvador Guardiola ◽

Monica Varese ◽

Xavier Roig ◽

Macarena Sánchez-Navarro ◽

Jesús García ◽

...

Keyword(s):

Specific Surface ◽

Protein Interactions ◽

In Silico ◽

Cyclic Peptides ◽

De Novo ◽

De Novo Design ◽

Target Protein ◽

Surface Patch ◽

Protein Protein Interactions

In silico design of heterochiral cyclic peptides that bind to a specific surface patch on the target protein (PD-1, in this case) and disrupt protein–protein interactions.

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Structure-Based Design, Synthesis and Bioactivity of a New Anti-TNFα Cyclopeptide

Molecules ◽

10.3390/molecules25040922 ◽

2020 ◽

Vol 25 (4) ◽

pp. 922 ◽

Cited By ~ 2

Author(s):

Mohannad Idress ◽

Bruce F. Milne ◽

Gary S. Thompson ◽

Laurent Trembleau ◽

Marcel Jaspars ◽

...

Keyword(s):

Protein Interactions ◽

De Novo ◽

Tissue Injury ◽

Causative Factor ◽

Specific Protein ◽

Docking Studies ◽

Protein Protein Interactions ◽

Necrosis Factor Alpha ◽

Design Synthesis ◽

Macrocyclic Peptides

As opposed to small molecules, macrocyclic peptides possess a large surface area and are recognised as promising candidates to selectively treat diseases by disrupting specific protein–protein interactions (PPIs). Due to the difficulty in predicting cyclopeptide conformations in solution, the de novo design of bioactive cyclopeptides remains significantly challenging. In this study, we used the combination of conformational analyses and molecular docking studies to design a new cyclopeptide inhibitor of the interaction between the human tumour necrosis factor alpha (TNFα) and its receptor TNFR-1. This interaction is a key in mediating the inflammatory response to tissue injury and infection in humans, and it is also an important causative factor of rheumatoid arthritis, psoriasis and inflammatory bowel disease. The solution state NMR structure of the cyclopeptide was determined, which helped to deduce its mode of interaction with TNFα. TNFα sensor cells were used to evaluate the biological activity of the peptide.

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Passing the Barrier – How Computer Simulations Can Help to Understand and Improve the Passive Membrane Permeability of Cyclic Peptides

CHIMIA International Journal for Chemistry ◽

10.2533/chimia.2021.518 ◽

2021 ◽

Vol 75 (6) ◽

pp. 518-521

Author(s):

Stephanie M. Linker ◽

Shuzhe Wang ◽

Benjamin Ries ◽

Thomas Stadelmann ◽

Sereina Riniker

Keyword(s):

Membrane Permeability ◽

Protein Interactions ◽

Cyclic Peptides ◽

Rational Design ◽

Cyclic Peptide ◽

Md Simulations ◽

Protein Protein Interactions ◽

Small Molecule Drugs ◽

Conformational Dynamic ◽

Passive Membrane

Proteins with large and flat binding sites as well as protein–protein interactions are considered ' undruggable ' with conventional small-molecule drugs. Cyclic peptides have been found to be capable of binding to such targets with high affinity, making this class of compounds an interesting source for possible therapeutics. However, the oftentimes poor passive membrane permeability of cyclic peptides still imposes restrictions on the applicability of cyclic peptide drugs. Here, we describe how computational methods in combination with experimental data can be used to improve our understanding of the structure–permeability relationship. Especially the conformational dynamic and chameleonic nature of cyclic peptides, which we investigate by a combination of MD simulations and kinetic modeling, is important for their ability to permeate passively through the membrane. The insights from such studies may enable the formulation of design principles for the rational design of permeable cyclic peptides.

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Protein–protein inhibitor designed de novo to target the MEEVD region on the C-terminus of Hsp90 and block co-chaperone activity

Chemical Communications ◽

10.1039/c8cc07576j ◽

2019 ◽

Vol 55 (6) ◽

pp. 846-849 ◽

Cited By ~ 7

Author(s):

Marwa N. Rahimi ◽

Shelli R. McAlpine

Keyword(s):

Protein Folding ◽

Protein Interactions ◽

Cyclic Peptide ◽

De Novo ◽

Protein Inhibitor ◽

Protein Protein Interactions ◽

Cellular Functions ◽

P Protein ◽

Cellular Environment ◽

C Terminus

Protein–protein interactions control all cellular functions. The designed cyclic peptide LB76 is shown to disrupt key PPI between Hsp90 and co-chaperones. LB76 binds selectively to Hsp90 in the cellular environment and disrupts Hsp90's protein folding activity.

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Faculty Opinions recommendation of Comparative assessment of large-scale data sets of protein-protein interactions.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1006598.82257 ◽

2002 ◽

Author(s):

Rob Russell

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Comparative Assessment ◽

Data Sets ◽

Protein Protein Interactions ◽

Large Scale Data ◽

Scale Data ◽

Large Scale Data Sets

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Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Sun Sook Chung ◽

Joseph C F Ng ◽

Anna Laddach ◽

N Shaun B Thomas ◽

Franca Fraternali

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Interaction Network ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Short Loop ◽

New Strategy ◽

Loop Network ◽

Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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