AlphaFold2 enables accurate deorphanization of ligands to single-pass receptors

Danneskiold-Samsoee, Niels Banhos; Kavi, Deniz; Jude, Kevin; Nissen, Silas Boye; Wat, Lianna W; Voilquin, Laetitia; Zhao, Meng; Santana-Oikawa, Galia Asae; Broido, Beatrice Blythe; García, K. Christopher; Svensson, Katrin J.

doi:10.1101/2023.03.16.531341

Cited by 12 publications

(3 citation statements)

References 60 publications

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“…In the latest version of the alphafold2_multimer_v3 model (AF2_v3) training set, only peptides with fewer than four amino acid residues were excluded, including most cyclic peptide complexes registered in the Protein Data Bank (PDB). However, there were no training sets for the alphafold2_ptm model (AF2_ptm) and alphafold2_multimer_v2 model (AF2_v2), because the training excluded peptides with fewer than 16 amino acid residues [53,55]. The AF2_ptm was developed to predict single proteins [40].…”

Section: Structure Prediction Of the Protein And Cyclic Peptide Complexmentioning

confidence: 99%

Design of Cyclic Peptides Targeting Protein–Protein Interactions Using AlphaFold

Kosugi,

Ohue

2023

IJMS

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More than 930,000 protein–protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein–cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein–peptide complexes and one protein–protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.

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Section: Structure Prediction Of the Protein And Cyclic Peptide Complexmentioning

confidence: 99%

Design of Cyclic Peptides Targeting Protein–Protein Interactions Using AlphaFold

Kosugi,

Ohue

2023

IJMS

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“…AF-M was trained using five distinct regimens, yielding five models, each of which makes a structure prediction. Many groups are now using AF-M to uncover novel PPIs on the scale of pathways and organisms [12][13][14][15][16][17][18][19] . While many studies have examined AF-M's ability to correctly model individual protein complexes and predict structures for complexes in curated PPI databases [20][21][22] , there has been considerably less focus on finding systematic ways to separate true from false interactions in large-scale unbiased screens.…”

Section: Introductionmentioning

confidence: 99%

Predictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions

Schmid,

Walter

2024

Preprint

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Protein-protein interactions (PPIs) are ubiquitous in biology, yet a comprehensive structural characterization of the PPIs underlying biochemical processes is lacking. Although AlphaFold-Multimer (AF-M) has the potential to fill this knowledge gap, standard AF-M confidence metrics do not reliably separate relevant PPIs from an abundance of false positive predictions. To address this limitation, we used machine learning on well curated datasets to train a Structure Prediction and Omics informed Classifier called SPOC that shows excellent performance in separating true and false PPIs, including in proteome-wide screens. We applied SPOC to an all-by-all matrix of nearly 300 human genome maintenance proteins, generating ~40,000 predictions that can be viewed at predictomes.org, where users can also score their own predictions with SPOC. High confidence PPIs discovered using our approach suggest novel hypotheses in genome maintenance. Our results provide a framework for interpreting large scale AF-M screens and help lay the foundation for a proteome-wide structural interactome.

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“…The recent emergence of computational structure prediction methods has led to a dramatic increase in the availability of predicted structures (Danneskiold-Samsøe et al, 2023;Rossi Sebastiano et al, 2022;Varadi et al, 2022), offering unparalleled insights into biological mechanisms at a molecular level (Bordin et al, 2023;Mosalaganti et al, 2022). New-generation AI-powered tools like AlphaFold (Jumper et al, 2021), ESMFold (Lin et al, 2023), RoseTTAFold (Baek et al, 2021), and others employ advanced algorithms to provide a bounty of predicted protein structures (or computed structure models) that have significantly broadened the scope of accessible molecular data.…”

Section: Introductionmentioning

confidence: 99%

Describing and Sharing Molecular Visualizations Using the MolViewSpec Toolkit

Bittrich,

Midlik,

Varadi

et al. 2024

Current Protocols

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With the ever‐expanding toolkit of molecular viewers, the ability to visualize macromolecular structures has never been more accessible. Yet, the idiosyncratic technical intricacies across tools and the integration complexities associated with handling structure annotation data present significant barriers to seamless interoperability and steep learning curves for many users. The necessity for reproducible data visualizations is at the forefront of the current challenges. Recently, we introduced MolViewSpec (homepage: https://molstar.org/mol‐view‐spec/, GitHub project: https://github.com/molstar/mol‐view‐spec), a specification approach that defines molecular visualizations, decoupling them from the varying implementation details of different molecular viewers. Through the protocols presented herein, we demonstrate how to use MolViewSpec and its 3D view–building Python library for creating sophisticated, customized 3D views covering all standard molecular visualizations. MolViewSpec supports representations like cartoon and ball‐and‐stick with coloring, labeling, and applying complex transformations such as superposition to any macromolecular structure file in mmCIF, BinaryCIF, and PDB formats. These examples showcase progress towards reusability and interoperability of molecular 3D visualization in an era when handling molecular structures at scale is a timely and pressing matter in structural bioinformatics as well as research and education across the life sciences. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Creating a MolViewSpec view using the MolViewSpec Python packageBasic Protocol 2: Creating a MolViewSpec view with reference to MolViewSpec annotation filesBasic Protocol 3: Creating a MolViewSpec view with labels and other advanced featuresSupport Protocol 1: Computing rotation and translation vectorsSupport Protocol 2: Creating a MolViewSpec annotation file

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AlphaFold2 enables accurate deorphanization of ligands to single-pass receptors

Cited by 12 publications

References 60 publications

Design of Cyclic Peptides Targeting Protein–Protein Interactions Using AlphaFold

Design of Cyclic Peptides Targeting Protein–Protein Interactions Using AlphaFold

Predictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions

Describing and Sharing Molecular Visualizations Using the MolViewSpec Toolkit

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