Protein–protein interaction prediction methods: from docking-based to AI-based approaches

Tsuchiya, Yuko; Yamamori, Yu; Tomii, Kentaro

doi:10.1007/s12551-022-01032-7

Cited by 13 publications

(5 citation statements)

References 74 publications

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“…In docking, it is advantageous to use or predict hot spots (small sets of residues that contribute significantly to protein-protein interaction formation), as mentioned by Tsuchiya et al, 55 to increase the accuracy and reliability of docking predictions. In the future, we will analyze interaction hot spots to provide more grounding to the potential interactions between HDA15 and XAL1.…”

Section: Perspectivesmentioning

confidence: 99%

HDACs MADS-domain protein interaction: a case study of HDA15 and XAL1 in Arabidopsis thaliana

Sanjuan-Badillo,

P. Martínez-Castilla,

García-Sandoval

et al. 2024

Plant Signaling & Behavior

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Cellular behavior, cell differentiation and ontogenetic development in eukaryotes result from complex interactions between epigenetic and classic molecular genetic mechanisms, with many of these interactions still to be elucidated. Histone deacetylase enzymes (HDACs) promote the interaction of histones with DNA by compacting the nucleosome, thus causing transcriptional repression. MADS-domain transcription factors are highly conserved in eukaryotes and participate in controlling diverse developmental processes in animals and plants, as well as regulating stress responses in plants. In this work, we focused on finding out putative interactions of Arabidopsis thaliana HDACs and MADS-domain proteins using an evolutionary perspective combined with bioinformatics analyses and testing the more promising predicted interactions through classic molecular biology tools. Through bioinformatic analyses, we found similarities between HDACs proteins from different organisms, which allowed us to predict a putative protein-protein interaction between the Arabidopsis thaliana deacetylase HDA15 and the MADS-domain protein XAANTAL1 (XAL1). The results of two-hybrid and Bimolecular Fluorescence Complementation analysis demonstrated in vitro and in vivo HDA15-XAL1 interaction in the nucleus. Likely, this interaction might regulate developmental processes in plants as is the case for this type of interaction in animals.

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Section: Perspectivesmentioning

confidence: 99%

HDACs MADS-domain protein interaction: a case study of HDA15 and XAL1 in Arabidopsis thaliana

Sanjuan-Badillo,

P. Martínez-Castilla,

García-Sandoval

et al. 2024

Plant Signaling & Behavior

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“…On the one hand, it has been shown that MSA can be denoised . On the other hand, the recent development of predictors relying on large language models (LLM) opens new perspectives in that field. , In particular, the lower quality of the ESMFold predictions might be compensated by the considerable decrease of the calculation time required by language models. Finally, ColabDock has been proposed to perform a form of docking restrained by experimental data .…”

Section: Predicting Oligomeric Modelsmentioning

confidence: 99%

A Perspective on the Prospective Use of AI in Protein Structure Prediction

Versini,

Sritharan,

Aykac Fas

et al. 2023

J. Chem. Inf. Model.

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AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

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“…The success of such virtual screening campaigns greatly depends on the prediction accuracy of the docking and scoring algorithms for antibody ranking. As recently reviewed 92 and evaluated 93 , structural prediction of protein-protein complexes using docking and AI-based approaches still leaves room for improvement, especially in antibody-antigen complexes derived from structural antibody homology-based models. Similar to any docking problem, aspects like the accurate prediction of flexibility, the role of water and further factors might represent potential challenges 94 in this case as well.…”

Section: The Emerging Approach -De Novo Design Of Developable Antibod...mentioning

confidence: 99%

SUMO –In SilicoSequence Assessment Using Multiple Optimization Parameters

Evers

Malhotra

Bolick

et al. 2022

Preprint

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To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

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Protein–protein interaction prediction methods: from docking-based to AI-based approaches

Cited by 13 publications

References 74 publications

HDACs MADS-domain protein interaction: a case study of HDA15 and XAL1 in Arabidopsis thaliana

HDACs MADS-domain protein interaction: a case study of HDA15 and XAL1 in Arabidopsis thaliana

A Perspective on the Prospective Use of AI in Protein Structure Prediction

SUMO –In SilicoSequence Assessment Using Multiple Optimization Parameters

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