ArticleBioinformatics (Oxford, England)2011
Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 162 papers, 1 of them a synthesis that pooled it.
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
162 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Article
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Deep learning in next-generation vaccine development for infectious diseases.Molecular therapy. Nucleic acids · 2025Review
- In Silico Analysis and Validation of A Disintegrin and Metalloprotease (ADAM) 17 Gene Missense Variants: Structural Bioinformatics Study.JMIR bioinformatics and biotechnology · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Functional and structural analysis of missense variants in the humanJournal of public health in Africa · 2025Article
- Prediction of Disordered Linkers Using APOD.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Parameterized hypercomplex convolutional network for accurate protein backbone torsion angle prediction.Scientific reports · 2024Article
- A privacy-preserving approach for cloud-based protein fold recognition.Patterns (New York, N.Y.) · 2024Article
- Advances in Computational Intelligence-Based Methods of Structure and Function Prediction of Proteins.Biomolecules · 2024Article
- AI-Driven Deep Learning Techniques in Protein Structure Prediction.International journal of molecular sciences · 2024Review
- Insights into structural vaccinology harnessed for universal coronavirus vaccine development.Clinical and experimental vaccine research · 2024Review
- Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.Interdisciplinary sciences, computational life sciences · 2024Review
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality.Biomolecules · 2024Article
- An interactive visualization tool for educational outreach in protein contact map overlap analysis.Frontiers in bioinformatics · 2024Article
- Designing a novel and combinatorial multi-antigenic epitope-based vaccine "MarVax" against Marburg virus-a reverse vaccinology and immunoinformatics approach.Journal, genetic engineering & biotechnology · 2023Article
- A sequence-based evolutionary distance method for Phylogenetic analysis of highly divergent proteins.Scientific reports · 2023Article
102 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
motivationIn recent years, development of a single-method fold-recognition server lags behind consensus and multiple template techniques. However, a good consensus prediction relies on the accuracy of individual methods. This article reports our efforts to further improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area.
resultsThe new method called SPARKS-X was tested with the SALIGN benchmark for alignment accuracy, Lindahl and SCOP benchmarks for fold recognition, and CASP 9 blind test for structure prediction. The method is compared to several state-of-the-art techniques such as HHPRED and BoostThreader. Results show that SPARKS-X is one of the best single-method fold recognition techniques. We further note that incorporating multiple templates and refinement in model building will likely further improve SPARKS-X. AVAILABILITY: The method is available as a SPARKS-X server at http://sparks.informatics.iupui.edu/
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.