ArticleProteins2014
Accurate single-sequence prediction of solvent accessible surface area using local and global features.
Article in Proteins, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
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- Prediction of Intrinsically Disordered Regions That Bind RNA with DisoRDPbind.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins.Frontiers in cellular and infection microbiology · 2025Article
- Accurate Prediction of Protein-Binding Residues in Protein Sequences Using SCRIBER.Methods in molecular biology (Clifton, N.J.) · 2025Article
- AlphaFold2, SPINE-X, and Seder on Four Hard CASP Targets.Methods in molecular biology (Clifton, N.J.) · 2025Article
- DescribePROT Database of Residue-Level Protein Structure and Function Annotations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Intrinsically Disordered Lipid Binding Residues with DisoLipPred.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Comprehensive assessment of AlphaFold's predictions of secondary structure and solvent accessibility at the amino acid-level in eukaryotic, bacterial and archaeal proteins.Computational and structural biotechnology journal · 2025Article
- Machine learning approaches for predicting protein-ligand binding sites from sequence data.Frontiers in bioinformatics · 2025Review
- Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.Human genomics · 2024Article
- Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler.Current genomics · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins.Nucleic acids research · 2024Article
- DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options.Nucleic acids research · 2024Article
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids.Protein science : a publication of the Protein Society · 2023Article
- PITHIA: Protein Interaction Site Prediction Using Multiple Sequence Alignments and Attention.International journal of molecular sciences · 2022Article
- Global, in situ analysis of the structural proteome in individuals with Parkinson's disease to identify a new class of biomarker.Nature structural & molecular biology · 2022Article
- Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning.BMC biology · 2022Article
- Protein-RNA interaction prediction with deep learning: structure matters.Briefings in bioinformatics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment-based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at http://mamiris.com, from the SPARKS Lab at http://sparks-lab.org, and from the Battelle Center for Mathematical Medicine at http://mathmed.org.
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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.