A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation

Wang, Menglun; Cang, Zixuan; Wei, Guowei

doi:10.1038/s42256-020-0149-6

Cited by 164 publications

(229 citation statements)

References 50 publications

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“…The topological representation uses element-and site-specific persistent homology to simplify the structural complexity of protein-protein complexes and encode vital biological information into topological invariants [40]. NetTree is a recently developed deep learning algorithm that integrates the advantages of convolutional neural networks and gradient-boosting trees [29]. Details of the method can be found in the literature [29].…”

Section: Topnettree Model For Ppi Bfe Changes Upon Mutationmentioning

confidence: 99%

“…NetTree is a recently developed deep learning algorithm that integrates the advantages of convolutional neural networks and gradient-boosting trees [29]. Details of the method can be found in the literature [29]. New parametrization and validation are given in the Supporting Material.…”

Section: Topnettree Model For Ppi Bfe Changes Upon Mutationmentioning

confidence: 99%

“…These databases have been used as benchmarks for evaluating the predictive power of various computational methods [18][19][20][21][22][23]. To simplify the structural complexity of PPI complexes, we have recently introduced element-specific and site-specific persistent homology, a new branch of algebraic topology [27,28], to embed molecular mechanisms into topological invariants [29]. This approach was paired with a new deep learning algorithm called NetTree, to combined convolutional neural networks and gradient boosting trees.…”

Section: Introductionmentioning

confidence: 99%

“…This approach was paired with a new deep learning algorithm called NetTree, to combined convolutional neural networks and gradient boosting trees. The resulting method, called TopNetTree, was about 22% better than the previous best result for the AB-Bind dataset and significantly outperformed the state-of-the-art in the literature on the SKEMPI database [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

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Mutations Strengthened SARS-CoV-2 Infectivity

Chen

Wang

et al. 2020

Journal of Molecular Biology

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446

538

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is a major concern in coronavirus disease 2019 (COVID-19) prevention and economic reopening. However, rigorous determination of SARS-CoV-2 infectivity is very difficult owing to its continuous evolution with over 10,000 single nucleotide polymorphisms (SNP) variants in many subtypes. We employ an algebraic topology-based machine learning model to quantitatively evaluate the binding free energy changes of SARS-CoV-2 spike glycoprotein (S protein) and host angiotensinconverting enzyme 2 receptor following mutations. We reveal that the SARS-CoV-2 virus becomes more infectious. Three out of six SARS-CoV-2 subtypes have become slightly more infectious, while the other three subtypes have significantly strengthened their infectivity. We also find that SARS-CoV-2 is slightly more infectious than SARS-CoV according to computed S protein-angiotensin-converting enzyme 2 binding free energy changes. Based on a systematic evaluation of all possible 3686 future mutations on the S protein receptor-binding domain, we show that most likely future mutations will make SARS-CoV-2 more infectious. Combining sequence alignment, probability analysis, and binding free energy calculation, we predict that a few residues on the receptor-binding motif, i.e., 452, 489, 500, 501, and 505, have high chances to mutate into significantly more infectious COVID-19 strains.

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Section: Topnettree Model For Ppi Bfe Changes Upon Mutationmentioning

confidence: 99%

Section: Topnettree Model For Ppi Bfe Changes Upon Mutationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mutations Strengthened SARS-CoV-2 Infectivity

Chen

Wang

et al. 2020

Journal of Molecular Biology

Self Cite

446

538

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“…The study demonstrated that mCSM-PPI2 outperforms 26 previous methods on CAPRI blind tests [49]. TopNetTree [51] is another new method that utilized a topology-based network tree by integrating a deep learning algorithm, NetTree, and the topological representation. TopNetTree achieved PCC of 0.82 on SKEMPI v2.0 dataset.…”

Section: Introductionmentioning

confidence: 97%

SAAMBE-3D: Predicting Effect of Mutations on Protein–Protein Interactions

Pahari

Murthy

et al. 2020

IJMS

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Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.

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MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning

Li,

Liu

2024

Advanced Science

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Assessing changes in protein–protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism‐aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS‐CoV‐2 variants and the ACE2 complex.

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A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation

Cited by 164 publications

References 50 publications

Mutations Strengthened SARS-CoV-2 Infectivity

Mutations Strengthened SARS-CoV-2 Infectivity

SAAMBE-3D: Predicting Effect of Mutations on Protein–Protein Interactions

MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning

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