ArticleBMC bioinformatics2008
Impact of residue accessible surface area on the prediction of protein secondary structures.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- RINAMI: Residue-attributed interpretable neural network for predicting absolute folding free energy by merging structure and sequence information.Protein science : a publication of the Protein Society · 2026Article
- Insight into improved specificity and thermostability of3 Biotech · 2025Article
- ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations.Computational and structural biotechnology journal · 2023Article
- Identification of SARS-CoV-2 surface therapeutic targets and drugs using molecular modeling methods for inhibition of the virus entry.Journal of molecular structure · 2022Article
- Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs.Computational and structural biotechnology journal · 2022Article
- Deep learning for protein secondary structure prediction: Pre and post-AlphaFold.Computational and structural biotechnology journal · 2022Review
- Single Molecule Measurements of the Accessibility of Molecular Surfaces.Frontiers in molecular biosciences · 2021Article
- Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction.PloS one · 2020Article
- Insight into Improved Thermostability of Cold-Adapted Staphylococcal Lipase by Glycine to Cysteine Mutation.Molecules (Basel, Switzerland) · 2019Article
- Characterization of recombinant endo-1,4-β-xylanase of Bacillus halodurans C-125 and rational identification of hot spot amino acid residues responsible for enhancing thermostability by an in-silico approach.Molecular biology reports · 2019Article
- HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons.Nucleic acids research · 2017Article
- Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids.International journal of molecular sciences · 2016Article
- CSI 2.0: a significantly improved version of the Chemical Shift Index.Journal of biomolecular NMR · 2014Article
- From local structure to a global framework: recognition of protein folds.Journal of the Royal Society, Interface · 2014Review
- Mutational effect of structural parameters on coiled-coil stability of proteins.Proteomics insights · 2013Article
- Prediction of deleterious nonsynonymous single-nucleotide polymorphism for human diseases.TheScientificWorldJournal · 2013Review
- The rate of the molecular clock and the cost of gratuitous protein synthesis.Genome biology · 2010Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested that amino acid relative solvent accessibility (RSA) might be an effective factor for increasing the accuracy of protein secondary structure prediction. Previous studies have either used a single constant threshold to classify residues into discrete classes (buries vs. exposed), or used the real-value predicted RSAs in their prediction method.
resultsWe studied the effect of applying different RSA threshold types (namely, fixed thresholds vs. residue-dependent thresholds) on a variety of secondary structure prediction methods. With the consideration of DSSP-assigned RSA values we realized that improvement in the accuracy of prediction strictly depends on the selected threshold(s). Furthermore, we showed that choosing a single threshold for all amino acids is not the best possible parameter. We therefore used residue-dependent thresholds and most of residues showed improvement in prediction. Next, we tried to consider predicted RSA values, since in the real-world problem, protein sequence is the only available information. We first predicted the RSA classes by RVP-net program and then used these data in our method. Using this approach, improvement in prediction was also obtained.
conclusionThe success of applying the RSA information on different secondary structure prediction methods suggest that prediction accuracy can be improved independent of prediction approaches. Thus, solvent accessibility can be considered as a rich source of information to help the improvement of these methods.
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