scholarly journals Prediction of the RNA Tertiary Structure Based on a Random Sampling Strategy and Parallel Mechanism

2022 ◽  
Vol 12 ◽  
Author(s):  
Zhendong Liu ◽  
Yurong Yang ◽  
Dongyan Li ◽  
Xinrong Lv ◽  
Xi Chen ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Structure Prediction ◽  
Parallel Mechanism ◽  
Tertiary Structure ◽  
De Novo ◽  
Sampling Strategy ◽  
Monte Carlo Algorithm ◽  
General Ability ◽  
Rna Tertiary Structure ◽  
New Research

Background: Macromolecule structure prediction remains a fundamental challenge of bioinformatics. Over the past several decades, the Rosetta framework has provided solutions to diverse challenges in computational biology. However, it is challenging to model RNA tertiary structures effectively when the de novo modeling of RNA involves solving a well-defined small puzzle.Methods: In this study, we introduce a stepwise Monte Carlo parallelization (SMCP) algorithm for RNA tertiary structure prediction. Millions of conformations were randomly searched using the Monte Carlo algorithm and stepwise ansatz hypothesis, and SMCP uses a parallel mechanism for efficient sampling. Moreover, to achieve better prediction accuracy and completeness, we judged and processed the modeling results.Results: A benchmark of nine single-stranded RNA loops drawn from riboswitches establishes the general ability of the algorithm to model RNA with high accuracy and integrity, including six motifs that cannot be solved by knowledge mining–based modeling algorithms. Experimental results show that the modeling accuracy of the SMCP algorithm is up to 0.14 Å, and the modeling integrity on this benchmark is extremely high.Conclusion: SMCP is an ab initio modeling algorithm that substantially outperforms previous algorithms in the Rosetta framework, especially in improving the accuracy and completeness of the model. It is expected that the work will provide new research ideas for macromolecular structure prediction in the future. In addition, this work will provide theoretical basis for the development of the biomedical field.

GPU-based acceleration of an RNA tertiary structure prediction algorithm

2013 ◽  
Vol 43 (8) ◽  
pp. 1011-1022 ◽  
Author(s):  
Yongkweon Jeon ◽  
Eesuk Jung ◽  
Hyeyoung Min ◽  
Eui-Young Chung ◽  
Sungroh Yoon
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
Prediction Algorithm ◽  
Tertiary Structure Prediction ◽  
Rna Tertiary Structure ◽  
Structure Prediction Algorithm

State-of-the-art web services for de novo protein structure prediction

2020 ◽  
Author(s):  
Luciano A Abriata ◽  
Matteo Dal Peraro
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
De Novo ◽  
State Of The Art ◽  
Data Bank ◽  
End Users ◽  
Model Quality ◽  
Uncharacterized Protein

Abstract Residue coevolution estimations coupled to machine learning methods are revolutionizing the ability of protein structure prediction approaches to model proteins that lack clear homologous templates in the Protein Data Bank (PDB). This has been patent in the last round of the Critical Assessment of Structure Prediction (CASP), which presented several very good models for the hardest targets. Unfortunately, literature reporting on these advances often lacks digests tailored to lay end users; moreover, some of the top-ranking predictors do not provide webservers that can be used by nonexperts. How can then end users benefit from these advances and correctly interpret the predicted models? Here we review the web resources that biologists can use today to take advantage of these state-of-the-art methods in their research, including not only the best de novo modeling servers but also datasets of models precomputed by experts for structurally uncharacterized protein families. We highlight their features, advantages and pitfalls for predicting structures of proteins without clear templates. We present a broad number of applications that span from driving forward biochemical investigations that lack experimental structures to actually assisting experimental structure determination in X-ray diffraction, cryo-EM and other forms of integrative modeling. We also discuss issues that must be considered by users yet still require further developments, such as global and residue-wise model quality estimates and sources of residue coevolution other than monomeric tertiary structure.

De novo tertiary structure prediction using RNA123—benchmarking and application to Macugen

2014 ◽  
Vol 20 (8) ◽  
Author(s):  
Emma S. E. Eriksson ◽  
Lokesh Joshi ◽  
Martin Billeter ◽  
Leif A. Eriksson
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
De Novo ◽  
Tertiary Structure Prediction

scholarly journals RNA tertiary structure prediction with ModeRNA

2011 ◽  
Vol 12 (6) ◽  
pp. 601-613 ◽  
Author(s):  
M. Rother ◽  
K. Rother ◽  
T. Puton ◽  
J. M. Bujnicki
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
Tertiary Structure Prediction ◽  
Rna Tertiary Structure

scholarly journals Generating, Maintaining, and Exploiting Diversity in a Memetic Algorithm for Protein Structure Prediction

2016 ◽  
Vol 24 (4) ◽  
pp. 577-607 ◽  
Author(s):  
Mario Garza-Fabre ◽  
Shaun M. Kandathil ◽  
Julia Handl ◽  
Joshua Knowles ◽  
Simon C. Lovell
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
De Novo ◽  
Memetic Algorithm ◽  
Scale Up ◽  
Three Dimensional ◽  
Limiting Factors ◽  
Genetic Operators

Computational approaches to de novo protein tertiary structure prediction, including those based on the preeminent “fragment-assembly” technique, have failed to scale up fully to larger proteins (on the order of 100 residues and above). A number of limiting factors are thought to contribute to the scaling problem over and above the simple combinatorial explosion, but the key ones relate to the lack of exploration of properly diverse protein folds, and to an acute form of “deception” in the energy function, whereby low-energy conformations do not reliably equate with native structures. In this article, solutions to both of these problems are investigated through a multistage memetic algorithm incorporating the successful Rosetta method as a local search routine. We found that specialised genetic operators significantly add to structural diversity and that this translates well to reaching low energies. The use of a generalised stochastic ranking procedure for selection enables the memetic algorithm to handle and traverse deep energy wells that can be considered deceptive, which further adds to the ability of the algorithm to obtain a much-improved diversity of folds. The results should translate to a tangible improvement in the performance of protein structure prediction algorithms in blind experiments such as CASP, and potentially to a further step towards the more challenging problem of predicting the three-dimensional shape of large proteins.

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