scholarly journals SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

2014 ◽  
Vol 15 (1) ◽  
Author(s):  
Ian Reid ◽  
Nicholas O’Toole ◽  
Omar Zabaneh ◽  
Reza Nourzadeh ◽  
Mahmoud Dahdouli ◽  
...  
Keyword(s):  
Ab Initio ◽  
Accurate Prediction ◽  
Rna Seq ◽  
Homology Information ◽  
Fungal Genes ◽  
Ab Initio Models

scholarly journals A Peptides Prediction Methodology for Tertiary Structure Based on Simulated Annealing

2021 ◽  
Vol 26 (2) ◽  
pp. 39
Author(s):  
Juan P. Sánchez-Hernández ◽  
Juan Frausto-Solís ◽  
Juan J. González-Barbosa ◽  
Diego A. Soto-Monterrubio ◽  
Fanny G. Maldonado-Nava ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Ab Initio ◽  
Primary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Golden Ratio ◽  
Small Peptides ◽  
Hybrid Simulated Annealing ◽  
Ab Initio Models

The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.

scholarly journals Refined ab initio gene predictions of Heterorhabditis bacteriophora using RNA-seq

2018 ◽  
Vol 48 (8) ◽  
pp. 585-590 ◽  
Author(s):  
Jonathan Vadnal ◽  
Olivia G. Granger ◽  
Ramesh Ratnappan ◽  
Ioannis Eleftherianos ◽  
Damien M. O'Halloran ◽  
...  
Keyword(s):  
Ab Initio ◽  
Heterorhabditis Bacteriophora ◽  
Rna Seq

Ab initio tools for the accurate prediction of the visible spectra of anthraquinones

2007 ◽  
Vol 67 (2) ◽  
pp. 334-341 ◽  
Author(s):  
Denis Jacquemin ◽  
Valérie Wathelet ◽  
Julien Preat ◽  
Eric A. Perpète
Keyword(s):  
Ab Initio ◽  
Accurate Prediction ◽  
Visible Spectra

scholarly journals Revealing Missing Human Protein Isoforms Based on Ab Initio Prediction, RNA-seq and Proteomics

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Zhiqiang Hu ◽  
Hamish S. Scott ◽  
Guangrong Qin ◽  
Guangyong Zheng ◽  
Xixia Chu ◽  
...  
Keyword(s):  
Ab Initio ◽  
Protein Isoforms ◽  
Human Protein ◽  
Rna Seq ◽  
Ab Initio Prediction

scholarly journals Combining unconventional MR with AMPLE ab initio models and experimental phasing

2016 ◽  
Vol 72 (a1) ◽  
pp. s200-s200
Author(s):  
Adam J. Simpkin ◽  
Olga Mayans ◽  
Daniel J. Rigden ◽  
Martin Savko ◽  
William E. Shepard ◽  
...  
Keyword(s):  
Ab Initio ◽  
Experimental Phasing ◽  
Ab Initio Models

Accurate prediction of Pressure and Temperature T1/2 Variation in Solid State Spin Crossover by Ab Initio Methods: the [CoII(dpzca)2] case

2021 ◽  
Author(s):  
Luca Bondì ◽  
Sally Brooker ◽  
Federico Totti
Keyword(s):  
Solid State ◽  
Ab Initio ◽  
First Principles ◽  
Spin Crossover ◽  
Second Order ◽  
Accurate Prediction ◽  
Ab Initio Methods ◽  
State Spin

Spin crossover (SCO) is among the most complicated second-order transitions to be modelled from first principles; especially solid state SCO with the added complexity of (a) interacting molecules and (b)...

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