@article {Latek2007,
	title = {Protein structure prediction: combining de novo modeling with sparse experimental data},
	journal = {Journal of Computational Chemistry},
	volume = {28},
	number = {10},
	year = {2007},
	month = {jul},
	pages = {1668{\textendash}76},
	abstract = {Routine structure prediction of new folds is still a challenging task for computational biology. The challenge is not only in the proper determination of overall fold but also in building models of acceptable resolution, useful for modeling the drug interactions and protein-protein complexes. In this work we propose and test a comprehensive approach to protein structure modeling supported by sparse, and relatively easy to obtain, experimental data. We focus on chemical shift-based restraints from NMR, although other sparse restraints could be easily included. In particular, we demonstrate that combining the typical NMR software with artificial intelligence-based prediction of secondary structure enhances significantly the accuracy of the restraints for molecular modeling. The computational procedure is based on the reduced representation approach implemented in the CABS modeling software, which proved to be a versatile tool for protein structure prediction during the CASP (CASP stands for critical assessment of techniques for protein structure prediction) experiments (see http://predictioncenter/CASP6/org). The method is successfully tested on a small set of representative globular proteins of different size and topology, including the two CASP6 targets, for which the required NMR data already exist. The method is implemented in a semi-automated pipeline applicable to a large scale structural annotation of genomic data. Here, we limit the computations to relatively small set. This enabled, without a loss of generality, a detailed discussion of various factors determining accuracy of the proposed approach to the protein structure prediction.},
	keywords = {Algorithms, Computer Simulation, Magnetic Resonance Spectroscopy, Models, Molecular, Protein Folding, Protein Structure, Proteins, Proteins: chemistry, Secondary, Software},
	issn = {0192-8651},
	doi = {10.1002/jcc.20657},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17342709},
	author = {Dorota Latek and Dariusz Ekonomiuk and Andrzej Koli{\'n}ski}
}
@article {Ekonomiuk2005,
	title = {Protein modeling with reduced representation: statistical potentials and protein folding mechanism},
	journal = {Acta Biochimica Polonica},
	volume = {52},
	number = {4},
	year = {2005},
	month = {jan},
	pages = {741{\textendash}8},
	abstract = {A high resolution reduced model of proteins is used in Monte Carlo dynamics studies of the folding mechanism of a small globular protein, the B1 immunoglobulin-binding domain of streptococcal protein G. It is shown that in order to reproduce the physics of the folding transition, the united atom based model requires a set of knowledge-based potentials mimicking the short-range conformational propensities and protein-like chain stiffness, a model of directional and cooperative hydrogen bonds, and properly designed knowledge-based potentials of the long-range interactions between the side groups. The folding of the model protein is cooperative and very fast. In a single trajectory, a number of folding/unfolding cycles were observed. Typically, the folding process is initiated by assembly of a native-like structure of the C-terminal hairpin. In the next stage the rest of the four-ribbon beta-sheet folds. The slowest step of this pathway is the assembly of the central helix on the scaffold of the beta-sheet.},
	keywords = {Biophysical Phenomena, Biophysics, Computer Simulation, Models, Molecular, Monte Carlo Method, Protein Conformation, Protein Folding, Proteins, Proteins: chemistry, Proteins: metabolism},
	issn = {0001-527X},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/15933762},
	author = {Dariusz Ekonomiuk and Marcin Kielbasinski and Andrzej Koli{\'n}ski}
}
