miPEPPred-FRL: A Novel Method for Predicting Plant MiRNA-Encoded Peptides Using Adaptive Feature Representation Learning

Li, Haibin; Meng, Jun; Wang, Zhaowei; Tang, Youwei; Xia, Shihao; Wang, Yu; Qin, Zhaojing; Luan, Yushi

doi:10.1021/acs.jcim.3c01020

J. Chem. Inf. Model.

2023

DOI: 10.1021/acs.jcim.3c01020

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miPEPPred-FRL: A Novel Method for Predicting Plant MiRNA-Encoded Peptides Using Adaptive Feature Representation Learning

Haibin Li,

Jun Meng,

Zhaowei Wang

et al.

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2024

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Cited by 2 publications

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“…Yang et al discovered human miRNA target sites by learning the interaction patterns between miRNAs and mRNA fragments. Li et al used adaptive feature representation learning to predict plant miRNA-encoded peptides. Wang et al enabled the identification of plant-secreted peptides using contrastive learning and feature-correction strategies.…”

mentioning

confidence: 99%

Editorial: Machine Learning in Bio-cheminformatics

Merz,

Wei,

Zhu

2024

J. Chem. Inf. Model.

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mentioning

confidence: 99%

Editorial: Machine Learning in Bio-cheminformatics

Merz,

Wei,

Zhu

2024

J. Chem. Inf. Model.

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Deciphering GB1’s Single Mutational Landscape: Insights from MuMi Analysis

Guclu,

Atilgan,

Atilgan

2024

J. Phys. Chem. B

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Mutational changes that affect the binding of the C2 fragment of Streptococcal protein G (GB1) to the Fc domain of human IgG (IgG-Fc) have been extensively studied using deep mutational scanning (DMS), and the binding affinity of all single mutations has been measured experimentally in the literature. To investigate the underlying molecular basis, we perform in silico mutational scanning for all possible single mutations, along with 2 μs-long molecular dynamics (WT-MD) of the wild-type (WT) GB1 in both unbound and IgG-Fc bound forms. We compute the hydrogen bonds between GB1 and IgG-Fc in WT-MD to identify the dominant hydrogen bonds for binding, which we then assess in conformations produced by Mutation and Minimization (MuMi) to explain the fitness landscape of GB1 and IgG-Fc binding. Furthermore, we analyze MuMi and WT-MD to investigate the dynamics of binding, focusing on the relative solvent accessibility of residues and the probability of residues being located at the binding interface. With these analyses, we explain the interactions between GB1 and IgG-Fc and display the structural features of binding. In sum, our findings highlight the potential of MuMi as a reliable and computationally efficient tool for predicting protein fitness landscapes, offering significant advantages over traditional methods. The methodologies and results presented in this study pave the way for improved predictive accuracy in protein stability and interaction studies, which are crucial for advancements in drug design and synthetic biology.

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miPEPPred-FRL: A Novel Method for Predicting Plant MiRNA-Encoded Peptides Using Adaptive Feature Representation Learning

Cited by 2 publications

References 46 publications

Editorial: Machine Learning in Bio-cheminformatics

Editorial: Machine Learning in Bio-cheminformatics

Deciphering GB1’s Single Mutational Landscape: Insights from MuMi Analysis

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