AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility

Martin, Juliette

doi:10.1021/acs.jcim.3c01805

Cited by 2 publications

References 68 publications

(101 reference statements)

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MSA clustering enhances AF-Multimer’s ability to predict conformational landscapes of protein–protein interactions

Rustamov,

Baev

2024

Bioinformatics Advances

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Motivation Understanding the conformational landscape of protein-ligand interactions is critical for elucidating the binding mechanisms that govern these interactions. Traditional methods like molecular dynamics (MD) simulations are computationally intensive, leading to a demand for more efficient approaches. This study explores how MSA (multiple sequence alignment) clustering enhance AF-Multimer's ability to predict conformational landscapes, particularly for proteins with multiple conformational states. Results We verified this approach by predicting the conformational landscapes of chemokine (CXCR4) and glucagon (GCGR) receptors in the presence of their agonists and antagonists. In our experiments AF-Multimer predicted the structures of CXCR4 and GCGR predominantly in active state in the presence of agonists and in inactive state in the presence of antagonists. Moreover, we tested our approach with proteins known to switch between monomeric and dimeric states, such as lymphotactin, SH3 and thermonuclease. AFcluster-Multimer accurately predicted conformational states during oligomerization, which AFcluster with AlphaFold2 alone fails to achieve. In conclusion, MSA clustering enhances AF-Multimer's ability to predict protein conformational landscapes and mechanistic effects of ligand binding, offering a robust tool for understanding protein-ligand interactions. Availability and Implementation Code for running AFcluster-Multimer is available at https://github.com/KhondamirRustamov/AF-Multimer-cluster Supplementary information Supplementary figures are available at Bioinformatics Advances online.

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MSA clustering enhances AF-Multimer’s ability to predict conformational landscapes of protein–protein interactions

Rustamov,

Baev

2024

Bioinformatics Advances

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What NLR you recognizing? Predicted binding affinities- and energies may be used to identify novel NLR-effector interactions

Fick,

Swart

et al. 2024

Preprint

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Nucleotide binding Leucine-rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity in plants following pathogen infection. Advances in genome sequencing have led to the identification of a myriad NLRs in numerous agriculturally important plant species. However, deciphering which NLR proteins recognize specific pathogen effectors remains a challenge. Predicting NLR-effector interactions in silico would provide a more targeted approach for experimental validation, critical for elucidating function, and advancing our understanding of NLR-triggered immunity. In this study, NLR-effector protein complex structures were predicted using Alphafold2-Multimer for all experimentally validated NLR-effector interactions. Binding affinities- and energies were predicted using 97 machine learning models from Area-affinity. We show that predicted structures with an AlphaFold confidence score > 0.42 have acceptable accuracy, and can be used to investigate NLR-effector interactions in silico. Binding affinities for 58 NLR-effector complexes ranged between -8.5 and -10.6 log(K), and binding energies between -11.8 and -14.4 kcal/mol, depending on the Area-Affinity model used. For 2427 forced NLR-effector complexes, these estimates showed larger variability, enabling the identification of novel NLR-effector complexes with 99% accuracy using an Ensemble machine learning model. The narrow range of binding energies- and affinities for true interactions suggest a specific change in Gibbs free energy, and thus conformational change, is required for NLR activation. This is the first study to provide a method for predicting NLR-effector interactions, applicable to all plant-pathogen interactions. Finally, the NLR-Effector Interaction Classification (NEIC) resource can streamline research efforts by identifying NLRs important for providing resistance against plant pathogens, advancing our understanding of plant immunity.

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AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility

Cited by 2 publications

References 68 publications

MSA clustering enhances AF-Multimer’s ability to predict conformational landscapes of protein–protein interactions

MSA clustering enhances AF-Multimer’s ability to predict conformational landscapes of protein–protein interactions

What NLR you recognizing? Predicted binding affinities- and energies may be used to identify novel NLR-effector interactions

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