Protein structure prediction by a data-level parallel algorithm
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Journal of Computational Chemistry
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)
CReF: a central-residue-fragment-based method for predicting approximate 3-D polypeptides structures
Proceedings of the 2008 ACM symposium on Applied computing
A Hybrid Method for the Protein Structure Prediction Problem
BSB '08 Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Mining the Protein Data Bank with CReF to predict approximate 3-D structures of polypeptides
International Journal of Data Mining and Bioinformatics
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Predicting the three-dimensional 3-D structure of a protein that has no templates in the Protein Data Bank PDB is a very hard, still an impossible task. Computational prediction methods have been developed during the last years, but the problem still remains challenging. In this paper we present a new strategy based on Interval Arithmetic to store structural information obtained from experimental protein templates and predict native-like approximate three-dimensional structures of proteins. Our objective is to perform the prediction in a very fast manner and predict native-like structures that can be used as starting point structures to ab initio methods. We illustrate the efficacy of our method in five case studies of polypeptides.