Recognizing protein-metal ion ligands binding residues by random forest algorithm with adding orthogonal properties-Reference-Cited by-同舟云学术

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Recognizing protein-metal ion ligands binding residues by random forest algorithm with adding orthogonal properties

Published:2022-06 Issue: Volume:98 Page:107693
ISSN:1476-9271
Container-title:Computational Biology and Chemistry
language:en
Short-container-title:Computational Biology and Chemistry

Author:

You Xiaoxiao,Hu Xiuzhen,Feng Zhenxing,Wang Ziyang,Hao Sixi,Yang Caiyun

Funder

Natural Science Foundation of Inner Mongolia

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computational Mathematics,Organic Chemistry,Biochemistry,Structural Biology

Reference41 articles.

1. Prediction of transition metal‐binding sites from apo protein structures;Babor;Proteins-Struct. Funct. Bioinforma.,2007

2. Machine learning for predicting lifespan-extending chemical compounds;Barardo;Aging,2017

3. Identification of metal ion binding sites based on amino acid sequences;Cao;Plos One,2017

4. Prediction of solvent accessibility and sites of deleterious mutations from protein sequence;Chen;Nucleic Acids Res.,2005

5. Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field;Dong;Protein.: Struct., Funct., Bioinforma.,2012

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier;ACS Omega;2024-02-08

2. Integrating reduced amino acid composition into PSSM for improving copper ion-binding protein prediction;International Journal of Biological Macromolecules;2023-07

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