Evidence map›Paper›PMID 26493701›Full record

ArticleBMC bioinformatics2015

A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11.

Jilong Li, Renzhi Cao, Jianlin Cheng

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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.

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12citing papers in PubMed
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3 · Its place in the literature

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12 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Jilong LiDepartment of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA. jl482@mail.missouri.edu.
Renzhi CaoDepartment of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA. rcrg4@mail.missouri.edu.
Jianlin ChengDepartment of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA. chengji@missouri.edu.

Funding

Integrated Prediction of Protein Struture at 1D, 2D and 3D LevelsR01GM093123 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI CHENG, JIANLIN · 2010 to 2023
$3.8M
NIGMS NIH HHS R01 GM093123NIGMS NIH HHS R01GM093123
6 · The paper itself

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

Protein Structure, TertiaryAmino Acid SequenceGenomicsModels, ChemicalProtein ConformationProteinsProteins

Identifiers

PMID26493701
PMCPMC4619059

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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.