Protein–Protein Docking: Past, Present, and Future

Sunny, Sharon; Jayaraj, P. B.

doi:10.1007/s10930-021-10031-8

Cited by 25 publications

(17 citation statements)

References 179 publications

(185 reference statements)

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“…There are different approaches to studying and predicting PPIs [20]. Experimental techniques are reliable for detecting structures of small proteins or single monomers, but they have intrinsic limits regarding protein complexes (see, e.g., [21,22]) and are timeand resource-intensive [23]. To overcome these limitations, computational methods to predict 3D protein complexes and the corresponding PPIs have been developed.…”

Section: Introductionmentioning

confidence: 99%

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Orasch¹,

Weber²,

Müller³

et al. 2022

IJMS

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Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.

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Section: Introductionmentioning

confidence: 99%

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Orasch¹,

Weber²,

Müller³

et al. 2022

IJMS

View full text Add to dashboard Cite

show abstract

“…There are different approaches to studying and predicting PPIs [20]. Experimental techniques are reliable for detecting structures of small proteins or single monomers, but they have intrinsic limits regarding protein complexes (see, e.g., [21, 22]) and are time- and resource-intensive [23]. To overcome these limitations, com-putational methods to predict 3D protein complexes and the corresponding PPIs have been developed.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these limitations, com-putational methods to predict 3D protein complexes and the corresponding PPIs have been developed. Many current methods rely on machine learning and can be classified into one of two groups based on the available input: template-free and template-based approaches [23]. Template-free methods provide predictions based on the peptide sequence only.…”

Section: Introductionmentioning

confidence: 99%

“…Template-free methods provide predictions based on the peptide sequence only. While current machine learning-based template methods for protein structure and PPI prediction have shown promising results [24], they are often computationally costly, limiting their use when many inputs need to be evaluated [23]. On the other hand, template-based (e.g., [25]) approaches compute their outputs using 3D structural information as input.…”

Section: Introductionmentioning

confidence: 99%

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Protein-protein interaction prediction for targeted protein degradation

Orasch¹,

Weber²,

Müller³

et al. 2022

Preprint

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Protein-protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, computer-aided approaches have been developed as screening tools, but these tools are primarily based on sequence information and are therefore limited in their ability to address spatial requirements and have thus far not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.

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“…Scoring protein-protein docking poses is another important task in studying protein-protein interaction. Various criteria [16] have been proposed, such as force-field based criteria [17, 18], knowledge-based criteria [19, 20], and machine learning-based criteria [21]. Since the side chains of the residues, especially those located at the interface, are crucial for protein-protein interaction, a scoring term that is mainly based on side chains may be an effective term for better scoring the protein-protein docking poses.…”

Section: Introductionmentioning

confidence: 99%

Studying protein-protein interaction through side-chain modeling method OPUS-Mut

Wang²,

Wang

et al. 2022

Preprint

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Protein side chains are vitally important to many biological processes such as protein-protein interaction. In this study, we evaluate the performance of our previous released side-chain modeling method OPUS-Mut, together with some other methods, on three oligomer datasets, CASP14 (11), CAMEO-Homo (65), and CAMEO-Hetero (21). The results show that OPUS-Mut outperforms other methods measured by all residues or by the interfacial residues. We also demonstrate our method on evaluating protein-protein docking pose on a dataset Oligomer-Dock (75) created using the top 10 predictions from ZDOCK 3.0.2. Our scoring function correctly identifies the native pose as the top-1 in 45 out of 75 targets. Different from traditional scoring functions, our method is based on the overall side-chain packing favorableness in accordance with the local packing environment. It emphasizes the significance of side chains and provides a new and effective scoring term for studying protein-protein interaction.

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Protein–Protein Docking: Past, Present, and Future

Cited by 25 publications

References 179 publications

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein-protein interaction prediction for targeted protein degradation

Studying protein-protein interaction through side-chain modeling method OPUS-Mut

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