scholarly journals Genetic algorithm and Tabu search based methods for molecular 3D-structure prediction

2011 ◽  
Vol 1 (1) ◽  
pp. 191-209 ◽  
Author(s):  
Abdel-Rahman Hedar ◽  
◽  
Ahmed Fouad Ali ◽  
Taysir Hassan Abdel-Hamid ◽  
◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Structure Prediction ◽  
3D Structure ◽  
3D Structure Prediction

In silico 3D structure prediction and hydrogen peroxide binding study of wheat catalase

2013 ◽  
Vol 5 (1) ◽  
pp. 77-83 ◽  
Author(s):  
Gopal Krishna Sahu ◽  
Bibhuti Bhusan Sahoo ◽  
Sneha Bhandari ◽  
Shruti Pandey
Keyword(s):  
Hydrogen Peroxide ◽  
In Silico ◽  
Structure Prediction ◽  
3D Structure ◽  
Binding Study ◽  
3D Structure Prediction

scholarly journals Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 432 ◽  
Author(s):  
Chandran Nithin ◽  
Pritha Ghosh ◽  
Janusz Bujnicki
Keyword(s):  
Rna Splicing ◽  
Structure Prediction ◽  
Protein Complexes ◽  
3D Structure ◽  
Structural Models ◽  
Computational Prediction ◽  
Computational Docking ◽  
3D Structure Prediction ◽  
Rna Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

Automated RNA 3D Structure Prediction with RNAComposer

2016 ◽  
pp. 199-215 ◽  
Author(s):  
Marcin Biesiada ◽  
Katarzyna J. Purzycka ◽  
Marta Szachniuk ◽  
Jacek Blazewicz ◽  
Ryszard W. Adamiak
Keyword(s):  
Structure Prediction ◽  
3D Structure ◽  
3D Structure Prediction

scholarly journals RNA 3D structure prediction guided by independent folding of homologous sequences

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Marcin Magnus ◽  
Kalli Kappel ◽  
Rhiju Das ◽  
Janusz M. Bujnicki
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
3D Structure ◽  
3D Models ◽  
Target Sequence ◽  
Rna Sequences ◽  
Homologous Sequences ◽  
3D Structure Prediction ◽  
Folding Simulations

Abstract Background The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule’s sequence. The prediction of tertiary structures of complex RNAs is still a challenging task. Results Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence. Conclusion This work, for the first time to our knowledge, demonstrates the importance of the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure “foldability” or “predictability” of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identifying limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

scholarly journals 3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction

2019 ◽  
Vol 20 (17) ◽  
pp. 4116 ◽  
Author(s):  
Jun Wang ◽  
Jian Wang ◽  
Yanzhao Huang ◽  
Yi Xiao
Keyword(s):  
Structure Prediction ◽  
3D Structure ◽  
Web Server ◽  
Structure Optimization ◽  
Optimization Procedure ◽  
Biological Functions ◽  
Rna Sequences ◽  
3D Structures ◽  
3D Structure Prediction ◽  
Sampling Structure

3D structures of RNAs are the basis for understanding their biological functions. However, experimentally solved RNA 3D structures are very limited in comparison with known RNA sequences up to now. Therefore, many computational methods have been proposed to solve this problem, including our 3dRNA. In recent years, 3dRNA has been greatly improved by adding several important features, including structure sampling, structure ranking and structure optimization under residue-residue restraints. Particularly, the optimization procedure with restraints enables 3dRNA to treat pseudoknots in a new way. These new features of 3dRNA can greatly promote its performance and have been integrated into the 3dRNA v2.0 web server. Here we introduce these new features in the 3dRNA v2.0 web server for the users.

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