ReviewEvolutionary bioinformatics online2015
Soft Computing Methods for Disulfide Connectivity Prediction.
Review in Evolutionary bioinformatics online, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 8 citations in OpenAlex.
- Hard nut to crack: Solving the disulfide linkage pattern of the Neosartorya (Aspergillus) fischeri antifungal protein 2.Protein science : a publication of the Protein Society · 2023Article
- Residue Adjacency Matrix Based Feature Engineering for Predicting Cysteine Reactivity in Proteins.Computational and structural biotechnology journal · 2019Article
- Bacterial thiol oxidoreductases - from basic research to new antibacterial strategies.Applied microbiology and biotechnology · 2017Review
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
The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.
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Registered trials
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