Structure prediction and analysis of mouse amiloride-sensitive cation channel 2, neuronal using bioinformatics tools

2009 ◽  
Author(s):  
Hina Naz Khan ◽  
Muhammad Haroon Khan ◽  
Hamid Rashid
Keyword(s):  
Structure Prediction ◽  
Cation Channel ◽  
Bioinformatics Tools

scholarly journals Bioinformatics Resources for Cancer Research with an Emphasis on Gene Function and Structure Prediction Tools

2006 ◽  
Vol 2 ◽  
pp. 117693510600200 ◽  
Author(s):  
Daisuke Kihara ◽  
Yifeng David Yang ◽  
Troy Hawkins
Keyword(s):  
Cancer Research ◽  
Structure Prediction ◽  
Pattern Mining ◽  
Large Data ◽  
Real Data ◽  
Microarray Data Analysis ◽  
Proteomics Data ◽  
Data Repositories ◽  
Prediction Tools ◽  
Bioinformatics Tools

The immensely popular fields of cancer research and bioinformatics overlap in many different areas, e.g. large data repositories that allow for users to analyze data from many experiments (data handling, databases), pattern mining, microarray data analysis, and interpretation of proteomics data. There are many newly available resources in these areas that may be unfamiliar to most cancer researchers wanting to incorporate bioinformatics tools and analyses into their work, and also to bioinformaticians looking for real data to develop and test algorithms. This review reveals the interdependence of cancer research and bioinformatics, and highlight the most appropriate and useful resources available to cancer researchers. These include not only public databases, but general and specific bioinformatics tools which can be useful to the cancer researcher. The primary foci are function and structure prediction tools of protein genes. The result is a useful reference to cancer researchers and bioinformaticians studying cancer alike.

Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

1970 ◽  
Vol 19 (2) ◽  
pp. 217-226
Author(s):  
S. M. Minhaz Ud-Dean ◽  
Mahdi Muhammad Moosa
Keyword(s):  
Protein Structure ◽  
Dihedral Angle ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Angle Distribution ◽  
Ramachandran Plot ◽  
Specific Data ◽  
Specific Distribution ◽  
Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

Protein structure prediction of human connexin 30 and its mutations in hearing system

2014 ◽  
Vol 3 (5) ◽  
Author(s):  
S. Reiisi ◽  
M. Hashemzade-chaleshtori ◽  
S. Reisi ◽  
H. Shahi ◽  
S. Parchami ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Connexin 30 ◽  
Hearing System

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

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