In silico prediction method for plant Nucleotide‐binding leucine‐rich repeat‐ and pathogen effector interactions DOI Creative Commons
Alicia Fick,

Jacobus Lukas Marthinus Fick,

Velushka Swart

et al.

The Plant Journal, Journal Year: 2025, Volume and Issue: 122(2)

Published: April 1, 2025

SUMMARY Plant Nucleotide‐binding leucine‐rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity following pathogen infection. Genome sequencing advancements have led to the identification myriad NLRs numerous agriculturally important plant species. However, deciphering which recognize specific effectors remains challenging. Predicting NLR–effector interactions silico will provide more targeted approach for experimental validation, critical elucidating function, advancing our understanding NLR‐triggered immunity. In this study, protein complex structures were predicted using AlphaFold2‐Multimer all experimentally validated reported literature. Binding affinities‐ energies 97 machine learning models from Area‐Affinity. We show that acceptable accuracy can be used investigate . affinities 58 complexes ranged between −8.5 −10.6 log(K), binding −11.8 −14.4 kcal/mol −1 , depending on Area‐Affinity model used. For 2427 “forced” complexes, these estimates showed larger variability, enabling novel with 99% an Ensemble model. The narrow range energies‐ “true” suggest change Gibbs free energy, thus conformational change, is required NLR activation. This first study method predicting interactions, applicable pathosystems. Finally, NLR–Effector Interaction Classification (NEIC) resource streamline research efforts by identifying plant–pathogen resistance,

Language: Английский

Unlocking protein networks with Predictomes: The SPOC advantage DOI
Arne Elofsson

Molecular Cell, Journal Year: 2025, Volume and Issue: 85(6), P. 1050 - 1051

Published: March 1, 2025

Language: Английский

Citations

0
Emerging frontiers in protein structure prediction following the AlphaFold revolution DOI Creative Commons
Martin L. Rennie, Michael R. Oliver

Journal of The Royal Society Interface, Journal Year: 2025, Volume and Issue: 22(225)

Published: April 1, 2025

Models of protein structures enable molecular understanding biological processes. Current structure prediction tools lie at the interface biology, chemistry and computer science. Millions models have been generated in a very short space time through revolution driven by deep learning, led AlphaFold. This has provided wealth new structural information. Interpreting these predictions is critical to determining where when this information useful. But proteins are not static nor do they act alone, interacting with other biomolecules complete their function level. review focuses on application state-of-the-art advanced applications. We also suggest set guidelines for reporting AlphaFold predictions.

Language: Английский

Citations

0
Mechanistic Insights into Proteomic Mutation-Phenotype Linkages from Tiling Mutagenesis Screens DOI Creative Commons
Wei He, Jen‐Wei Huang, Yalong Wang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

ABSTRACT High-throughput mutagenesis screens are powerful tools for mapping mutations to phenotypes. However, deciphering the molecular mechanisms that link phenotypic outcomes remains a significant challenge. Here, we present ProTiler-Mut, versatile computational framework harnesses tiling screens, which introduce variants across entire protein sequences, facilitate investigation of mutation-to-phenotype associations at multiple levels, including individual residues, substructures, and protein-protein interactions (PPIs). As demonstrated through our analyses base editing (BE) targeting DNA Damage Response (DDR) proteins T cell regulators, ProTiler-Mut provides novel insights into mutation-phenotype linkages, including: i) refined classification mutation reveals separation-of-function (SOF) category beyond conventional binary loss-of-function (LOF) gain-of-function (GOF); ii) definition phenotype-associated hotspot substructures enable inference function unscreened pathogenic mutations; iii) identification PPIs disrupted by functional mutations. Through analyses, identified substructure harboring GOF disrupt between kinases MAPK1 RSK1, leading activation elevated expression immune checkpoint receptor PD-1. Furthermore, demonstrate applicability various screening platforms, highlighting its broad utility generalizability.

Language: Английский

Citations

0
In silico prediction method for plant Nucleotide‐binding leucine‐rich repeat‐ and pathogen effector interactions DOI Creative Commons
Alicia Fick,

Jacobus Lukas Marthinus Fick,

Velushka Swart

et al.

The Plant Journal, Journal Year: 2025, Volume and Issue: 122(2)

Published: April 1, 2025

SUMMARY Plant Nucleotide‐binding leucine‐rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity following pathogen infection. Genome sequencing advancements have led to the identification myriad NLRs numerous agriculturally important plant species. However, deciphering which recognize specific effectors remains challenging. Predicting NLR–effector interactions silico will provide more targeted approach for experimental validation, critical elucidating function, advancing our understanding NLR‐triggered immunity. In this study, protein complex structures were predicted using AlphaFold2‐Multimer all experimentally validated reported literature. Binding affinities‐ energies 97 machine learning models from Area‐Affinity. We show that acceptable accuracy can be used investigate . affinities 58 complexes ranged between −8.5 −10.6 log(K), binding −11.8 −14.4 kcal/mol −1 , depending on Area‐Affinity model used. For 2427 “forced” complexes, these estimates showed larger variability, enabling novel with 99% an Ensemble model. The narrow range energies‐ “true” suggest change Gibbs free energy, thus conformational change, is required NLR activation. This first study method predicting interactions, applicable pathosystems. Finally, NLR–Effector Interaction Classification (NEIC) resource streamline research efforts by identifying plant–pathogen resistance,

Language: Английский

Citations

0