ArticleBMC bioinformatics2015
A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Performance of pyDock in 8th CAPRI: Energy-Based Scoring Applied to Docking and AlphaFold Models.Proteins · 2026Article
- Transmembrane Prostate Androgen-Induced Protein 1 Molecular Modeling and Refinement Using Coarse-Grained Molecular Dynamics.ACS omega · 2025Article
- In silico and in vitro study of Mycobacterium tuberculosis H37Rv uncharacterized protein (RipD): an insight on tuberculosis therapeutics.Journal of molecular modeling · 2022Article
- Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14.Scientific reports · 2021Article
- A single-model quality assessment method for poor quality protein structure.BMC bioinformatics · 2020Article
- Identification and localization of Tospovirus genus-wide conserved residues in 3D models of the nucleocapsid and the silencing suppressor proteins.Virology journal · 2019Article
- An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences.Acta crystallographica. Section D, Structural biology · 2017Review
- QAcon: single model quality assessment using protein structural and contact information with machine learning techniques.Bioinformatics (Oxford, England) · 2017Article
- DeepQA: improving the estimation of single protein model quality with deep belief networks.BMC bioinformatics · 2016Article
- A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning.Journal of proteomics & bioinformatics · 2016Article
- A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling.Scientific reports · 2016Article
- Protein single-model quality assessment by feature-based probability density functions.Scientific reports · 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
Abstract
backgroundWith more and more protein sequences produced in the genomic era, predicting protein structures from sequences becomes very important for elucidating the molecular details and functions of these proteins for biomedical research. Traditional template-based protein structure prediction methods tend to focus on identifying the best templates, generating the best alignments, and applying the best energy function to rank models, which often cannot achieve the best performance because of the difficulty of obtaining best templates, alignments, and models.
methodsWe developed a large-scale conformation sampling and evaluation method and its servers to improve the reliability and robustness of protein structure prediction. In the first step, our method used a variety of alignment methods to sample relevant and complementary templates and to generate alternative and diverse target-template alignments, used a template and alignment combination protocol to combine alignments, and used template-based and template-free modeling methods to generate a pool of conformations for a target protein. In the second step, it used a large number of protein model quality assessment methods to evaluate and rank the models in the protein model pool, in conjunction with an exception handling strategy to deal with any additional failure in model ranking.
resultsThe method was implemented as two protein structure prediction servers: MULTICOM-CONSTRUCT and MULTICOM-CLUSTER that participated in the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014. The two servers were ranked among the best 10 server predictors.
conclusionsThe good performance of our servers in CASP11 demonstrates the effectiveness and robustness of the large-scale conformation sampling and evaluation. The MULTICOM server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/.
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.