How DNA affects the hyperthermophilic protein Ape10b2 for oligomerization: an investigation using multiple short molecular dynamics simulations

Zhang, Xue; Zheng, Qing‐Chuan

doi:10.1039/d1cp04341b

Cited by 1 publication

(1 citation statement)

References 46 publications

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“…Protein conformation undergoes changes when binding with a partner [ 44 , 65 , 66 ], and currently available structure-based PPI sites prediction tools were typically trained using protein complex structures, which limits their accuracy and generalization ability when predicting PPI sites on unbound-form proteins [ 35 ]. To elaborate the second limitation of structure-based methods, we compared the predictive performance of RGN and GraphPPIS on the same protein with different conformations (i.e., bound and unbound conformations).…”

Section: Resultsmentioning

confidence: 99%

A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites

Mou,

Pan,

Zhou

et al. 2023

Research

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The identification of protein–protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis .

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Section: Resultsmentioning

confidence: 99%

A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites

Mou,

Pan,

Zhou

et al. 2023

Research

View full text Add to dashboard Cite

show abstract

How DNA affects the hyperthermophilic protein Ape10b2 for oligomerization: an investigation using multiple short molecular dynamics simulations

Abstract: Ape10b2 can stabilize DNA at high temperature and DNA disturbance of the protein is beneficial for oligomerization.

Cited by 1 publication

References 46 publications

A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites

A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites

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