ArticlePloS one2013
AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed.
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- Deep Learning-Based Prediction of Enzyme Optimal pH and Design of Point Mutations to Improve Acid Resistance.ACS synthetic biology · 2025Article
- Classification of Acid and Alkaline Enzymes Based on Normalized Van der Waals Volume Features.Proteomics. Clinical applications · 2025Article
- A Deep Retrieval-Enhanced Meta-Learning Framework for Enzyme Optimum pH Prediction.Journal of chemical information and modeling · 2025Article
- Approaching Optimal pH Enzyme Prediction with Large Language Models.ACS synthetic biology · 2024Article
- Article
- Acid-resistant enzymes: the acquisition strategies and applications.Applied microbiology and biotechnology · 2023Review
- NeuroCNN_GNB: an ensemble model to predict neuropeptides based on a convolution neural network and Gaussian naive Bayes.Frontiers in genetics · 2023Article
- Robust host source tracking building on the divergent and non-stochastic assembly of gut microbiomes in wild and farmed large yellow croaker.Microbiome · 2022Article
- Identification of intelligence-related proteins through a robust two-layer predictor.Communicative & integrative biology · 2022Article
- Some characters of bacterial cellulases in goats' rumen elucidated by metagenomic DNA analysis and the role of fibronectin 3 module for endoglucanase function.Animal bioscience · 2021Article
- Characterization of a thermophilic cytochrome P450 of the CYP203A subfamily from Binh Chau hot spring in Vietnam.FEBS open bio · 2021Article
- Identifying Antioxidant Proteins by Combining Multiple Methods.Frontiers in bioengineering and biotechnology · 2020Article
- MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence.Frontiers in microbiology · 2020Article
- Autophosphorylation of Orphan Receptor ERBB2 Can Be Induced by Extracellular Treatment with Mildly Alkaline Media.International journal of molecular sciences · 2019Article
- Identification of Bacteriophage Virion Proteins Using Multinomial Naïve Bayes with g-Gap Feature Tree.International journal of molecular sciences · 2018Article
- PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach.Scientific reports · 2018Article
- HBPred: a tool to identify growth hormone-binding proteins.International journal of biological sciences · 2018Article
- SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots.Scientific reports · 2017Article
- Identifying NMolecular genetics and genomics : MGG · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The structure and activity of enzymes are influenced by pH value of their surroundings. Although many enzymes work well in the pH range from 6 to 8, some specific enzymes have good efficiencies only in acidic (pH<5) or alkaline (pH>9) solution. Studies have demonstrated that the activities of enzymes correlate with their primary sequences. It is crucial to judge enzyme adaptation to acidic or alkaline environment from its amino acid sequence in molecular mechanism clarification and the design of high efficient enzymes. In this study, we developed a sequence-based method to discriminate acidic enzymes from alkaline enzymes. The analysis of variance was used to choose the optimized discriminating features derived from g-gap dipeptide compositions. And support vector machine was utilized to establish the prediction model. In the rigorous jackknife cross-validation, the overall accuracy of 96.7% was achieved. The method can correctly predict 96.3% acidic and 97.1% alkaline enzymes. Through the comparison between the proposed method and previous methods, it is demonstrated that the proposed method is more accurate. On the basis of this proposed method, we have built an online web-server called AcalPred which can be freely accessed from the website (http://lin.uestc.edu.cn/server/AcalPred). We believe that the AcalPred will become a powerful tool to study enzyme adaptation to acidic or alkaline environment.
Indexed as
Identifiers
What OpenQuestion holds
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.