Prospects for Tertiary Structure Prediction of RNA Based on Secondary Structure Information

2012 ◽  
Vol 52 (2) ◽  
pp. 557-567 ◽  
Author(s):  
Satoshi Yamasaki ◽  
Shugo Nakamura ◽  
Kazuhiko Fukui
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Information ◽  
Tertiary Structure Prediction ◽  
Structure Information

scholarly journals TMBpro: secondary structure, β-contact and tertiary structure prediction of transmembrane β-barrel proteins

2007 ◽  
Vol 24 (4) ◽  
pp. 513-520 ◽  
Author(s):  
Arlo Randall ◽  
Jianlin Cheng ◽  
Michael Sweredoski ◽  
Pierre Baldi
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Tertiary Structure Prediction

scholarly journals Improved computational methods of protein sequence alignment, model selection and tertiary structure prediction

2013 ◽  
Author(s):  
◽  
Xin Deng
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Model Selection ◽  
Sequence Alignment ◽  
Protein Sequence ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Solvent Accessibility ◽  
Relative Solvent Accessibility ◽  
Tertiary Structure Prediction

Protein sequence and profile alignment has been used essentially in most bioinformatics tasks such as protein structure modeling, function prediction, and phylogenetic analysis. We designed a new algorithm MSACompro to incorporate predicted secondary structure, relative solvent accessibility, and residue-residue contact information into multiple protein sequence alignment. Our experiments showed that it improved multiple sequence alignment accuracy over most existing methods without using the structural information and performed comparably to the method using structural features and additional homologous sequences by slightly lower scores. We also developed HHpacom, a new profile-profile pairwise alignment by integrating secondary structure, solvent accessibility, torsion angle and inferred residue pair coupling information. The evaluation showed that the secondary structure, relative solvent accessibility and torsion angle information significantly improved the alignment accuracy in comparison with the state of the art methods HHsearch and HHsuite. The evolutionary constraint information did help in some cases, especially the alignments of the proteins which are of short lengths, typically 100 to 500 residues. Protein Model selection is also a key step in protein tertiary structure prediction. We developed two SVM model quality assessment methods taking query-template alignment as input. The assessment results illustrated that this could help improve the model selection, protein structure prediction and many other bioinformatics problems. Moreover, we also developed a protein tertiary structure prediction pipeline, of which many components were built in our group’s MULTICOM system. The MULTICOM performed well in the CASP10 (Critical Assessment of Techniques for Protein Structure Prediction) competition.

Protein Structure Prediction Based on Profile HMM and QPSO

2014 ◽  
Vol 1004-1005 ◽  
pp. 853-856
Author(s):  
Hai Xia Long ◽  
Shu Lei Wu ◽  
Yan Lv
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Protein Structure Prediction ◽  
Protein Function ◽  
Structure Prediction ◽  
Secondary Structure Information ◽  
Structure Information ◽  
Significant Process ◽  
Profile Hmm ◽  
Made In

Protein structure prediction is a challenging field strongly associated with protein function and evolution determination, which is crucial for biologists. Despite significant process made in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. In this study, we have developed a method for protein structure prediction based on 7-state HMM which can reduce the number of states using secondary structure information about proteins for each fold. The QPSO is an efficient optimization algorithm which is used to train profile HMM. Experiment results show that the proposed method is reasonable.

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