Protein structure prediction by a data-level parallel algorithm
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Biopython: Python tools for computational biology
ACM SIGBIO Newsletter
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Expert Systems with Applications: An International Journal
Combining machine learning and optimization techniques to determine 3-D structures of polypeptides
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An expert protein loop refinement protocol by molecular dynamics simulations with restraints
Expert Systems with Applications: An International Journal
A sampling approach for protein backbone fragment conformations
International Journal of Data Mining and Bioinformatics
International Journal of Bioinformatics Research and Applications
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In this paper we describe CReF, a Central Residue Fragmentbased method to predict approximate 3-D structures of polypeptides by mining the Protein Data Bank (PDB). The approximate predicted structures are good enough to be used as starting conformations in refinement procedures employing state-of-the-art molecular mechanics methods such as molecular dynamics simulations. CReF is very fast and we illustrate its efficacy in three case studies of polypeptides whose sizes vary from 34 to 70 amino acids. As indicated by the RMSD values, our initial results show that the predicted structures adopt the expected fold, similar to the experimental ones.