ArticleNucleic acids research2019
PSICA: a fast and accurate web service for protein model quality analysis.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- DeepUMQA-X: Comprehensive and insightful estimation of model accuracy for protein single-chain and complex.Nucleic acids research · 2025Article
- Investigations on genomic, topological and structural properties of diguanylate cyclases involved inCurrent research in structural biology · 2025Article
- Recent advances and challenges in protein complex model accuracy estimation.Computational and structural biotechnology journal · 2024Review
- Biochemical characterization and gene structure analysis of the 24-kDa glutathione transferase sigma from Taenia solium.FEBS open bio · 2024Article
- An In Silico Bioremediation Study to Identify Essential Residues of Metallothionein Enhancing the Bioaccumulation of Heavy Metals inMicroorganisms · 2023Article
- DE-STRESS: a user-friendly web application for the evaluation of protein designs.Protein engineering, design & selection : PEDS · 2021Article
- MUfoldQA_G: High-accuracy protein model QA via retraining and transformation.Computational and structural biotechnology journal · 2021Article
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5 authors.
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Abstract
This paper presents a new fast and accurate web service for protein model quality analysis, called PSICA (Protein Structural Information Conformity Analysis). It is designed to evaluate how much a tertiary model of a given protein primary sequence conforms to the known protein structures of similar protein sequences, and to evaluate the quality of predicted protein models. PSICA implements the MUfoldQA_S method, an efficient state-of-the-art protein model quality assessment (QA) method. In CASP12, MUfoldQA_S ranked No. 1 in the protein model QA select-20 category in terms of the difference between the predicted and true GDT-TS value of each model. For a given predicted 3D model, PSICA generates (i) predicted global GDT-TS value; (ii) interactive comparison between the model and other known protein structures; (iii) visualization of the predicted local quality of the model; and (iv) JSmol rendering of the model. Additionally, PSICA implements MUfoldQA_C, a new consensus method based on MUfoldQA_S. In CASP12, MUfoldQA_C ranked No. 1 in top 1 model GDT-TS loss on the select-20 QA category and No. 2 in the average difference between the predicted and true GDT-TS value of each model for both select-20 and best-150 QA categories. The PSICA server is freely available at http://qas.wangwb.com/∼wwr34/mufoldqa/index.html.
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