ArticleThe protein journal2013
Protein hypersaline adaptation: insight from amino acids with machine learning algorithms.
Article in The protein journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 26 citations in OpenAlex.
- Article
- HaloClass: Salt-Tolerant Protein Classification with Protein Language Models.The protein journal · 2024Article
- Functional Properties of Amino Acid Side Chains as Biomarkers of Extraterrestrial Life.Astrobiology · 2018Article
- Structural and Mechanistic Insights into the Improvement of the Halotolerance of a Marine Microbial Esterase by Increasing Intra- and Interdomain Hydrophobic Interactions.Applied and environmental microbiology · 2017Article
- Halophiles: biology, adaptation, and their role in decontamination of hypersaline environments.World journal of microbiology & biotechnology · 2016Review
- A novel salt-tolerant chitobiosidase discovered by genetic screening of a metagenomic library derived from chitin-amended agricultural soil.Applied microbiology and biotechnology · 2015Article
- Structural changes in halophilic and non-halophilic proteases in response to chaotropic reagents.The protein journal · 2014Article
- Article
- Protein adaptations in archaeal extremophiles.Archaea (Vancouver, B.C.) · 2013Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional bioinformatics methods performed systematic comparison between the halophilic proteins and their non-halophilic homologues, to investigate the features related to hypersaline adaptation. Therefore, proposing some quantitative models to explain the sequence-characteristic relationship of halophilic proteins might shed new light on haloadaptation and help to design new biocatalysts adapt to high salt concentration. Five machine learning algorithm, including three linear and two non-linear methods were used to discriminate halophilic and their non-halophilic counterparts and the prediction accuracy was encouraging. The best prediction reliability for halophilic proteins was achieved by artificial neural network and support vector machine and reached 80 %, for non-halophilic proteins, it was achieved by linear regression and reached 100 %. Besides, the linear models have captured some clues for protein halo-stability. Among them, lower frequency of Ser in halophilic protein has not been report before.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.