Optimal Mutation Sites for PRE Data Collection and Membrane Protein Structure Prediction
نویسندگان
چکیده
منابع مشابه
Active Learning for Membrane Protein Structure Prediction
Background: About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution ...
متن کاملOptimal neural networks for protein-structure prediction.
The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model ...
متن کاملProtein-Protein Binding-Sites Prediction by Protein Surface Structure Conservation
A new algorithm to predict protein-protein binding sites using conservation of both protein surface structure and physical-chemical properties in structurally similar proteins is developed. Binding-site residues in proteins are known to be more conserved than the rest of the surface, and finding local surface similarities by comparing a protein to its structural neighbors can potentially reveal...
متن کاملPrediction of 3D protein Structure based on Mutation of AKAP3 and PLOD3 Gene in Case of Non-Obstructive Azoospermia
Background: The present study has been designed with the aim of evaluating A-kinase anchoring proteins 3 (AKAP3)and Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase 3 (PLOD3) gene mutations and prediction of 3D proteinstructure for ligand binding activity in the cases of non-obstructive azoospermic male.Materials and Methods: Clinically diagnosed cases of non-obstructive azoos...
متن کاملMultipass membrane protein structure prediction using Rosetta.
We describe the adaptation of the Rosetta de novo structure prediction method for prediction of helical transmembrane protein structures. The membrane environment is modeled by embedding the protein chain into a model membrane represented by parallel planes defining hydrophobic, interface, and polar membrane layers for each energy evaluation. The optimal embedding is determined by maximizing th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Structure
سال: 2011
ISSN: 0969-2126
DOI: 10.1016/j.str.2011.02.002